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Genomic Studies on Floral and Vegetative Development in the Genus Streptocarpus (Gesneriaceae)

Yun-Yu Chen

Doctor of Philosophy The University of Edinburgh Royal Botanic Garden Edinburgh 2019

Declaration I declare that this thesis has been composed solely by myself and that it has not been submitted, in whole or in part, in any previous application for a degree. Except where stated otherwised by reference or acknowledgement, the work presented is entirely my own.

______Yun-Yu Chen

Date 15/07/2019

Acknowledgements I would like to express my utmost gratitude to the wonderful people who joined and contributed to this project and helped me throughout the PhD. First are my supervisors, Michael Möller, Catherine Kidner, and Kanae Nishii – thank you for giving me the opportunity, guidance, joy, intellectual support, and sometimes physical hard work! - throughout my study. Thank you all very much for your time and effort in discussing and commenting the works we produced together, and in helping me to practice and improve myself through numerous posters, presentations, and written pieces of work. This thesis is a result of all of our dedications. I would also like to thank our collaborators, Christine Hackett from the BioSS for the course and discussion on linkage mapping and QTL mapping, and Atsushi Nagano from Ryukoku University for the RAD-Seq experiments. I feel extremely lucky and grateful to have worked with you all, and the memory will forever stay dearly in my heart. My sincerest thanks also goes to my examiners, Michelle Hart and Minsung Kim, on reviewing this brick of papers and for stimulating the questions and discussions during the viva. You have helped me improve the work and myself even more. My gratitude also goes to the supporting teams at the University of Edinburgh and the Royal Botanic Garden Edinburgh. To my thesis committee member, Prof Andrew Hudson, who joined all my PhD milestones, providing invaluable discussions about the project. To all members in the RBGE horticultural team, particularly Sadie Barber, Nate Kelso and Andy Ensoll for taking care of the plant materials – without you the project would not have happened. Thanks to the RBGE STS team, Laura Forrest and Ruth Holland for the support in molecular lab, and to Frieda Christie for the training and discussion in microscopy and SEM. Thanks to the ICT team members for fulfilling my endless software and server requests. A big thank you to all the friends in Edinburgh - Julieth, Roosevelt, Javier, Karina, Flávia, Kristine, Pakkapol, Andrés, Mauricio, Lucía, Surabhi, Nadia, Thibauld and so many more - I will always remember the happiness and time we shared together in the BO8 office as well as in the pubs. To people in the tropical science group: Axel, Peter, Mark, Tiina, Toby and others, for the fun times and intellectual discussions. Special thanks to my former landlady Susan and her family for their hospitality. Also to Moth, their little granny cat for the cuddling service provided. Finally, my sincerest gratitude goes to my family - for my parents’ love and endless supports, for waiting for me at the other side of the world patiently. To my wife Andrea, who appeared at the most surprising moment, and accompanied me to complete this journey. Thank you for your patience and understanding, going through all the hard work and stressful moments with me. Wherever I am, I know I can look to you and feel the calmness and supports you reserved for me. I owe you everything.

Abbreviations BAM Binary sequence alignment map (file format) BLAST Basic local alignment search tool BLAT BLAST-like alignment tool BM Basal meristem Bp Base pairs BTL Binary trait loci BUSCO Benchmarking universal single-copy orthologs cM Centimorgan CTAB Cetyl trimethylammonium bromide DBG De Bruijn graph DNA Deoxyribonucleic acid EDTA Ethylenediaminetetraacetic acid Gbp Giga base pairs gDNA Genomic DNA GM Groove meristem GWAS Genome wide association study KEGG Kyoto Encyclopedia of Genes and Genomes Mbp Mega base pairs NGS Next generation sequencing ORF Open reading frames PCR Polymerase chain reaction QTL Quantitative trait loci RAD-Seq Restriction-site associated DNA sequencing RBGE Royal Botanic Garden Edinburgh RNA Ribonucleic acid RNA-Seq RNA sequencing Rpm Revolution per minute SAM Sequence alignment map (file format) SEM Scanning electron microscope

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Abstract The genus Streptocarpus consists of around 180 species with diverse morphologies. At least three main types of vegetative growth forms can be distinguished: caulescent, rosulate (acaulescents with multiple leaves), and unifoliate (acaulescents with one leaf). Floral size, shape, and pigmentation pattern are also highly variable between species. Previous studies have suggested that some of the morphological characters are inherited as Mendelian traits. For instance, the rosulate growth form is dominant over the unifoliate, and the rosulate / unifoliate growth form was hypothesised to be determined by two genetic loci, based on the Mendelian segregation ratios recorded in backcross and F2 populations. However, the identity of the loci and the underlying molecular mechanisms remain unknown. In this study, Streptocarpus rexii (rosulate) and Streptocarpus grandis (unifoliate) were used to study the genetic basis of morphological variation in Streptocarpus . The aim is to use modern next generation sequencing (NGS) technologies to build draft genomes, transcriptomes, and genetic maps for the non-model Streptocarpus plants, and carry out quantitative trait loci (QTL) mapping to locate the causative loci. First, suitable DNA and RNA extraction methods for obtaining NGS-quality nucleic acids from Streptocarpus were established. For DNA extraction this was a modified protocol of the ChargeSwitch gDNA Plant Kit, and for RNA extraction a TRIzol reagent plus phenol:chloroform:isoamyl alcohol wash protocol was devised. The nucleic acid samples extracted were subsequently used for library preparation and NGS sequencing experiments. Whole genome shotgun sequencing was performed for S. rexii and S. grandis using Illumina HiSeq 4000 and HiSeq X. De novo assembly of the sequence data produced a S. rexii draft genome of 596,583,869 bp, with 95,845 scaffolds and an N50 value of 35,609 bp. The S. grandis draft genome had a total span of 843,329,708 bp, with 127,951 scaffolds and an N50 value of 31,638 bp. The genome assemblies served as references for subsequent NGS data analysis. The RNA samples derived from various vegetative and floral tissues of S. rexii and S. grandis were sequenced on MiSeq and HiSeq 4000 platforms. The transcriptome assembly was carried out using de novo and reference-based methods (i.e. mapped to the obtained draft genomes), followed by putative protein-coding open reading frame identification and annotation. For S. rexii , 60,500 and 53,322 transcripts were constructed in the de novo and reference-based assemblies respectively. For S. grandis , 51,267 and 46,429 transcripts were constructed respectively. A Streptocarpus genetic map was constructed using restriction-site associated DNA sequencing (RAD-Seq) genotyping of a backcross population (( S. grandis × S. rexii ) × S. grandis ). The RAD-Seq data were analysed using a de novo approach and reference-based approaches with two different aligners, and the RAD-markers recovered from the three

approaches were combined to maximise the genetic map density. Different marker-filtering strategies with varying stringencies were also tested and compared. The results showed that the most stringently filtered map had 377 mapped markers in 17 linkage groups, and a total distance of 1,144.2 cM. On the other hand, the densest map consisted of 853 markers in 16 linkage groups (matching the basic haploid chromosome number of the Streptocarpus species used here), and a total distance of 1,389.9 cM. The maps constructed were used for QTL mapping of growth form variation, identifying up to 5 effective loci for the rosulate / unifoliate phenotypes, with two of the loci on LG2 and LG14 consistently found in all mapping attempts. The results suggest that the variation in growth form may be regulated by two major loci, but a few additional minor loci might also be associated with the trait. Several QTLs for floral dimension, flowering time, and floral pigmentation patterns were also found, and the genetic regions associated with the floral traits of Streptocarpus were revealed for the first time. During this study valuable genomic resources were generated for future research to identify the genes underlying different morphologies in the genus Streptocarpus . The reported QTLs narrow down the genetic region for fine-mapping studies, and the genome and transcriptome resources will aid the isolation of candidate gene sequences. Identifying the genetic loci and their crosstalk behind the variable morphologies in future work will greatly add to our knowledge on how the highly diverse genus Streptocarpus has evolved and on how fundamental developmental processes of plants are regulated.

Lay summary An important question in biology is how differences in shape and form between species have evolved. To answer this question, a key step is to investigate how shape and form develop and understand the genetic mechanisms regulating these processes. The identification of the genes responsible for the developmental differences underlying the morphological differences is a milestone in understanding the evolution of diversity. Streptocarpus is a group of plants including some which have become popular houseplants (e.g. African Violet and Cape Primroses). Some species in the genus have unconventional developments that have attracted botanical research for over 70 years. Most flowering plants form shoots with clearly defined growing tips which produce ‘conventional’ leaves and flowers but some Streptocarpus species produce leaves in unconventional ways – from meristems that develop at the base of leaves, or plants that produce a single leaf that grows for the whole life span of the plant. However, the genetic changes which cause this dramatic shift in form remain unknown. Here, we used modern next generation sequencing (NGS) technologies to build genetic resources for Streptocarpus that are required for gene identification, and to isolate the causative genes inferring morphological variation in the genome. This study describes the generation of the first draft genome sequence for Streptocarpus, identifies the expressed part of the genome through RNA sequencing, and creates a genetic map (i.e. the graphical representation of a chromosome with linear arrangements of genetic markers). By studying two Streptocarpus species with distinctive morphologies, S. rexii with multiple leaves in an irregular rosette and S. grandis with only one leaf, we identified genomic regions where the causative genes were most likely to be located. This is the first study reporting association mapping, identifying association between morphologies and genome sequences, of vegetative and floral traits in Streptocarpus to aid the isolation of candidate gene sequences. The presented work provides the basis for further studies to understand the molecular mechanism of Streptocarpus development, which will greatly add to our knowledge on how this morphologically highly diverse genus has evolved and on how fundamental developmental processes are regulated in plants.

Table of contents Declaration ...... i Acknowledgements ...... ii Abbreviations ...... iii Abstract ...... iv Lay summary ...... vi Table of contents ...... vii Chapter 1 Introduction ...... 1 1.1 Background ...... 1 1.2 The genus Streptocarpus ...... 1 1.2.1 Overview ...... 1 1.2.2 Variation and inheritance of vegetative forms...... 3 1.2.3 Variation and inheritance of floral colour and morphology ...... 12 1.3 Applications of next generation sequencing technologies to study interspecific genetics...... 18 1.3.1 Next generation sequencing technologies and Gesneriaceae resources 18 1.3.2 Next generation sequencing genomic resources for Streptocarpus ...... 20 1.4 Objectives ...... 21 Chapter 2 Establishing DNA and RNA extraction methods for Streptocarpus for next generation sequencing (NGS) ...... 24 2.1 Introduction ...... 24 2.1.1 Impact of DNA and RNA quality on NGS experiments ...... 24 2.1.2 Quality requirement of DNA and RNA for NGS experiments ...... 24 2.1.3 Nucleic acid extraction methods used for Gesneriaceae species ...... 25 2.2 Materials and methods ...... 27 2.2.1 Plant materials ...... 27 2.2.2 DNA extraction protocols ...... 28 2.2.3 RNA extraction protocol ...... 31 2.2.4 Quantification and quality control of the nucleic acid samples ...... 33 2.3 Results ...... 34 2.3.1 Testing different DNA extraction protocols ...... 34 2.3.2 DNA extraction for whole genome sequencing of S. grandis and S. rexii 39 2.3.3 RNA extraction for RNA-Seq of S. rexii ...... 43 2.4 Discussion ...... 45 2.4.1 Comparisons between different DNA extraction methods ...... 45

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2.4.2 Revaluating the RNA extraction protocol ...... 47 2.4.3 Discrepancies between quantification by spectrophotometer and Qubit assay ...... 48 2.4.4 Conclusion ...... 49 Chapter 3 Building genome resources – Genome sequencing and de novo genome assembly of Streptocarpus rexii and S. grandis ...... 50 3.1 Introduction ...... 50 3.1.1 Genome size and chromosome count of S. rexii and S. grandis ...... 50 3.1.2 Currently available genome resources for Gesneriaceae and Lamiales ..50 3.1.3 Whole genome sequencing and its applications ...... 51 3.1.4 Genome sequencing and assembly strategy ...... 51 3.2 Materials and methods ...... 55 3.2.1 Plant materials ...... 55 3.2.2 DNA extraction, library preparation and genome sequencing ...... 55 3.2.3 Assembly and analysis of the organellar genome ...... 55 3.2.4 De novo assembly of the nuclear genome ...... 59 3.2.5 Post-assembly filtering and assessment of assembly quality ...... 62 3.3 Results ...... 65 3.3.1 Quality check of the whole genome shotgun sequencing reads ...... 65 3.3.2 Organellar genome assembly ...... 67 3.3.3 Preliminary assembly tests for the plant nuclear genome ...... 71 3.3.4 Assembly and quality control of the S. rexii draft genome ...... 75 3.3.5 Assembly and quality control of the S. grandis draft genome ...... 84 3.4 Discussion ...... 93 3.4.1 Assembly of the Streptocarpus organellar genomes ...... 93 3.4.2 Assembly of the Streptocarpus nuclear genome ...... 95 3.4.3 Identification of contaminant species in the nuclear genome assemblies 98 3.4.4 Comparisons with other closely related genomes and possible future improvement for genome assembly ...... 99 Chapter 4 Building transcriptome resources – RNA sequencing transcriptome analysis of Streptocarpus rexii and Streptocarpus grandis ...... 102 4.1 Introduction ...... 102 4.1.1 RNA sequencing and its applications ...... 102 4.1.2 Currently available Streptocarpus and Gesneriaceae transcriptomes ... 102 4.1.3 RNA-seq analysis strategy ...... 104

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4.2 Materials and methods ...... 106 4.2.1 Plant materials ...... 106 4.2.2 RNA extraction, library preparation and RNA-Seq ...... 106 4.2.3 Sequence data pre-processing ...... 106 4.2.4 Transcriptome de novo and reference-guided assembly ...... 107 4.2.5 Preliminary assembly quality evaluation ...... 108 4.2.6 Post-assembly filtering and functional annotation ...... 109 4.2.7 Orthogroup identification ...... 110 4.3 Results ...... 112 4.3.1 RNA-Seq data preprocessing ...... 112 4.3.2 De novo assembly of the S. rexii and S. grandis transcriptomes ...... 116 4.3.3 Reference-guided transcriptome assembly of S. rexii and S. grandis .... 122 4.4 Discussion ...... 133 4.4.1 Comparison of the transcriptome assemblies of S. rexii and S. grandis 133 4.4.2 Comparison with other transcriptome resources ...... 135 4.4.3 Conclusions ...... 137 Chapter 5 Building a genetic map – Linkage analysis for constructing a genetic map from a S. rexii  S. grandis backcross population ...... 138 5.1 Introduction ...... 138 5.1.1 Applications of genetic mapping in candidate gene identification ...... 138 5.1.2 RAD-Seq for linkage analysis in non-model organisms ...... 139 5.1.3 Objectives ...... 142 5.2 Materials and methods ...... 144 5.2.1 Plant materials ...... 144 5.2.2 DNA extraction, library preparation and sequencing ...... 146 5.2.3 Quality control and preprocessing of the RAD-Seq data ...... 146 5.2.4 Genotyping of RAD-Seq data - de novo approach ...... 147 5.2.5 Genotyping of RAD-Seq data – Reference-based approach using BWA aligner...... 149 5.2.6 Genotyping of RAD-Seq data – Reference-based approach using Stampy aligner...... 150 5.2.7 Combining the genotype locus file and calculation of the genetic map .. 151 5.2.8 Synteny analysis of the genetic maps ...... 156 5.3 Results ...... 158 5.3.1 Quality check and preprocessing of the RAD-Seq reads ...... 158

5.3.2 MapA calculation – de novo approach ...... 160 5.3.3 MapA calculation – reference-based approach using BWA and Stampy aligners ...... 166 5.3.4 MapA calculation – combined approaches ...... 173 5.3.5 MapB calculation – de novo approach optimisation ...... 176 5.3.6 MapB calculation – reference-based approach using BWA and Stampy aligners ...... 177 5.3.7 MapB calculation – combined approaches ...... 180 5.3.8 Genetic map synteny analyses ...... 191 5.4 Discussion ...... 195 5.4.1 RAD-Seq and data preprocessing ...... 195 5.4.2 Comparison between RAD-Seq data analysis approaches ...... 195 5.4.3 Comparison between MapA and MapB maps ...... 199 5.4.4 Difficulties in reconstructing linkage groups LG15 and LG16 ...... 200 5.4.5 Comparison of the Streptocarpus genetic map to other Gesneriaceae maps ...... 201 5.4.6 Conclusion ...... 202 Chapter 6 Studies of marker-trait association – morphological variations and QTL mapping in the Streptocarpus backcross population ...... 203 6.1 Introduction ...... 203 6.1.1 Quantitative trait loci (QTL) and binary trait loci (BTL) mapping overview ...... 203 6.1.2 Morphological differences between S. rexii and S. grandis and their hybrids ...... 205 6.1.3 Objectives of this chapter ...... 207 6.2 Materials and methods ...... 208 6.2.1 Plant materials ...... 208 6.2.3 Morphology scoring of the parental plants ...... 209 6.2.4 Morphology scoring and examination of trait distribution in the BC mapping population ...... 213 6.2.5 Phenotypic distribution, segregation ratio, and phenotypic correlation in the BC population ...... 216 6.2.6 QTL mapping ...... 218 6.2.7 Genome annotation for the rosulate / unifoliate loci ...... 221 6.2.8 Scaffold to scaffold alignments ...... 221

6.3 Results ...... 222 6.3.1 Morphological variation between S. rexii , S. grandis , and their F1 hybrid ...... 222 6.3.2 Segregation of morphological variations in the backcross population .... 225 6.3.3 Mapping of the rosulate / unifoliate loci ...... 231 6.3.4 Mapping of the floral and other vegetative trait loci ...... 239 6.3.5 Genome annotation for the rosulate / unifoliate genetic regions using three genetic maps ...... 254 6.4 Discussion ...... 269 6.4.1 Genetic architecture of the rosulate and unifoliate growth form ...... 269 6.4.2 Candidate genes identified for the rosulate and unifoliate growth form . 272 6.4.3 Genetic architectures of the floral traits and other vegetative traits ...... 276 6.4.4 Conclusion ...... 279 Chapter 7 Discussion and conclusions ...... 280 7.1 Streptocarpus as a model system for developmental studies ...... 280 7.2 Directions for future studies ...... 282 References ...... 285 Appendices ...... 308 Appendix 2.1 ...... 308 Appendix 2.2 ...... 309 Appendix 2.3 ...... 310 Appendix 2.4 ...... 311 Appendix 2.5 ...... 312 Appendix 2.6 ...... 314 Appendix 2.7 ...... 316 Appendix 2.8 ...... 318 Appendix 2.9 ...... 318 Appendix 3.1 ...... 319 Appendix 3.2 ...... 320 Appendix 3.3 ...... 321 Appendix 3.4 ...... 324 Appendix 3.5 ...... 325 Appendix 3.6 ...... 325 Appendix 4.1 ...... 327 Appendix 5.1 ...... 328

Appendix 5.2 ...... 353 Appendix 5.3 ...... 358 Appendix 5.4 ...... 359 Appendix 5.5 ...... 360 Appendix 6.1 ...... 361 Appendix 6.2 ...... 362 Appendix 6.3 ...... 363 Appendix 6.4 ...... 364 Appendix 6.5 ...... 367 Appendix 6.6 ...... 372 Appendix 6.7 ...... 373 Appendix 6.8 ...... 376 Appendix 6.9 ...... 379

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Chapter 1: Introduction Chapter 1 Introduction

1.1 Background Studies in model organisms provide important insights into fundamental biological processes. On the other hand, non-model organisms may have unusual yet interesting properties that are not observed in model systems (Russell et al., 2017). For instance, non- model organisms may show unique morphologies, and can serve as valuable materials to study the evolution of morphological diversity (Mauricio, 2001; Bolger et al., 2017a). In my thesis, I focus on genomics and genetics analyses in the non-model genus Streptocarpus . Streptocarpus belongs to the family Gesneriaceae and shows a wide range of morphological variations (Hilliard and Burtt, 1971; Möller and Cronk 2001; Nishii et al., 2015). Some of the species display an unordinary growth of their above-ground shoots, such as one-leaf plants, which retain an enlarged cotyledon as a sole above ground vegetative organ (Jong, 1970; Jong and Burtt, 1975; Möller and Cronk, 2001; Nishii et al., 2015). The aim of this study is to increase our understanding of the genetic mechanisms that regulate the morphological variations in these Streptocarpus species.

1.2 The genus Streptocarpus 1.2.1 Overview The genus Streptocarpus was first reported by John Lindley in the 1828 edition of The Botanical Register (Figure 1.1). He described it as rivalling the famous ornamental Gloxinia species in looks, while “surpasses it in the elegance of its figure, and the delicacy of its colouring” (Lindley, 1828). The name Streptocarpus was derived from the Greek στρεπτός (twisted) καρπός (fruit) characterising their twisted capsule, which was a traditional taxonomical character for this genus although it was later found to be lost in some species in the light of its new delineation (Möller and Cronk, 1997; Nishii et al., 2015). Streptocarpus includes some popular ornamental plants with important horticultural value, such as the African Violets (section Saintpaulia ) and the Cape Primroses in section Streptocarpus (Nishii et al., 2015). Both are known commercially important flowering plants, and are of particular interest to plant breeders world-wide (Buta et al., 2010; Currey and Flax, 2015). Many cultivars based on Streptocarpus hybrids are commonly cultivated throughout Europe, America, and Asia for ornamental purposes (Reinten et al., 2011; Maria et al., 2004).

Chapter 1: Introduction

Figure 1.1 Illustration of Streptocarpus rexii , the type species of the genus Streptocarpus . (Figure from Plate 1173, Lindley 1828, Botanical Register)

In Gesneriaceae, the genus belongs to subfamily Didymocarpoideae, tribe Trichosporeae, subtribe Streptocarpinae (Weber, et al., 2013). The latest phylogenetic study using molecular markers, the Internal Transcribed Spacer region (ITS) and three chloroplast sequences, revealed the relationships among more than 130 (out of >176 described) species in the genus (Nishii et al., 2015). The genus is divided into two subgenera, Streptocarpella and Streptocarpus , consisting of seven and five sections, respectively. Geographically, the genus is distributed across Africa, Madagascar and the Comoro Islands, with the subgenus Streptocarpus found throughout eastern and southern Africa, and the subgenus Streptocarpella distributed widely in central and eastern Africa; both subgenera are found in the Madagascar and the Comoro Islands (Hilliard and Burtt, 1971). Streptocarpus plants are either monocarpic (i.e. die after flowering and fruiting) or perennial plants, with herbaceous or shrubby-woody habits (Hilliard and Burtt, 1971; Humbert 1971; Jong et al. 2012). In their natural habitat they are generally found growing on rocks, ravines, forest floors, less commonly under rock boulders outside forests, and rarely epiphytically in shady gorges (Lawrence, 1940; Hilliard and Burtt, 1971).

Chapter 1: Introduction Streptocarpus species show several notable properties, and have been studied in many different disciplines and subject areas. These include studies on phylogeography (e.g. Hughes et al., 2005), population genetics (e.g. Hughes et al., 2004; 2007), morphology and evolution (e.g. Jong, 1970; Jong and Burtt, 1975; Möller and Cronk, 2001; Nishii et al., 2017), meristem development (e.g. Jong 1970; Jong and Burtt 1975; Imaichi et al., 2000; Rauh and Basile, 2003; Nishii et al., 2004; Nishii and Nagata, 2007; Mantegazza et al., 2007; Mantegazza et al., 2009; Nishii et al., 2010a; Tononi et al., 2010; Nishii et al., 2012a), floral development and evolution (e.g. Harrison et al., 1999; Hughes et al., 2006), physiology such as photoperiodism (Nitsch, 1967), hormone responses (e.g. Rosenblum and Basile, 1984; Nishii et al., 2012a; Nishii et al., 2014; Chen et al., 2017), biochemistry (Scott-Moncrieff, 1936; Stöckigt et al., 1973; Inoue et al., 1984; Sheridan et al., 2011; Inoue et al., 1982), cytology (e.g. Lawrence et al., 1939; Ratter, 1963; Jong and Möller, 2000; Möller and Pullan, 2015; Möller, 2018), transcriptomics (Chiara et al., 2013), and chloroplast genomics (Kyalo et al., 2018). In particular, the morphological variations observed in Streptocarpus are extraordinary for land plants, and suggests a greater flexibility of developmental programs than those revealed in model plant systems. In my PhD project, I focus on studying the morphological variations and the genetic basis of vegetative and floral characters.

1.2.2 Variation and inheritance of vegetative forms One of the most unusual features observed in the development of Streptocarpus species is their diverse vegetative growth forms (Figure 1.2). The classification of their growth forms attracted early taxonomic attention (Fritsch 1893-1894), and later detailed morphological studies were carried out (Jong, 1970; Jong and Burtt, 1975). In general, Streptocarpus species can be roughly grouped into caulescents (with typical shoot apical meristems, SAM; Figure 1.2 a, d) and acaulescents (lacking a conventional SAM; Figure 1.2 b, c, e, f). Acaulescent species can further be distinguished into two subgroups: rosulates (with multiple leaves; Figure 1.2 b, e) and unifoliates (with a single leaf; Figure 1.2 c, f) (Jong, 1970; Hilliard and Burtt, 1971). The latest study identified over 30 caulescents, 50 rosulate and 40 unifoliate species and some intermediate forms in a total set of 167 species (Nishii et al., 2015). The evolutionary history of the growth forms is not fully resolved, and hybridisation, ecological niche adaptation, frequent transition between the growth forms may all be involved in shaping the species’ vegetative habit (Möller and Cronk, 2001; Nishii et al., 2017).

Chapter 1: Introduction

Figure 1.2 Examples of variation of growth forms observed in Streptocarpus . (a) Streptocarpus thysanotus , caulescent. (b) Streptocarpus johannis , excentric rosulate. (c) Streptocarpus wendlandii , unifoliate. (d) - (f) Schematic illustrations of the growth forms. (d) Caulescent. (e) Rosulate. (f) Unifoliate. Red circles and arrows indicate location of leaf- forming meristems, Mc macrocotyledon, ap additional phyllomorphs. Bars = 5 cm. (Illustrations modified from Nishii et al. 2016)

The classification of the genus is closely linked to growth form and has been revised several times over the years. The different morphs of Streptocarpus were first recognised by De Candolle (1845), who noticed the short stem of some of the species and grouped the known species into the caule abbreviato (abbreviated stem) and the caulescentes (caulescents). Fritch 1893-1894 further divided the growth forms into three taxonomical groups: the caulescentes (caulescents), unifoliati (unifoliates), and rosulati (rosulates). It was later recognised that there is no hard line between unifoliate and rosulate growth forms, with some rosulate species showing unifoliate morphology at early life stages, or unifoliate

Chapter 1: Introduction species that can produce additional leaves (Burtt, 1939). Nevertheless, this classification system was further extended to include the sub-unifoliate growth form, describing some small-leaved unifoliates that produce additional leaves occasionally (Lawrence, 1958). Later, extensive morphological studies were carried out on Streptocarpus fanniniae and other species, and the species categorised into four major groups: unifoliates, plurifoliates, rosulates, and caulescents (Jong, 1970; Jong and Burtt, 1975). Humbert (1971) described additional growth forms in Madagascar including the species with leaves in basal rosette with long petioles, species with leaves in basal rosette with veins ascending from the base and shrubby species with short filaments and non-coherent anthers. Hilliard and Burtt (1971) placed all the caulescents and the petioled Madagascan species into subgenus Streptocarpella and the remaining acaulescent species in subgenus Streptocarpus without further formal subdivision. In the latest study, a total of six fundamental growth patterns were defined, including the caulescent, rosulate, unifoliate, creeping rhizomatous stem, shrubby, and Saintpaulia -like rosette (Nishii et al., 2015). On the basis of phylogenetic results, floral and growth patterns, a classification with two subgenera and 12 sections was proposed, in which unifoliates and rosulates occurred in mixed sections, the African section Streptocarpus and the Madagascan sections Colpogyne and Plantaginei (Figure 1.3 Nishii et al., 2015). The subgenus division of Nishii et al. (2015) is fully supported by cytology with subgenus Streptocarpella possessing a basic chromosome number of x = 15, while those of subgenus Streptocarpus have x = 16.

(Next page) Figure 1.3 Latest molecular systematics of Streptocarpus with the growth habits mapped on each taxon (a) subgenus Streptocarpella , (b) subgenus Streptocarpus . Growth habit: ● caulescent, ★ creeping rhizomatous stem, ■ rosulate, ▬ Saintpaulia-like rosette, ▲ unifoliate, ✦ shrubby. (Figure modified from Nishii et al., 2015)

Chapter 1: Introduction

Figure 1.3 Latest molecular systematics of Streptocarpus with the growth habits mapped on each taxon. Full legend given on previous page.

Chapter 1: Introduction My PhD project focuses on the developmental differences between the two acaulescent growth forms, rosulate and unifoliate. The above-ground vegetative body of acaulescent Streptocarpus is composed of specialised organs named phyllomorphs (Figure 1.4; Jong, 1970). A phyllomorph is a leaf/stem construct bearing several meristems. Each phyllomorph consists of a lamina and a petiolode, the latter with functions of petiole and stem. Three meristems are found on a phyllomorph: the basal meristem maintains lamina growth and is located at the proximal end of the lamina (Figure 1.4; bm). The petiolode meristem controls the petiolode and midrib extension and thickening (Figure 1.4; pm). The groove meristem located at the juxtaposition between the lamina and petiolode, gives rise to additional phyllomorphs and/or inflorescences (Figure 1.4; gm).

Lamina

bm pm ap gm Petiolode r

Figure 1.4 Schematic illustration of a phyllomorph, with morphology based on Streptocarpus fanniniae C. B. Clarke. bm basal meristem, ap additional phyllomorph, gm groove meristem, pm petiolode meristem, r root. (modified from Jong and Burtt, 1975)

A major distinction between the rosulate and unifoliate growth forms is the differentiation of the groove meristem (Jong, 1970). At seed germination and early seedling development stages, the morphology between the two growth forms are very similar (Jong, 1970), which both rosulate and unifoliate species showing anisocotylous development (Figure 1.5, Figure 1.6). Anisocotyly is the unequal growth in a pair of cotyledons, where one of the two cotyledons grows large and becomes a major photosynthetic organ (cotyledonary phyllomorph; Caspary, 1858; Fritsch, 1904; Jong, 1970; reviewed in Nishii et al., 2010b). But as the plant grows, the difference between rosulate and unifoliate becomes apparent; in rosulate species the groove meristem will develop additional phyllomorphs, and the successive production of further phyllomorphs from the groove meristem of the preceding phyllomorph arranges them in either a more-or-less regular (centric rosulate) or irregular (excentric rosulate) rosette (Figure 1.5; Jong, 1970; Nishii and Nagata, 2007). Later

Chapter 1: Introduction on, each phyllomorph will produce inflorescences at the base of the lamina. On the other hand, in unifoliate species the enlarged cotyledonary phyllomorph is the only above-ground vegetative organ and the groove meristem will differentiate into inflorescences (Figure 1.5; Jong, 1970; Jong and Burtt, 1975; Imaichi et al., 2000).

Figure 1.5 Schematic illustration of rosulate and unifoliate development, using S. rexii and S. grandis as example. The actual development time may vary depending on the growth condition.

Chapter 1: Introduction

Figure 1.6 Seedling morphologies of the parental lineages. (a) 5 DAU isocotylous seedling of S. rexii . (b) 5 DAU isocotylous seedling of S. grandis . (c) 5 DAU isocotylous seedling of S. grandis × S. rexii F1 hybrid. (d) 20 DAU anisocotylous seedling of S. rexii . (e) 20 DAU anisocotylous seedling of S. grandis . (f) 20 DAU anisocotylous seedling of F1 hybrid. (g) 40 DAU anisocotylous seedling of S. rexii . (h) 40 DAU anisocotylous seedling of S. grandis . (i) 20 DAU anisocotylous seedling of F1 hybrid. Mc: macrocotyledon. mc: microcotyledon. Bar: 200 m.

The differentiation in the groove meristem can be well illustrated under electron microscope (Figure 1.7). In 60 DAU seedlings of S. rexii and S. rexii × S. grandis F1 hybrid, a patch of small cells was seen on the adaxial side of the petiolode, adjacent to the proximal end of the macrocotyledon (Figure 1.7 a, c; arrows). The cells formed a bulging round- shaped cluster of around 150 µm to 200 µm in diameter (Figure 1.7 d, f). These cells presumably represent the groove meristem, and do not carry trichomes in contrast to the tissues surrounding them that are densely covered with short glandular and long eglandular trichomes. On the other hand, S. grandis seedlings showed no apparent sign of bulging in the groove meristem area (Figure 1.7 b), only a patch of cells that were not covered with trichomes, about 50 µm in diameter (Figure 1.7 b arrow). At 65 DAU seedlings of S. rexii and F1 hybrid showed apparent signs for the formation of a bulged GM of about 200 µm in diameter (Figure 1.7 f and g). The first primary phyllomorph emerged from the bulge, and showed adaxial-abaxial polarity with trichomes appearing from abaxial side (Figure 1.7 j, l, m; P1). On the other hand, the groove meristem area of S. grandis appeared flat and dormant

Chapter 1: Introduction at 65 DAU (Figure 1.7 e), 90 DAU (Figure 1.7 h), and 150 DAU (Figure 1.7 k). At the same time, the groove meristem area without trichome-growth seemed to enlarged in older materials. In 90 DAU S. grandis plant, the groove meristem area was about 100 µm in diameter and showed slightly bulge-shape morphology (Figure 1.7 h). In 150 DAU plant, the groove meristem area became about 200 µm in diameter though appeared to be flatten again (Figure 1.7 k).

Figure 1.7 Development of the groove meristem of the three parental Streptocarpus parental lineages. All images are oriented to show the macrocotyledon (Mc) at the top. The microcotyledon and the trichomes surrounding the groove meristem tissue were removed. (a) S. rexii 60 DAU. (b) S. grandis 60 DAU. (c) F1 hybrid 60 DAU. (d) S. rexii 65 DAU, close

Chapter 1: Introduction up view of the groove meristem. (e) S. grandis 65 DAU, close up of the groove part. (f) F1 hybrid 65 DAU, close up view of the groove meristem. (g) S. rexii 65 DAU, with a bulge shape groove meristem. (h) S. grandis 90 DAU, with a bulge shape groove meristem. (i) F1 65 DAU, with a bulge shape groove meristem. (j) S. rexii 65 DAU, with a growing phyllomorph primordium. (k) S. grandis 150 DAU, with flat groove meristem. (l) F1 65 DAU, with a developed primary phyllomorph. (m) S. rexii 65 DAU, with a developed primary phyllomorph. Mc: macrocotyledon. mc: microcotyledon, which was removed to reveal the groove meristem tissue. Yellow arrows: groove meristem. P1: primary phyllomorph. ad: adaxial. ab: abaxial. Bars: 200 m.

As rosulates and unifoliate species of subgenus Streptocarpus both have the same chromosome count of 2n = 32, viable off-springs can be produced (Lawrence et al., 1939; Möller and Pullan, 2015). Oehlkers (1938; 1942) carried out some of the earliest studies of the inheritance of the growth forms. In hybridisation experiments between rosulate and unifoliate Streptocarpus , all F1 hybrids were rosulate in form, indicating that rosulate is the dominant phenotype. Furthermore, their backcross progenies were reported to segregate in a Mendelian ratio, with the rosulate:unifoliate ratio of 3:1 in backcross progenies. And in F2 progenies, the segregation ratio was 15:1 (Table 1.1; Oehlkers, 1938; 1942; Harrison et al., 2005). Both ratios indicate that two unlinked genetic loci define the growth form, and that the growth form variation is a Mendelian trait (Oehlkers, 1938; 1942; Harrison et al., 2005). In addition, it was reported that one of the loci may act at an early stage, which resulted in rosulate individuals appearing at about 6 months after sowing, and the other locus at a later stage, at about 9 months after sowing (Oehlkers, 1942).

Table 1.1 Segregation ratios of rosulate  unifoliate experimental crosses

No. No. Population* Rosulate:unifoliate Reference rosulate unifoliate

S. wendlandii  3:1 318 120 Oehlkers, 1938 (wendlandii  rexii ) (P = 0.246, X 2 test)

(S. wendlandii  rexii) 15:1 48 3 Oehlkers, 1938  (rexii  wendlandii ) (P = 0.9136, X 2 test)

S. grandis  3:1 145 41 Oehlkers, 1942 (grandis  rexii ) (P = 0.351, X 2 test)

S. wittei  3:1 Harrison et al. , 98 30 (wittei  rexii ) (P = 0.683, X 2 test) 2005 * All species except for S. rexii (rosulate) are unifoliate

Chapter 1: Introduction However, the identity of the two rosulate loci remains unknown to date, and plant hormones, sugar signalling and environmental factors may all have an effect on the rosulate and unifoliate morphologies. In terms of hormonal signalling, external treatment of gibberellin (GA) on unifoliate seedlings was found to induce the formation of an additional leaf (Dubuc-Lebreux, 1978; Rosenblum and Basile, 1984; Nishii et al., 2012a), and also induced the formation of an apical leaf bud in the rosulate S. rexii (Nishii et al., 2014). Sugar signalling may have a similar effect, with the treatment of -glucosyl phenyglycoside ( -D-

Glc) 3, a sugar molecule that specifically bind to the membrane Arabinogalactan-Proteins (AGPs), fascilitate the formation of additional leaf shoots in S. prolixus , which in natural condition produce 2 – 3 leaves (Rauh, 2001; Rauh and Basile, 2003). On the other hand, treatment of a structurally-similar β-galactosyl Yariv reagent that does not bind to AGPs failed to produce the same phenotype (Rauh, 2001; Rauh and Basile, 2003). In certain cases the growth form can be affected by environmental factors, such as in the caulescent species Streptocarpus nobilis , which when grown under adverse condition grows no additional leaves (Hilliard and Burtt, 1971). A most direct attempt to identify the loci was the genetic association with the meristematic class I KNOX (KNOXI) gene. Mutation of the KNOX gene SHOOT MERISTEMLESS (STM ) was shown to produce a phenotype lacking the SAM during embryogenesis in A. thaliana (Barton and Poethig, 1993). The gene homolog SSTM1 was studied in the backcross populations S. dunnii  (S. dunnii  S. rexii ) and S. wittei  (S. wittei  S. rexii ) and were found expressing in the groove meristems, but was also found to be unlinked to the unifoliate growth form (Harrison, 2002; Harrison et al., 2005). Other developmental genes studied in Streptocarpus sp. include WUSCEL (as SrWUS ; Mantegazza et al., 2009), AS1 / ROUGH SHEATH2 / PHANTASTICA (as SrARP ; Nishii et al., 2010), GA20-oxidase and GA2-oxidase (as SrGA20ox and SrGA2ox ; Nishii et al., 2014), and ISOPENTENYLTRANSFERASE (as SrIPT ; Chen et al., 2017). Among these the gene transcripts of SrWUS , SrARP , SrGA20ox , SrIPT5 and SrIPT9 were found located in the groove meristem and basal meristems of rosulate S. rexii (while the gibberellin synthesising SrGA2ox expressed in the surrounding tissues outside of the meristem). In addition, the KNOX1 homolog STM was also found in the groove and basal meristems of the unifoliate S. wendlandii (Nishii et al., 2017).

1.2.3 Variation and inheritance of floral colour and morphology The flowers of Streptocarpus species have several features in common: they are zygomorphic (with bilateral symmetry), gamopetalous, five-lobed, two-lipped, and are produced in pair-flowered cymes (Hilliard and Burtt, 1971; Haston and Ronse de Craene, 2007). On top of these features, the genus shows a wide range of variation in terms of floral

Chapter 1: Introduction dimension, corolla shape and colour, pigmentation pattern, and scent (Hilliard and Burtt, 1971; Harrison et al., 1999; Möller et al., 2019). Hilliard and Burtt (1971) first described the floral types of Streptocarpus based on the shape of the corolla mouth. Three floral types were characterised as open (funnel shape flower), the key-hole-type (the opening of the tube is laterally compressed to a narrow slit), and personate (the ridges of lower lobe mask the corolla tube entrance). Later, Harrison et al. (1999) conducted measurements on the floral shape of 39 Streptocarpus and Saintpaulia species, and grouped them into six floral types based on morphometric analyses. These types included the open-tube type, the key-hole type, the personate type, the Saintpaulia type (reduced corolla tube, enantiostyly), the small pouch type (small size, pale colour and relatively wide tube), and the dunnii type (distinctive flower of Streptocarpus dunnii with red colour). Nishii et al. (2015) further expanded on this with two additional floral types, the Acanth-type (for the inclusion of the unusual corolla shape of S. lilliputana that matches certain genera in Acanthaceae ) and the labellanthus-type (with a forward directing lip and reduced upper lip) (Figure 1.8). Since this categorisation did not account for the wide range of corolla shapes in the open tube, Möller et al. (2019) reassessed the flower classification and recognised seven main types of Nishii et al. (2015) and subdivided the open-tube type into six subtypes that included the Acanth-type and the new Acicularis-type.

Figure 1.8 Different floral types of the genus Streptocarpus (a) Small pouch type, S. beampingaratrensis subsp. beampingaratrensis (b) Open cylindric tube, with narrow tube, S. kentaniensis (c) Open cylindric tube, with broad tube, S. grandis (d) Open tube with pollination chamber, S. pumilus (e) Inverted V-type, S. wendlandii (f) Acanth-type, S. lilliputana (g) Acicularis-type, S. acicularis (h) labellanthus-type, S. thysanotus (i) Key-hole type, S. saxorum (j) Personate type, S. glandulosissimus (k) Flat-faced type, S. shumensis (l)

Chapter 1: Introduction Bird-pollination-type, S. dunnii . (Figure (b) (c) (e) were modified from Möller et al., 2019; figure (g) was modified from Darbyshire and Massingue, 2014; all other figures were modified from Nishii et al., 2015)

Between 1940 and 1960, a series of investigations were carried out by Lawrence to study the genetic inheritance of floral colour and pigmentation patterns of Streptocarpus species. These studies examined the segregation ratio of floral traits in multiple crosses, and aimed to find their inheritance patterns (Lawrence et al., 1939; Lawrence, 1947, 1957, 1958; Lawrence and Sturgess, 1957). Streptocarpus flowers usually have different intensities of purple to blue and pink shades with occasional exceptions, e.g. S. lutea and S. bindseilii which have ivory white flowers, and S. dunnii has red flowers (Hilliard and Burtt, 1971; Lawrence et al., 1939). The underlying pigmentation molecules are possibly delphinidin derivatives (a kind of anthocyanin), predominantly malvidin, followed by pelargonidin and peonidin (Scott-Moncrieff, 1936; Lawrence et al., 1939; Lawrence and Sturgess, 1957). The inheritance of floral colours was studied in S. rexii (blue) and S. dunnii (red) crosses, and several different colour classes were identified in the backcross and F2 populations, including blue, mauve, magenta, rose, pink, salmon and ivory (varying from the most intense blue to white, i.e. acyanic). Through studying the crosses and segregation ratios it was suggested that these colour classes were controlled by at least nine genes (Table 1.2; Lawrence et al., 1939; Lawrence and Sturgess, 1957). However, the identity of these loci remains unknown to date, and it has not been further examined using molecular marker or other genotyping methodology. The genetics of the pigmentation patterns in the flower were also studied, including (1) the anthocyanin blotch in the corolla tube, (2) anthocyanin accumulation in glandular hairs of the pistil, (3) anthocyanin-coloured lines on the petals, and (4) the yellow pigment in the central stripe of the corolla tube (Lawrence, 1957). This study was carried out with multiple crosses between garden forms and acyanic forms of Streptocarpus (cultivars originated from hybridisation between S. dunnii , S. rexii and S. parviflorus ). The inheritance of the anthocyanin blotch, hair colours (on the pistil), and anthocyanin lines on lower petals were all found to segregate in a 1:1 and 3:1 ratio in the backcross and F2 populations respectively (Lawrence, 1957). The yellow pigment (inside the lower petal of the corolla tube) was found to segregate in a 1:1 ratio in the backcross, but deviates from the expected 3:1 ratio in the F2 population, possibly due to a more complicated genetic basis. These characters also showed varying degrees of linkage (Lawrence, 1957), and it was concluded that the pigmentation patterns possibly follow Mendelian inheritance, and are probably

Chapter 1: Introduction controlled by a ‘supergene’ consisting of five individual genes that are closely located on a chromosome and are genetically linked (Table 1.3; Lawrence, 1957, 1958). Later, the inheritance of the yellow spot was studied in crosses using S. rexii , S. parviflorus , S. montigena and S. cyaneus (Oehlkers, 1966; 1967). The result suggests that the presence of the yellow spot is a dominant phenotype, and is likely to be monogenic with the segregation ratio of absence to presence of 1:1 and 3:1, in backcross and F2 population respectively, in contrast to Lawrence’s study.

Table 1.2 Summary of the hypothetical genes involved in Streptocarpus floral colouration

Hypothetic Hypothetic function Phenotype Reference gene code

General production of Dominant Coloured (red) inflorescence and flower V Lawrence and Sturgess 1957 anthocyanin in all tissues Recessive Green inflorescence stem and white flower

F General production of Dominant Non-white flower Lawrence 1939 (or A) anthocyanin in flowers Recessive White flower Lawrence and Sturgess 1957

Increase production of Dominant Medium to intense anthocyanin colour in corolla I Lawrence and Sturgess 1957 anthocyanin in flower Recessive Pale anthocyanin colour in corolla

Production of anthoxanthin Dominant Presence of anthoxanthin (white to yellowish pigments) C Lawrence and Sturgess 1957 co-pigment Recessive Absence of anthoxanthin

Convert pelargonidin Dominant Presence of cyanidin R Lawrence 1939 to cyaniding Recessive Absence (or traces) of cyanidin

Convert pelargonidin Dominant Presence of delphinidin, resulted in mauve or blue flower O Lawrence 1939 to delphinidin Recessive Absence of delphinidin, resulted in lighter-colour flower

Dominant Presence of solely 3:5 dimonoside pigments D Produce 3:5 dimonoside Lawrence 1939 Recessive 3:5 dimonoside, 3-pentoseglycoside and 3-monoside mix

Complementary for Dominant Produce limited amount of 3:5 dimonoside pigments X,Z Lawrence and Sturgess 1957 3:5 dimonoside production Recessive 3:5 dimonoside, 3-pentoseglycoside and 3-monoside mix Note. In the presence of both dominant R and dominant O, the pigment malvidin is produced

Table 1.3 Summary of the hypothetical genes involved in Streptocarpus floral pigmentation patterns Hypothetic Hypothetic function Phenotype Reference gene

Production of anthocyanin blotch Dominant Presence of blotch or a deeper anthocyanin Lawrence B at the anterior part of corolla Recessive Absence of the trait 1957

Production of anthocyanin Dominant Presence of colour in the stalk of glandular hairs on pistil Lawrence H accumulation in hairs on the pistil Recessive Absence of the trait 1957

Production of anthocyanin lines Dominant Presence of lines on the lower petal Lawrence L at the posterior part of lower petal Recessive Absence of the trait 1957

Production of yellow pigment Dominant Yellow spot presence if both loci have at least one dominant allele Lawrence Y down the central part of corolla Recessive Yellow spot absence if either of the loci are having two recessive allele 1957

Chapter 1: Introduction In summary, Streptocarpus species show distinctive caulescent and acaulescent vegetative growth forms and diverse floral morphological characters. These morphologies are well documented, and preliminary genetic studies suggest the possible underlying genetic mechanism, i.e. two genetic loci for rosulate / unifoliate growth, nine genetic loci for floral colour, and a supergene for pigmentation pattern. However, the important question of physical identification of the actual loci remains unresolved. Gaining further knowledge of the genetic regulation of these phenotypic traits will increase our understanding of the evolution of the genus Streptocarpus and the Gesneriaceae family. It will also provide a broader understanding of how plant development is regulated to produce the unique morphologies observed in Streptocarpus and relate these to model plant systems.

1.3 Applications of next generation sequencing technologies to study interspecific genetics 1.3.1 Next generation sequencing technologies and Gesneriaceae resources Next generation sequencing (NGS) refers to a wide range of high-throughput and in- parallel sequencing methods that emerged around 2004 (reviewed in Reuter et al., 2015; Kulski, 2016). These methods are distinct from the traditional Sanger sequencing method (Sanger et al., 1977) in their chemical reactions, and can generate giga base pairs (Gbp) of sequence data overnight at much lower cost. While the Sanger method sequences longer strands of DNA fragments based on the polymerase-chain reaction (PCR, usually around 1000 bp), NGS methods often involve shearing of DNA into much smaller fragments (from 25 bp to 500 bp), and each fragment is sequenced in parallel (reviewed in Glenn, 2011; Reuter et al., 2015). Thereby the number of base pairs (bp) sequenced per unit cost is greatly increased (Figure 1.9; Stein, 2010).

Figure 1.9 The trend of DNA sequencing cost versus the cost for hard disk storage. The cost is in US dollar (Stein, 2010)

Chapter 1: Introduction Prior to the emergence of NGS, genome-scale sequencing and analysis have been restricted to a few selected model species, e.g. fruit fly, Arabidopsis thaliana , and humans (Adams et al., 2000; Arabidopsis Genome Initiative, 2000; International Human Genome Sequencing Consortium, 2001). However, with the lowered price of sequencing and the development of more user-friendly bioinformatics software, whole genome sequencing projects of non-model organisms became popular (Figure 1.10; Genome 10K Community of Scientists, 2009; Ellegren, 2014; Smith, 2016). As of April 2016, there were more than 60,000 prokaryotic genomes and over 2,700 eukaryotic genomes stored in GenBank, and between 2010 and 2015 alone, more than 2,000 mitochondrion genomes were published (Smith, 2016). Hence, the emergence of NGS technologies is an important milestone for genomic studies of non-model organisms (Sboner et al., 2011; Van Nimwegen et al., 2016).

Figure 1.10 Number of base pairs (bp) stored in the GenBank database that were derived from whole genome shotgun sequencing experiments (from GenBank and WGS statistics https://www.ncbi.nlm.nih.gov/genbank/statistics/)

The usage of NGS technologies greatly accelerated research progress and resource availability in Gesneriaceae. For instance, the nuclear genome assembly of Dorcoceras hygrometricum (as Boea hygrometrica , see also Puglisi et al., 2016) is constructed (Xiao et al., 2015). The species belongs to the Loxocarpinae, closely related to subtribe Streptocarpinae where Streptocarpus resides (Möller et al., 2009), and has nine pairs of chromosomes (Kiehn et al., 1998). RNA sequencing-derived transcriptomes have been produced for several genera of Gesneriaceae, such as Streptocarpus (Chiara et al., 2013; Matasci et al., 2014), Dorcoceras (Xiao et al., 2015), and Primulina (Ai et al., 2014). A genetic map was constructed for the genera Rhytidophyllum and Primulina , using genetic

Chapter 1: Introduction markers derived from Genotyping-By-Sequencing (GBS) and transcriptome-derived single- nucleotide polymorphism (SNP) markers respectively (Alexandre et al., 2015; Feng et al., 2016).

1.3.2 Next generation sequencing genomic resources for Streptocarpus Compared to other Gesneriaceae species or model plants, the available NGS derived genomic resources for the genus Streptocarpus are limited. A transcriptome of S. rexii is available at the online database ANGeLDUST (Chiara et al., 2013). The only genome resource available so far is the circular chloroplast sequence of S. teitensis (Kyalo et al., 2018). At the beginning of my PhD project, there was no nuclear genome reference or genetic map available for Streptocarpus (Chen et al., 2018). Nevertheless, sequence resources are fundamental for genetic and genomic studies in the genus. Genome sequences can serve as backbone for reference-based SNP calling, and for the assembly of genotyping or RNA sequencing data (Davey et al., 2011, Korpelainen et al., 2014). A well annotated genome is very useful for the identification of functioning genes and gene structure (Ekblom and Wolf, 2014). With advanced NGS platforms such as Illumina HiSeq 4000 and HiSeq X, one lane of sequencing can generate up to 900 Gbp of data, that can provide ~900  depth of coverage for a 1 Gbp genome (Shen et al., 2014), which is suitable for assembly of the medium sized genome of Streptocarpus species which is on average ~0.8 Gbp for diploids (Möller, 2018). Transcriptome profiles provide important information on the expressed genes in a genome. RNA sequencing (RNA-Seq) is a powerful approach for building a transcriptome database, which yields the sequence and structural information of genes. The RNA-Seq reads and the assembled transcriptome will be beneficial for annotating the nuclear genome (Hoff et al., 2016). In addition, the gene sequence information are valuable resources for gene isolation for future candidate gene studies (Wolf, 2013; Korpelainen et al., 2014). Well established bioinformatics tools are readily available for RNA-Seq data analysis, allowing the sequence to be assembled without the need of a complete reference genome (Haas et al., 2013), and annotation of the transcripts can be performed using existing pipelines (Conesa et al., 2005; Lohse et al., 2013; Kanehisa et al., 2016; Bolger et al., 2017b). Thus, RNA-Seq would be a feasible approach to generate the transcriptome database as a fundamental resource. A genetic map is an essential resource for studying the genetic basis of phenotypic variation. It is required for mapping causative loci conferring a phenotype, such as quantitative trait loci analysis (QTL) or the mapping of simple-inherited trait loci (reviewed in Lynch and Walsh, 1998; Broman and Sen, 2009). Traditionally, most of these mapping experiments involved relatively labour-intensive genotyping methods, such as Restriction Fragment Length Polymorphism (RFLP), Amplified Fragment Length Polymorphism

Chapter 1: Introduction (AFLP), or microsatellite markers (Kole and Abbott, 2008). The incorporation of NGS technologies allows sequence-based genotyping of a mapping population, and has been proven to be a successful approach for evolutionary genetic studies in non-model species (e.g. Chutimanitsakun et al., 2011; Kakioka et al., 2013; Palaiokostas et al., 2013; Campbell et al., 2014; Gonen et al., 2014). New methodologies, such as Reduced Representative Libraries (RRL; first described in Van Tassell et al., 2008), Restriction-site Associated DNA sequencing (RAD-Seq; first described in Baird et al., 2008), and Genotyping-By-Sequencing (GBS; first described in Elshire et al., 2011) enables the genotyping of thousands to tens of thousands of markers (Davey et al., 2011). This greatly enhances the linkage map density and the resolution of QTL mapping, with the great advantage that the data analysis can be done without the need of a complete reference genome (reviewed in Davey et al., 2011; Nielsen et al., 2011; Leggett and Maclean, 2014). Among these methods, the RAD-Seq approach has well developed and established analysis pipelines and has been successfully applied many times for constructing ultra-dense linkage maps (Davey and Blaxter, 2010; Davey et al., 2011; Catchen et al., 2011, 2013). It utilises the availability of diverse restriction enzymes, for fragmenting the genomic DNA and sequences hundreds to thousands of genetic markers (Reviewed in Lowry et al., 2016; Catchen et al., 2017; McKinney et al., 2017; Lowry et al., 2017). Thus, RAD-Seq is a promising approach for genetic mapping of traits for Streptocarpus . The quality of NGS data is affected by the initial quality of DNA or RNA for library preparation (Healey et al., 2014). Some plant material may have high polysaccharide and secondary metabolite content, which affects the quality and quantity of extracted nucleic acids (Križman et al., 2006; Elshire et al., 2011). These contaminants inhibit the downstream experiments such as restriction digestion and PCR, thus reducing the efficiency of NGS library preparation (Zhang et al., 2000; Healey et al., 2014). The extraction of high molecular weight nuclear DNA is important for whole genome shotgun sequencing and RAD-Seq. Severely degraded DNA can cause the loss of genetic polymorphisms and loss of important genome information (Yang et al., 2014), and can also lead to reduced RAD-tags and sites of variance (Etter et al., 2011; Graham et al., 2015). Since DNA and RNA extraction methodologies for NGS experiments have not been established for Streptocarpus , different extraction methods will be tested and optimised in this thesis.

1.4 Objectives In this study, essential genomic resources for genetic studies in the genus Streptocarpus will be acquired. Using NGS technologies, we will assemble reference genome sequences, transcriptome data, and build a genetic map for our target Streptocarpus species to carry out QTL mapping of the target traits. Two Streptocarpus species were chosen as the study material. One is the type species Streptocarpus rexii , which has an

Chapter 1: Introduction excentric rosulate growth form (Figure 1.11 a), with open-tube type flowers with pollination chambers (Figure 1.11 b). The other is Streptocarpus grandis , which has a unifoliate growth form (Figure 1.11 c), with open-type flowers with broad cylindrical tubes (Figure 1.11 d). Both species represent the section Streptocarpus in sub-genus Streptocarpus (Nishii et al., 2015), with 16 pairs of chromosome and viable interspecies hybrids can be produced (Oehlkers, 1938; 1942; Möller and Pullan, 2015). The following are the specific objectives for my PhD project:

1. Determine the DNA and RNA extraction methods optimal for Streptocarpus NGS experiments. 2. Construct draft genomes for S. rexii and S. grandis . 3. Assemble transcriptomes of S. rexii and S. grandis , based on a range of tissue types to obtain as wide as possible gene expression profiles. 4. Calculate a genetic map for Streptocarpus using a mapping population generated from a backcross population ( S. grandis Õ S. rexii ) Õ S. grandis. 5. Perform QTL mapping for vegetative and floral characters, and search of candidate genes for future fine mapping approaches.

These data obtained in the study will not only be useful for the isolation of specific genetic loci in future studies, but will also serve as an important resource for future genomic studies across this morphologically challenging group.

Chapter 1: Introduction

Figure 1.11 Study materials (a) S. rexii , mature flowering plant (b) Flower of S. rexii . Top: front view. Middle: Side view. Bottom: Ventral corollas of a dissected flower (c) S. grandis , mature flowering plant (d) Flower of S. grandis . Top: front view. Middle: Side view. Bottom: Ventral corollas of a dissected flower. Bars = 2 cm.

Chapter 2: DNA and RNA extraction Chapter 2 Establishing DNA and RNA extraction methods for Streptocarpus for next generation sequencing (NGS)

2.1 Introduction 2.1.1 Impact of DNA and RNA quality on NGS experiments High quality nucleic acids are an essential prerequisite for NGS experiments. Contamination and nucleic acid degradation during extraction can have profound negative impacts on an NGS run, such as reducing the efficiency of NGS library preparation (Zhang et al., 2000; Healey et al., 2014). DNA degradation may results in the loss of important genome regions to be sequenced, reducing the detection of genetic polymorphisms and number of single nucleotide polymorphisms (SNPs) recovered (Yang et al., 2014; Graham et al., 2015; Hart et al., 2016). Large amounts of DNA and RNA are required for library preparation and library quality check. For instance, a minimum amount of 1 µg of DNA is needed to prepare a TruSeq PCR-free whole genome shotgun sequencing library (User manual, Illumina, San Diego, CA, USA), on the contrary to traditional Sanger sequencing where as little as 100 ng of DNA is needed as sequencing template (Platt et al., 2007). Accurate quantification of the nucleic acids is also important, since pooling different samples with different amounts of DNA for the same sequencing run (e.g. RAD-Seq) can cause bias in sequencing, resulting in samples with lower DNA concertation having lower sequencing coverage, thus reducing the reliability of the genotyping results and number of markers recovered (Davey et al., 2011; Fountain et al., 2016). Previous studies of Streptocarpus species have been restricted to non-NGS genotyping method or traditional Sanger sequencing approaches (e.g. Harrison et al., 2005), which does not have such strict sample quality and quantity requirements. In order to successfully carry out NGS experiments in the Streptocarpus materials, the establishment of DNA and RNA extraction methods suitable for NGS experiments were seen as a prerequisite for this project.

2.1.2 Quality requirement of DNA and RNA for NGS experiments The quality and quantity of the nucleic acid sample can be checked by spectrophotometer, electrophoresis, and fluorometer (Endrullat et al., 2016; Hart et al., 2016). Spectrophotometer is used to assess the purity of the samples. It measures and calculates the absorbance ratio at specific wave lengths, i.e. A260/A280 and A260/A230. For pure DNA and RNA, the A260/A280 ratio is 1.8 and 2.0, respectively. On the other hand, the A260/A230 ratio should be around 2.0 for both DNA and RNA samples (Endrullat et al.,

Chapter 2: DNA and RNA extraction 2016). For NGS experiments, the A260/A280 ratio should be in the range of 1.8 – 2.0, and the A260/A230 ratio between 2.0 – 2.2. A lower A260/A280 ratio suggests contamination such as polysaccharides, phenols or ethylenediaminetetraacetic acid (EDTA), and a lower A260/A230 ratio suggests the presence of proteins and phenols (Endrullat et al., 2016). The integrity (absence of degradation) of samples can be checked by gel electrophoresis and the Agilent TapeStation system (Hart et al., 2016). For genomic DNA, the gel electrophoresis should show a sharply defined band at high molecular weight without smearing (degradation). For total RNA, the gel should show two intact bands, representing 18S and 28S rRNA that comprises 80-90% of the total RNA (Buckingham and Flaws, 2007). The TapeStation system gives a quantitative measurement of the integrity. The DNA Integrity Number (DIN) and RNA Integrity Number Equivalent (RIN e) are scales ranging from 1 (severely degraded) to 10 (highly intact), thus higher value suggest better sample quality that is suitable for NGS experiment (Hart et al., 2016). For NGS samples, DIN and RIN e values above 7 are recommended (Keats et al., 2018). The concentration of the samples can be measured by using fluorescent dye and fluorometer (O'Neill et al., 2011; Simbolo et al., 2013). The Qubit assay system utilises a fluorescent dye that binds to DNA or RNA specifically. Once bound, the dye emits fluorescence which the intensity is fluorometrically measured. Thus, it provides an accurate and specific measurement of DNA and RNA concentration with minimised interference of other contaminants (O'Neill et al., 2011; Simbolo et al., 2013).

2.1.3 Nucleic acid extraction methods used for Gesneriaceae species Plant tissues can contain high contents of polysaccharide and phenolic components that co-precipitate with nucleic acids, making the extraction of high quality DNA and RNA extra difficult (Križman et al., 2006; Elshire et al., 2011). C etyltrimethylammonium bromide extraction (CTAB; Doyle and Doyle, 1987) is a commonly used method for DNA extraction from plants, where the CTAB molecules trap proteins and polysaccharides and separate them from the nucleic acids (Tan and Yiap, 2009). However, for Gesneriaceae samples, a modified CTAB protocol or other extraction techniques are known to be used for NGS, which includes the phenol purification step (Allen et al., 2006), and was used for DNA extraction for Dorcoceras hygrometricum genome sequencing (Xiao et al., 2015). DNeasy silica-membrane spin columns were used for the extraction of DNA from Rhytidophyllum samples for genotyping-by-sequencing experiments (Alexandre et al., 2015); here, the DNA is bound to silica-membranes while the contaminants pass through and washed away (Tan and Yiap, 2009). For the sequencing of the Streptocarpus teitensis chloroplast genome, a magnetic beads-based extraction method was used (Kyalo et al., 2018): in this method, the DNA molecules are bound to magnetic beads coated with ligands or biopolymers. Contaminants are washed away with wash buffer, while the magnetic beads, with their DNA

Chapter 2: DNA and RNA extraction load, are immobilised by a magnet, thus the nucleic acid purified (Tan and Yiap, 2009). For RNA extraction, the Sigma Spectrum Plant Total RNA Kit was used for sample preparation for RNA-Seq of S. rexii (Chiara et al., 2013). This method is based on silica column purification, though the protocol has only being tested on vegetative tissues so far, i.e. leaves and cotyledons (Chiara et al., 2013), but not on floral tissues. In our research group, RNA extraction is frequently carried out using guanidium isothiocyanate-phenol- chloroform extraction (Ullrich et al., 1977; Chomczynski and Sacchi, 1987; Nishii et al., 2010a), followed by acidic phenol:chloroform (5:1) purification and a final clean-up with the PureLink RNA Mini Kit (Invitrogen, Waltham, MA, USA). This method has been tested for the extraction of both floral and vegetative tissues of the S. grandis for RNA-Seq. In this chapter, I tested and compared the DNA extraction methods mentioned above, in order to find the optimal DNA extraction protocols for the sample preparation for whole genome sequencing and RAD-Seq. The existing RNA extraction protocol was also tested on the S. rexii materials to evaluate the efficiency for the RNA-Seq sample preparation.

Chapter 2: DNA and RNA extraction 2.2 Materials and methods 2.2.1 Plant materials All plant materials were grown and maintained in the Royal Botanic Garden Edinburgh (RBGE) research glasshouses. Streptocarpus rexii (RBGE accession 20150819) and Streptocarpus grandis (RBGE accession 20150821) were used for optimising the nucleic acid extraction methods. The materials were sown and grown from seeds. All samples were collected from young actively growing leaf or cotyledons with the length within 5 cm, except for S. grandis which only a single enlarged cotyledon can be used (Figure 2.1). For DNA extraction, the leaf materials were collected from the proximal part of developing phyllomorphs, which is the area around the actively dividing basal meristem and groove meristem tissue: For S. grandis , the tissue was collected from the only phyllomorph, i.e. macrocotyledon (Figure 2.1 a). For S. rexii , young actively developing phyllomorphs were used, with the midrib length roughly 5 cm and smaller (Figure 2.1 b). To ensure uniform sample sizes, the lid of a sterile 2 ml Eppendorf tube was used to punch out discs of leaf tissue (Kim et al., 1997). The midrib and vein tissues were avoided, as they cause difficulty for grinding (Figure 2.1 c - f). For DNA extraction, the collected leaf discs were frozen immediately in liquid nitrogen after collection to prevent DNA degradation.

Figure 2.1 Standardisation of sampling of leaf disc material from Streptocarpus for DNA extraction. (a) Sampling area for S. grandis ; tissue for DNA extraction was collected from the proximal area of the leaf (yellow dashed circle). (b) Sampling area for S. rexii ; the tissue was collected from the proximal area of young actively growing leaves of 5 cm in length and smaller (yellow dashed circle). (c) – (f) Collection of leaf disc samples, (c) Leaf discs were punched out from leaves with the lid of a 2 ml Eppendorf tube. (d) The punched-out leaf disc was equal to the size of the lid. (e) The leaf disc was separated from the lid and collected for later DNA extraction. (f) The process was repeated until the desired amount of tissue was collected. Bars = 2cm.

Chapter 2: DNA and RNA extraction

For RNA extraction, leaf material was collected as described above. Root material was collected from leaf cuttings grown in perlite for about 2 weeks, and prior to RNA extraction, the roots were dug out and the perlite thoroughly washed off with tap water. Flower buds (length 0.5 – 5 cm), open flowers (length about 5.5 cm) and developing fruits (length 2 – 5 cm) were collected directly from flowering plants (Figure 2.2). For each tissue types, roughly 1 – 1.5 g (fresh weight) of materials were collected for 6 – 12 tubes of RNA extraction reactions (see section 2.2.3). All materials were frozen immediately in liquid nitrogen after collection.

Figure 2.2 Different types of tissue of S. rexii used for RNA extraction and RNA-Seq. (a) seedlings approximately 30 days after sowing (b) Leaf tissues, collected from young developing leaves smaller than 1 cm in length to medium sized leaves up to 5 cm in length (c) actively growing roots from young adventitious plantlets (d) floral buds, 1-5 mm in length (e) open flowers (f) developing fruits. Bars = 2 cm.

2.2.2 DNA extraction protocols Tissue grinding For the testing of different extraction protocols, 1 to 2 tubes of extractions of each method were performed. For each tube of extraction, 2 – 4 leaf discs collected from young actively growing leaves were used. The tissues were ground using Eppendorf tube and pellet pestle (Sigma-Aldrich, Merck, Darmstadt, Germany) with liquid nitrogen. For DNA extraction for whole genome sequencing, since a larger amount of tissues were required (i.e. 32 leaf discs for S. grandis , and 168 leaf discs for S. rexii ), the tissues were ground using mortar and pestle with liquid nitrogen.

DNA extraction (1) CTAB method (for detailed lab protocol format see Appendix 2.1) The solutions and reagents required included 4% CTAB solution (100 mM Tris HCl pH 8.0, 1.4 M NaCl, 20 mM EDTA, 4% CTAB), β-mercaptoethanol (Sigma-Aldrich),

Chapter 2: DNA and RNA extraction chloroform:isoamyl alcohol (24:1), isopropanol (Sigma-Aldrich), wash buffer (10 mM ammonium acetate in 76% ethanol), and TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich). 2 µl β-mercaptoethanol and a 2% of PVPP were freshly added into 1 ml of 4% CTAB solution immediately before the start of the experiment. The solution was preheated to 65°C before tissue grinding. The ground leaf tissue was transferred to an 2 ml Eppendorf tube containing 1 ml of the pre-heated CTAB solution, and the mixture incubated in a 65°C heat- block for 60 minutes. During incubation, the mixture was occasionally mixed by inversion of the tube. After incubation, 500 µl chloroform:isoamyl alcohol (24:1) was added and the tube placed on an orbital shaker at minimum speed for 30 minutes. The tube was then centrifuged at 11,000 rpm for 10 minutes and the aqueous phase (~700 µl) transferred to a new 1.5 ml Eppendorf tube. The chloroform:isoamyl alcohol steps were repeated, with an equal amount (~700 µl) of chilled isopropanol added to the aqueous phase, and was mixed by inversion. The sample was then stored at -20°C overnight. The next day, the sample was centrifuged at 8,000 rpm for 10 minutes for pelleting the precipitate. The supernatant was discarded, and 500 µl of wash buffer added to the tube. The tube was shaken vigorously and incubated at room temperature for 30 minutes, followed by centrifuging at 8,000 rpm for 10 minutes. The supernatant was discarded and the DNA pellet dried using a SpeedVac concentrator (Thermo Fisher Scientific, Waltham, MA, USA) for 10 to 15 minutes. The dry pellet was finally dissolved in 100 µl TE buffer, and stored at -20°C.

(2) ChargeSwitch gDNA Plant Kit (for detailed lab protocol format see Appendix 2.2) Two protocols based on the ChargeSwitch gDNA Plant Kit (Thermo Fisher Scientific) were tested; one following the manufacturer’s instructions, and the other with modifications on the incubation time, which the lysis step was extended to 60 minutes and the rest of the incubation steps extended to 30 minutes (named the ChargeSwitch Kit Extended time protocol). The protocol is as follows: The ground leaf tissue was transferred to a 2 ml Eppendorf tube containing 1 ml of L18 lysis buffer. The sample was vortexed and incubated at room temperature for 1 hour. 100 µl of 10% SDS buffer was then added to the tube and incubated at room temperature for 30 minutes. 400 µl of N5 precipitation buffer (pre-chilled) was then added, and the sample incubated in ice for 30 minutes. The sample was centrifuged at maximum speed for 5 minutes, and the lysate transferred to a clean 2 ml Eppendorf tube. 100 µl of D1 detergent was added to the tube, followed by 40 µl of resuspended ChargeSwitch Magnetic Beads, and mixed by gentle pipetting. The mixture was incubated at room temperature for 30 minutes, followed by the use of the MagnaRack TM (Thermo Fisher Scientific) to pelletise the magnetic beads, thus separating the beads from the solution. The solution was discarded, and 1 ml of W12 wash buffer added to the tube and mixed with the magnetic beads by gentle pipetting. The MagnaRack TM was used again to remove the wash

Chapter 2: DNA and RNA extraction buffer, and the washing step was repeated once. After discarding the wash buffer, 150 µl of E6 elution buffer was added and well mixed with the magnetic beads by gentle pipetting. The mixture was incubated at room temperature for 30 minutes, and then the MagnaRack TM was used to separate the beads from the DNA-containing elution buffer. The DNA elution was transferred to a clean 1.5 ml Eppendorf tube and stored in -20°C.

(3) DNAzol method (for detailed lab protocol format see Appendix 2.3) The solutions and reagents required include the Plant DNAzol TM reagent (Thermo Fisher Scientific), 100% ethanol, 75% ethanol, and TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich). The ground leaf tissue was added to an Eppendorf tube containing 300 µl DNAzol reagent and the mixture vigorously shaken. The mixture was incubated with constant shaking for 30 minutes, followed by the addition of 300 µl chloroform and mixed vigorously, and shaken again for 5 minutes. The tube was then centrifuged at 10,000 rpm for 10 minutes, and the viscous supernatant transferred to a clean 1.5 ml Eppendorf tube. 225 µl of 100% ethanol were added to the tube and mixed by inverting the tube 6 to 8 times for the precipitation of the DNA. The mixture was incubated at room temperature for 5 minutes, followed by centrifugation at 7,000 rpm for 4 minutes. The supernatant was discarded, and the precipitated DNA was washed with freshly prepared wash buffer (contains 1 volume of DNAzol with 0.75 volumes of 100% ethanol). 300 µl of the prepared wash buffer was added to the tube containing the DNA pellet, and the tube was vortexed. The sample was kept at room temperature for 5 minutes, and then centrifuged at 7,000 rpm for 4 minutes. The wash buffer was discarded, and the pellet dissolved in 70 µl TE buffer and stored at -20°C.

(4) DNeasy Plant Mini Kit (for detailed lab protocol format see Appendix 2.4) Two protocols based on the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) were tested; one following the manufacturer’s instructions and the other with an extended incubation time of 30 minutes (DNeasy Kit Extended time protocol). The DNeasy Kit Extended time protocol was as follows: The ground leaf tissue was mixed with 400 µl of AP1 buffer in an 1.5 ml Eppendorf tube and mixed by vortexing. The mixture was incubated at 65°C for 30 minutes, with occasional inversion 2 to 3 times. 130 µl of P3 buffer was added and mixed, and the tube placed on ice for 5 minutes. The sample was then centrifuged at 13,000 rpm for 5 minutes, and the lysate transferred to a QIAshredder Mini Spin Column provided in the kit. The column was centrifuged at 13,000 rpm for 2 minutes, and the flow-through transferred to a clean 1.5 ml Eppendorf tube. 1.5 volumes of AW1 buffer were added to the flow- through and mixed well by pipetting. 650 µl of the mixture was then transferred to a DNeasy Mini Spin column, followed by centrifugation at ≥8,000 rpm for 1 minute, and the flow- through discarded. The step was repeated with the remaining mixture until all was processed.

Chapter 2: DNA and RNA extraction 500 µl of AW2 buffer was added to the column for washing, then centrifuged at ≥8,000 rpm for 1 minute and the flow-through discarded. The washing step was repeated twice. After discarding the flow-through from the second wash, the column was transferred to a clean 2 ml Eppendorf tube. 100 µl of AE buffer was added to the column and incubated at room temperature for 5 minutes to elute the DNA. The column was centrifuged at ≥8,000 rpm for 1 minute to collect the DNA solution.

RNase A treatment and phenol:chloroform:isoamyl alcohol purification RNase A treatment and phenol:chloroform:isoamyl alcohol purification (PCI; 25:24:1) was performed for samples extracted using CTAB and ChargeSwitch methods. First, the volume of the DNA elution was adjusted to 300 µl by adding TE buffer. This is then followed by adding 2 µl of 4 mg / ml RNase A (#12091-021, Thermo Fisher Scientific; 1 / 5 dilution of the original stock using auto-claved distilled water) to the DNA and mixed by repeated tube inversions, and the sample was incubated at room temperature for 5 to 10 minutes. The RNase A reaction was stopped by adding 300 µl of PCI (pH 8.0) and mixed on an orbital shaker for 30 minutes. The sample was centrifuged at 11,000 rpm for 10 minutes and the aqueous phase transferred to a clean 1.5 ml Eppendorf tube (ap. 250 µl). The PCI step was repeated once. 0.1× volumes of 3 M sodium acetate (NaOAc, about 25 µl) were added to the sample, followed by 2.5× volumes of 100% ethanol and the solution mixed. The sample was kept at -20°C overnight for DNA precipitation. The sample was then centrifuged at 11,000 rpm for 10 minutes. The supernatant was discarded and 1 ml of 70% ethanol was added to the pellet and left for 30 minutes for washing. The sample was centrifuged at 11,000 rpm for 10 minutes. The supernatant was discarded and the pellet dissolved in TE buffer. The DNA eluate was incubated in a heat block at 65°C to help dissolve the DNA. The dissolved DNA was used for quality check. In addition, the ChargeSwitch extended time protocol was eventually used for the DNA extraction for whole genome sequencing (detail extraction protocol described in Appendix 2.6).

2.2.3 RNA extraction protocol RNA was extracted using the TRIzol reagent (Thermo Fisher Scientific) following the manufacturer’s manual with modifications. The RNA was then further purified using phenol:chloroform (5:1, pH 4.3 – 4.7, Sigma-Aldrich) and PureLink RNA Mini Kit (Thermo Fisher Scientific). The protocol is described in brief below and in detail in Appendix 2.7:

TRIzol extraction The tissue samples for RNA extraction were freshly collected from healthy growing plants and frozen in liquid nitrogen immediately after collection. The tissue was ground in

Chapter 2: DNA and RNA extraction liquid nitrogen using a pestle and mortar. The ground tissue was transferred to a 1.5 ml Eppendorf tube containing 1 ml of TRIzol reagent. The sample was incubated at room temperature with constant gentle shaking on an orbital shaker for 50 minutes to 1 hour. The sample was then centrifuged at 11,000 rpm and 4°C for 10 minutes, and the supernatant transferred to a clean 1.5 ml Eppendorf tube. 200 µl of chloroform (BDH, VWR International, Radnor, PA, USA) were added to the sample and mixed by shaking the tube vigorously, and the mixture kept for 2 to 3 minutes before being centrifuged at 11,000 rpm and 4°C for 10 minutes. The aqueous phase was transferred to a clean 1.5 ml Eppendorf tube, and 500 µl of ice-cold isopropanol (Sigma-Aldrich) added for RNA precipitation. The sample was stored at -20°C for over 1 hour or overnight, followed by centrifugation at 11,000 rpm and 4°C for 10 minutes. After removing the supernatant, the pellet was dissolved in 50 µl of diethyl pyrocarbonate (DEPC) treated-water for preliminary quality check.

Phenol:chloroform (5:1) solution treatment DEPC water was added to the RNA sample, or by RNA extracts from the same tissue type was combined, to make up the total volume of RNA extract to 300 µl per tube. 300 µl of phenol:chloroform (5:1, pH 4.3 - 4.7, Sigma-Aldrich) solution was then added to the sample and mixed by shaking the tube vigorously. The sample was centrifuged at 11,000 rpm for 10 minutes, and the aqueous phase transferred to a clean 1.5 ml Eppendorf tube. This step was repeated once. 300 µl of ice-cold isopropanol was added to the sample, and the sample stored at -80°C overnight for RNA precipitation. After the overnight incubation, the sample was centrifuged at 11,000 rpm for 10 minutes. The supernatant was discarded. This was followed by adding 500 µl of 75% ethanol to the sample for washing. The sample was centrifuged at 9,000 rpm for 5 min. The supernatant was discarded and the remaining liquid carefully removed with a pipette. The pellet was dissolved in 100 µl of DEPC water for preliminary quality checks.

PureLink RNA Mini Kit purification The RNA sample was further purified using the PureLink RNA Mini Kit (Thermo Fisher Scientifc) with modifications of the original protocol. The lysis buffer was first prepared by adding 4 µl of β-mercaptoethanol (Sigma-Aldrich) into 400 µl of Lysis Buffer provided in the kit. The prepared lysis buffer was then added to the RNA sample, mixed by vortexing and incubated for 3 minutes. 200 µl of ethanol was added to the sample, and the sample mixture was transferred to the spin cartridge and centrifuged at 11,000 rpm for 1 minute. The flow-through was discarded and 600 µl of Wash Buffer I were added to the cartridge. The sample was centrifuged at 11,000 rpm for 1 minute. The flow-through was discarded and the collection tube (tube below the spin cartridge) was replaced with a clean one. 400 µl of Wash Buffer II was added to the spin cartridge and the sample centrifuged at

Chapter 2: DNA and RNA extraction 11,000 rpm for 1 minute. The flow-through was discarded, and the Wash Buffer II step was repeated once. The spin cartridge was centrifuged at 11,000 rpm for 2 minutes, and the column of the spin cartridge was transferred to a recovery tube. 40 µl of RNase-free water was added to the centre of the column and incubated at room temperature for 10 minutes to elute the RNA. The column was centrifuged at 11,000 rpm for 2 minutes and the collected RNA was used for final quality checks.

2.2.4 Quantification and quality control of the nucleic acid samples For both DNA and RNA, the absorbance ratios (i.e. A260/A280 and A260/A230) of the extracted samples were measured using the NanoVue TM Plus spectrophotometer (GE Healthcare, Chicago, IL, USA). The concentration measured by the spectrophotometer was also recorded. The final concentration was determined using the Qubit dsDNA HS Assay Kit or the Qubit RNA HS Assay Kit with the Qubit 2.0 fluorometer (Thermo Fisher Scientific). For the Qubit assay, 1/2 dilutions of the samples were first prepared and 2 µl of the dilution were loaded on the machine according to the manufacturer’s manual. The integrity of the samples was first checked by agarose gel electrophoresis with 1% agarose gels at 100 volts for 45 minutes. Finally, the DIN and RIN e values of the DNA and RNA samples were assessed using Agilent TapeStation system (Agilent Genomics, Santa Clara, CA, USA) installed in Edinburgh Genomics (The University of Edinburgh, Edinburgh, UK). The DIN and RIN e values were only checked for the selected samples used for whole genome sequencing and RNA-Seq.

Chapter 2: DNA and RNA extraction 2.3 Results 2.3.1 Testing different DNA extraction protocols The different DNA extraction protocols were first tested on fresh S. grandis material. The CTAB method resulted in an overall medium quality DNA (A260/A280 ratio around 2.0; A260/A230 ratio around 1.1 – 1.2) and medium quantity (4,000 – 5,000 ng of total DNA) with little difference between using 2 or 4 leaf discs (Table 2.1). However, the gel electrophoresis result showed intensely smeared bands at around 500 to 1,000 bp and below 200 bp (Figure 2.3 a). The ChargeSwitch Kit extraction results varied greatly depending on the protocol used: the original ChargeSwitch Kit protocol gave poor DNA quality and quantity (A260/A280 = 4.450; A260/A230 = 0.640; 660 ng of total DNA). By extending the incubation time, the ChargeSwitch KitExtended time protocol gave the best DNA extraction results among the methods tested (A260/A280 = 2.144; A260/A230 = 1.739; 37,950 ng of total DNA). The gel electrophoresis of the ChargeSwitch Kit Extended time protocol showed a single intact band of DNA (Figure 2.3 b). The DNAzol protocol extracted a large amount of DNA but of poor quality: The amount extracted DNA was high and ranged from around 18,000 to 26,000 ng with 2 and 4 leaf discs respectively, and the A260/A280 ratio was around 1.7, but the A260/A230 ratio was very low (< 0.8 for both 2 and 4 leaf discs). The gel electrophoresis showed a single band of DNA (Figure 2.3 c). The DNeasy Kit yielded the least usable DNA in terms of quality and quantity, with highly variable A260/A280 ratio (1.5 and 4.5 for 2 and 4 leaf discs respectively) and a much lower A260/A230 ratio compared the the requirement (0.017 and 0.018 respectively). The total amount of DNA extracted was also low (50 ng for both 2 and 4 leaf discs). The result improved slightly after the incubation time was extended but still showed poor quality and quantity (A260/A280 = 2.700; A260/A230 = 0.953; 2,075 ng of total DNA). The gel electrophoresis did not show any visible band (Appendix 2.8). The two overall best methods (CTAB method and ChargeSwitch Kit) were also tested on the S. rexii material (Table 2.1 lower part). The performance of the CTAB protocol was similar to that in S. grandis , generating a medium quality and quantity of DNA (A260/A280 = 2.218; A260/A230 = 1.271; 3,050 ng of total DNA). The gel electrophoresis pattern is cleaner, with a single sharp high-molecular-weight band and only little smearing below 200 bp (Figure 2.3 b, left). The original ChargeSwitch protocol produced a poor quality and low quantity of extracted DNA similar to that in S. grandis . The ChargeSwitch Kit Extended time protocol again gave the best quality DNA (A260/A280 around 1.8; A260/A230 around 1.7 to 1.9) and quantity depending on the use of 2 leaf discs (3,750 ng total DNA) or 4 leaf discs (6,225 ng of total DNA). The gel electrophoresis result shows a single band with only weak smearing below 200 bp (Figure 2.3 b, right).

Table 2.1 Quality check results of DNA extracted from S. grandis and S. rexii using different protocols Elution Total DNA extracted as Species Starting material Extraction method A260/A280 A260/A230 volume† measured by NanoVue (ng) S. grandis 2 leaf discs CTAB 2.062 1.250 50 4,125 S. grandis 4 leaf discs CTAB 2.073 1.137 50 4,975 S. grandis 4 leaf discs ChargeSwitch Kit 4.450 0.640 150 660 S. grandis 4 leaf disc s ChargeSwitch Kit Extended time 2.144 1.739 150 37,950 S. grandis 2 leaf discs DNAzol 1.718 0.202 70 26,757 S. grandis 4 leaf discs DNAzol 1.734 0.734 70 18,165 S. grandis 2 leaf discs DNeasy Kit 1.500 0.018 100 50 S. grandis 4 leaf discs DN easy Kit 4.500 0.017 100 50 S. grandis 2 leaf discs DNeasy Kit Extended time 2.700 0.953 100 2,075 S. rexii 4 leaf discs CTAB 2.218 1.271 50 3,050 S. rexii 4 leaf discs ChargeSwitch Kit 5.462 0.640 150 615 S. rexii 2 leaf discs ChargeS witch Kit Extended time 1.805 1.689 150 3,750 S. rexii 4 leaf discs ChargeSwitch Kit Extended time 1.886 1.976 150 6,225 † The volume for elution varied among the different DNA extraction protocols

Chapter 2: DNA and RNA extraction

Figure 2.3 Gel electrophoresis results of DNA extracted using different extraction protocols. (a) S. grandis CTAB extraction. (b) S. grandis ChargeSwitch Extended time extraction. (c) S. grandis DNAzol extraction. (d) S. rexii CTAB extraction. (e) S. rexii ChargeSwitch Extended time extraction. Numbers beside the 1Kb+ Ladder indicates the molecular weight in base pairs.

To further purify the extracted DNA, RNase A treatment and PCI purification were performed on the extracted DNA using the CTAB and ChargeSwtich Kit Extended time methods in both S. grandis and S. rexii (Table 2.2). In S. grandis , the CTAB-extracted DNA had an A260/A280 ratio of 2.125 and an A260/A230 ratio of 1.708 prior to the treatment (Table 2.2). After the RNase A and phenol purification, the A260/A280 ratio was improved to 1.826 but the A260/A230 ratio decreased to 1.613, though the recovery rate of the DNA was about one fifth after the treatment (from 16,100 ng of DNA prior to 3,000 ng DNA post purification). For the ChargeSwtich Kit Extended time method, the A260/A280 and A260/A230 ratios were 2.144 and 1.739 respectively prior to the purification treatment. After the treatment, the values improved to 1.897 and 2.395 respectively. The total amount of DNA extracted measured by the Qubit assay showed great discrepancies in values compared to NanoVue spectrophotometry (Table 2.2). In the CTAB extraction, the DNA measured by Qubit was 482 ng in total, which is only about 16% of the

Chapter 2: DNA and RNA extraction value measured by NanoVue (3,000 ng). In the ChargeSwitch Kit Extended time extraction, the Qubit measurement was 518 ng of total DNA, which was only about 3% of the value from NanoVue (17,130 ng). Gel electrophoresis indicated that RNase A and phenol purification successfully removed the smearing observed in both extraction methods prior purification (Figure 2.4 a). For the S. rexii samples, the DNA quality also improved after PCI treatment (Table 2.2). In the CTAB extraction, prior to the treatment the DNA sample had A260/A280 and A260/A230 ratios of 2.218 and 1.271, respectively. After the treatment, the A260/A280 ratio decreased to 1.562, and the A260/A230 ratio increased to 1.444. The ChargeSwitch Kit Extended time method gave the best result; prior and after the treatment the A260/A280 and A260/A230 ratios were 1.886 and 1.728, and 1.976 and 2.188, respectively. In the CTAB extraction the Qubit measurement for total extracted DNA (182 ng) was only about 14% of that measured by NanoVue (1,300 ng). In the ChargeSwitch Kit Extended time extraction, the amount measured by Qubit (328 ng) was about 21% of the NanoVue measurement (1,520 ng, Table 2.2). The smearing observed prior to the treatment was also removed in both extractions (Figure 2.4 b).

Table 2.2 Quality check results of the S. grandis and S. rexii DNA before and after RNase A treatment and phenol purification.* Total DNA Concentration as Total DNA extracted A260/ A260/ Elution extracted as Species Extraction method* measured by as measured A280 A230 volume† measured by Qubit (ng/µl) by Qubit (ng) NanoVue (ng) S. grandis CTAB 2.125 1.708 50 16,100 N/A N/A

S. grandis CTAB + RNase A + PCI 1.826 1.613 15 3,000 32.1 482

S. grandisº ChargeSwitch Kit Extended time 2.144 1.739 150 37,950 N/A N/A ChargeSwitch Kit Extended time S. grandis 1.897 2.395 20 17,130 25.9 518 + RNase A + PCI S. rexiiº CTAB 2.218 1.271 50 3,050 N/A N/A

S. rexii CTAB + RNase A + PCI 1.562 1.444 15 1,300 12.1 182

S. rexiiº ChargeSwitch Kit Extended time 1.886 1.976 150 6,225 N/A N/A ChargeSwitch Kit Extended time S. rexii 1.728 2.188 20 1,520 16.4 328 + RNase A + PCI N/A - The values were not measured; PCI - phenol:chloroform:isoamyl alcohol treatment; * - starting material in all cases was 4 leaf discs. º - values taken from first experiment for comparison.

Chapter 2: DNA and RNA extraction

Figure 2.4 Gel electrophoresis results of the extracted DNA after RNase A treatment and phenol purification. (a) S. grandis . (b) S. rexii . Numbers beside the 1Kb+ Ladder indicates the molecular weight in base pairs. +R RNase A treatment, +P Phenol purification.

2.3.2 DNA extraction for whole genome sequencing of S. grandis and S. rexii The ChargeSwitch Kit Extended time with RNase A treatment and phenol purification protocol (Appendix 2.6) was used to extract the DNA samples required for the whole genome shotgun sequencing of S. grandis and S. rexii . For the S. grandis extraction, 8 tubes of ChargeSwitch reactions (totally 32 leaf discs) were processed and the DNA combined. The extracted DNA had an A260/A280 ratio of 1.887, and an A260/A230 ratio of 1.879. The

Chapter 2: DNA and RNA extraction concentration measured using the Qubit assay was 37.1 ng/µl, and the total amount of DNA extracted was 2.7454 µg (37.1 ng/µl × 74 µl). On average, each ChargeSwitch reaction provided approximately 343 ng of DNA. The gel electrophoresis showed a single high- molecular-weight DNA band (Figure 2.5 a). The sample had a DIN value of 7.8 and successfully passed the quality control test required (Figure 2.5 b and c), and was used for the whole genome sequencing library preparation. The same method was applied for the extraction of S. rexii DNA. 42 tubes of ChargeSwitch reactions were carried out and combined (about 168 leaf discs in total). The extracted DNA had an A260/A280 value of 1.898 and an A260/A230 value of 1.915. The Qubit concentration was 20 ng/µl, and the total amount of DNA extracted 9.565 µg (20 ng/µl × 478 µl, Table 2.4). On average, each ChargeSwitch reaction produced about 227 ng of DNA. The gel electrophoresis of the sample showed a single intact band (Figure 2.6 a). The TapeStation system gave a DIN value of 7.6 and a clear electropherogram (Figure 2.6 b, c). The sample successfully passed the quality control requirement and was used for the whole genome sequencing library preparation.

Chapter 2: DNA and RNA extraction

Figure 2.5 Gel electrophoresis and TapeStation quality check results of the S. grandis DNA sample used for whole genome sequencing. (a) Gel electrophoresis image. (b) Gel image of the TapeStation run. (c) The electropherogram of the samples from the TapeStation run. (d) NanoVue measurement results.

Chapter 2: DNA and RNA extraction

Figure 2.6 Gel electrophoresis and TapeStation quality check results of the S. rexii DNA sample used for whole genome sequencing. (a) Gel electrophoresis image. (b) Gel image of the TapeStation run. (c) The electropherogram of the samples from the TapeStation run. (d) NanoVue measurement results.

Chapter 2: DNA and RNA extraction

2.3.3 RNA extraction for RNA-Seq of S. rexii RNA extraction was performed on different tissues of S. rexii (Table 2.3). Among these extractions, the seedling and flower tissues gave the best quality RNA, with an A260/A280 ratio around 2.0 and an A260/A230 ratio above 2.0. Leaves and root tissue extractions have a good A260/A280 ratio, but both showed lower than expected A260/A230 ratios (1.227 and 1.454, respectively). The floral bud and fruit showed poor quality RNA, with low A260/A280 and A260/A230 ratios (Table 2.3). In total, 3,936 ng of RNA were extracted according to the NanoVue measurement.

Table 2.3 Quality check results of the RNA extractions from different S. rexii tissues Elution Total RNA extracted Tissue A260/ A260/ Species volume measured by type A280 A230 (µl) NanoVue (ng) S. rexii Seedlings 2.114 2.070 30 901 Leaves 2.245 1.227 30 152 Roots 2.299 1.454 30 409 Buds 1.147 1.146 20 1,179 Flowers 1.981 2.304 20 1,017 Fruits 1.478 0.831 15 278 Total 145 3,936

The resulting RNA samples were combined, and the quality and quantity measured again. The A260/A280 ratio was 2.117, and the A260/A230 ratio was 1.822. The concentration measured by Qubit was 602.5 ng/µl, and the total amount measured as 80,132 ng of RNA. The value is about 20-fold higher than the previous NanoVue measurements combined (totally 3,936 ng of RNA, Table 2.5). The gel electrophoresis showed a clear 18S and 28S rRNA banding pattern (Figure 2.7 a). The sample had a RIN e value of 8.6 (Figure 2.7 b, c). This sample successfully passed the quality test and was used for library preparation for RNA-Seq.

Chapter 2: DNA and RNA extraction

Figure 2.7 Gel electrophoresis and TapeStation quality check results of the S. rexii RNA sample for RNA-Seq. (a) Gel electrophoresis image. (b) Gel image of the TapeStation run. (c) The electropherogram of the samples from the TapeStation run. (d) NanoVue measurement results.

Chapter 2: DNA and RNA extraction

2.4 Discussion 2.4.1 Comparisons between different DNA extraction methods Among all the DNA extraction protocols tested, the ChargeSwitch gDNA Plant Kit with extended incubation time (ChargeSwitch Kit Extended time ) followed by RNase A treatment and phenol purification gave the best DNA extraction results (Table 2.4). This protocol gave an intact high-molecular-weight DNA band, a reasonable DIN value, and absorbance ratios within the recommended ranges. The method was successfully applied for the extraction of the whole genome sequencing samples and passed the quality check. Thus, a method was established to successfully prepare NGS-grade quality DNA from S. grandis and S. rexii . Extensive modifications of the protocol for the supplier of the ChargeSwitch Kit were required for maximal results, as the original protocol only extracted low quality and quantity of DNA from both Streptocarpus species (Table 2.1). One possibility is that the amount of leaf tissue used per extraction, 4 leaf discs, exceeds the suggested starting material amount (i.e. 100 mg, ChargeSwitch gDNA Plant Kit manual). The average weight per leaf disc of S. grandis and S. rexii was around 30 to 50 mg (Appendix 2.9), larger than the suggested 100 mg if four leaf discs were combined. Similar results were observed in CTAB extractions, which the DNA yield from two and four leaf discs were very similar (Table 2.1). Yet, since four leaf discs still gave higher DNA yield, four instead of two leaf discs was chosen for the rest of the DNA extraction testing. It should be noted that the original ChargeSwitch protocol has not been tested using appropriate amount of leaf disc yet (e.g. 2 or 3 leaf discs, Table 2.1). In ChargeSwitch protocol, the potential limitation on starting material was overcame by extending the incubation time from the original 1 minute up to 60 minutes at the lysis step and 30 minutes at the rest of the incubation steps. After the modification, the quality of the DNA improved and the total DNA extracted increased. In addition, RNase A treatment and phenol purification were shown to be crucial for the sample preparation. Similar results were observed in CTAB extraction, which ribosomal RNA-like smear presented in the gel electrophoresis result prior to the treatment (Figure 2.3 a). The NanoVue A260/A280 ratio is also higher than 2.0 prior to RNase A, suggesting the presence of RNA in the sample. After the RNase A and phenol treatments, the smear disappeared and the NanoVue values improved (Figure 2.4 and Table 2.2). In general, longer incubation time and phenol purification is required for extracting high quality DNA from the Streptocarpus materials. Even with these modifications, the performance of the CTAB extraction protocol is still unstable (e.g. varying total DNA yield and A260/A230 ratio; Table 2.1 and 2.2). There are many factors that may contribute to the final extraction quality and quantity, for instance, the age and general condition of the leaf material used, or high levels of phenolic compounds may present in the leaves tissues (Inoue

Chapter 2: DNA and RNA extraction et al., 1982; 1984; Sheridan et al., 2011). The Streptocarpus species, S. dunnii and S. saxorum , are known to have the phenolic compounds quinones (Hook et al., 2014). Furthermore, phenolic glucosides are known to present in many Gesneriaceae species that might be related to anti-fungal and anti-microbial activity (Verdan and Stefanello, 2012). It is not known whether these compounds accumulate in S. grandis and S. rexii , and whether the presence of these compounds affects the extraction quality or not. But in general phenolic and polysaccharides can make the DNA extracts appeared as viscous, glue-like, and brown in colour (Zhang et al., 2000; Healey et al., 2014). Interestingly, DNA yield difference was found between S. rexii and S. grandis samples when both were extracted using ChargeSwitch extended time protocol (Table 2.2). S. rexii tissues tend to yield less DNA (totally ~9,500 ng DNA from 168 leaf discs; 56.5 ng per leaf disc) comparing to the S. grandis (totally ~2,700 ng DNA from 32 leaf discs; 84.3 ng per leaf disc). Since leaf tissues of similar age and properties (i.e. proximal leaf, near the actively dividing basal and groove meristem) were used in all extractions, it is unlikely that the difference was caused by sampling bias. One possible explanation is that the vein tissues of S. grandis are easier to remove comparing to the S. rexii due to their larger size. The vein tissues are difficult to grind, and if the majority of the vein tissues were excluded in S. grandis extractions, it may contributed better grinding and higher DNA recovery. Another possibility is the two species may share dissimilar physiological properties, such as different secondary metabolites, that may affect the purity of extracted DNA. The established ChargeSwitch Kit Extended time protocol is more similar to the protocol used in Kyalo et al. (2018) for the sequencing of the S. teitensis chloroplast genome, where the DNA was extracted using the magnetic bead-based MagicMag Genomic DNA Micro Kit (Sangon Biotech Co.). On the other hand, while the CTAB protocol and the DNeasy Kit were reported to have worked for the DNA extraction from Dorcoceras and Rhytidophyllum species, respectively (Xiao et al., 2015; Alexandre et al., 2015), they failed to do so on the Streptocarpus materials in the present study. It is known that the CTAB method can remove neutral-pH polysaccharides, but cannot separate acidic polysaccharides from the DNA (Tan and Yiap, 2009). It is possible that the polysaccharides of the other Gesneriaceae genera have different pH properties to those in Streptocarpus , and thus CTAB protocol could worked but not in Streptocarpus . A disadvantage of the ChargeSwitch Kit extraction protocol is the high unit cost per reaction; according to the results obtained here, each ChargeSwitch reaction extracted about 200 – 300 ng of DNA from 4 leaf discs, and each reaction costs about £3.00. For a genotyping experiment which requires the extraction of hundreds of samples with at least a minimum yield of 500 ng of DNA each (for e.g. RAD-Seq), the cost for the extraction kit itself would be over 1,000 pounds for 200 samples. In addition, the CTAB-extracted-DNA

Chapter 2: DNA and RNA extraction was successfully digested when tested with restriction enzyme, which is a key step during RAD-Seq library preparation (Baird et al., 2008; Peterson et al., 2012). Thus, the modified CTAB method was later chosen to be used for the DNA extraction of the mapping population for RAD-Seq experiments (Table 2.4 and Appendix 2.5, see details in Chapter 5). On the other hand, the ChargeSwitch Kit Extended time protocol remained the best method for DNA extraction and was used to prepare the DNA samples for whole genome shotgun sequencing.

Table 2.4 Comparison of the DNA extraction results for different methods for the Streptocarpus leaf material Protocol Results Usage in this thesis ChargeSwitch Kit Ex tend ed time A260/A280 1.7 to 1.9 Used to extract DNA from S. + RNase A treatment A260/A230 ~2.0 grandis and S. rexii for + phenol purification Extracts ~300 ng DNA from 4 whole genome sequencing (Appendix 2.6) leaf discs (see Chapter 3) Modified CTAB method A260/A280 1.5 to 1.9 Used to extract DNA from + RNase A treatment A260/A230 < 2.0 the mapping population for + phenol purification Extracts ~500 ng DNA from 4 RAD-Seq (see Chapter 5) (Appendix 2.5) leaf discs ChargeSwitch Kit A260/A280 > 4 N/A A260/A230 < 1 Very low DNA recovery

DNAzol A260/A280 ~1.7 N/A A260/A230 < 1 High DNA recovery

DNeasy Kit Extend time A260/A280 2.7 N/A A260/A230 < 1 Low DNA recovery

DNeasy Kit Highly variable A260/A280 N/A Very low A260/A230 Very low DNA recovery

2.4.2 Revaluating the RNA extraction protocol The RNA extraction protocol established for RNA-Seq of S. grandis (Appendix 2.7) was shown to be suitable for the extraction from S. rexii materials (Figure 2.7), although quality variation was observed among the different tissue types (Table 2.3). The same observation was made in other crop species, such as strawberries and cardamom, in which the RNA extracted from fruits are particularly low in quality even when using optimised protocols (Nadiya et al., 2015; Christou et al., 2014). In S. rexii , our method was most

Chapter 2: DNA and RNA extraction effective for the extraction of RNA from seedlings and open flowers, the products of which had reasonable absorbance ratios. The method was applicable to the developing leaves and root tissues, but the extracted RNA had low A260/A230 ratios. On the other hand, the method only resulted in RNA of poor quality from the floral buds and developing fruits (Table 2.3). This implies that current protocols cannot fully remove the polysaccharide and phenolic contaminants. Some studies suggested that CTAB-based methods produce high quality RNA in the presence of high concentration of PVP and β-mercaptoethanol (Nadiya et al., 2015; Sánchez et al., 2016). Other methods, such as the RNeasy Kit, may be an option to be tested for future extractions. Nevertheless, our combined RNA samples passed the NGS quality requirements, indicating that the current protocol is suitable for our material.

2.4.3 Discrepancies between quantification by spectrophotometer and Qubit assay The DNA concentrations measured by Qubit assay were significantly lower than the measurements obtained from the NanoVue spectrophotometer (Table 2.2). It is frequently reported that the NanoVue overestimates DNA concertation, as the spectrophotometer cannot distinguish target nucleic acids from the accompanying contaminants that also absorb lights of 260 nm wavelength (O'Neill et al., 2011; Simbolo et al., 2013). On the other hand, Qubit assay has been demonstrated to be more accurate, and the measurement result is closer to that obtained from highly-specific PicoGreen nucleic acid quantification method (O'Neill et al., 2011; Garcia-Elias et al., 2017). This suggests that there may still be contamination remained in the ChargeSwitch-extracted DNA samples, thus causing the overestimation of DNA concentration in NanoVue measurement. However, NanoVue may have underestimated the RNA concentration of our RNA extractions, which the NanoVue measurement was 20 folds lower than the concentration obtained from Qubit assay (Table 2.3). This is unlikely to be an experimental error, as it was observed repeatedly in other Streptocarpus RNA extractions (> 25 species; unpublished data). It was reported that the secondary structure of RNA may prevents UV absorbance thus lowering the NanoVue measured quantity, and the solution is to denature the RNA sample at 70°C for 2 minutes prior to the measurement, which may enhance the accuracy and increase the measured concentration by up to 25% (Aranda et al., 2009). Still, this does not explain the 20 folds difference observed in our measurements. Overestimation of the RNA concentration by Qubit assay has so far only been reported in non-peer reviewed technical reports (Fischer et al., 2016). Comparisons of multiple quantification methods (e.g. qPCR and PicoGreen) on quantifying serial diluted samples may be needed to determine which methods can more accurately reflect the actual RNA concentration (Aranda et al., 2009; Garcia-Elias et al., 2017).

Chapter 2: DNA and RNA extraction

2.4.4 Conclusion Nucleic acid sample preparation is the first step of any sequencing experiment. Here the optimised DNA and RNA extraction methods for Streptocarpus materials were found, which were successfully used to extract DNA and RNA suitable for NGS experiments. The ChargeSwitch Kit Extended time protocol was selected for DNA extraction for whole genome shotgun sequencing, and a modified CTAB method with RNase A treatment and phenol purification was chosen for the DNA extraction for RAD-Seq experiments. For RNA extraction of sufficient quality for RNA-Seq for S. rexii , the protocol set up for Streptocarpus materials by the Royal Botanic Garden Edinburgh Gesneriaceae research group was found suitable. These methods were applied in sequencing experiments reported in the following chapters of this thesis.

Chapter 3: Building genome resource Chapter 3 Building genome resources – Genome sequencing and de novo genome assembly of Streptocarpus rexii and S. grandis

3.1 Introduction 3.1.1 Genome size and chromosome count of S. rexii and S. grandis Plant genome size and complexity vary greatly among species. For example, the smallest angiosperm genome currently known is that of the Genlisea margaretae approximately 63 mega base pairs (Mbp) (Greilhuber et al., 2006), while the largest genome reported so far is that of the octoploid lily Paris japonica with about 148.8 giga base pairs (Gbp) (Pellicer et al., 2010). Large and polyploid genomes are more costly in terms of sequencing to the same depth of coverage comparing to smaller genome, and are more difficult to assemble comparing to smaller and diploid genomes (Li and Harkess, 2018). The Streptocarpus materials chosen in this study, S. rexii and S. grandis , have medium sized genomes among angiosperms. The monoploid genome contents (1C value) are 0.95 pg and 1.289 pg respectively (Möller, 2018), which correspond to an estimated genome size of 929.1 Mbp for S. rexii , and 1,260.6 Mbp for S. grandis (Cavaller-Smith, 1985). Streptocarpus rexii and S. grandis both belong to the subgenus Streptocarpus , and are both diploid with 16 pairs of chromosomes (2n = 32; Lawrence et al., 1939).

3.1.2 Currently available genome resources for Gesneriaceae and Lamiales Genome assemblies are available for one Gesneriaceae and several Lamiales species. For the Gesneriaceae family, the genome of Dorcoceras hygrometricum was sequenced and consists of 520,969 scaffolds, with a total span of 1,548 Mbp and an N50 value of 110,988 bp (Xiao et al., 2015). In the order Lamiales, genomes such as that of sesame ( Sesamum indicum, Pedaliaceae; Wang et al., 2016) and spotted monkey flower (Mimulus guttatus ; Hellsten et al., 2013. now Erythranthe guttata , Phrymaceae; Nesom, 2012) are available. Sesame has a high quality genome with 17 chromosomes and a total span of about 270 Mbp (Wang et al., 2016). The E. guttata genome, a recently established model organism, consists of 1,507 scaffolds, with a total span of 312.7 Mbp and an N50 value of 21.2 Mbp. However, due to the distant phylogenetic relationship of these species with Streptocarpus , their genomes may show little homology to that of Streptocarpus , and thus may not be a suitable reference sequence for this study.

Chapter 3: Building genome resource 3.1.3 Whole genome sequencing and its applications A major application of a genome assembly is to serve as reference sequence for the analyses of other NGS data, including RNA-Seq and RAD-Seq (Ekblom and Wolf, 2014). Eventhough genome-scale screening can be conducted without a reference (Davey et al., 2011; Haas et al., 2013), analyses using a reference sequence can improve the results by recover genes or genotypes with lower sequencing coverage, and to correct sequencing errors (Lu et al., 2013; Haas et al., 2013; Florea and Salzberg, 2013). The combination of de novo assembly and reference-guided assembly of RNA-Seq data was shown to provide the best quality transcriptome in terms of both transcript length and number (Lu et al., 2013). For the analysis of RAD-Seq data, by mapping the reads to the reference genome, the chance of genotyping error is decreased as the required depth of coverage for correct genotyping is lower (Fountain et al., 2016). Reference-guided analysis can also increase the number of markers recovered and improve the resolution of the resulting genetic map (Shafer et al., 2016). Reference genomes for the Streptocarpus species are thus invaluable for this study. It can improve the resolution and reduce the chances of error in RNA-Seq and RAD-Seq data analyses, and the assembly itself can provide sequence information at the targeted genetic regions that QTL mapping identifies. To obtain the genome assemblies of both S. rexii and S. grandis is also useful, since the comparative information may help identifying sequence differences of developmental regulating genes, untranslated introns or promoter regions, or for designing fine-mapping markers for further study once a genetic region of interest is identified. Therefore, the genomes of both S. rexii and S. grandis will be sequenced and analysed here.

3.1.4 Genome sequencing and assembly strategy The output of different NGS technologies varies in read length, amount of data output, sequencing error rate, and cost (Goodwin et al., 2016; Van Dijk et al., 2018). Third generation sequencing technologies are suitable for resolving complex genomes with many repetitive elements, but they are more costly and have lower throughput per run thus reducing the depth of coverage of the target genome. Approaches such as mate-pair libraries (Illumina, San Diego, CA, USA), chromosome conformation capture (Dovetail Genomics, Santa Cruz, CA, USA) and optical mapping (Bionano Genomics, San Diego, CA, USA) are suitable for the production of chromosome-level scaffoldings based on preliminary genome assemblies (Jiao et al., 2017), and are thus not considered in this study. On the other hand, the Illumina sequencing-by-synthesis approach provides higher data output per unit cost of sequencing (Goodwin et al., 2016), allowing higher sequencing coverage for the medium- sized genome of Streptocarpus (Sims et al., 2014). By using the Patterned Flow Cell technology such as the HiSeq 4000 and HiSeq X, up to 650 to 900 Gbp of data can be

Chapter 3: Building genome resource generated in a sequencing experiment from a single flow cell with 8 lanes (Product specification, Illumina). By sequencing the Streptocarpus genome in a single lane, the 80 to 112 Gbp data generated can provide an approximately 100× sequencing depth for the ~1 Gbp genome of Streptocarpus (Goodwin et al., 2016). Since no reference genome exists of a species closely related to Streptocarpus , de novo assembly is required to produce a draft genome from the sequencing data. De novo assembly describes the process of reconstructing contiguous sequences from shorter nucleotide fragments (‘reads’ from sequencing experiments) without the guidance of a reference (Ekblom and Wolf, 2014). Many difficulties can be encountered throughout this process, such as sequencing errors, low depth of coverage, repetitive elements or cross- species contamination. Hence, the aim during de novo assembly is to minimise these errors (Ekblom and Wolf, 2014; Laurence et al., 2014; Sims et al., 2014; Compeau et al., 2017). This can be achieved through a cautious choice of assembly tools, assembly parameters, and stringent quality control and filtering (e.g. Baker, 2012; Ekblom and Wolf, 2014; Dominguez Del Angel et al., 2018). Bioinformatics tools for genome assembly using NGS data have also been developed and are readily available. For example, SOAPdenovo (Luo et al., 2012) and ABySS (Simpson et al., 2009; Jackman et al., 2017) are commonly used for assembling medium to large-sized genomes without the requirement of enormous amounts of computing resources. SOAPdenovo was used for the de novo assembly of the D. hygrometricum and Nicotiana tabacum genomes (Sierro et al., 2014; Xiao et al., 2015), while ABySS has also been proven to be able to deal with highly complex plant genomes such as that of bread wheat ( Triticum aestivum ) (IWGSC, 2014). Both SOAPdenovo and ABySS assemblers use the de Bruijn graph method (DBG) for de novo genome assembly (Li et al., 2012). In brief, the DBG approach starts by breaking down the sequencing reads into a series of “ k-mers” (substrings of the original read with a length of k). A de Bruijn graph is then constructed from these k-mers, where each k-mer represents a node and the overlapping region between k-mers are indicated by arrows (called directed edges). By walking through the directed edges, clipping off stranded node tips, and resolving the graph structures such as bubbles and low coverage nodes, a long-contiguous “contig” is reconstructed (Figure 3.1; Li 2012; Luo et al., 2012; Jackman et al., 2017; Compeau et al., 2017). Usually, this is followed by using the read-pair information, possibly from the paired-end library or an additional long-insert library (Goodwin et al., 2016). The reads/contigs from the same read pair are joined together, and the gaps between the sequences are estimated from the insert size of the library, and replaced with “N” bases, thus forming the “scaffolds”, meaning noncontiguous sequences with gaps of known length (Van Dijk et al., 2018).

Chapter 3: Building genome resource

Figure 3.1 A simplified example of a DBG method assembly. In this example the target is to obtain the 20 bp-length genomic region on the top sequence. Sixteen k-mers can be obtained with the k value of 5 bp. The k-mers were used to construct the de Bruijn graph (bottom graph), where each node represents one k-mer, and the arrows (directed edges) indicate the direction of the assembly. Each pair of connected nodes differs only by one nucleotide at the beginning and at the end. By walking through the directed edges from the beginning to the end, the original sequence is reconstructed (modified from Li et al., 2012).

In practice, the k value of the k-mers is a major parameter to be tested and optimised for different assemblers (Li et al., 2012; Compeau et al., 2017; Mapleson et al., 2017). Larger k values retain more unique k-mers, which helps to resolve low-complexity short tandem repeats and reconstruct longer contigs; smaller k values may not span across the regions, but can recover assemblies with lower sequencing depth (Li et al., 2012; Compeau et al., 2017). In any case, the k value should be larger than half of the read length, and it should be an odd number to avoid sequence palindromes (Simpson et al., 2009; Reuter et al., 2015). The optimal k value can be determined by checking the shape of the k-mer histograms and by comparing assemblies reconstructed with iterative k values (Mapleson et al., 2017). Additionally, k-mer information is useful for the estimation of genome properties, i.e. genome size, heterozygosity, repeat content (Marçais and Kingsford, 2011; Mapleson et al., 2017; Vurture et al., 2017). Another common problem associated with whole genome assembly is cross-species contamination. Biological samples are usually contaminated with traces of bacteria, algae, fungi, symbionts, and arthropods from the surrounding environment or introduced during nucleic acid sample preparation (Laurence et al., 2014; Laetsch and Blaxter, 2017). NGS technologies cannot distinguish between these and the target sequences and this can result in contaminant sequences being misassembled as part of the target genome of interest (Merchant et al., 2014; Laetsch and Blaxter, 2017). This error may lead to overestimating the genome size, overestimating gene copy number, or erroneous biological conclusions such as horizontal-gene-transfers (Koutsovoulos et al., 2016). Hence, a crucial step for genome analysis is to identify and remove these non-target contaminants (Kumar et al., 2013; Laurence et al., 2014; Ekblom and Wolf, 2014). Assessing the GC content of the raw reads

Chapter 3: Building genome resource (Andrews, 2010) or the assembled contigs is a common way to check for contaminants that have different GC ratios to the target species (Ekblom and Wolf, 2014). Removing contigs and scaffolds smaller than a given size threshold also greatly helps removing potential contaminants or misassemblies that tend to be small in size (Koutsovoulos et al., 2016). Software packages like Blobtools (Kumar et al., 2013; Laetsch and Blaxter, 2017) can be used to visualise the GC content of each scaffold against its sequence coverage. Together with BLAST-assigned taxonomical information, the taxon-annotated GC-coverage plot (blobplot) is a useful approach to remove cross-species contamination from the assemblies. The quality of the assemblies should be compared quantitatively. Tools such as Quast can be used to assess the assembly metrics including total assembly size, N50, L50, GC content, and percentage of N bases (Gurevich et al., 2013). The N50 and L50 values represent a measurement of the contiguity and length of the assembled contig; N50 is defined as a length L so that the summation of all contigs with length ≥ L is at least half the length of the total assembly; L50 is defined as the number of contigs required to meet above criteria (Gurevich et al., 2013; Ekblom and Wolf, 2014). The GC content, as described above, can be a measurement of potential contaminant species. The percentage of N bases represent the proportion of gaps in the assembly (Ekblom and Wolf, 2014). Another frequently used assembly quality metric is the completeness of house- keeping genes that are highly conserved across most of the organisms (Parra et al., 2007; Simão et al., 2015). Software packages such as BUSCO searches for the Benchmarking Universal Single Copy Orthologs in a genome assembly, and the percentage of BUSCO completeness provides a rough estimation of the completeness of the assembly (Simão et al., 2015; Koutsovoulos et al., 2016). There is no standard value for the BUSCO completeness of a draft genome, although a BUSCO completeness of ~90% can generally be considered as a good assembly (M Blaxter, personal communication). Another way to compare two genome assemblies is through genome-to-genome alignment, where the similarity between two sequences can be compared and the alignment can be visualised as a dot plot to identify local sequence rearrangement (Delcher et al., 2002; Marçais et al., 2018; Cabanettes and Klopp, 2018). In addition to the nuclear genome assembly, whole genome sequencing data are usually accompanied by a high proportion of reads derived from the organellar genomes, i.e. plastid and mitochondria (McPherson et al., 2013). These genomes are much smaller in size and are typically circular. The plastid genomes are known to range from about 120 to 160 Kbp (Wicke et al., 2011), and the mitochondrial genomes vary much more in size and typically range between 200 to 750 Kbp (Gualberto et al., 2014; Dierckxsens et al., 2017). The organellar genome reads can be assembled, and the produced genomes could provide potential resources for barcoding, phylogeny or population genetics research (Jansen et al., 2006; Shaw et al., 2007; Zhao et al., 2018). So far in the genus Streptocarpus , only the

Chapter 3: Building genome resource plastid genome of S. teitensis has been assembled and annotated (Kyalo et al., 2018). In this study, the organellar genomes of the S. rexii and S. grandis will also be assembled and characterised. In summary, whole genome data analysis requires cautious optimisation and quality control during assembly. In this chapter I attempt to assemble the first whole genome sequence for S. rexii and S. grandis in the genus Streptocarpus . The resulting nuclear and organellar genomes will serve as reference sequences for later chapters, as well as provide invaluable resources for future studies.

3.2 Materials and methods 3.2.1 Plant materials The Streptocarpus rexii (accession 20150819*A) and Streptocarpus grandis (accession 20150821*A) were used as the materials for whole genome shotgun sequencing (Detail information of the materials are in Appendix 3.1). Both accessions are direct descendants of inbred lineages (S. rexii : selfed F2, S. grandis : selfed F3) that are later used for genetic studies in Chapter 4 and Chapter 5. Plants of both species were grown in the research glasshouse of the Royal Botanic Garden Edinburgh.

3.2.2 DNA extraction, library preparation and genome sequencing Approaches to the DNA extraction and quality assessment of Streptocarpus are described in Chapter 2 (see also Appendix 2.6). In brief, the plant DNA was extracted using ChargeSwitch gDNA Plant Kit (Thermo Fisher Scientific), followed by RNase A treatment and phenol:chloroform:isoamyl alcohol (25:24:1) purification before finally eluted in TE buffer. The extracted DNA samples were submitted to Edinburgh Genomics (University of Edinburgh, UK) for library preparation and whole genome shotgun sequencing. Short-insert paired-end libraries were prepared using the TruSeq DNA PCR free Library Prep Kit (Illumina, San Diego, CA, USA). For S. rexii , two libraries with insert sizes of 350 bp and 550 bp were prepared. For S. grandis , one library with an insert size of 350 bp was prepared. Paired-end reads of length 150 bp were generated from the libraries. The S. rexii library with insert size 550 bp was sequenced on HiSeq 4000, and both libraries with insert sizes of 350 bp were sequenced on the HiSeq X (Illumina). All libraries were sequenced in individual lanes to ensure maximum coverage. The read data was returned in fastq format and the software FastQC v.0.11.7 (Andrews, 2010) was used to evaluate the quality of the reads and the results were summarised using MultiQC v.1.5 (Ewels et al., 2016).

3.2.3 Assembly and analysis of the organellar genome The plastid and mitochondrial genomes were assembled using the software NOVOPlasty v.2.6.5 (Dierckxsens et al., 2017). In brief, this software takes a seed sequence

Chapter 3: Building genome resource and carries out seed-and-extend algorithm for assembly, and can incorporate a reference sequence to resolve the structure of repetitive regions. For both Streptocarpus species, the HiSeq X data were used as the input. The input file for the assembler was first prepared according to Dierckxsens et al. (2017), which a subset of 20 million unprocessed read pairs was prepared by extracting the first 20 million reads from both forward and reverse read files of the paired-end data, as described in Box 3.1.

Box 3.1 Extracting 20 million read pairs from the whole genome sequencing data head -n 80000000 <(gunzip -c [READ1.fq.gz] ) > read1_20M.fq head -n 80000000 <(gunzip -c [READ2.fq.gz] ) > read2_20M.fq Note: As each read entry has four lines of information in fastq format, a total number of 20 × 4 = 80 million lines were specified.

The S. rexii and S. grandis plastid genomes were assembled with the D. hygrometricum plastid sequence (153,493 bp, GenBank accession: JN107811; Zhang et al., 2012) as the seed and reference in NOVOPlasty. The “Genome range” parameter value was set to 120,000 to 200,000 bp, so that a genome larger than that of D. hygrometricum was considered by the software. The rest of the parameters remained unchanged as default. The detailed configuration file for the assembler is shown in Box 3.2. The assembly of the mitochondrial genome was carried out in a step-wise approach. For the S. rexii mitochondrion, the reads were first mapped to the D. hygrometricum mitochondrial genome (510,519 bp, GenBank accession: JN107812; Zhang et al., 2012) to identify a mitochondrion-specific read that was later used as the seed sequence to initiate the assembly. The mapped reads were BLAST searched against the nucleotide (nt) database on the NCBI webpage (Altschul et al., 1990) and the BLAST report checked to ensure that the read only matched plant mitochondrial but not plastid sequences, for confirming that the origin of the read is a mitochondrion-specific region. Finally, the following read sequence was chosen as the seed for assembling the S. rexii mitochondrial genome: CATAAGGGCCATGCGGACTTGACGTCATCCCCACCTTCCTCCAGTATATC ACTGACAGTCCTTCGTGAGTGCGGCACGCACCTTTTTCTTTCTTTTGGAGCTGTTT TGTCGGGGCGTACTAAACCCACTACGTACCACACCACCGGGCAG The D. hygrometricum mitochondrial sequence was used as reference, and the “Genome range” parameter value was set to 150,000 to 700,000 bp, so that a genome size larger than the D. hygrometricum mitochondrion was considered. The assembled S. rexii plastid genome was provided to the NOVOPlasty software for filtering out plastid reads from the raw data (parameter “Chloroplast sequence”), so the software can assemble the mitochondrial genome without incorporating the plastid sequences. The assembly began with the default k-mer size of 39, and gradually increased by 10 bp intervals if the mitochondrial assembly was not circularised. Finally, the circularised S. rexii mitochondrial assembly was

Chapter 3: Building genome resource created with a k-mer size of 79. The detailed configuration file for the assembler is shown in Box 3.3. The same procedure was repeated to obtain the S. grandis mitochondrial genome. The following sequence was chosen as the seed to initiate the assembly: GGCCATGCGGACTTGACGTCATCCCCACCTTCCTCCAGTATATCACTGAC AGTCCTTCGTGAGTGCGGCACGCACCTTTTTCTTTCTTTTGGAGCTGTTTTGTCGG GGCGTACTAAACCCACTACGTACCACACCACCGGGCAGATCGCC The other parameters were the same as those used for the S. rexii mitochondrial assembly, with the S. grandis plastid genome provided for the “Chloroplast sequence” parameter to filter out plastid reads. Finally, the circularised genome was assembled with a k-mer value of 59 (Box 3.3).

Box 3.2 NOVOPlasty configuration file and commands for the plastid genome assembly of Streptocarpus species. # For the config file Project: ------Project name = Plastid_assembly Type = chloro Genome Range = 120000-200000 K-mer = 39 Max memory = Extended log = 0 Save assembled reads = no Seed Input = [INPUT_SEED.fa] Reference sequence = [INPUT_REFERENCE.fa] Variance detection = no Heteroplasmy = Chloroplast sequence =

Dataset 1: ------Read Length = 150 Insert size = 350 Platform = illumina Single/Paired = PE Combined reads = Forward reads = [READ1_20M.fastq] Reverse reads = [READ2_20M.fastq]

Optional: ------Insert size auto = yes Insert Range = 1.8 Insert Range strict = 1.3

# To execute the software for the assembly perl novoplasty.pl –c [CONFIG_FILE]

Chapter 3: Building genome resource Box 3.3 NOVOPlasty parameter settings for the mitochondrial genome assembly. The main differences to the plastid assembly are (1) Type, (2) Genome range, (3) Chloroplast sequence, as the plastid sequence should be provided for the mitochondrial assembly. # For the config file Project: ------Project name = Mitochondrial_assembly Type = mito_plant Genome Range = 150000-700000 K-mer = [K-MER_SIZE] Max memory = Extended log = 0 Save assembled reads = no Seed Input = [INPUT_SEED.fa] Reference sequence = [INPUT_REFERENCE.fa] Variance detection = no Heteroplasmy = Chloroplast sequence = [ASSEMBLED_PLASTID_GENOME.fa]

Dataset 1: ------Read Length = 150 Insert size = 350 Platform = illumina Single/Paired = PE Combined reads = Forward reads = [READ1_20M.fastq] Reverse reads = [READ2_20M.fastq]

Optional: ------Insert size auto = yes Insert Range = 1.8 Insert Range strict = 1.3

# To execute the software for the assembly perl novoplasty.pl –c [CONFIG_FILE]

The annotation of the organellar genome assemblies was carried out with the webtool GeSeq v.1.50 (Tillich et al., 2017). This tool provides visualisation of the genome annotation using OGDRAW v.1.2 (Lohse et al., 2007; 2013) and generates an annotated GenBank annotation file. The annotation of the plastid genome was carried out under default parameters plus enabling the plastid-specific functions, i.e. HMMER profile search (Wheeler and Eddy, 2013) and the coding gene sequence and rRNA BLAT search (Kent, 2002) using the MPI-MP plastid references (Tillich et al. 2017). Plastid genomes of model plant species were also included as references for the BLAT search for gene prediction (Kent, 2002), which the A. thaliana (GenBank accession: NC_000932; Sato et al., 1999), Nicotiana tabacum L. (accession: NC_001879; Shinozaki et al., 1986), and Solanum lycopersicum L.

Chapter 3: Building genome resource (accession: AC_000188; Kahlau et al., 2006) were chosen. tRNAscan-SE v.2.0 was enabled for tRNA annotation (Lowe, 1997; Lowe and Chan, 2016). The annotation of the mitochondrial genome was also carried out under default settings. tRNAscan-SE v.2.0 was used for tRNA search, and the mitochondrial genomes of model species were included for the BLAT searches for gene prediction, including A. thaliana (accession: NC_001284; Unseld et al., 1997), N. tabacum (accession: NC_006581; Sugiyama et al., 2004), and S. lycopersicum (accession: NC_035963; Mueller et al., 2005). The assembled plastid / mitochondrial sequences were aligned to compare the genome structures between S. rexii and S. grandis . The alignment was done using the “progressiveMauve alignment” function in the program Mauve v.2.4.0 under default settings (Darling et al., 2004; 2010). The aligned sequences were further checked for identity and similarity by the Sequence Identity And Similarity webtool (last update 20 September 2017, http://imed.med.ucm.es/Tools/sias.html), and uncorrected distance between sequences by the EMBOSS DISTMAT v.6.6.0.0 (http://www.hpa-bioinfotools.org.uk/pise/distmat.html).

3.2.4 De novo assembly of the nuclear genome A summarised flowchart of the whole process of genome assembly and filtering is in Appendix 3.2, and each step is described in detail below.

Preliminary S. rexii genome assembly To assess the assembler performance and the overall genome properties, a preliminary genome assembly was performed using the S. rexii HiSeq 4000 reads. Prior to the assembling, the reads were quality checked and adapter trimmed (removed) using Trimmomatic v.0.36 (ILLUMINACLIP: TruSeq3-SE.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:30:20 AVGQUAL:20 MINLEN:51; Bolger et al., 2014). The trimmed reads were then assembled using SOAPdenovo2 with a k value of 55 (Luo et al., 2012). A second preliminary assembly attempt was taken to compare the assembly performance between SOAPdenovo2 and ABySS2 (Simpson et al., 2009; Jackman et al., 2017). The Hiseq 4000 and Hiseq X reads of S. rexii were used but without trimming, as the assemblers can automatically exclude lowly supported (likely error) k-mers (Simpson et al., 2009; Jackman et al., 2017). The assemblers ABySS2 v.2.0.2 (Simpson et al., 2009; Jackman et al., 2017) and SOAPdenovo2 (Luo et al., 2012) were compared for their product assemblies’ metrics. A k value of k = 77 was chosen as the value represents about the half value of the read length (150 bp). The rest of the assembly parameters remained unchanged as default. The quality metrics of all the assemblies were assessed using QUAST v.4.6.3 (Gurevich et al., 2013). The remapping rate was calculated by mapping the reads onto the

Chapter 3: Building genome resource genome using the BWA v.0.7.17 “mem” function (Li and Durbin, 2009). The average depth of coverage was calculated based on the remapped BAM file via the “genomecov” function in BEDtools (Quinlan and Hall, 2010).

K-mer size optimisation The optimal k-mer value was estimated using the software Kmer Analysis Toolkit KAT v.2.3.4 (Mapleson et al., 2017). This software was used generate k-mer distribution histograms from the raw reads, using k values between 77 bp and 147 bp with a 10 bp interval. The generated histograms were compared to check the number of distinct k-mers and the frequency of k-mer occurrence. In addition, the k-mer counting result was used to estimate the genome properties i.e. genome size, heterozygosity and repeat content, using the webtool GenomeScope (Vurture et al., 2017).

Genome assembly of S. rexii and S. grandis ABySS2 v.2.0.2 (Simpson et al., 2009; Jackman et al., 2017) was used for the final assembling of S. rexii and S. grandis genomes. For S. rexii , both the HiSeq 4000 and HiSeq X data were used and the assembly was carried out with k values between k = 77 and k = 147, with k = 10 intervals. For S. grandis , the HiSeq X data was used and the assembly was carried out with k values between k = 107 and k = 137, with k = 10 intervals. The Bloom filter was enabled to reduce the maximum memory usage (parameter B, H, and kc). The commands used for the assembly are given in Box 3.4.

Chapter 3: Building genome resource Box 3.4 Parameter setting for the genome assembly of Streptocarpus species using ABySS2 and SOAPdenovo2 ## Genome assembly using ABySS2 with two paired-end libraries # The Bloom filter was enabled to reduce the memory usage abyss-pe -j j= [NO._THREADS] k= [K_VALUE] name= [OUTPUT_NAME] \ lib=' [LIBRARY_A] [LIBRARY_B] ' \ LIBRARY_A=' [LIB_A_READ1] [LIB_A_READ2] ' \ LIBRARY_B=' [LIB_B_READ1] [LIB_B_READ2] ' \ B=20000M H=3 kc=3

## Genome assembly using SOAPdenovo2 # The configuration file for SOAPdenovo2 max_rd_len=150 [LIB] reverse_seq=0 asm_flags=3 rd_len_cutoff=100 rank=1 pair_num_cutoff=3 map_len=32 q1= [LIB_A_READ1] q2= [LIB_A_READ2]

[LIB] reverse_seq=0 asm_flags=3 rd_len_cutoff=100 rank=1 pair_num_cutoff=3 map_len=32 q1= [LIB_B_READ1] q2= [LIB_B_READ2]

# Assemble the genome using SOAPdenovo2 SOAPdenovo-63mer all -s [CONFIG_FILE] -o [OUTPUT_NAME] \ -K [K_VALUE] -p [NO._THREADS]

## Calculating the remapping rate using BWA and samtools # Index the genome assembly bwa index [GENOME_ASSEMBLY] # Remap the raw reads to the assembly and write the output in BAM format bwa mem -t [NO._THREADS] [GENOME_ASSEMBLY] [READ1] [READ2] \ | samtools view -Sb \ | samtools sort -O bam -o [OUTPUT_NAME.bam] # Calculate the mapping rate Samtools flagstat [OUTPUT_NAME.bam]

## Calculating the average depth of coverage of the genome # Computes the depth of coverage of the genome bedtools genomecov [OUTPUT_NAME.bam] > [OUTPUT_FILE] # Calculate the average depth of coverage awk '{ total += $2 } END { print total/NR }' [OUTPUT_FILE]

Chapter 3: Building genome resource 3.2.5 Post-assembly filtering and assessment of assembly quality For all the produced assemblies, scaffolds smaller than 500 bp were removed. QUAST v.4.6.3 (Gurevich et al., 2013) was used to calculate the assembly quality metrics. The remapping rate was calculated by mapping the reads onto the genome using the BWA v.0.7.17 “mem” function (Li and Durbin, 2009). The average depth of coverage was calculated based on the remapped BAM file via the “genomecov” function in BEDtools (Quinlan and Hall, 2010). The software Blobtools v.1.0 (Kumar et al., 2013; Koutsovoulos et al., 2016; Laetsch and Blaxter, 2017) was used to remove potential contaminant scaffolds from the assemblies. For the preparation of the Blobtools input file, (1) the coverage file (.cov file) was prepared by aligning raw reads to the assembly using BWA “mem” (Li and Durbin, 2009) with default settings. SAMtools v.1.7 (Li et al., 2009) was then used to process the SAM format to BAM format. The resultant bam file was converted to coverage file (.cov file) using the Blobtools map2cov function; (2) the hits file containing the taxonomic information on the scaffolds was prepared by performing local BLAST searches of the draft genome against the NCBI nt database (downloaded on 26-Mar-2018). BLASTn megablast was performed using BLAST+ v2.7.1+ (Camacho et al., 2009) with an E-value threshold of 1e-25; (3) the assembly file was the genome assembly generated from the ABySS assembler in fasta format. With the three input files, Blobtools was used to construct the blobplot for both S. rexii and S. grandis assemblies. The detailed commands are provided in Box 3.5.

Box 3.5 Commands for Blobtools for blobplot generation # Preparing the .cov file bwa index -p [INDEX_PREFIX] [GENOME_ASSEMBLY.fasta] bwa mem [INDEX_PREFIX] [READ1.fastq.gz] [READ2.fastq.gz] \ | samtools view –Sb \ | samtools sort –O bam –o [FINAL_BAM.bam] blobtools map2cov –b [FINAL_BAM.bam] –o [FINAL_COV_FILE]

# Preparing the hits file blastn -task megablast –db nt –evalue 1e-25 –culling_limit 5 \ -query [GENOME_ASSEMBLY.fa] \ -outfmt '6 qseqid staxids bitscore std \ sscinames sskingdoms stitle' \ -out [OUTPUT_HIT_FILE]

# Run blobtools create to create blobDB data structure blobtools create –x bestsum \ -i [GENOME_ASSEMBLY.fasta] -c [FINAL_COV_FILE] \ -t [OUTPUT_FIT_FILE ] \ -o [BLOBDB_OUTPUT_PREFIX]

# Run blobtools view on the output blobDB.json # to create the summary table

Chapter 3: Building genome resource blobtools view –i [BLOBDB]

# Run blobtools blobplot on the output blobDB.jason # to create the blobplot blobtools blobplot –i [BLBODB] –o [OUTPUT_GRAPH_PREFIX]

After identifying the scaffolds of potential contaminant origin (which were labelled as non-plant origin by the Blobtools analysis), they were removed, and only the scaffolds labelled as ‘Streptophyta’ or ‘no-hit’ (unidentifiable in the searched database) were kept. The filtered assemblies were analysed with Blobtools again to ensure the complete removal of the contaminants. The detailed commands including the filtering of the assembly are shown in Box 3.6.

Box 3.6 Commands for filtering genome assemblies based on blobplot results ## Filtering the genome assembly # Retrieve the fasta entries that were labelled as Streptophyta # or no-hit in the summary table awk '$6=="Streptophyta" || $6=="no-hit"' [BLOB_TABLE] | \ awk {print $1} > Sequence.to.keep

# Retrieve the actual fasta sequence from the unfiltered # genome assembly perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' Sequence.to.keep [GENOME_ASSEMBLY.fasta ] > \ [FILTERED_GENOME.fasta]

# Retrieve the Blast result from the original hit file, keeping # only the filtered assemblies for i in $(cat Sequence.to.keep);do awk -v num="$i" '$1==num' [OUTPUT_HIT_FILE] >> [FILTERED_HIT] done

# Retrieve the coverage information from the original cov file, # keeping only the filtered assemblies for i in $(cat Sequence.to.keep);do awk -v num="$i" '$1==num' [FINAL_COV_FILE] >> [FILTERED_COV] done

## Generate the blobplot for filtered assembly blobtools create –x bestsum \ -i [FILTERED_GENOME.fasta] -c [FILTERED_COV] -t [FILTERED_HIT] \ -o [FILTERED_BLOBDB_PREFIX] blobtools view –i [FILTERED_BLOBDB] blobtools blobplot –i [FILTERED_BLBODB] \ –o [FILTERED_OUTPUT_PREFIX]

The completeness of the genome assemblies, in terms of the presence of core genes (essential for biological functions), was assessed using BUSCO v.3 (Simão et al., 2015). The core genes for the plant dataset were downloaded from the BUSCO webpage

Chapter 3: Building genome resource (Embryophyta_odb9), which a total number of 1,440 core genes were used as reference for the search (Box 3.7).

Box 3.7 Commands for BUSCO analysis for completeness of core genes of the assemblies python BUSCO.py -i [GENOME.fasta] -o [OUTPUT_NAME] -m geno \ -cpu [NO._THREADS] -l embryophyta_odb9/

For genome-to-genome alignment, the webtool D-GENIES v.1.1.1 was used (Cabanettes and Klopp, 2018) which carries out alignments using Minimap v.2 (Li, 2018), and the results were visualised as dot plots. The alignment was carried out between (1) S. rexii and S. grandis , (2) S. rexii and D. hygrometricum , and (3) S. grandis and D. hygrometricum (GCA_001598015; Xiao et al., 2015), all under default parameters.

Chapter 3: Building genome resource 3.3 Results 3.3.1 Quality check of the whole genome shotgun sequencing reads The number of reads obtained from the sequencing experiments are summarised in Table 3.1. For S. rexii , about 401.8 million read pairs (c. 803 million reads) and 260.4 million read pairs (c. 520 million reads) were obtained from HiSeq X and HiSeq 4000 sequencing, respectively. For S. grandis , almost 452.7 million read pairs (c. 905 million reads) were obtained from the HiSeq X sequencing. The obtained reads had an average Phred quality score above Q30 except for the region at the end of read 2 (Figure 3.2). On the other hand, the GC distribution of the reads indicated that both S. rexii datasets had a GC content distribution which deviates from expected. The major peak was at around 39% GC, but a smaller fraction of the reads had a higher GC content of around 67%-71% (Figure 3.2 a and b). The GC content of S. grandis appeared as normal distribution, with a central peak at around 39% (Figure 3.2 c).

Table 3.1 Amount of read counts generated from the whole genome shotgun sequencing S. rexii S. grandis HiSeq X HiSeq 4000 HiSeq X Library insert size (bp) 350 550 350 Read length (bp) 150 150 150 Read pairs obtained 401,838,795 260,476,261 452,684,043

Chapter 3: Building genome resource

(a) (b) S. rexii, HiSeq X (350 bp insert) S. rexii, HiSeq 4000 (550 bp insert)

Read 1 Read 1 Read 2 Read 2 Phred score Phred score

Position (bp) Position (bp) Percentage of reads Percentage of reads

GC% GC%

Read 1 Read 2 Phred score

Position (bp) Percentage of reads

GC% Figure 3.2 FastQC quality check of the reads generated from the three whole genome shotgun sequencing experiments. (a) S. rexii HiSeq X. (b) S. rexii HiSeq 4000. (c) S. grandis HiSeq X. The upper graph shows the mean quality score (Phred score), and the lower graph shows the distribution of the per read GC% content. The two lines represent the forward and reverse reads from the paired-end sequencing. The colours of the lines indicate pass (green) or warning (orange) of the quality check results.

Chapter 3: Building genome resource 3.3.2 Organellar genome assembly The complete circular plastid genomes were assembled for S. rexii and S. grandis . For S. rexii, the assembled sequence had a total length of 152,724 bp, with 107 protein coding genes, 8 rRNA and 33 tRNA annotated sequences (Table 3.2 and Figure 3.3 a). The S. grandis plastid genome had similar metrics, and the assembly spanned 152,770 bp, and the annotation is identical to that of the S. rexii assembly (Table 3.2 and Figure 3.3 b). The two assembled sequences can be aligned where a large synteny block was identified that covers the entire plastid genome (Figure 3.4). In total, 267 SNPs and 63 gaps were found in the alignment. The two sequences shared 99.65% identity and similarity, and the uncorrected distance was 0.18.

Table 3.2 Metrics of the S. rexii and S. grandis chloroplast genome assemblies based on HiSeq X data. S. rexii S. grandis No. contigs 1 1 Total base pairs (bp) 152,724 152,770 No. protein coding genes 107 107 No. rRNA annotated 8 8 No. tRNA annotated 33 33

Chapter 3: Building genome resource

(a)

Streptocarpus rexii Chloroplast 152,724 bp

(b)

Streptocarpus grandis Chloroplast 152,770 bp

Figure 3.3 Gene map of the Streptocarpus plastid genome assemblies, with gene annotations in colour. (a) S. rexii . (b) S. grandis .

Chapter 3: Building genome resource

S. rexii chloroplast

S. grandis chloroplast

Figure 3.4 Synteny between the S. rexii and S. grandis plastid genome assemblies. The large red rectangles represent the synteny blocks identified by Mauve. The vertical lines inside the synteny blocks indicate the similarities between the two aligned sequences, where longer and darker lines suggest lower sequence similarity or gaps in the alignment. The numbers above the blocks indicate base pairs.

The complete mitochondrial genomes of S. rexii and S. grandis were also assembled. The S. rexii mitochondrial genome spanned 314,134 bp, with 72 protein coding genes, 2 rRNA and 17 tRNA annotated (Table 3.3 and Figure 3.5 a). The S. grandis mitochondria spanned 352,540 bp, with 79 protein coding genes, 3 rRNA and 17 tRNA annotated (Table 3.3 and Figure 3.5 b). Strangely, the mitochondrial complex III was only identified in S. grandis (Figure 3.5 b; red arrow) assembly but not in S. rexii . The synteny analysis suggested that the orientation of the identified synteny blocks were very different between the two assemblies, which 13 synteny blocks identified and they were different in terms of strand, position, and order (Figure 3.6). Among the aligned region, 1,080 SNPs and 289 gaps were found. Due to the fact that the sequence cannot really be aligned and the conserved region is too fragmented, the sequence identity, similarity and the uncorrected distance were not calculated.

Table 3.3 Metrics of the S. rexii and S. grandis mitochondrial genome assemblies based on HiSeq X data. S. rexii S. grandis No. contigs 1 1 Total base pairs (bp) 314,134 352,540 No. protein coding genes 72 79 No. rRNA annotated 2 3 No. tRNA annotated 17 17

Chapter 3: Building genome resource

(a)

Streptocarpus rexii Mitochondrion 314,134 bp

(b)

Streptocarpus grandis Mitochondrion 352,540 bp

Figure 3.5 Gene map of the Streptocarpus mitochondrial genome assemblies, with gene annotations in colour. (a) S. rexii . (b) S. grandis . Red arrow: mitochondrial complex III.

Chapter 3: Building genome resource

S. rexii mitochondrion

S. grandis mitochondrion

Figure 3.6 Synteny between the S. rexii and S. grandis mitochondrial genome assemblies. The rectangles of different colours represent the synteny blocks identified by Mauve. The vertical lines inside the synteny blocks indicate the similarities between the two aligned sequences, where longer and darker lines suggest lower sequence similarity or gaps in the alignment). The numbers above the blocks indicate base pairs.

3.3.3 Preliminary assembly tests for the plant nuclear genome Among the initial 260,476,261 read-pairs, 257,355,603 were kept after quality and adapter trimming (98.8%). The trimmed reads showed an overall good quality with an average Phred quality score above Q30, but the abnormal GC content distribution remained the same (Figure 3.7).

(a) (b) S. rexii, HiSeq 4000 (before trimming) S. rexii, HiSeq 4000 (after trimming)

Read 1 Read 1 Read 2 Read 2

Figure 3.7 FastQC quality check of the S. rexii HiSeq 4000 reads. (a) Before quality check and adapter trimming. (b) After quality check and adapter trimming. The upper graph shows the mean quality score, and the lower graph shows the per sequence GC% content. The two

Chapter 3: Building genome resource lines represent the forward and reverse reads from the paired-end sequencing. The colours of the lines indicate pass (green) or warning (orange) of the quality check result.

Assembly of the preprocessed reads resulted in 1,380,875 scaffolds (Table 3.4). The total span was 902,891,804 bp, and the longest scaffold was about 1.6 Mbp. After filtering out the small scaffolds shorter than 500 bp, 97,377 scaffolds were kept. The filtered assembly had a total span of 716,373,945 bp, an N50 value of 25,903 bp, and a L50 value of 5,871. About 64.7% of the input reads (166,613,685 out of 257,355,603 read pairs) were mapped to the draft genome assembly, and the mean depth of coverage of the assembled genome was 177×. However, the assembly contained a high proportion of Ns, i.e. 206,359,657 N bases which comprised about 29% of the assembly (Table 3.4,). In addition, the GC% distribution of the assembly indicated the presence of a group of scaffolds with an abnormally high GC content at 60% to 65% (Figure 3.8).

Table 3.4 Metrics of the S. rexii SOAPdenovo2 preliminary assembly on preprocessed reads S. rexii SOAPdenovo2 preliminary assembly Dataset used HiSeq 4000 (preprocessed) No. input read pairs 257,355,603 Assembler SOAPdenovo2 v2.04-r240 Assembly parameter k = 55 Assembly metrics Total no. scaffolds 1,380,875 Total span (bp) 902,891,804 No. scaffolds (≥ 500 bp) 97,377 Total span (≥ 500 bp) (bp) 716,373,945 Largest scaffold (bp) 1,647,276 N50 (bp) 25,903 L50 5,871 GC (%) 39.61 N base count (bp) 206,359,657 No. N bases per 100 kbp 28,342

Chapter 3: Building genome resource

30,000

24,000

18,000

12,000

6,000 Number of scaffolds Numberof

GC% content

Figure 3.8 Distribution of the GC% of the assembled scaffolds in the S. rexii SOAPdenovo2 preliminary assembly

To further explore the possibility of improving the genome assembly, both S. rexii datasets generated from HiSeq 4000 and HiSeq X platforms were analysed using ABySS2 and SOAPdenovo2 (Table 3.5 and Figure 3.9). The ABySS2 assembly resulted in 8,985,595 scaffolds comparing to the 3,377,520 scaffolds of the SOAPdenovo2 assembly. The total span of the ABySS2 assembly was 1,610,664,622 bp, which is about 300 Mbp longer than the SOAPdenovo2 assembly (Table 3.5). After filtering out short scaffolds smaller than 500 bp, the metrics of the two assemblies were similar as shown in the cumulative plot and the Nx plot, except for that the ABySS2 assembly was about 200 Mbp shorter (Figure 3.9 a and b). After filtering, there were 191,825 and 271,821 scaffolds remaining in the ABySS2 and SOAPdenovo2 assemblies, respectively. The total span of the ABySS2 assembly was 623 Mbp, and for the SOAPdenovo2 assembly 830 Mbp. The SOAPdenovo2 assembly had the longest scaffold of 3,627,799 bp, compared to the ABySS2 assembly with 1,582,816 bp. The ABySS2 assembly showed a slightly better contiguity with a N50 value of 13,689 bp and a L50 value of 10,075. For the SOAPdenovo2 the N50 value was 8,462 bp and the L50 value 21,884. The presence of the double peaks in the GC% graph was again seen in both assemblies, with a smaller peak at around 60% to 70% GC (Figure 3.9 c). A major difference between the two assemblies was in the number of N bases (Table 3.5). The SOAPdenovo2 assembly contained about 40 times more N bases than that of the ABySS2 assembly (40,191,947 to 1,482,696 N bases). On average 4,836 N bases were present in every 100 Kbp of the SOAPdenovo2 assembly, which was about 20 times higher than the value of the ABySS2 assembly (237 Ns per 100 Kbp). Overall the two assemblies were similar in their metrics. Even though the SOAPdenovo2 assembly had a longer total span, it also tended to have a higher proportion of N bases, indicating that many of the assembled sequences were non-informative. In addition, the ABySS2 assembly showed a slightly better contiguity using the same datasets and

Chapter 3: Building genome resource parameter i.e. better N50 and L50 values (Table 3.5 and Figure 3.9 d). Thus, ABySS2 was chosen for further optimisation to generate the S. rexii draft genome using both the HiSeq 4000 and HiSeq X datasets.

Table 3.5 Metrics of the preliminary ABySS2 and SOAPdenovo2 S. rexii genome assemblies S. rexii S. rexii ABySS2 SOAPdenovo2 Preliminary assembly Preliminary assembly 2 Dataset used HiSeq 4000 + HiSeq X HiSeq 4000 + HiSeq X

No. input read pairs 662,315,056 662,315,056 SOAPdenovo2 v2.04- Assembler ABySS2 v2.0.2 r240 Assembly parameter k = 77 k = 77

Assembly metrics

Total no. scaffolds 8,985,595 3,377,520

Total span (bp) 1,610,664,622 1,331,851,305

No. scaffolds (≥ 500 bp) 191,825 271,821

Total span (≥ 500 bp) (bp) 623,869,921 830,971,992

Largest scaffold (bp) 1,582,816 3,627,799

N50 (%) 13,689 8,462

L50 10,075 21,884

GC (%) 42.17 44.83

N base count (bp) 1,482,696 40,191,947

No. N bases per 100 kbp 237.66 4,836.74

Chapter 3: Building genome resource

(a) Cumulative length (Mbp) (b) N(x) length (Kbp)

3,2003,200

2,4002,400

1,6001,600

800800

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0 0 00 Total pairs (Mbp) base ABySS2 SOAPdenovo2 ABySS2 Assemblies SOAPdenovo2 GC% content

ABySS2 assembly SOAPdenovo2 assembly

Figure 3.9 Comparisons between the ABySS2 and the SOAPdenovo2 assemblies ( k = 77). (a) Cumulative length plot. (b) N(x) length plot. (c) GC% frequency distribution. (d) N50 value and total base pairs assembled. The bars indicate the N50 values and the black diamonds indicate the total length of the assemblies.

3.3.4 Assembly and quality control of the S. rexii draft genome The k-mer histograms of the raw reads were first generated. The k-mer histograms of both datasets showed that the number of distinct k-mers kept increasing until k = 137, and the shape collapsed completely at k = 147 (Figure 3.10). From k-mer counting results, S. rexii was estimated to have a genome size between 542 Mbp and 710 Mbp. The estimated heterozygosity of the genome ranged between 0.03% and 0.13%, and the estimated repeat content was between 13% and 27% (Table 3.6).

Chapter 3: Building genome resource

(a)

100

k=137, 69.0 Million 70

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K=77 K=87 K=97 K=107 K=117 K=127 K=137 K=147

Figure 3.10 K-mer histograms of S. rexii sequencing data. (A) Histogram of HiSeq X dataset. (B) Histogram of HiSeq 4000 dataset.

Chapter 3: Building genome resource Table 3.6 Estimation of S. rexii genome size, heterozygosity, and repeat content from k-mer count data, from HiSeq X (upper half) and HiSeq 4000 (lower half) data. HiSeq X k = 77 k = 87 k = 97 k = 107 k = 117 k = 127 k = 137 k = 147 Genome size (Mbp) 666 674 680 686 701 710 N/A N/A Heterozygosity (%) 0.04 0.04 0.05 0.04 0.03 0.03 N/A N/A Repeat content (%) 14.94 14.05 13.44 12.92 13.69 13.92 N/A N/A Model fit (%) 95.30 96.11 96.80 97.83 98.73 99.25 N/A N/A HiSeq 4000 k = 77 k = 87 k = 97 k = 107 k = 117 k = 127 k = 137 k = 147 Genome size (Mbp) 664 673 685 692 690 692 N/A N/A Heterozygosity (%) 0.06 0.05 0.05 0.05 0.07 0.04 N/A N/A Repeat content (%) 14.78 14.36 14.67 14.50 14.92 14.50 N/A N/A Model fit (%) 98.48 98.81 99.09 99.41 98.84 99.41 N/A N/A Note. N/A - The GenomeScope tool failed to fit the model to the k-mer distribution at k = 137 and k = 147

ABySS2 was used to assemble the S. rexii nuclear genome based on the k value range tested above. The HiSeq 4000 and HiSeq X datasets were combined to generate the assembly. After removing short scaffolds < 500 bp, the assembly metrics of the assemblies improved together with higher k values in general (Table 3.7 and Figure 3.11): k = 117 gave the maximum total span (total span = 637,572,950 bp), as well as the longest scaffold (1,380,762 bp). The best N50 and L50 values were achieved with k = 137, giving an N50 value of 35,890 bp and L50 value of 4,568 (Table 3.7 and Figure 3.11 b). Interestingly, a gradual disappearance of scaffolds with 60% - 70% GC content was observed in assemblies with higher k values, which in the assembly at k = 137 the small lump of high GC content almost disappeared completely (Figure 3.11 c). On the other hand, the assembly at k = 147 showed the worst metrics, with the smallest total span and worst contiguity (Table 3.7 and Figure 3.11 d). Overall, the assembly at k = 137 showed the best contiguity metrics, and also a high remapping rate of 95.6% (633,607,562 out of 662,315,056 mapped read pairs), and the mean depth of coverage was 867×. Its shorter total span was possibly due to the absence of the suspiciously high-GC content sequences, i.e. may represent cross-species contamination, rather than the missing of the target plant nuclear genome (Figure 3.11 c). Thus, this assembly was chosen for further quality checks and filtering.

Chapter 3: Building genome resource Table 3.7 Metrics of the ABySS2 genome assemblies of S. rexii based on HiSeq 4000 + HiSeq X data S. rexii S. rexii S. rexii S. rexii ABySS2 ABySS2 ABySS2 ABySS2 k = 77 k = 87 k = 97 k = 107 Total no. scaffolds 8,985,595 7,457,747 6,126,393 5,045,696 Total span (bp) 1,610,664,622 1,539,402,274 1,451,571,257 1,358,773,807 No. scaffolds (≥500 bp) 1,582,816 1,104,780 1,105,045 1,274,067 Total span (≥500 bp) (bp) 623,869,921 636,030,438 640,577,967 638,481,047 Longest scaffold (bp) 191,825 168,900 145,355 120,713 N50 (bp) 13,689 17,784 22,373 26,570 L50 10,075 8,227 6,925 6,026 GC (%) 42.17 41.55 40.95 40.3 N base count (bp) 1,482,696 1,197,906 1,018,561 943,364 No. N bases per 100 kbp 237.66 188.34 159.01 147.75

S. rexii S. rexii S. rexii S. rexii Table 3.7 continued ABySS2 ABySS2 ABySS2 ABySS k = 117 k = 127 k = 137 k = 147 Total no. scaffolds 4,082,404 3,125,951 2,604,776 40,527,013 Total span (bp) 1,255,664,373 1,133,015,757 1,051,896,697 6,380,703,103 No. scaffolds (≥500 bp) 103,468 98,936 99,001 7,168 Total span (≥500 bp) (bp) 637,572,950 633,669,601 612,844,571 6,809,895 Longest scaffold (bp) 1,380,762 874,602 886,437 117,219 N50 (bp) 29,452 33,037 35,890 853 L50 5,525 4,984 4,568 1,661 GC (%) 39.75 39.33 38.48 40.06 N base count (bp) 979,516 855,495 961,842 10,110 No. N bases per 100 kbp 153.63 135.01 156.95 148.46

Chapter 3: Building genome resource

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K=77 K=87 K=97 K=107 K=117 K=127 K=137 K=147

Figure 3.11 Comparisons of S. rexii assemblies generated from ABySS2 using different k values. All assemblies were generated from HiSeq 4000 and HiSeq X datasets combined. (a) Cumulative length plot. (b) N(x) length plot. (c) GC% frequency distribution. (d) N50 value and total base pairs assembled. The bars indicate the N50 values and the black diamonds indicate the total span of the assemblies.

The blobplot of the assembly at k = 137 indicated that the major source of contaminants was bacteria, and predominantly Proteobacteria and Actinobacteria (Figure 3.12). These bacterial scaffolds are the large red and green clusters on the right-hand-side of the plot, with a GC content ranging from 60% to 70%. This corresponded well with the suspiciously high GC distribution observed in previous figures (Figure 3.8, 3.9 c and 3.11 c). The Proteobacteria assemblies consisted of 1,300 scaffolds and spanned about 8.4 Mbp, while the Actinobacteria consisted of 1,753 scaffolds and spanned about 7.4 Mbp (Table 3.8). Other sources of contaminants identified included fungi (Basidiomycota and Ascomycota) and undefined Eukaryotic species. The Basidiomycota consisted of 32 scaffolds (spanning 87,109 bp), the Ascomycota 29 scaffolds (spanning 73,614 bp), and for undefined Eukaryota 28 scaffolds (spanning 144,514 bp). In total, 3,156 scaffolds (spanning c. 16.3 Mbp) were identified as potential contaminants. This comprised about 2.7% of the original assembly (Table 3.8). The complete list of the contaminant species identified is summarised in Appendix 3.3.

Chapter 3: Building genome resource

Figure 3.12 Blobplot of the S. rexii genome assembly ( k = 137) before filtering. The colour code indicates the taxon assigned to the scaffolds (shown as circles). The size of the circles indicates the length of the scaffolds. The figure on the top shows the GC distribution of the assembly, and the figure on the right shows the coverage distributions.

Chapter 3: Building genome resource Table 3.8 Summary of the potential contaminant scaffolds identified in the S. rexii genome assembly.

Average Category No. scaffolds Total span (bp) Total span (%) scaffold length (bp) Actinobacteria 1,753 7,444,060 45.574 4,246 Proteobacteria 1,300 8,425,972 51.586 6,481 Basidiomycota 32 87,109 0.533 2,722 Ascomycota 29 73,614 0.451 2,538 Undefined Eukaryota 28 144,514 0.885 5,161 Arthropoda 6 4,165 0.025 694 Mucoromycota 4 2,056 0.013 514 Undefined bacteria 4 25,732 0.158 6,433 Undefined viruses 4 40,410 0.247 10,102 Chordata 3 42,696 0. 261 14,232 Nematoda 3 2,005 0.012 668 Chlorophyta 2 1,068 0.007 534 Apicomplexa 1 38,912 0.238 38,912 Bacteroidetes 1 601 0.004 601 Undefined 1 556 0.003 556 Unresolved 1 517 0.003 517 TOTAL 3,172 16,333,987 100.000 5,149

After removing the contaminant scaffolds (keeping only the Streptophyta sequences and “no-hit” scaffolds), the filtered assembly had 95,845 scaffolds remaining with a total span of 596.6 Mbp (Table 3.9). The N50 value after filtering was 35,609 bp, which was only slightly lower than the unfiltered assembly. The average GC content after filtering was 37.75% (Table 3.9). The GC distribution graph shows that the high-GC peak disappeared after filtering (Figure 3.13 c). The unfiltered and filtered genomes had similar BUSCO completeness percentages of approximately 88%. Interestingly, after filtering, the BUSCO completeness was slightly higher (87.3% versus 88.8%) (Table 3.9). Finally, the blobplot of the filtered genome assembly showed a cleaner pattern without bacterial scaffolds (Figure 3.14). At this point, the finalised S. rexii draft genome assembly was generated in this study.

Chapter 3: Building genome resource Table 3.9 Metrics of the unfiltered and filtered S. rexii genome assemblies S. rexii S. rexii ABySS2 ABySS2

k = 137 k = 137 unfiltered filtered Assembly metrics Total no. scaffolds 2,604,776 95,845 Total span (bp) 1,051,896,697 596,583,869 No. scaffolds (≥500 bp) 99,001 95,845 Total span (≥500 bp) (bp) 612,844,571 596,583,869 Largest scaffold (bp) 886,437 421,987 N50 (bp) 35,890 35,609 L50 4,568 4,571 GC (%) 38.48 37.75% N base count (bp) 961,842 907,432 No. N bases per 100 kbp 156.95 152.10 Genome completeness BUSCO completeness (%) 87.3 88.8 No. complete BUSCOs 1,257 1,322 No. fragmented BUSCOs 41 43 No. missing BUSCOs 183 161 Note. A total of 1,440 BUSCOs were searched

Chapter 3: Building genome resource

(a) Cumulative length (Mbp) (b) N(x) length (Kbp)

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200 Base%pairs%(>500%bp) 10,00010000 50,000 100 5000 100

0 0 0 (Mbp) Total pairs base ABySS2 SOAPdenovo2 0 UnfilteredAssemblies Filtered GC% content

Unfiltered assembly Filtered assembly

Figure 3.13 Comparisons of the unfiltered and filtered S. rexii genome assemblies. (a) Cumulative length plot. (b) N(x) length plot. (c) GC% frequency distribution. (d) N50 value and total base pairs assembled. The bars indicate the N50 value and the black diamonds indicate the total span of the assemblies.

Chapter 3: Building genome resource

Figure 3.14 Blobplot of the S. rexii genome assembly ( k = 137) after filtering. The colour code indicates the taxon assigned to the scaffolds (shown as circles). The size of the circles indicates the length of the scaffolds. The figure on the top shows the GC distribution of the assembly, and the figure on the right shows the coverage distributions.

3.3.5 Assembly and quality control of the S. grandis draft genome The data generated from the HiSeq X sequencing experiments was used for the assembly. First, k-mer histograms were analysed, which suggested that optimal k values for the assembly were probably around 127 to 137, as these two values gave the highest number of distinct k-mers (Figure 3.15). The estimated genome size was about 990 Mbp to 1,003 Mbp, with 0.02% - 0.03% heterozygosity, and 12% - 15% repeat content (Table 3.10).

Chapter 3: Building genome resource

100 k=137, 96.8 Million

70 k=127, 65.0 Million

50 k=117, 49.2 Million

40 k=107, 39.5 Million k=97, 32.8 Million

Number of distinctk-mersof Number 30 Millions k=87, 28.1 Million k=77, 24.2 Million 20

0 0 20 40 60 80 100 120 140 160 180 200 Frequency

K=147 K=77 K=87 K=97 K=107 K=117 K=127 K=137 Figure 3.15 K-mer histogram of the S. grandis sequencing data generated from the HiSeq X experiment.

Table 3.10 Estimation of S. grandis genome size, heterozygosity, and repeat content from k- mer count data based on the HiSeq X dataset. HiSeq X k = 77 k = 87 k = 97 k = 107 k = 117 k = 127 k = 137 k = 147 Genome size (Mbp) 974 985 992 997 1,003 996 N/A N/A Heterozygosity (%) 0.07 0.02 0.02 0.02 0.03 0.03 N/A N/A Repeat content (%) 16.84 16.34 15.22 13.94 12.56 14.85 N/A N/A Model fit (%) 94.82 95.64 96.84 97.92 98.79 98.65 N/A N/A Note. N/A - The GenomeScope tool failed to fit the model to the k-mer distribution at k = 137 and k = 147

Chapter 3: Building genome resource Based on the k-mer histogram results, the genome assembly was carried out at k values ranging from 107 to 137 (Table 3.11, Figure 3.16). The assembly with k = 127 resulted in the overall best assembly. After removing short scaffolds (< 500 bp), the assembly had 738,916 scaffolds and a total span of around 845 Mbp (Table 3.11, Figure 3.16 a), and had the highest N50 value (31,553 bp) and lowest L50 value (7,246) among the four assemblies. On the other hand, the assembly with k = 137 was highly fragmented, with 276,087 scaffolds and an N50 value of 7,271 bp (Table 3.11, Figure 3.16). The GC plot showed a normal distributed single peak centred at 36% to 38% GC content (Figure 3.16 c). As the assembly at k = 127 gave the best results for other parameters, this assembly was chosen for contaminant identification and further quality checks. This assembly also had a high remapping rate of 99.4% (450,658,777 mapped among 452,684,043 read pairs), and a depth of average coverage of 140×.

Table 3.11 Metrics the ABySS2 S. grandis genome assemblies based on HiSeq X dataset. S. grandis S. grandis S. grandis S. grandis ABySS2 ABySS2 ABySS2 ABySS2 k = 107 k = 117 k = 127 k = 137 Total no. scaffolds 3,397,906 2,794,551 2,252,545 4,790,734 Total span (bp) 1,287,619,502 1,253,615,696 1,211,718,685 1,533,120,309 No. scaffolds (≥500 bp) 128,374 121,887 128,388 276,087 Total span (≥500 bp) (bp) 795,183,443 824,378,614 844,897,632 682,013,812 Largest scaffold (bp) 526,028 530,645 738,916 391,136 N50 (bp) 26,343 30,389 31,553 7,271 L50 8,065 7,310 7,246 17,910 GC (%) 38.25 38.31 38.33 36.70 N base count (bp) 721,914 738,806 859,536 1,969,208 No. N bases per 100 kbp 90.79 89.62 101.73 288.73

Chapter 3: Building genome resource

(a)Cumulative length (Mbp) (b) N(x) length (Kbp)

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400,000 35,000 900 30,000 320,000 700 25,000 240,000 20,000 500 160,000 15,000 10,000 300 80,000 5,000 100 0 0 (Mbp) pairs Total base GC% content Assemblies

K=107 K=117 K=127 K=137

Figure 3.16 Comparisons of S. grandis assemblies generated from ABySS2 using different k values. All assemblies were generated from HiSeq X dataset. (A) Cumulative length plot. (B) N(x) length plot. (C) GC% frequency distribution. (D) N50 value and total base pairs assembled. The bars indicate the N50 value and the black diamonds indicate the total span of the assemblies.

Analysis of the blobplot revealed 437 potential contaminant scaffolds, with a total span of about 1.6 Mbp (Figure 3.17 and Table 3.12). The major source of contamination was from Proteobacteria (Figure 3.17 green circles), which contained 426 scaffolds spanning 1.4 Mbp (Table 3.12). A detailed list of the contaminant species identified are summarised in Appendix 3.4.

Chapter 3: Building genome resource

Figure 3.17 Blobplot of the S. grandis genome assembly ( k = 127) before filtering. The colour code indicates the taxon assigned to the scaffolds (shown as circles). The size of the circles indicates the length of the scaffolds. The figure on the top shows the GC distribution of the assembly, and the figure on the right shows the coverage distributions.

Chapter 3: Building genome resource Table 3.12 Summary of the potential contaminant scaffolds identified in S. grandis genome assembly based on HiSeq X dataset. Average Category No. scaffolds Total span (bp) Total span (%) scaffold length (bp) Proteobacteria 426 1,484,488 94.68 3,484.7 Undefined Viruses 3 15,357 0.98 5, 119 Undefined Eukaryota 3 4,149 0.26 1,383 Undefined 2 42,177 2.69 21,088.5 Unresolved 1 17,138 1.09 17,138 Chordata 1 3,881 0.25 3,881 Undefined bacteria 1 734 0.05 734 TOTAL 437 1,567,924 100.00 3,587.9

The identified contaminant sequences were removed, and only the Streptophyta sequences and “no-hit” scaffolds were kept. Since there were very little contaminations, the metrics of the filtered and unfiltered genomes were nearly identical (Table 3.13, Figure 3.18). The filtered assembly consisted of 127,951 scaffolds with a total span of 843.3 Mbp (Table 3.13, Figure 3.18). The N50 and L50 was 31,638 bp and 7,221, respectively, and the average GC proportion was 38.31% (Table 3.13). Both filtered and unfiltered assemblies had similar BUSCO completeness of 88.5%. Inspection of the post-filtering blobplot confirmed that the bacterial and other contaminant sequences were effectively removed (Figure 3.19). Here the finalised S. grandis genome assembly with the data generated in this study was obtained.

Table 3.13 Metrics of the unfiltered and filtered S. grandis genome assemblies based on HiSeq X dataset. A total of 1,440 BUSCOs were searched. S. grandis S. grandis ABySS2 ABySS2

k = 127 k = 127 unfiltered filtered Assembly metrics Total no. scaffolds 2,252,545 127,951 Total span (bp) 1,211,718,685 843,329,708 No. scaffolds ( ≥500 bp) 128,388 127,951 Total span ( ≥500 bp) (bp) 844,897,632 843,329,708 Largest scaffold (bp) 738,916 738,916 N50 (bp) 31,553 31,638 L50 7,246 7,221 GC (%) 38.33 38.31 N base count (bp) 1,232,681 874,532 No. N bases per 100 kbp 101 .73 100.37 Genome completeness BUSCO completeness (%) 88.5 88.6 No. complete BUSCOs 1,275 1,276 No. fragmented BUSCOs 214 36

Chapter 3: Building genome resource No. missing BUSCOs 128 128 (a) Cumulative length (Mbp) (b) N(x) length (Kbp)

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15,000 400 160,000 15,000N50%(bp) 300300 10,00010,000 Base%pairs%(>500%bp) 80,000 200 5,0005,000 100100 00 00 pairs (Mbp) Total base ABySS2 SOAPdenovo2 Unfiltered Filtered GC% content Assemblies

Unfiltered assembly Filtered assembly

Figure 3.18 Comparison of the unfiltered and filtered S. grandis genome assemblies based on HiSeq X data. (a) Cumulative length plot. (b) N(x) length plot. (c) GC% frequency distribution. (d) N50 value and total base pairs assembled. The bars indicate the N50 value and the black diamonds indicate the total span of the assembly. Because the two assemblies are nearly identical, in (a) – (c) the two lines overlap completely and cannot be distinguished.

Chapter 3: Building genome resource

Figure 3.19 Blobplot of the S. grandis genome assembly ( k = 127) based on HiSeq X data after filtering. The colour code indicates the taxon assigned to the scaffolds (shown as circles). The size of the circle indicates the length of the scaffolds. The figure on the top shows the GC distribution of the assembly, and the figure on the right shows the coverage distributions.

A rough comparison between the filtered S. rexii and S. grandis genome assemblies was made by mapping the S. grandis to the S. rexii assembly (Figure 3.20). The two assemblies were rather dissimilar with only about 20.7% (about 174 Mbp) of the assemblies matching. Among these, 12.7% (about 107 Mbp) showed an above 75% similarity. Also, a large proportion of the S. grandis assemblies were not identified in the S. rexii assembly (Figure 3.20 a). However, this is still relatively similar comparing to the results where the two Streptocarpus genomes were aligned to the D. hygrometricum genome (Figure 3.20 b and c). In these comparisons, the S. rexii and S. grandis assemblies matched only about 3%

Chapter 3: Building genome resource to the D. hygrometricum genome. This indicated that the Streptocarpus assemblies were very different from the Dorcoceras assembly, and the two Streptocarpus assemblies might also be dissimilar between themselves, but less so.

Figure 3.20 Dot plot of the comparison between two Streptocarpus genome assemblies and the Dorcorceras genome assembly (NCBI accession: GCA_001598015.1). (a) S. rexii v.s. S. grandis . (b) S. rexii v.s. D. hygrometricum . (c) S. grandis v.s. D. hygrometricum .

Chapter 3: Building genome resource 3.4 Discussion 3.4.1 Assembly of the Streptocarpus organellar genomes The S. rexii and S. grandis plastid genomes share similar assembly metrics (Table 3.14). The two assemblies also share a high sequence identity of 99.65%, and can be aligned and identified as a single synteny block, indicating their identical genome structure (Figure 3.4). The differences found concerned deletions and SNPs between S. rexii and S. grandis plastid assemblies. For instance, a 65 bp deletion in the trn L-F region is present in the S. rexii plastid but not in S. grandis , which is congruent with previous observations (Möller et al., 2004). It is known that the plastid structure and sequences are conserved among currently sequenced Lamiales species including Gesneriaceae, and their total plastid genome size is around 153 Kbp (Kyalo et al., 2018). This was also observed in our results (Table 3.14). In terms of gene content, the plastid genomes of the three Streptocarpus species and the D. hygrometricum have 107 and 103 protein coding genes annotated, respectively, roughly 20 proteins more compared to Haberlea rhodopensis Friv. and Lysionotus pauciflorus Maxim. (Table 3.14; Ren et al., 2016; Ivanova et al., 2017). Interestingly, all species had eight rRNAs except for S. teitensis and H. rhodopensis with four rRNAs. It is possible that this difference was due to the different annotation pipelines used. As shown in Appendix 3.5, when annotating the S. teitensis and H. rhodopensis plastids using GeSeq tool (method described in section 3.2.3), eight rRNA genes were identified in these assemblies. A more comprehensive comparison is required, such as annotating all genomes using the same annotation pipeline, to confirm whether the differences observed is due to pipeline or actual genetic differences. S. teitensis was placed in subgenus Streptocarpella in Streptocarpus , while S. rexii and S. grandis reside in subgenus Streptocarpus (Nishii et al., 2015). This classification was reflected in the high similarities of the plastid genomes of S. rexii and S. grandis , and wider distance to S. teitensis whose plastid genome was about 500 bp longer than in the two Streptocarpus species in this study (Table 3.14). Comparative study of these three plastids sequences also in relation to other Gesneriaceae genomes may provide interesting insights into the evolution of the chloroplast genome in Gesneriaceae (Kyalo et al., 2018).

Chapter 3: Building genome resource Table 3.14 Plastid assembly metrics across species gathered from the present study and previously published studies Streptocarpus Streptocarpus Streptocarpus rexii grandis teitensis Total span (bp) 152,724 152,770 153,207 No. protein coding genes 107 107 116 No. rRNA annotated 8 8 4 No. tRNA annotated 33 33 32 Reference This study This study Kyalo et al., 2018

Dorcoceras Lysionotus Haberlea

hygrometricum pauciflorus rhodopensis Total span (bp) 153,493 153,856 153,099 No. protein coding genes 103 88 86 No. rRNA annotated 8 8 4 No. tRNA annotated 36 37 36 Reference Zhang et al., 2012 Ren et al., 2016 Ivanova et al., 2017

While the chloroplast genome organisation was highly conserved between the two species studied here, the mitochondrial assemblies on the other hand were highly variable between S. rexii and S. grandis and differed in their statistics and annotation results (Table 3.15). The S. rexii mitochondrial genome was about 40,000 bp shorter than that of the S. grandis , and had 7 fewer protein coding genes and missed 1 rRNA gene compared to S. grandis (Table 3.15). The sequence alignment between the two assemblies revealed large gaps and 1,080 SNPs. The synteny analysis between the two assemblies indicated that the two genomes greatly differed in their structure. One large proportion of the S. rexii assembly was not even identified in S. grandis , and vice versa (Figure 3.6). In particular, the mitochondrial complex III was not found in the S. rexii mitochondrial genome (Figure 3.5). This complex consisted of cytochrome c reductase, involved in the electron transport chain reaction, which is a key component for mitochondrion functioning (Siedow and Umbach, 1995). It is possible that the absence of complex III in S. rexii is related to the 40,000 bp difference between the S. rexii and S. grandis mitochondrial assemblies. A detailed examination should be made of the sequence alignment between the two assemblies to ascertain whether the S. rexii assembly has failed to recover the sequence of complex III due to misassembly. When comparing the Streptocarpus mitochondrial genome assemblies with other Lamiales species, both Streptocarpus assemblies were found to be much smaller (about 200 Kbp shorter) than those of D. hygrometricum (Zhang et al., 2012) and E. guttata (Mower et al., 2012) (Table 3.15). Despite the difference in assembly size, the two Streptocarpus assemblies are still within the typical range of angiosperm mitochondrial genomes (200 Kbp to 750 Kbp; Gualberto et al., 2014). However, both Streptocarpus assemblies had almost

Chapter 3: Building genome resource twice as many protein coding genes identified (Table 3.15). This is again likely to be due to the different annotation pipelines used. As shown in Appendix 3.6, when annotating the D. hygrometricum and E. guttata mitochondrial genomes using the method described in this study, 152 and 149 protein coding genes were identified in the two assemblies, respectively.

Table 3.15 Mitochondria assembly metrics across species gathered from the present study and previously published studies Streptocarpus Streptocarpus Dorcoceras Erythrante

rexii grandis hygrometricum guttata Total span (bp) 314,134 352,540 510,519 525,671 No. protein coding genes 72 79 33 35 No. rRNA annotated 2 3 4 3 No. tRNA annotated 17 17 28 24 Zhang Mower Reference This study This study et al., 2012 et al., 2012

Since the main objective of this study was not to analyse the organellar genomes themselves, only the basic assemblies and annotation metrics were analysed and compared here. A more thorough analysis and characterisation of the Streptocarpus plastid and mitochondrial genomes would be desirable but is beyond the scope of the present study.

3.4.2 Assembly of the Streptocarpus nuclear genome The S. rexii and S. grandis were sequenced, assembled, and compared. The ABySS2 and SOAPdenovo2 assemblers were first tested on the S. rexii dataset, which the ABySS2 assembler was chosen for further optimisation. The ABySS2 assembler was finally used to reconstruct the S. rexii genome at k = 137 and the S. grandis genome at k = 127. For both assemblies their contaminants were filtered out, and the quality and BUSCO completeness assessed. The metrics of the final assembly for both species are summarised in Table 3.16.

Chapter 3: Building genome resource Table 3.16 Assembly metrics of the S. rexii and S. grandis genomes. A total of 1,440 BUSCOs were searched. S. rexii S. grandis ABySS2 ABySS2 k = 137 k = 127 filtered filtered Assembly metrics Total no. scaffolds 95,845 127,951 Total span (bp) 596,583,869 843,329,708 No. scaffolds (≥500 bp) 95,845 127,951 Total span (≥500 bp) (bp) 596,583,869 843,329,708 Largest scaffold (bp) 421,987 738,916 N50 (bp) 35,609 31,638 L50 4,571 7,221 GC (%) 37.75 38.31 N base count (bp) 907,432 874,532 No. N bases per 100 kbp 152.10 100.37 Genome completeness BUSCO completeness (%) 88.8 88.6 No. complete BUSCOs 1,279 1,276 No. fragmented BUSCOs 43 36 No. missing BUSCOs 118 128

The total span of the S. rexii assembly was about 596 Mbp, which was 247 Mbp smaller than the S. grandis assembly (843 Mbp). The S. rexii assembly had fewer scaffolds assembled; the N50 value was about 4,000 bp longer than the S. grandis assembly, and the L50 value lower, suggesting an overall better contiguity of the S. rexii assembly. However, S. rexii also had more N bases in the assembly. This is possibly due to the usage of a library with longer insert size (550 bp), which was not used for the S. grandis (only with insert size of 350 bp), thus more read pair information was utilised for scaffolding and more gaps were created. There was a discrepancy between the estimated genome size and the assembly total spans. The S. rexii and S. grandis genome size estimation by flow cytometry gave C-values of 929 Mbp and 1,260 Mbp respectively (Möller, 2018); the estimations obtained from the k- mer histograms were significantly lower, 542 Mbp to 710 Mbp for S. rexii , and 990 Mbp to 1,003 Mbp for S. grandis (Tables 3.6, 3.10). Both C-value and k-mer estimations were larger than the final genome assemblies (Table 3.16). It is known that repeat content of genomes are difficult to be assembled (Claros et al., 2012; Compeau et al., 2017). Plants with smaller

Chapter 3: Building genome resource genomes such as A. thaliana (~135 Mbp; Rhee et al., 2003) tend to have fewer but longer repeat sequences (2 Kbp to 6 Kbp) interspersed among longer non-repetitive regions, while plants with relatively large genome sizes such as maize (~2.1 Gbp; Hirsch et al., 2016) tend to have many shorter repeated sequences (50 bp to 2 Kbp) interspersed among shorter non- repetitive sequences, and are more difficult to assemble (Lapitanz, 1992). Both Streptocarpus species have genome sizes around 1 Gbp, and the repeat content estimated from the k-mer histograms was about 12% to 16% (Table 3.6 and 3.10). It is possible that the repeat content of the Streptocarpus genomed were not reconstructed, thus the final assembly size is smaller than the estimated genome size. Alternatively, it is possible that the flow cytometry results are inconsistent and the genome size was estimated incorrectly. For example, two very different 1C value for Streptocarpus cyaneus were reported, 0.875 pg (Möller, 2018) and 0.675 pg (Hansen et al., 2001), which correspond to 855 Mbp and 660 Mbp respectively. Another example is Streptocarpus ionantha , which its 1C value was independently calculated as 0.87 pg (Möller, 2018) and 0.75 pg (Loureiro et al., 2007), corresponding to 850 Mbp and 733 Mbp respectively. This suggests that the flow cytometry results can have 100 Mbp to 200 Mbp variations, and maybe the flow cytometry has overestimated the Streptocarpus genome size while the assemblies presented here underestimated it. Both Streptocarpus assemblies showed similar BUSCO completeness: the S. rexii had 1,279 BUSCO identified (88.8%) and S. grandis 1,276 BUSCO identified (88.6%) among the 1,440 BUSCOs searched. The S. rexii assembly also had more fragmented BUSCOs (43) and less missing BUSCOs (118) than the S. grandis assembly (36 fragmented and 128 missing), implying that the S. rexii assembly was able to reconstruct more core genes even if they were fragmented. Interestingly, in both assemblies the BUSCO completeness improved after filtering out short scaffolds and cross-species contaminants. This suggests that our filtering strategy was able to improve the assembly quality, but at the same time was not too stringent as to remove key information from the genome. Genome-to-genome alignment between S. rexii and S. grandis assemblies suggested a low similarity, where only 12.18% of the S. rexii assembly matching the S. grandis assembly with an identity higher than 50%. Likewise, 79.33% of the S. rexii assembly failed to match S. grandis (Figure 3.20 a). The dot plots suggested that there was a large proportion of the S. grandis genome that cannot be identified in S. rexii genome, and vice versa (Figure 3.20 a). This difficulty encountered in aligning the two genomes may be related to the phylogenetic distance between the two species. As previously described, the two species differ in 45 nucleotides and 4 insertions/deletions in their ribosomal Internal Transcribed Spacer (ITS) region (Chen et al., 2018). Using the average substitution rate for ITS for herbaceous plants (Kay et al., 2006), this would result in a divergence time of c. 9.8 (±1.4SE) million years ( c.f . Puglisi et al., 2011). This is presumably long enough for the two

Chapter 3: Building genome resource genomes to diverge considerably. Nevertheless, the genome-to-genome alignment analysis may need to be optimised, such as changing the parameters and comparisons with other alignment tool (Marçais et al., 2018). It is also possible that the repeat content of the genomes were not assembled as previously discussed, and reanalysis of genome-to-genome alignment with improved genome assemblies (such as inclusion of PacBio or Nanopore data) may be required.

3.4.3 Identification of contaminant species in the nuclear genome assemblies Cross-species contamination was observed in both Streptocarpus genome assemblies. FastQC results for the S. rexii sequencing data indicated the presence of contaminants that had a high GC content (~65%) (Figure 3.2), as well as the ‘2 heaped’ GC distribution observed in the unfiltered assemblies (Figure 3.11 c and 3.13 c). Analysis using Blobtools identified the major sources of contamination in the S. rexii assembly as stemming from Actinobacteria and Proteobacteria (Figure 3.12 and Table 3.8). In addition, there were several other contaminants that shared a similar GC content to that of the plant genomes, including fungi and arthropods (Figure 3.12 and 3.17). The S. grandis assembly showed much less cross-species contamination than that of S. rexii (Figure 3.17 and Table 3.12). One possibility is that the plant material used for DNA extraction were maintained under different conditions: the S. rexii plants were grown in a glasshouse, where the plants are exposed a wide range of organisms, including to insects, animals, fungi, bacteria and other microbes present in and on other plants kept in the glasshouses. On the other hand, the S. grandis material had always been kept isolated in a growth chamber under fixed environmental conditions prior to DNA extraction, and was thus exposed to fewer contaminants. Interestingly, the Proteobacterial scaffolds identified in S. rexii and S. grandis have different GC percentage and may have came from different species. In S. rexii the scaffolds have around 60% to 70% GC percentage (Figure 3.12), while in S. grandis they have approximately 40% to 50% (Figure 3.17). Further examination revealed that the Proteobacteria in S. rexii is mainly Methylobacterium extorquens , with ~65% GC percentage, whereas in S. grandis they are mainly an unidentified Methylophilus sp. TWE2, with ~45% GC percentage. The list of contaminants identified could represent a potential resource for horticultural pest control purposes (Appendix 3.3 and 3.4). In both Streptocarpus assemblies, many plant pathogenic microbes were identified. In terms of bacteria, the list includes Janthinobacterium agaricidamnosum which causes soft rot disease in common mushrooms (Lincoln et al., 1999); Pectobacterium polaris causes soft-rot disease in potato (Dees et al., 2017); Pseudomonas cichorii a non-host specific pathogen causing water-soaked lesions on leaves (Li et al., 2014); Acidovorax citrulli causing fruit blotch in melons (Eckshtain-Levi et al., 2016); Serratia marcescens the causative agent for yellow vine disease in melons

Chapter 3: Building genome resource (Rascoe et al., 2003), and the Xanthomonas sp. that produce spots and blights on different plant organs of a wide range of hosts (Da Silva et al., 2002). As for the potential fungal pathogen identified, Peronospora tabacina is known for causing the blue mold disease in tobacco (Ristaino et al., 2007), and other Peronospora sp. are known to cause Downy mildew (Slusarenko and Schlaich, 2003). Pythium ultimum causes rot disease on a wide range of crop and ornamental plants (Lévesque et al., 2010). The Dahlia mosaic virus causes mosaic patterns on leaves (Brunt, 1971). Finally, the Aphelenchoides fragariae is a parasitic nematode found in strawberries and several ornamental plants (Sánchez-Monge et al., 2015). Besides the pathogenic organisms, sequences of plant growth stimulating-microbes and possible symbionts were also found in the assemblies. For example, Methylobacterium sp., Azorhizobium sp., Rhizobium sp., Sinorhizobium sp., Bradyrhizobium sp., and Mesorhizobium sp. that are known symbionts of legumes involved in nitrogen fixation (Masson-Boivin et al., 2009). However, it should be noted that the accuracy of the identification is limited to the availability of reference genomes, i.e. if the reference genome of the actual species does not exist in the NCBI nucleotide database, the sequence will be identified as the most closely related species. A problem with the current contamination-filtering strategy is that many assembled scaffolds remained unidentified or undefined (no-hit). This may possibly be improved by BLAST searching of the genome assemblies against other available genomic databases, such as UniProt (Apweiler et al., 2004). The BLAST search results of multiple databases can be integrated using the ‘bestsumorder’ option in Blobtools and may increase the proportion of taxonomical rank-assigned scaffolds (Laetsch and Blaxter, 2017). Another possible improvement for the overall genome assembly strategy, is to remove the contaminant reads instead of the scaffolds, and repeat the genome assembly with the cleaned reads which may increase the assembly contiguity (Koutsovoulos et al., 2016).

3.4.4 Comparisons with other closely related genomes and possible future improvement for genome assembly When comparing the metrics of the Streptocarpus genome assemblies to those of the closely related D. hygrometricum and to E. guttata (Table 3.17), the former assemblies were found to be much more fragmented. The longest scaffold was less than half the length of those in Dorcoceras and Erythrante , and the N50 value was about one third of the other two assemblies. This was likely due to the limitation of the data availability in our project. Differences in the library construction and sequencing strategy of the other two genomes may provide us with guidance for our future sequencing experiments. For the Dorcoceras assembly, both short-insert paired-end library and long-insert mate-pair libraries for Illumina sequencing were used, as well as 454 Pyrosequencing to produce data with longer read length of up to 1 Kbp (Xiao et al., 2015). For the Erythrante genome, even though next

Chapter 3: Building genome resource generation sequencing technologies were not used, libraries of long insert size (3.3 Kbp insert size to 105 Kbp insert size) were used (Hellsten et al., 2013). In both cases, long insert size libraries were used to improve the scaffold assembly (Ekblom and Wolf, 2014). Thus, in addition to the usage of third generation sequencing data to improve our genome assembly, long insert size libraries such as mate-pair might be another option. In terms of sequence similarity, genome-to-genome alignments between the Streptocarpus sp. and the D. hygrometricum indicated that the two Streptocarpus genome assemblies were very different from the D. hygrometricum genome. Especially with a large proportion of the D. hygrometricum genome that cannot be found in the Streptocarpus assemblies (Figure 3.20 b and c), though again the alignment procedure may also require optimisation.

Table 3.17 Comparison of the Streptocarpus assemblies with other Lamiales genomes Streptocarpus Streptocarpus Dorcoceras Erythrante

rexii grandis hygrometricum guttata 350 bp and 350 bp insert 170 – 800 bp Three 3.3 Kbp 550 bp insert library insert library insert libraries libraries Sequenced on Sequenced on Two 6.6 Kbp Sequenced on HiSeq X HiSeq 2000 insert libraries HiSeq X/2500 600 and 1,000 One 7.9 Kbp bp shotgun insert library libraries Sequencing approach Six 33 – 36 Kbp Sequenced by insert fosmid 454 GF FLX libraries

Two BAC 64 – 105 Kbp insert libraries

Sanger sequencing Assembly metrics Total span (Mbp) 596 843 1,548 322 Total no. scaffolds 95,845 127,951 520,969 2,212 Largest scaffold (bp) 421,987 738,916 1,434,191 4,921,564 N50 (bp) 35,609 31,638 110,988 1,123,783 GC (%) 37.75 38.31 42.30 36.31 To summarise this chapter, here we generated the first draft genome assemblies for the genus Streptocarpus . These represent only the second and third Gesneriaceae genome to be sequenced and assembled alongside that of D. hygrometricum . The assemblies will serve

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Chapter 3: Building genome resource as an important reference resource for the analysis of the RNA-Seq and RAD-Seq data in the following chapters. The organellar genomes could be assembled and circularised as a by- product, making them invaluable resources for future studies.

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Chapter 4: Building transcriptome resource Chapter 4 Building transcriptome resources – RNA sequencing transcriptome analysis of Streptocarpus rexii and Streptocarpus grandis

4.1 Introduction 4.1.1 RNA sequencing and its applications RNA sequencing (RNA-Seq) uses NGS technologies to sequence and quantify transcripts (Wang et al., 2009). Compared to traditional transcriptomic study approaches, such as microarrays or genomic tiling arrays, RNA-Seq does not require a reference genome or design of hybridisation probes, thus is suitable for studies in non-model organisms (Wang et al., 2009; Korpelainen et al., 2014). RNA-Seq reads can be assembled into a transcriptome providing transcribed mRNA sequence information, and can potentially be used for identifying differentially expressed genes or investigating isoforms and alternative splicing events (Korpelainen et al., 2014). For example, RNA-Seq derived transcriptomes from a developmental series have been used for comparative transcriptomics to identify candidate regulators of phyllotaxy in Antirrhinum (Wang et al., 2017a) and shoot apical meristem and floral development in legumes (Singh and Jain, 2014). A Streptocarpus rexii transcriptome database has previously been generated to identify candidate gene sequences for hormone biosynthesis and cotyledonary development (Chiara et al., 2013; Chen et al., 2017). However, so far no transcriptome resource is available for the comparison between rosulate and unifoliate growth forms in the genus. The transcriptomic data of S. rexii and S. grandis would thus be invaluable in this aspect, as it would be the first transcriptome of a unifoliate species. In this chapter the transcriptome of both S. rexii and S. grandis are generated using RNA-seq performed on pooled RNA from different tissue types (vegetative+reproductive).

4.1.2 Currently available Streptocarpus and Gesneriaceae transcriptomes For twenty-three Gesneriaceae species across 6 genera RNA-Seq derived transcriptomes are published (Figure 4.1), including S. rexii and Streptocarpus ionanthus (formerly Saintpaulia ionantha ; see also Nishii et al., 2015) (Table 4.1). However, the RNA used for both these Streptocarpus transcriptomes were extracted soley from vegetative tissues (Chiara et al., 2013; Matasci et al., 2014). Other Gesneriaceae transcriptomes include Damrongia clarkeana (as Boea clarkeana , see also Puglisi et al., 2016) and Dorcoceras hygrometricum (as Boea hygrometrica , see also Puglisi et al., 2016), were used for studying drought tolerance and rehydration processes (Xiao et al., 2015; Wang et al., 2017b). Transcriptomes of Achimenes were used for the study of floral development (Roberts and

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Chapter 4: Building transcriptome resource Roalson, 2017). Sinningia eumorpha , Sinningia magnifica , and Primulina transcriptomes were derived from a mixture of tissue types, e.g. leaf and flower or leaf and root (Ai et al., 2014; Serrano-Serrano et al., 2017). The Primulina transcriptomes were used for studying species and population genetic diversity (Ai et al., 2014) .

Figure 4.1 Summary phylogeny of the Gesneriaceae family with indication of the genera for which transcriptome resources are available (Figure modified from Weber et al., 2013)

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Chapter 4: Building transcriptome resource Table 4.1 List of available Gesneriaceae transcriptomes Species Tissues Reference Achimenes cettoana Flower Roberts and Roalson, 2017 Achimenes erecta Flower Roberts and Roalson, 2017 Achimenes misera Flower Roberts and Roalson, 2017 Achimenes patens Flower Roberts and Roalson, 2017 Damrongia clarkeana Leaf Wang et al., 2017 Dorcoceras hygrometricum Leaf Xiao et al., 2015 Sinningia tuberosa Leaf Matasci et al., 2014 Sinningia eumorpha Leaf and flower Serrano-Serrano et al., 2017 Sinningia magnifica Leaf and flower Serrano-Serrano et al., 2017 Streptocarpus rexii Leaf and cotyledon Chiara et al., 2013 Streptocarpus ionantha Leaf Matasci et al., 2014 Primulina eburnea Leaf and root Ai et al., 2014 Primulina fimbrisepala Leaf and root Ai et al., 2014 Primulina heterotricha Leaf and root Ai et al., 2014 Primulina huaijiensis Leaf and root Ai et al., 2014 Primulina lobulata Leaf and root Ai et al., 2014 Primulina lutea Leaf and root Ai et al., 2014 Primulina pteropoda Leaf and root Ai et al., 2014 Primulina sinensis Leaf and root Ai et al., 2014 Primulina swinglei Leaf and root Ai et al., 2014 Primulina tabacum Leaf and root Ai et al., 2014 Primulina villosissima Leaf and root Ai et al., 2014

4.1.3 RNA-seq analysis strategy In this chapter, analysis of the RNA-seq data involves four steps: read preprocessing (i.e., trimming), assemblying, isolation of open reading frames of genes (ORFs), and fuctional annotation. Read preprocessing step focuses on removing low quality reads (to remove potential sequencing error) and Illumina adaptor sequences, which if not filtered can causes misassembly (Andrews, 2010). The assembly step is done through de novo assembly and reference guided assembly: de novo transcriptome assembly is performed in a similar fashion to that of de novo genome assembly, which RNA-seq reads are break down into K- mers and transcripts are reconstructed using de Bruijn graph approach (see section 3.1.4; Haas et al., 2013); reference guided assembly is carried out in the presence of a reference genome sequence. First RNA-seq reads are aligned to the genome sequence, and transcripts

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Chapter 4: Building transcriptome resource can be reconstructed from overlapped-mapped reads (Korpelainen et al., 2014). An essential consideration for reference guided assembly is the aligner’s capability in creating gaps during alignment process. As the reference genome sequence usually contains intron-exon gene structures, which is not presented in the RNA-seq reads, the aligners must be able to create gaps at the intron position, thus separates a read into 2 or potentially more exons regions in order to achieve correct read mapping (Korpelainen et al., 2014). Software such as HISAT2 has such function and is designed for aligning RNA-seq data to a reference genome (Kim et al., 2015). Assemblers such as Trinity (Haas et al., 2013) is designed for both de novo and reference-guided assembly. ORF identification step aims to isolate translated proportion of the assembled transcripts, which the isolated sequences (as amino acids) can later be used for protein functional annotation. The software TransDecoder is designed for this purpose and can effectively remove untranslated regions (UTRs; Haas et al., 2013). Finally, the functional annotation step aims to assign (annotate) a possible biological function to the protein sequences by comparing them to existing protein sequence database (Bolger et al., 2018). For example, the webtool Mercator assign ‘MapMan Bin ontology’ to assembled sequences, a plant specific collection of protein functions catagorised by biological processes (Loshe et al., 2014); the Kyoto Encyclopedia of Genes and Genomes ontology, KEGG, is a database of genes and related biological pathways, and can be used to assign ‘KO terms’ to the assembled proteins (Kanehisa et al., 2016). As the scope of this study is to generate reference gene transcripts dataset for S. rexii and S. grandis . For both species, total RNA was extracted from seedlings, roots, shoots, floral buds, flowers and developing fruits to cover as wide an expression profile as possible. Since the intention was not to compare gene expression differences between different tissue types, no biological repeat was made for gene differential expression analysis, and all RNA were pooled and sequenced together. The RNA-Seq reads were assembled de novo and through a reference-guided approach (i.e. mapping the RNA-Seq reads to a reference genome). The assemblies were filtered for ORFs and cross-species contaminants. The resulting transcriptomes were compared to those of other Gesneriaceae and to model species and will serve as a fundamental genomic resource for the genus Streptocarpus .

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Chapter 4: Building transcriptome resource 4.2 Materials and methods 4.2.1 Plant materials All plant materials were harvested from plants grown in the research glasshouses and growth chambers at the Royal Botanic Garden Edinburgh. For S. rexii , the accession 20150819 was used, and for S. grandis accession 20020577. The sample collection was performed as described in Chapter 2, section 2.2.1. In brief, the seedlings, roots, shoots, floral buds, flowers, and developing fruit tissues of S. rexii and S. grandis were collected from young and mature plants. The RNA extraction and the sequencing of S. grandis materials was done and the data kindly provided by K. Nishii.

4.2.2 RNA extraction, library preparation and RNA-Seq The RNA extraction and quality assessment of the RNA samples were described in Chapter 2. In brief, RNA were extracted using TRIzol reagents followed by acid phenol:chloroform purification and PureLink Kit clean up. The extracted total RNA were pooled and delivered to Edinburgh Genomics (University of Edinburgh, Edinburgh, UK) for library preparation and sequencing. The library was prepared using TruSeq Stranded mRNA Prep Kit (Illumina, San Diego, CA, USA). The S. rexii library was first sequenced in one lane of MiSeq (Illumina) together with two other libraries. However, the read yield was not sufficient to generate a good transcriptome. Thus, an additional sequencing run was carried out, which was performed in one lane of HiSeq 4000 (Illumina) together with 13 other libraries outside the scope of this thesis. For the S. grandis , the sequencing was performed using one lane of MiSeq (Illumina). The sequencing results was returned in Fastq format and the read quality was accessed by FastQC v0.11.5 (Andrews, 2010).

4.2.3 Sequence data pre-processing Trimmomatic-v0.35 (Bolger et al., 2014) was used for adopter and quality trimming with the following settings: ILLUMINACLIP:TruSeq3-PE2.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:50. The pre-processed reads were stored in fastq.gz format (Box 4.1).

Box 4.1 Script for Trimmomatic preprocessing of the RNAseq reads. Text in bold with brackets [Text] indicate the input files and output file names to be specified. java –jar trimmomatic-0.35.jar PE -threads [NO_CPU] –phred33 \ [READ1fastq] [READ2.fastq] \ [TRIMMED_READ1.fastq] [UNPAIR_TRIMMED_READ1.fastq] \ [TRIMMED_READ2.fastq] [UNPAIR_TRIMMED_READ2.fastq] \ ILLUMINACLIP:/TruSeq3-PE-2.fa:2:30:10 \ LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:50

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Chapter 4: Building transcriptome resource 4.2.4 Transcriptome de novo and reference-guided assembly Trinity v2.4.0 (Haas et al., 2013) was used for both de novo and reference-guided assembly of the transcriptomes. For de novo assembly, the preprocessed reads were used and the assembly carried out under default parameter settings (Box 4.2). For the reference-guided assembly, the software STAR (Dobin et al., 2013) was used to map the RNA-Seq reads onto the corresponding genome assemblies (i.e. S. rexii RNA-Seq reads mapped to S. rexii genome, and S. grandis reads to S. grandis genome). The assembly was carried out in two stages. First, mapping and assembly was performed under default parameter settings. Second, three STAR mapping parameters were tested to improve the mapping percentage; this includes the ‘minimum and maximum intron size’, ‘maximum mismatch allowed during alignment’ and the ‘2nd pass mode’. The detail parameter values tested for the assembly optimisation are summarised in Table 4.2. Optimal parameter values were chosen to generate a bam file of the assembly to the genome, which was then used for transcriptome assembly via Trinity v2.4.0 (Haas et al., 2013). The detailed commands used are listed in Box 4.2 ( de novo assembly) and Box 4.3 (reference-guided assembly).

Table 4.2 STAR parameters tested for the mapping of RNA-Seq reads to reference genome Parameter Parameter definition Values tested --alignIntronMin / The minimum / maximum intron 21 / 288 (default), --alignIntronMax length allowed when mapping RNA- 10 / 10,000, Seq reads 10 / 20,000

--outFilterMismatchNmax The maximum number of mismatch 5, 10 (default), allowed when writing a read to the 20, 30, 40, 50, output file (i.e. the mapping 60, 70, 80 procedure was not affected, but by altering this parameter one can decide whether to consider a reads with more mismatches to be considered as mapped or not) --twopassMode 2nd pass mode. Enabled to improve Disabled the mapping of reads on splice (default), junctions. When enabled, STAR will Enabled carry out a normal mapping procedure first (1st mapping) and identify the splice junctions. The splicing information was then used for the 2nd mapping, which will not identify new junction but will align the spliced reads with short overhangs across the previously detected junctions

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Chapter 4: Building transcriptome resource Box 4.2 Script for transcriptome de novo assembly. Text in bold with brackets [Text] indicate the input files and output file names to be specified. ## For Trinity de novo assembly Trinity --seqType fq --CPU [NO_CPU] \ --left [READ1.fastq] --right [READ2.fastq]

Box 4.3 Script for transcriptome reference-guided assembly. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Build STAR genome indices STAR --runThreadN [NO_THREADS] --runMode genomeGenerate \ --genomeDir [OUTPUT_DIRECTORY] \ --genomeFastaFiles [GENOME_ASSEMBLY.fasta] \ --limitGenomeGenerateRAM 300000000000 \ --genomeSAindexNbases 13

# STAR mapping of RNA-Seq reads STAR --runThreadN [NO_THREADS] \ --genomeDir [GENOME_INDEX_DIRECTORY] \ --readFilesIn [READ1.fq] [READ2.fq] \ --outFilterMismatchNmax [MISMATCH_ALLOWED] --outFileNamePrefix [OUTPUT_NAME]

# Convert the STAR output sam file into bam format samtools view -Sb -@ [NO_THREADS] [STAR_OUTPUT.sam] | \ samtools sort -O bam -@ [NO_THREADS] -o [OUTPUT_NAME.bam]

# Trinity genome-guided assembly Trinity --seqType fq --left [READ1.fq.gz] --right [READ2.fq.gz] \ --genome_guided_bam [OUTPUT_NAME.bam] \ --genome_guided_max_intron 20000 \ --max_memory 200G –CPU [NO_THREADS]

4.2.5 Preliminary assembly quality evaluation For both de novo and reference-guided assemblies, tools from the Trinity package v2.4.0 (Haas et al. 2013) were used to evaluate the metrics of the assemblies (1) the ‘analyze_blastPlus_topHit_coverage.pl’ was used to calculate the gene completeness. (2) ‘TrinityStats.pl’ was used to calculate the basic metrics, e.g. N50, total base pairs assembled, and number of predicted genes. In addition, Bowtie2 v2.2.8 (Langmead and Salzberg, 2012) was used to check the remapping rate of RNA-seq reads on the assembly under default settings. BUSCO v3 (Simão et al., 2015) was used to check the completeness of universal single copy orthologous genes. This involved BLASTing the assembled transcripts against the Embryophyta odb9 database (Last update date 13/02/2017) and assessing transcriptome completeness by the proportion of BUSCO genes found. The detailed commands are listed in Box 4.4.

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Chapter 4: Building transcriptome resource Box 4.4 Script for preliminary quality assessment of the transcriptome assembly. Text in bold with brackets [Text] indicate the input files and output file names to be specified. ## 1. Calculating gene completeness using Trinity script # Build the blastdb from uniprot dataset fasta file makeblastdb -in [uniprot_sprot.fasta] -dbtype prot # Blast the transcriptome against the database blastx -query [TRANSCRIPTOME.fasta] -db [uniprot_sprot_DB] \ -out [BLAST_OUTPUT_NAME] -evalue 1e-20 \ -max_target_seqs 1 -outfmt 6 # Analyse the output file Trinity/util/analyze_blastPlus_topHit_coverage.pl \ [BLAST_OUTPUT] \ [TRANSCRIPTOME.fasta] \ [uniprot_sprot.fasta] # Group BLAST hits to improve the coverage Trinity/util/misc/blast_outfmt6_group_segments.pl \ [BLAST_OUTPUT] [TRANSCRIPTOME.fasta] \ [uniprot_sprot.fasta] > [GROUPED_BLAST_OUTPUT] # Re-analyse the output file Trinity/util/misc/ blast_outfmt6_group_segments.tophit_coverage.pl \ [GROUPED_BLAST_OUTPUT] \ [TRANSCRIPTOME.fasta] \ [uniprot_sprot.fasta]

## 2. Basic assembly metrics calculation using Trinity script Trinity/util/TrinityStats.pl [TRANSCRIPTOME.fasta]

## 3. Checking remapping rate bowtie2-build [TRANSCRIPTOME.fasta] [OUTPUT_INDEX_NAME] bowtie2 -q -x [BOWTIE2_INDEX] -1 [READ1.fastq] -2 [READ2.fastq]

## 4. Checking gene completeness using BUSCO python BUSCO.py -i [TRANSCRIPTOME.fasta] -o [OUTPUT_NAME] -m tran \ --cpu [NO_CORES] -l embryophyta_odb9/

4.2.6 Post-assembly filtering and functional annotation For both de novo and reference-guided approaches, the assemblies were filtered for sequences containing open reading frames (ORFs) via TransDecoder v5.1.0 (Haas et al., 2013) with the ‘--single_best_orf’ option, which keeps the longest isoform among all the identified isoforms. The identified ORFs were then filtered for potential contaminant sequences using the KEGG Orthology And Link Annotation (KOALA) function on the KEGG server (Kyoto Encyclopedia of Genes and Genomes). The tool GhostKOALA v2.0 (Kanehisa et al., 2016) assigned taxonomical information to all the ORFs, and those ORFs labelled non-eudicot or non-monocot-originated were subsequently removed (i.e. assigned as basal plants, animals, fungi, protists, bacteria, archaea, viruses, or unknown). The assembly procedure used in this study is and filtering commands are listed in Box 4.5.

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Chapter 4: Building transcriptome resource Box 4.5 CDS identification and contaminant removal for the transcriptome. Text in bold with brackets [Text] indicate the input files and output file names to be specified. ## Using TransDecoder for CDS identification ./TransDecoder.LongOrfs -t [TRANSCRIPTOME.fasta] ./TransDecoder.Predict -t [TRANSCRIPTOME.fasta] --single_best_only

## Transcriptome contaminant filtering based on GhostKOALA results # Identify the angiosperm originated transcripts grep -i "monocot\|eudicot" [KOALA.TAXANOMY.OUTPUT] | \ awk '{print $1}' | sed 's/user://' > transcript.to.keep # Isolate the corresponding transcripts perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' \ transcript.to.keep [TRANSDECODER_PROCESSED_CDS.fasta] > filtered.fasta

The basic statistics of the assemblies and BUSCO completeness were evaluated as described in Box 4.4, and their functional annotation carried out using KEGG GhostKOALA v2.0 (Kanehisa et al., 2016a; 2016b) and the Mercator web tool (Lohse et al., 2013) under default settings. The transcriptome analysis flowchart is summarised in Figure 4.2.

4.2.7 Orthogroup identification Orthofinder v1.1.8 (Emms and Kelly, 2015) was used to identify conserved orthogroups in the four final assemblies (i.e. de novo and reference-guided S. rexii transcriptomes; de novo and reference-guided S. grandis transcriptomes). The TransDecoder output file, which consisted of the peptide sequences of the identified ORFs, was used as input. An OrthoFinder analysis was performed under default settings (Box 4.6). The output file ‘Orthogroups.csv’ was used for the visualisation as a Venn diagram using an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Box 4.6 Identification of orthologous transcripts using OrthoFinder. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Execute OrthoFinder, where all transcriptome fasta files are in the # same directory Orthofinder -f [FASTA_FILE_DIRECTORY] -t [NO._CPU]

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Figure 4.2 Flowchart of Streptocarpus transcriptome analysis

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Chapter 4: Building transcriptome resource 4.3 Results 4.3.1 RNA-Seq data preprocessing The MiSeq run of S. rexii generated about 4.3 million read pairs; the HiSeq 4000 run of S. rexii generated the highest read counts among all three sequencing experiments with approximately 24.7 million read pairs. The MiSeq S. grandis run gave about 16.5 million read pairs (Table 4.3). Prior to preprocessing, all three datasets showed good sequence quality with an average quality score above Q30 (Figure 4.3 to 4.5 a). Some biases in ‘per base sequenced content’ were observed, where the proportion of C bases did not corresponded to the proportion of G bases in the data, neither did the proportion of A to T bases (Figure 4.3 to 4.5 b and c). The ‘GC distribution’ graphs failed to fit a normal distribution, and showed skewed pattern towards the 55% to 65% GC content (Figure 4.3 to 4.5 d). Adopter contamination was found at the 3’ end of the raw reads (Figure 4.3 to 4.5 e). After preprocessing, only about half of the sequencing data remained for each dataset: this was approximately 2.3, 12.7, and 10.2 million read pairs for the S. rexii MiSeq, S. rexii HiSeq 4000, and S. grandis MiSeq sequencing datasets respectively (Table 4.3). The biased ‘per base sequence content’ and the skewed GC distributions persisted after preprocessing (Figure 4.3 to 4.5 b, c, d).

Table 4.3 S. rexii and S. grandis RNA-Seq experiment summary S. rexii S. rexii S. grandis Sequencer MiSeq HiSeq 4000 MiSeq

No. read pairs obtained 4,364,215 24,739,455 16,501,920

Total base pairs (bp) 1,309,264,000 7,313,836,500 4,950,576,000

No. read pairs after trimming 2,357,477 12,725,705 10,266,774

Total base pairs after 703,323,621 3,773,167,877 3,064,274,789 trimming (bp)

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Figure 4.3 RNA-Seq read quality check results of the S. rexii MiSeq experiment

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Figure 4.4 RNA-Seq read quality check results of the S. rexii HiSeq 4000 experiment

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Figure 4.5 RNA-Seq read quality check results of the S. grandis MiSeq experiment

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Chapter 4: Building transcriptome resource 4.3.2 De novo assembly of the S. rexii and S. grandis transcriptomes The de novo assembling of the S. rexii data using either MiSeq data alone gave the lowest number of reads, the HiSeq 4000 almost 6x more data, while the combined MiSeq + HiSeq 4000 data had the additive read numbers of the first two experiments (Table 4.4). The assembly metrics of total number of contigs and contig N50 were positively correlated with the number of input reads (Table 4.4; Figure 4.6). The analysis with MiSeq data alone recovered the lowest number of contigs (59,042), with an N50 value of 1,098 bp. Assembly using HiSeq 4000 data alone gave 101,640 contigs, and a longer N50 value of 1,580 bp. Assembly using MiSeq + HiSeq 4000 data combined generated the highest number of contigs and the highest contig N50 values (Table 4.4), and a total of 123,213,415 bp assembled with a BUSCO completeness of 79% (1,137 BUSCOs found). From the assembly including both MiSeq and HiSeq 4000 data, 64,516 ORFs were identified, with 4,016 of these labelled as possible cross-species contamination. After removing these ORFs, 60,500 ORFs remained (Table 4.4). For the S. grandis MiSeq dataset, 87,665 contigs with a total of 102,299,541 bp were assembled. The contig N50 value of about 1,500 was very similar to that of the S. rexii analysis using HiSeq 4000 data alone, while the BUSCO completeness was with 79% identical to the S. rexii MiSeq + HiSeq data analysis (Table 4.4). For S. grandis , 53,132 ORFs were identified of which 1,855 were found to be possible cross-species contaminations and were subsequently removed. A final 51,267 ORFs were retained for the de novo S. grandis assembly (Table 4.4).

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Chapter 4: Building transcriptome resource Table 4.4 Metrics of the S. rexii and S. grandis de novo assembled transcriptomes

S. rexii S. rexii S. rexii S. grandis

MiSeq + Dataset used MiSeq HiSeq 4000 MiSeq HiSeq 4000 No. read pairs used 2,357,477 12,725,705 15,083,182 10,266,774 Assembly metrics No. contigs 59,042 101,640 110,955 87,665 Total base pairs (bp) 53,293,812 103,654,725 123,213,415 102,299,541 Average contig length (bp) 902 1,019 1,110 1,166 Contigs N50 (bp) 1,098 1,580 1,716 1,541 No. gene with > 80% 5,724 7,777 8,343 7,798 length GC (%) 42.9 42.3 42.1 42.5 Reads remapping (%) 94.9 99.2 99.3 97.1 Transcriptome completeness BUSCO completeness (%) 54.8 72.0 79.0 79.0 No. completed BUSCOs 789 1,036 1,137 1,138 No. missing BUSCOs 651 404 303 302 ORF identification No. ORFs identified 35,277 58,383 64,516 53,132 Contaminant identification No. contaminant contigs 763 3,928 4,016 1,855 Archaea 21 38 38 23 Bacteria 264 851 888 656 Protist 94 244 232 193 Fungi 97 483 491 204 Animal 225 2,152 2,196 458 Other plants 49 119 108 282 Virus 9 37 42 32 Unidentified 12 27 21 17 Possible contaminant (%) 2.2 6.7 6.2 3.5 Final number of contigs 34,506 54,432 60,500 51,267 kept after filtering

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(a) 35,000,000120,000 110,955 120,000 1 contigs 101,640 30,166,364 30,000,000 contigs 100,000 reads 100,000 1

25,451,410 25,000,000 reads 80,000 80,000

20,000,000 59,042 60,000 contigs 60,000 15,000,000

40,000 40,000

Total number readsof after trimmingTotal 10,000,000 Number of contigs assembled Number assembled contigs of Number

20,000 20,000 5,000,000 4,714,954 reads

0 0 S. rexii S. rexii S. rexii MiSeq HiSeq 4000 MiSeq + HiSeq 4000 (b) 35,000,000 140,000 1 123,213 Kbp 30,166,364 30,000,000 reads 120,000 1

103,655 Kbp

25,451,410 25,000,000 reads 100,000 1 )

20,000,000 80,000

15,000,000 53,294 Kbp 60,000 Total base pairs (Kbp) pairs base Total

Total number of reads after trimming after reads of number Total 10,000,000 40,000

5,000,000 4,714,954 20,000 reads

0 0 S. rexii S. rexii S. rexii MiSeq HiSeq 4000 MiSeq + HiSeq 4000 Figure 4.6 Relationships between the number of input reads and number of base pairs assembled in the de novo assembly of the S. rexii transcriptomes. (a) Number of contigs assembled. (b) Number of base pairs assembled.

The assembly metrics of the post-filtering transcriptomes were recalculated (Table 4.5). The filtered S. rexii transcriptome had 60,500 transcripts with a total of 64,548,015 bp assembled. The average contig length was 1,066 bp, and the contig N50 1,323 bp. The average GC% was 44.6%. The number of core genes identified decreased, with the BUSCO completeness lowered to 76.4% compared to the unfiltered transcriptome (Table 4.4). The filtered S. grandis transcriptome had 51,267 transcripts and a total of 58,554,237 assembled base pairs (Table 4.5). The average contig length was 1,142 bp and the N50 1,410 bp. The average GC% was 44.3%. The transcriptome had a 79% BUSCO completeness, identical to the unfiltered transcriptome (Table 4.4). Functional annotation was performed for the filtered S. rexii and S. grandis transcriptome assemblies (Table 4.5; Transcriptome annotation). The S. rexii transcriptome

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Chapter 4: Building transcriptome resource had 27,811 contigs annotated (45.9% of all contigs) using the KEGG pipeline, and 41,002 contigs annotated (67.7%) using the Mercator pipeline (Figure 4.7). For the S. grandis transcriptome, these numbers were 23,520 (45.8%) and 34,898 contigs (68.0%) by KEGG and Mercator pipelines respectively (Figure 4.8).

Table 4.5 Metrics of the de novo transcriptomes assembled after filtering

S. rexii S. grandis MiSeq + Dataset used MiSeq HiSeq 4000 Assembly metrics No. transcripts 60,500 51,267 Total base pairs (bp) 64,548,015 58,554,237 Average transcript length (bp) 1,066 1,142 Longest transcript (bp) 10,947 11,934 Transcript N50 (bp) 1,323 1,410 GC (%) 44.6 44.3 Transcriptome completeness BUSCO completeness (%) 76.4% 79.0% No. completed BUSCOs 1,100 1,138 No. missing BUSCOs 340 302 Transcriptome annotation No. gene annotated by KEGG 27,811 23,520 No. gene annotated by Mercator 41,002 34,898

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Figure 4.7 Functional annotation results of the S. rexii de novo transcriptome assembly (a) KEGG annotation. Data labels show the number of transcripts of the given category (right- hand side), followed by the percentage of that category in the whole transcriptome. (b) Mercator annotation. The labels show functions annotated.

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Figure 4.8 Functional annotation result of the S. grandis de novo transcriptome assembly (a) KEGG annotation. Data labels show the number of transcripts of the given category (right- hand side), followed by the percentage of that category in the whole transcriptome. (b) Mercator annotation. The labels show functions annotated.

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Chapter 4: Building transcriptome resource 4.3.3 Reference-guided transcriptome assembly of S. rexii and S. grandis RNA-Seq reads of S. rexii and S. grandis were first mapped to the assembled draft genome under default parameters (Table 4.6). For S. rexii , the combined data of MiSeq and HiSeq 4000 runs were used (15,083,182 read pairs after preprocessing). For S. grandis , the MiSeq dataset was used (10,266,774 read pairs after preprocessing). 88.6% of the S. rexii reads (13,362,520 from 15,083,182 read pairs) were mapped to the S. rexii genome, and 93.5% S. grandis reads (9,600,119 from 10,266,774 read pairs) were mapped to the S. grandis genome. From these, 90,977 and 73,957 contigs were assembled for S. rexii and S. grandis respectively. In total, about 101 Mbp and 85 Mbp were assembled for S. rexii and S. grandis respectively (Table 4.6). The BUSCO completeness was 78.5% for S. rexii and 80.7% for S. grandis .

Table 4.6 Metrics of the reference-guided assemblies under default mapping parameters S. rexii S. grandis MiSeq + Sequencer MiSeq HiSeq4000 No. reads (after trimming) 15,083,182  2 10,266,774  2 No. reads mapped 13,362,520  2 9,600,119  2 Read mapped (%) 88.6 93.5 No. contigs 90,977 73,957 Total base pairs (bp) 101,157,269 85,843,471 Contigs N50 (bp) 1,644 1,662 GC (%) 41.8 42.2 Transcriptome completeness BUSCO completeness (%) 78.5 80.7 No. completed BUSCOs 1,131 1,163 No. missing BUSCOs 309 277

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Chapter 4: Building transcriptome resource The STAR mapping parameters were optimised using the S. rexii dataset (MiSeq + HiSeq 4000 data). First, the minimum and maximum intron size was tested, with the minimum intron size decreased and maximum intron size increased (Table 4.7). The number of mapped reads increased by 1,000 to 1,500 read pairs after the adjustment, and the total numbers of contigs and base pairs assembled decreased compared to the default parameter settings (Table 4.7). On the other hand, the contig N50 and GC% remained nearly identical (Table 4.7). The BUSCO completeness was unaffected. Since no actual improvement was found in the assembly metrics, the default parameter values (minimum intron length = 21, maximum intron = 288) were kept with the optimisation moved on to the next parameter.

Table 4.7 Effect of ‘intron size’ mapping parameters on the assembly of S. rexii RNA-Seq data S. rexii S. rexii S. rexii Parameter (Default) (Min intron length / 10 / 10,000 10 / 20,000 21 / 288 Max intron length)

No. reads mapped 13,362,520  2 13,364,087  2 13,363,238  2

Read mapped (%) 88.6 88.6 88.6

No. contigs 90,977 90,539 90,644

Total base pairs (bp) 101,157,269 100,960,080 101,027,579

Contigs N50 (bp) 1,644 1,645 1,645

GC (%) 41.8 41.8 41.8

Transcriptome completeness

BUSCO completeness (%) 78.5 78.5 78.5

No. completed BUSCOs 1,131 1,131 1,131

No. missing BUSCOs 309 309 309

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Chapter 4: Building transcriptome resource The number of mismatches allowed for STAR alignment was optimised. Under the default parameter (maximum 10 bp mismatches per read), 13,362,520 read pairs were mapped and 90,977 contigs assembled (Table 4.8). When the setting for mismatches allowed was decreased to 5 bp, fewer reads were mapped and the number of contigs reconstructed decreased by 431. When the value of maximum mismatch allowed was increased to 20 bp, about 7000 more read pairs were mapped (totalling 13,369,423 read pairs) and 481 more contigs were assembled. The total number of base pairs assembled also increased by around 150,000 bp (Table 4.8). The number of mapped read pair increased with high mismatches allowed, and the number was saturated once the value reached 50 bp, with 13,372,943 out of 15,083,182 read pairs mapped (Table 4.8). However, the number of total contigs assembled varied and the highest number of contigs assembled was achieved with 91,668 when the maximum mismatch allowance was set to 60 bp. The number of assembled contigs and the total base pairs decreased once the parameter was set above 70 (Table 4.8). The BUSCO completeness was not affected by any change of the mismatch parameter. Thus, the parameter value of 60 for the mismatch allowance was used as the optimal parameter, with the optimisation moving on to the next parameter.

Table 4.8 Effect of changing ‘mismatch allowed’ mapping parameter on the assembly of S. rexii RNA-Seq data S. rexii S. rexii S. rexii Parameter (Max mismatch allowed 5 (Default) 10 20 to be written into output file)

No. reads mapped 13,368,275  2 13,362,520  2 13,369,423  2

Read mapped (%) 88.6 88.6 88.6

No. contigs 90,546 90,977 91,458

Total base pairs (bp) 100,883,359 101,157,269 101,334,267

Contigs N50 (bp) 1,645 1,644 1,641

GC (%) 41.8 41.8 41.8

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Chapter 4: Building transcriptome resource Table 4.8 continued S. rexii S. rexii S. rexii Parameter (Maximum mismatch 30 40 50 allowed to be written into output file)

No. reads mapped 13,371,845  2 13,372,671  2 13,372,943  2

Reads mapped (%) 88.7 88.7 88.7

No. contigs 91,578 91,612 91,646

Total base pairs (bp) 101,430,383 101,370,348 101,421,998

Contigs N50 (bp) 1,641 1,640 1,640

GC (%) 41.8 41.8 41.8

Table 4.8 continued S. rexii S. rexii S. rexii Parameter (Maximum mismatch 60 70 80 allowed to be written into output file)

No. reads mapped 13,372,943  2 13,372,943  2 13,372,943  2

Reads mapped (%) 88.7 88.7 88.7

No. contigs 91,668 91,652 91,651

Total base pairs (bp) 101,460,759 101,433,335 101,426,394

Contigs N50 (bp) 1,640 1,640 1,640

GC (%) 41.8 41.8 41.8

The last parameter optimised was the 2nd pass mapping mode. When 2 nd mapping pass mode is enabled alone, the number of read pairs mapped increased from 13,362,520 to 13,410,025 and the mapping percentage improved from 88.7 to 88.9% (Table 4.9). The total number of contigs assembled was 91,036, 632 lower than that of the best settings with a maximum mismatch allowance of 60 (Table 4.9, 91,668 contigs) but higher than the default setting (Table 4.8; 90,977 contigs). When combining both ‘2nd pass mapping mode’ and

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Chapter 4: Building transcriptome resource ‘maximum mismatch allowed = 60’, 13,416,581 read pairs were mapped (89.0%) which was the highest among all the tested parameter settings (Table 4.9). However, the total number of contig assembled decreased to 91,491 and is lower than using the ‘maximum mismatch allowed = 60’ parameter alone (Table 4.9). The BUSCO completeness of all three assemblies were identical (78.5%). Since the aim of the optimisation was to reconstruct and rescue as many contigs as possible, and the 2nd pass mapping optimisation did not really improve the BUSCO completeness, the parameter settings that generates the highest number of contigs (i.e. maximum mismatch allowed = 60) was chosen for the final assembly of both S. rexii and S. grandis reference-guided transcriptomes.

Table 4.9 Effect of enabling ‘2 nd mapping pass’ mapping parameter on the assembly of S. rexii RNA-Seq data S. rexii S. rexii S. rexii 2nd mapping pass Maximum + Maximum Parameters 2nd mapping pass mismatch allowed mismatch allowed = 60 = 60

No. reads mapped 13,410,025  2 13,372,943  2 13,416,581  2

Read mapped (%) 88.9 88.7 89.0

No. contigs 91,036 91,668 91,491

Total base pairs (bp) 101,340,620 101,460,759 101,420,359

Contigs N50 (bp) 1,645 1,640 1,641

GC (%) 41.8 41.8 41.8

Transcriptome completeness

BUSCO completeness (%) 78.5 78.5 78.5

No. completed BUSCOs 1,131 1,131 1,131

No. missing BUSCOs 309 309 309

Using the optimised parameter setting for reads mapping (i.e. maximum mismatch allowed = 60), reference-guided assembly was carried out for both S. rexii and S. grandis (Table 4.10). In S. rexii 88.7% of the total reads were mapped, and in S. grandis this figure was 93.5% (Table 4.10). 91,668 contigs were assembled for S. rexii and 73,962 contigs for S.

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Chapter 4: Building transcriptome resource grandis . The N50 value was 1,640 bp and 1,661 bp, and the average GC percentage 41.8% and 42.2% for S. rexii and S. grandis respectively. In total, 1,131 and 1,163 BUSCO genes were found to be completed in the two assemblies, corresponding to 78.5% and 80.7% BUSCO completeness. 55,013 and 47,709 ORFs identified in the S. rexii and S. grandis assemblies respectively. Among these, 1,691 ORFs from S. rexii and 1,266 ORFs from S. grandis were classified as possible contamination and were removed (Table 4.10). Finally, 53,322 ORFs remained in the S. rexii transcriptome and 46,429 ORFs in the S. grandis transcriptome.

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Chapter 4: Building transcriptome resource Table 4.10 Filtering of the optimised reference-guided transcriptome assemblies of S. rexii and S. grandis S. rexii S. grandis MiSeq + Sequencing platform MiSeq HiSeq4000 No. reads (after trimming) 15,083,182  2 10,266,774  2 Parameters (Max mismatch allowed) 60 60 No. of reads mapped 13,372,943  2 9,600,119  2 Read mapped (%) 88.7 93.5 No. contigs 91,668 73,962 Total base pairs (bp) 101,460,759 85,831,430 Contig N50 (bp) 1,640 1,661 GC (%) 41.8 42.2 Transcriptome completeness BUSCO completeness (%) 78.5 80.7 No. completed BUSCOs 1,131 1,163 No. missing BUSCOs 309 277 ORF identification No. ORFs identified 55,013 47,709 Contaminant identification Total no. of contaminant contigs 1,691 1,266 Archaea 26 16 Bacteria 617 440 Protists 138 121 Fungis 211 144 Animal 588 476 Other plants 75 54 Viruses 28 13 Unidentified 8 2 Possible contaminant (%) 3.0 2.6 Final number of contigs kept after 53,322 46,429 filtering

For the filtered S. rexii transcriptome assembly, 53,322 contigs were retained with a total of 53,617,050 bp assembled. The average transcript length and transcript N50 was 1,005 bp and 1,242 bp, respectively. The GC percentage was 44.5% and the BUSCO

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Chapter 4: Building transcriptome resource completeness 76.7% (Table 4.11). For the S. grandis assembly, 46,429 contigs were kept with a total of 48,891,699 bp assembled (Table 4.11). The average transcript length was 1,053 bp, with the longest transcript of 10,758 bp, and the N50 value was 1,302 bp. The average GC percentage was 44.3%, and the BUSCO completeness 78.4%. For S. rexii 24,171 transcripts and 36,883 transcripts were annotated by the KEGG and Mercator pipelines respectively (Figure 4.9). These figures S. grandis were 20,971 transcripts and 31,026 transcripts respectively (Figure 4.10).

Table 4.11 Metrics of the filtered reference-guided assembled transcriptomes for S. rexii and S. grandis

S. rexii S. grandis MiSeq + Dataset used MiSeq HiSeq 4000 Assembly metrics No. transcripts 53,322 46,429 Total base pairs (bp) 53,617,050 48,891,699 Average transcript length (bp) 1,005 1,053 Longest transcript (bp) 10,944 10,758 Transcript N50 (bp) 1,242 1,302 GC (%) 44.5 44.3 Transcriptome completeness BUSCO completeness (%) 76.7 78.4 No. completed BUSCOs 1,104 1,129 No. missing BUSCOs 336 311 Transcriptome annotation No. gene annotated by KEGG 24,171 20,971 No. gene annotated by Mercator 36,883 31,026

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Figure 4.9 Functional annotation results of the S. rexii reference-guided transcriptome assembly (a) KEGG annotation. The data labels show the number of transcripts of the given category (right-hand side), followed by the percentage of that category in the whole transcriptome. (b) Mercator annotation. The labels show functions annotated.

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Figure 4.10 Functional annotation results of the S. grandis reference-guided transcriptome assembly (a) KEGG annotation. The data labels show the number of transcripts of the given category (right-hand side), followed by the percentage of that category in the whole transcriptome. (b) Mercator annotation. The labels show functions annotated.

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Chapter 4: Building transcriptome resource A total number of 39,921 orthogroups were identified where each orthogroup contained at least one transcript (Figure 4.11; Appendix 4.1). Among these, 18,947 orthogroups were found in all four transcriptomes (Figure 4.11). Unique orthogroups were found in each transcriptome (i.e. unique within the assemblies), with seven, three, one, and five transcriptome-specific orthogroups found in S. rexii de novo , S. rexii reference-guided, S. grandis de novo , S. grandis reference-guided transcriptome respectively. When comparing the two assemblies within species (i.e. de novo and reference-guided), very different gene sets were found: between S. rexii de novo and reference-guided transcriptomes, only 30,205 orthogroups were found conserved (Figure 4.11, overlap between blue and purple; 8,453 + 1,150 + 18,947 + 1,655); between S. grandis de novo and reference-giuded transcriptomes, only 26,721 groups were found to be conserved (Figure 4.11, red and green; 5,443 + 1,182 + 18,947 + 1,149). When comparing between species ( S. rexii to S. grandis ), 8,463 unique orthogroups were found for S. rexii (Figure 4.11; 3 + 8,453 + 7) and 5,449 were found in S. grandis (Figure 4.11; 1 + 5,443 + 5).

S. rexii, reference-guided assembly S. grandis, de novo assembly 3 1

330 8,453 5,443

1,150 1,182 7 5

18,947 633 601 1,149 1,655 S. rexii, de novo assembly S. grandis, reference-guided assembly 364

Figure 4.11 Venn diagram showing the number of shared and unique orthogroups identified in all four finalised transcriptome assemblies

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Chapter 4: Building transcriptome resource 4.4 Discussion 4.4.1 Comparison of the transcriptome assemblies of S. rexii and S. grandis In this chapter, RNA-Seq was performed for generating transcriptome resources for S. rexii and S. grandis . The RNA-Seq included transcripts from both vegetative and reproductive tissues, and de novo and reference-guided assembly approaches were pursued. The assembly metrics of all four finalised assemblies compared and summarised in Table 4.12. Overall, the de novo approach produced more transcripts and incorporated more base pairs compared to the reference-guided assemblies. The average length and N50 values were similar between the de novo and reference-guided assemblies, with the de novo assemblies showing slightly better contiguity (about 50 to 100 bp longer in both average length and N50 value). The length of the longest transcript assembled was similar among all four assemblies, as was the average GC content. A possible explaination for the higher transcript count observed in the de novo assembly was that de novo approaches tend to recover more isoforms per locus, and detect more short transcribed fragments that are possibly ignored by reference-guided assembly (Lu et al., 2013). In terms of gene completeness, the S. rexii the reference-guided assembly had four more BUSCO genes identified compared to the de novo assembly (Table 4.12). However, for S. grandis the reference-guided assembly had 9 fewer BUSCOs identified compared to the de novo assembly (Table 4.12). One possible explanation could be over-stringent filtering, as the BUSCO completeness of the S. grandis assembly prior to the filtering had the highest BUSCO completeness of 80.7% (Table 4.12). A similar trend was observed in the S. rexii assemblies, with the BUSCO completeness decreasing after ORF and contaminant filtering (Table 4.12). Interestingly, additional BUSCO analyses of the transcriptomes at different filtering stages suggested that the drop of the value occurred mainly at the ORF identification stage. This implies that the ORF identification process carried out by the TransDecoder software failed to identify the ORFs of some core genes. Possible improvements could be made for this step, is by decreasing the ORF length cut off value, and by incorporating homology searches of the transcripts to a known protein database to increase the sensitivity of ORF detection (Haas et al., 2013). Still, both prior- and post- filtering assemblies should be retained, in the case that the target gene of interest cannot be found in the post-filtering transcriptome one can still try to find it in prior-filtering assemblies. The orthogroup identification results suggested that de novo and reference-guided approaches recovered different sets of genes. For example, between the two S. rexii assemblies, the de novo and reference-guided assemblies consisted of 2,153 and 2,116 non- overlapping orthogroups respectively. On the other hand, the de novo and reference-guided S. grandis assemblies had 2,114 and 2,625 non-overlapping orthogroups respectively (Figure 4.11). One possibility is that in de novo assemblies, the reconstruction of the transcript was

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Chapter 4: Building transcriptome resource limited by the depth of sequencing coverage of some expressed genes, and by providing high-coverage data it may even out-perform the reference-guided assembly (Lu et al., 2013). On the other hand, the reference-guided assembly was good at recovering transcripts with low sequencing coverage. However, here the assembly quality was limited by the quality of the genome assembly, i.e. in the case that the genomic region was not assembled, the transcript cannot be reconstructed. The de novo and reference-guided assemblies were suggested to be complementary to each other (Lu et al., 2013; Visser et al., 2015). By keeping the results of both assembly approaches, a more complete transcriptome profile of S. rexii and S. grandis can be captured.

Table 4.12 Statistics summary of the transcriptome assemblies in this study S. rexii S. grandis S. rexii S. grandis Reference- Reference- De novo De novo guided guided MiSeq + MiSeq + Dataset used MiSeq MiSeq HiSeq 4000 HiSeq 4000 Assembly metrics (before filtering) No. transcripts 110,955 91,668 87,665 73,962 Total base pairs (bp) 123,213,415 101,460,759 102,299,541 85,831,430 Transcript N50 (bp) 1,716 1,640 1,541 1,661 GC (%) 42.1 41.8 42.5 42.2 BUSCO completeness (%) 79.0 78.5 79.0 80.7 Assembly metrics (after filtering)

No. transcripts 60,500 53,322 51,267 46,429

Total base pairs (bp) 64,548,015 53,617,050 58,554,237 48,891,699 Average transcript length 1,066 1,005 1,142 1,053 (bp) Longest transcript (bp) 10,947 10,944 11,934 10,758

Transcript N50 (bp) 1,323 1,242 1,410 1,302

GC (%) 44.6 44.5 44.3 44.3

BUSCO completeness (%) 76.4 76.7 79.0 78.4 No. annotated gene 27,811 24,171 23,520 20,971 (KEGG) No. annotated gene 41,002 36,883 34,898 31,026 (Mercator)

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Chapter 4: Building transcriptome resource 4.4.2 Comparison with other transcriptome resources When compared to other Gesneriaceae transcriptomes, our assemblies showed reasonable assembly metrics (Table 4.13). The S. rexii assemblies recovered about twice the amount of transcripts compared to the previous S. rexii transcriptome, although with less contiguity (Chiara et al., 2013). On the other hand, our S. rexii and S. grandis assemblies had a more reasonable number of transcripts (containing 46,429-60,500 transcripts) compared to the S. ionanthus transcriptome (120,278), but with a much better contiguity (1,005-1,142 versus 326 average length). When compared to other Gesneriaceae species, our assemblies had a medium number of transcript counts (Table 4.13; S. ionanthus , Primulina eburnea and Primulina pteropoda had over 100,000 transcripts; Achimenes cettoana about 29,000 transcripts) and a roughly similar transcript length and N50 value. When compared to the transcriptome of the model species Arabidopsis thaliana (Zhang et al., 2017), our assemblies had lower transcript counts and less continuity (Table 4.13). However, the A. thaliana transcriptome was based on multiple sequencing libraries of different strains of plants exposed to various growth conditions, which would have allowed for the retrieval of more genes (Zhang et al., 2017). A main feature of the Streptocarpus transcriptomes generated here was that the RNA samples were derived from various vegetative and reproductive tissues, compared to the other Gesneriaceae transcriptomes where the sample collections were limited to either vegetative or reproductive organs (Table 4.13). It is known that gene expression patterns vary widely between different cells and developmental stages (Yanofsky, 1995; Fletcher, 2002; Huijser and Schmid, 2011; Banks, 2015). Our inclusion of seedlings, roots, shoots, floral buds, flowers and developing fruits covered most of the cell types, and several of the main developmental stages of Streptocarpus . Thus, our transcriptome is likely largely complete and suitable for the purpose of genome annotation (Yandell and Ence, 2012; Hoff et al., 2016). However, a disadvantage is that since the extracted RNAs were pooled and sequenced together, the actual expression level of the genes in each tissue is no longer retrievable. Thus, more RNA-Seq experiments of specific tissues would be needed to examine the overall changes of gene expression levels at different developmental stages.

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Chapter 4: Building transcriptome resource Table 4.13 Statistical summary of the transcriptomes of Gesneriaceae and A. thaliana No. Avg. length Species Tissues Reference transcript / N50 (bp) Genus Streptocarpus S. rexii (de novo ) All* 60,500 1,066 / 1,323 This study S. rexii (ref -guided ) All 53,322 1,005 / 1,242 This study S. grandis (de novo ) All 51,267 1,142 / 1,410 This study S. grandis (ref -guided ) All 46,429 1,053 / 1,302 This study Leaf+ S. rexii 33,113 2,064 / 2,556 Chiara et al., 2013 cotyledon S. ionantha Leaf 120,278 326 / 488 Matasci et al., 2014 Genus Achimenes Roberts and Roalson, A. cettoana Flower 29,065 1,417 / 2,113 2017 Roberts and Roalson, A. erecta Flower 41,381 1,268 / 2,061 2017 Roberts and Roalson, A. misera Flower 41,285 1,260 / 1,990 2017 Roberts and Roalson, A. patens Flower 37,898 1,304 / 2,109 2017 Genus Damrongia D. clarkeana Leaf 94,546 487 / 1,075 Wang et al., 2017 Genus Dorcoceras D. hygrometricum Leaf 49,374 2,535 / - Xiao et al ., 2015 Genus Sinningia S. tuberosa Leaf 56,809 691 / 1,573 Matasci et al., 2014 Serrano-Serrano et al., S. eumorpha Leaf + flower 87,053 1,687 / 2,597 2017 Serrano-Serrano et al., S. magnifica Leaf + flower 97,023 1,545 / 2,794 2017 Genus Primulina P. eburnea Leaf + root 106,665 1,086 / 1,823 Ai et al., 2014 P. fimbrisepala Leaf + root 94,033 989 / 1,607 Ai et al., 2014 P. heterotricha Leaf + root 92,255 1,201 / 1,915 Ai et al., 2014 P. huaijiensis Leaf + root 76,495 962 / 1,582 Ai et al., 2014 P. lobulata Leaf + root 81,271 1,144 / 1,847 Ai et al., 2014 P. lutea Leaf + root 70,426 903 / 1,506 Ai et al., 2014 P. pteropoda Leaf + root 108,947 1,036 / 1,709 Ai et al., 2014 P. sinensis Leaf + root 75,523 965 / 1,609 Ai et al., 2014 P. swinglei Le af + root 91,113 921 / 1,538 Ai et al., 2014 P. tabacum Leaf + root 82,357 1,113 / 1,785 Ai et al., 2014 P. villosissima Leaf + root 75,614 926 / 1,470 Ai et al., 2014 Genus Arabidopsis A. thaliana All 82,190 1,858 / 2,163 Zhang et al., 2017 *: All includes seedlings, leaves, roots, flower bud, flowers and developing fruits -: Information was not provided

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Chapter 4: Building transcriptome resource 4.4.3 Conclusions Here we present four finalised transcriptome assemblies of S. rexii and S. grandis . The different gene sets recovered by different assembly approaches suggested that the assemblies were complimentary to each other, and together they provided a largely complete gene expression profile for both Streptocarpus species. The focus of the study here was to produce a preliminary resource for Streptocarpus . Further characterisation of the transcriptomes could be made to improve the datasets, such as transcript length and number of transcript / isoform per gene, identification of alternative splicing, gene ontology annotation, molecular pathway reconstruction, or orthogroup identifications with other species. Still, the RNA-Seq data and transcriptomes generated here represent invaluable resources for future studies on Streptocarpus and the Gesneriaceae family, either for candidate gene approaches or for the structural and functional annotation of the whole genome assembly.

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Chapter 5: Building genetic map Chapter 5 Building a genetic map – Linkage analysis for constructing a genetic map from a S. rexii  S. grandis backcross population

5.1 Introduction 5.1.1 Applications of genetic mapping in candidate gene identification A genetic map (or genetic linkage map) shows the relative positions of genes or genetic markers on the chromosomes in the genome (Van Ooijen and Jansen, 2013). The genetic distance is determined by the mean number of recombination events (crossovers) occuring per meiosis. In theory, the more frequently recombination between the markers occurs, the longer the relative physical distance (Semagn et al., 2006; Van Ooijen and Jansen, 2013). The first genetic map was calculated in 1913 for fruit fly by Alfred H. Sturtevant, who showed that the frequency of recombination events could be an index of the distance between two genes (Sturtevant, 1913). The ‘map unit’ for the genetic distance was termed centiMorgan (cM), in honour of Thomas H. Morgan, Sturtevant’s supervisor. The distance of 1 cM corresponds to an average of one crossover in every 100 gametes, or 1% recombination frequency (Kole and Abbott, 2008). Genetic mapping is widely used in plant research. To date, genetic maps have been constructed for hundreds of plant species, including key model plants such as Arabidopsis , rice, maize, and tomato (Koornneef et al., 1983; Beavis and Grant, 1991; Harushima et al., 1998; Meinke et al., 2003; Frary et al., 2005). Genetic maps serve five major purposes: (1) They allow the genetic analysis of quantitative traits and the mapping of the quantitative trait loci (QTL mapping). (2) They facilitate marker-assisted selection for the introgression of genes or QTLs for plant breeding. (3) They allow comparative mapping between species, to identify similarity and differences in gene order and distance. (4) They can be used to anchor the physical map of DNA scaffolds. (5) Building a genetic map is also the first step for positional or map-based cloning of genes (Lewis, 2002; Semagn et al., 2006; Broman and Sen, 2009). Genetic maps are widely used in molecular breeding. For example, the wheat genetic map was used to locate drought resistant alleles in wild wheat species, which were later introgressed into the bread wheat genome and successfully improved bread wheat stress resistance (Peleg et al., 2008; 2009; Merchuk-Ovnat et al., 2016). A rice genetic map constructed to identify QTLs related to biomass yield allowed QTL-based selection of rice lines to produce grains with higher biomass (Matsubara et al., 2016). Genetic maps have also been used for evolutionary studies. For instance, the map of Petunia was used to identified two floral scent related QTLs, which are associated with the pollinator preference (Klahre et al., 2011). The genetic map of Rhytidophyllum identified QTLs regulating pollination

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Chapter 5: Building genetic map syndromes including floral dimensions, nectar volumes and flower colour, potentially linked to speciation (Alexandre et al., 2015). Prior to this study, no genetic map was available for Streptocarpus (Chen et al., 2018). Genetic maps are available for other Gesneriaceae genera: Rhytidophyllum (Alexandre et al., 2015) and Primulina (Feng et al., 2016), however, these maps focused on floral characters, and are not informative for vegetative development. Streptocarpus material at RBGE provides the opportunity to identify the genetic basis of differences between rosulate and unifoliate growth forms by QTL mapping in a hybrid population between rosulate and unifoliate Streptocarpus species.

5.1.2 RAD-Seq for linkage analysis in non-model organisms The development of NGS technologies allow high-throughput-genotyping of hundreds to thousands of markers with reasonable costs and time, without the need of preliminary knowledge of genetic marker and reference sequences (Davey et al., 2011). Such high-throughput genotyping methods also enable the construction of ultra-dense genetic maps that greatly improve the precision of QTL localisation and help distinguish closely located QTLs (Stange et al., 2013). Restriction site-Associated DNA sequencing (RAD-Seq) is one of these high-throughput genotyping methods, which has been widely applied for genetic mapping in non-model organisms (Baird et al., 2008; Davey and Blaxter, 2010). For example, the recently published genetic map of oil palm ( Elaeis guineensis ) was constructed using a combination of RAD-Seq and microsatellite markers, which resulted in 10,023 mapped markers (of which 9,712 markers were derived from RAD-Seq data) across 16 linkage groups with a total span of 2,938.2 cM (Bai et al., 2018). RAD-Seq is a reduced representation sequencing approach, which sequences the subset of the genome flanking restriction sites of a selected restriction enzyme (Baird et al., 2008). This is achieved by the combination of restriction-enzyme reaction, ligation of a molecular identifier (MID), and Illumina sequencing technology (Illumina Inc, San Diego, CA, USA) (Baird et al., 2008). In brief, the genomic DNA of each individual of the mapping population is first digested by restriction enzymes, and the overhanging site tagged with an Illumina sequence primer site attached with an individual-specific barcode. The DNA samples are then pooled, and the standard Illumina library preparation protocol taken to prepare an Illumina sequencing library, which includes mechanical shearing, size-selection, ligation of adapters and PCR (Figure 5.1; Davey and Blaxter, 2010). The prepared library is then sequenced, and the reads demultiplexed into individuals based on the individual- specific barcodes, thus the genotype of each individual can be recovered (Davey and Blaxter, 2010). RAD-Seq allows the genotyping of hundreds to thousands of loci of multiple individuals within a single Illumina sequencing lane (Baird et al., 2008; Davey and Blaxter,

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Chapter 5: Building genetic map 2010). The number of markers genotyped can be regulated by the choice of restriction enzymes (i.e. 8-base cutter produces less markers than 6-bases cutters; Catchen et al., 2017). A RAD-Seq protocol was first used for genotyping of bulk segregant analysis in sticklebacks (Baird et al., 2008). Alternative protocols such as double digest RAD (ddRAD) were later developed. This method omits the random shearing step during library preparation to reduce the bias caused by randomisation, and incorporates digestion by two restriction enzymes to produce the DNA fragments allowing fine tuning of marker numbers by different enzyme combinations (Peterson et al., 2012). RAD-Seq data can be analysed to reconstruct the genetic marker by de novo or reference-guided approaches (Baird et al., 2008; Davey et al., 2011; Davey and Blaxter, 2010; Catchen et al., 2011). In de novo approach, RAD reads can be de novo assembled to form stacks of sequences, in which each stack represents a genetic locus. Single-nucleotide polymorphism (SNP) genotypes can be determined from the assembled sequences. For the reference-guided approach, the RAD reads are aligned to a reference sequence. The aligned reads form stacks, which can be used to retrieve the SNP genotypes from the corresponding genome location. The advantage of the de novo approach is it does not require a reference genome sequence, which most often is not available for non-model species. On the other hand, the reference-guided approach has been shown to recover more markers from the RAD-Seq data (Shafer et al., 2016; Fountain et al., 2016), and requires less sequencing depth for correct genotyping (Fountain et al., 2016).

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Chapter 5: Building genetic map

Genomic DNA of sample 1 and sample 2

Restriction enzyme digestion

P1 adaptor ligation (Sample 1 and sample 2 has different adaptor)

Sample pooling Restriction site Sample 1 P1 adaptor Sample 2 P1 adaptor P2 adaptor Random shearing

Size selection Keeping 300 – 700 bp fragments

Ligate P2 adaptor with divergent end

PCR using P1 and P2 primer (only fragments with P1 and P2 adaptors were amplified)

Illumina sequencing

Figure 5.1 Schematic illustration of the procedure of RAD-Seq library preparation, using two samples as example (Modified from Davey et al., 2011)

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Chapter 5: Building genetic map 5.1.3 Objectives In this chapter, a backcross (BC) population (( S. grandis × S. rexii ) × S. grandis ) was used for genetic linkage mapping. RAD-Seq was performed on the BC population and the parental materials for genotyping, and de novo and reference-based approaches were carried out with the obtained data (Figure 5.2). For the reference-based approaches, the S. rexii genome assemblies presented in Chapter 3 were used as references; S. rexii genome was chosen over the S. grandis one because the former shows better assembly contiguity, which is important for downstream analysis (e.g., designing new markers or gene mining). The alignment of the RAD reads was carried out using the software Burrow-Wheeler Aligner (BWA, Figure 5.2 a; Li and Durbin, 2009) and Stampy (Figure 5.2 a; Lunter and Goodson, 2011): The former uses Burrow-Wheeler Transform to perform string matching for alignment (Li and Durbin, 2009); the later uses a hash-based method, by building the hash table of the reads and reference, and alignment done by comparing the hashes (Lunter and Goodson, 2011). The software Stacks (Catchen et al., 2011, 2013) is used for genotype calling and filtering of both de novo and reference-based analyses (Figure 5.2). In brief, in de novo approach the software first (1) attempts to cluster RAD-Seq reads that share highly similar sequences to form ‘stacks’ (parameter M). These stacks can be interpreted as presented haplotypes. Also, filtering of the read depth of stacks is applied to remove those without sufficient read depth (parameter m). For example, a haplotype derived from a read depth of 8× stack can be more reliable than that derived from a read depth of 1× (which can possibly be a sequencing error). (2) The unaligned reads, named ‘secondary reads’, are attempted to be aligned again to ‘stacks’ formed in previous step using a user-defined base pair mismatch allowance (parameter N). This is meant to recue reads from being wasted. (3) The software then tries to merge similar ‘stacks’ (again using a user-defined number of mismatch base pair; parameter n) to determine which haplotypes are originated from the same locus/marker. (4) Finally, by comparing the genotype of each locus with the parental genotypes, the genotype of each indivudal at each locus is determined. In reference-based approach, as all the read alignment and adjustment is done by a chosen aligner, only the stacks read depth filter (i.e., parameter m) is used. After the genotypes of all individuals are called, one can improve the reliability of the result by filtering out markers that have too many missing data. Typically, a threshold of <20% missing data is recommended (Hackett and Broadfoot, 2003). The linkage analysis of the genotyping results is performed using software JoinMap (Van Ooijen, 2006; Van Ooijen and Jansen, 2013). The software performs further genetic marker filtering, such as removing markers showing segregation distortion or similar segregation patterns. The linkage group is then calculated using a maximum likelihood method, which gives LOD values (i.e., log value of the likelihood ratio) for each group defined; a LOD threshold ≥3 and ≤7 is generally recommended when selecting linkage

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Chapter 5: Building genetic map groups (Van Ooijen and Jansen, 2013). Later, the genetic linkage map is calculated for each group using either regression mapping algorithm or maximum likelihood mapping algorithm. The former calculates genetic map by adding one marker at a time, and test the goodness-of- fit at each possible position the marker is added; the later is a quicker approach for linkage groups with > 100 loci, and works by smart search algorithm, expectation-maximisation (EM), and spatial sampling to approach the global optimal loci order within the linkage group (Van Ooijen and Jansen, 2013). To maximise the number of markers recovered and the resolution of the final map, a series of mapping optimisations were carried out and the results compared. The first series of genetic map calculations (MapA) was done when only the preliminary genome assembly was available ( S. rexii preliminary genome assembly using SOAPdenovo2; Table 3.4). The de novo approach, reference-based approach using BWA, and reference-based approach using Stampy were used to analyse the data. Later, the combined approach (i.e. combining markers generated from the de novo and both reference-based approach) was taken to calculate the final map (MapB), where the improved and filtered S. rexii genome assembly was used as reference sequence ( S. rexii genome assembly using ABySS2; Table 3.16).

Figure 5.2 Schematic illustration of the RAD-Seq data analysis. (a) The three different approaches used to generate genetic markers from RAD-Seq data. (b) Type and quality of the recovered markers. 1 - Non-informative markers that do not contain SNP sites. 2 - Informative markers that have sufficient sequencing depth and contain SNP sites. 3 - Non- informative markers that do not have sufficient sequencing depth, or are unmapped reads. (Original figure from Chen et al., 2018)

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Chapter 5: Building genetic map 5.2 Materials and methods 5.2.1 Plant materials The backcross (BC) population (( S. grandis × S. rexii ) × S. grandis ) generated using the rosulate Streptocarpus rexii and unifoliate Streptocarpus grandis was used for genetic mapping (Figure 5.3). First, S. rexii (accession 19990270*I) pollen was transferred to the stigma of S. grandis flowers (denoted lineage S. grandis F1 ; accession 20020577*Q) to produce an F1 hybrid. Later, pollen of another S. grandis lineage (denoted S. grandis BC ; accession 20130764*B) was used to pollinate the F1 plant (accession 20071108*J), and 233 backcross individuals were cultivated in this study (accession 20150825). A second accession of S. grandis had to be used for backcrossing as the species is monocarpic and the first parent died after flowering and fruiting. The full list of plant materials used in this study are summarised in Table 5.1. All plant materials were cultivated in the Royal Botanic Garden Edinburgh living research collection throughout the experiments. The F1 hybrid S. grandis × S. rexii used for backcrossing to S. grandis and the BC seeds used in this study were generated in a previous study in 2007 and 2014 respectively (M. Möller, unpublished). The seeds of the BC population used for this study were sown in January 2015, and the DNA extractions of the BC individuals were carried out throughout 2015 until early 2016. For the parental lineages, S. rexii 19990270, S. grandis F1 20020577, and S. grandis 20130764 BC , were only available as silica-dried-leaf tissue at the time when this study was carried out.

Figure 5.3 Pedigree of the Streptocarpus genetic mapping population

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Table 5.1 List of plant materials used in the study Taxon Accession*Qualifier Note S. rexii 19990270*I Parent of F1. F1 descendant of 19870333, collected by K Jong Collection number: JNG s.n. From Grahamstown; ‘Faraway’ Estate, Cape Prov., SE, ZA

S. rexii 20150819*A Used for genome sequencing (Chapter 3); Descendent of 19990270*I

S. grandis F1 20020577*Q Parent of F1. Collected by M Möller. Collection number: MMO 2000-21. From Nkandla forest, KwaZulu-Natal Prov., ZA

S. grandis 20151810*C Descendant of 20020577*Q

S. grandis BC 20130764*B Parent of BC. F3 descendant of 19771210, collected by OM Hilliard and BL Burtt Collection number: HBT 5923. From Ngome forest, KwaZulu-Natal Prov., ZA

S. grandis 20150821*A Used for genome sequencing (Chapter 3); Descendent of 20130764*B

S. grandis F1 × rexii 20071108*J Parent of BC

(S. grandis F1 × rexii ) × grandis BC 20150825*A-IS Genetic mapping

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Chapter 5: Building genetic map 5.2.2 DNA extraction, library preparation and sequencing The DNA of the S. rexii , S. grandis , F1 hybrids and backcross individuals were extracted using the modified CTAB extraction method described in Chapter 2 (Appendix 2.5). In brief, the proximal regions of young-growing phyllomorphs were freshly collected and extracted using 4% CTAB solution with 2% PVPP. For the parental lineages, S. rexii 20020577, silica-dried-leaf tissues were used. The extracted DNA was further treated with RNase A and cleaned up using a phenol:chloroform:isoamyl alcohol 25:24:1 solution. The DNA of each sample was quantified using the Qubit 2.0 fluorometer with the Qubit dsDNA HS assay kit (Thermo Fisher Scientific). The samples were then diluted into 20 ng/ l based on the Qubit measurements to achieve the requested concentration for the RAD-Seq library preparation. The final concentration was confirmed again by Qubit fluorometry, and the results should fall between 15 – 21 ng/ l or the dilution step was repeated. The diluted DNA samples were sent to our collaborator Dr Atsushi Nagano’s group (Ryokoku University, Kyoto, Japan), where the library preparation and RAD-Seq was performed. The double digest RAD-Seq library was prepared following the protocols described previously (Peterson et al., 2012; Sakaguchi et al., 2015), in which the restriction enzymes Eco RI and BgI II were used. The libraries were sequenced on 3 lanes of HiSeq 2500 (Illumina), with 50 to 100 libraries per lane and 51 bp single-end sequencing of the BgI II restriction-end. In total 237 libraries were sequenced (233 BC individuals + 2 S. rexii + S. grandis F1 + S. grandis BC ). The data was demultiplexed and returned in fastq.gz format. The materials sequenced in each lane are listed in Appendix 5.1 a.

5.2.3 Quality control and preprocessing of the RAD-Seq data The quality and adapter trimming of the RAD-Seq data was carried out using Trimmomatic v0.35 (Bolger et al., 2014). The LEADING, TRAILING, SLIDINGWINDOW, and AVGQUAL parameters were used for quality trimming, and reads shorter than 51 bp were discarded (MINLEN:51). The output reads were then processed by PRINSEQ-lite v0.20.4 (Schmieder and Edwards, 2011) to remove reads containing any unidentified nucleotide ‘N’ as suggested in Catchen et al., 2011. Finally, Bowtie v2-2.2.8 (Langmead and Salzberg, 2012) was used to filter out reads generated by organellar genomes, by mapping RAD-Seq reads to the assembled S. rexii chloroplast and mitochondrial genomes presented in Chapter 3 and keeping the unmapped reads (i.e. hence the reads ) were kept thus removing the reads that was originated from the organellar genomes. The quality of the prior- and post-prepressed datasets was checked by FastQC v0.11.7 (Andrews, 2010}, and the results were summarised using MultiQC v1.5 (Ewels et al., 2016). The detailed commands and parameters used are listed in Box 5.1.

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Chapter 5: Building genetic map Box 5.1 Script for RAD-Seq data preprocessing. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Pipe for Trimmomatic, prinseq-lite, and Bowtie2-preprocessing java -jar trimmomatic-0.35.jar SE -phred33 \ [RADseq.fastq] [Output.fastq] \ ILLUMINACLIP: TruSeq3-SE.fa:2:30:10 \ LEADING:20 TRAILING:20 \ SLIDINGWINDOW:30:20 AVGQUAL:20 MINLEN:51 | \ prinseq-lite -ns_max_n 0 \ -fastq [Output.fastq] -out_format 3 -out_good [Output2.fastq] | \ bowtie2 -x [index of Organelle genome] -q [Output2.fastq] -N 1 \ --un-gz [RADseq_cleaned.fq.gz]

For S. grandis , the RAD-Seq data from the accession 20150821 (YYD17, Appendix 5.1 a) were used as the S. grandis parental lineage for all following RAD-Seq genotyping analysis via Stacks v1.47 (Catchen et al., 2011; 2013). For S. rexii , due to the low read count of both libraries, a ‘superparent’ file was constructed according to Catchen et al. (2011) by combining preprocessed RAD-Seq reads of accession 20150819 (YYD16, Appendix 5.1 a) and 19990270 (YYD19, Appendix 5.1 a). This ‘superparent’ file was used as the S. rexii parental lineage data for all the genotyping analysis described in the following sections. The 200 BC individuals with the highest read counts were identified and used for all the following genetic map calculations (Appendix 5.1 b).

5.2.4 Genotyping of RAD-Seq data - de novo approach Stacks v1.47 was used for the de novo analysis of the RAD-Seq data. More specifically, the script denovo_map.pl was used, which clusters the reads based on sequence alignment. Each cluster (i.e. stack) represents a genetic locus, and the genotype can be determined using the script genotypes (Catchen et al., 2011; 2013). For the genotyping of MapA (the preliminary map), three major parameters of denovo_map.pl were chosen for optimisation (Table 5.2; Catchen et al., 2011). These were ‘m’ (minimum stack depth), ‘M’ (mismatch allowed within an individual), and ‘N’ (mismatch allowed for merging secondary reads to the primary stacks). The 50 BC individuals with the highest read counts after preprocessing were chosen for the optimisation (Appendix 5.1 b). This involved testing different values of each parameter on the data of these 50 BC individuals and the two parents. The settings that generated the highest marker numbers in the optimisation tests were selected, and then applied for the analysis the data of the 200 BC individuals with the highest read counts (Appendix 5.1 b). The Stacks commands used are summarised in Box 5.2.

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Chapter 5: Building genetic map Table 5.2 Parameters tested for the de novo approach analysis for MapA Parameters Parameter definition Values tested m Minimum stack depth 1,2,3,4,5,9,10 M Mismatch allowed within individuals 1,2,3,4,5 N Mismatch allowed for aligning 2 nd reads to primary stacks M, M+1, M+2

Box 5.2 Script for the genotyping using the de novo approach. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # denovo_map.pl analysis using the trimmed RADseq files as input denovo_map.pl \ -p [S. grandis parent reads.fq.gz] -p [S. rexii parent reads.fq.gz] \ -r [BC individual 1 reads.fq.gz] \ -r [BC individual 2 reads.fq.gz] \ -r [BC individual 3 reads.fq.gz] \ … -r [BC individual 199 reads.fq.gz] \ -r [BC individual 200 reads.fq.gz] \ -o [Output directory] \ -T [no. threads] –m [m value] –M [M value] –N [N value] -A BC1 -S -b 1

# Genotype calling. Only retain markers with less than 20% missing data # (-r 160) genotypes -b 1 -P . -r 160 -t BC1 -o joinmap

For the genotyping of MapB, an additional parameter n (mismatch allowed when building catalog) of denovo_map.pl was optimised in addition to the three parameters tested for MapA (Table 5.3; m, M, N). The optimisation was carried out using the data of the 50 BC individuals and the two parents as previously described (Appendix 5.1). Parameter settings showing the highest marker number were selected and applied for the analysis of the 200 backcross population selected as above. The commands used are described in Box 5.2.

Table 5.3 Parameters tested for the de novo approach analysis for MapB Parameters Parameter definition Values tested m Minimum stack depth 1,2,3,4,5,6,8,10 M Mismatch allowed within individuals 1,2,3,4,5 N Mismatch allowed for aligning 2 nd reads to primary stacks M, M+1, M+2 n Mismatch allowed when building catalog 1,2,3,4,5

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Chapter 5: Building genetic map 5.2.5 Genotyping of RAD-Seq data – Reference-based approach using BWA aligner BWA v0.7.15 (Li and Durbin, 2009) was used to align the RAD-Seq reads to the S. rexii draft genome produced in Chapter 3. SAMtools v1.7 (Li et al., 2009) was used to convert the alignment into BAM files. The BAM files were used as the input files for the Stacks v1.47 script ref_map.pl , which reconstructed the genetic loci information based on the alignment. Finally, the Stacks script genotypes was used for the genotyping and the generation of the locus genotype file. For the genotyping of the MapA, the preliminary S. rexii genome assembly was used as the reference for the alignment (Table 3.4). The alignment was carried out using BWA aln under default parameters. The Stacks script ref_map.pl was used to reconstruct the loci from the BAM files with a minimum stack depth of 3 (-m 3). The detailed commands used are summarised in Box 5.4. For the genotyping of MapB, the finalized S. rexii genome assembly was used as reference for mapping the RAD-Seq reads (Table 3.16, S. rexii ). Furthermore, optimisation of two of the BWA alignment parameters was carried out. These were the maximum edit distance (n) and maximum edit distance in seed (k; Table 5.4). The data of 50 BC plants and the parents were used for their optimisation (Appendix 5.1 b). The optimal parameter settings that gave the highest number of markers were chosen, and subsequently applied for the alignment of the RAD-Seq data of 200 BC individuals (Appendix 5.1 b). In addition, the statistics of all the BAM files were accessed using SAMStats v1.5.1 (Lassmann et al., 2011). Data for the 200 BC individuals were combined and the number of mismatches per reads calculated and plotted. The Stacks script ref_map.pl was used to reconstruct the loci from the BAM files with a minimum stack depth of 3 (-m 3). The detailed commands used are summarised in Box 5.4.

Table 5.4 Parameters of the BWA aligner tested for the calculation of MapB Parameter Parameter definition Value tested n Maximum edit distance 3, 4, 6, 12 k Maximum edit distance in seed 1, 2 (default), 3

Box 5.4 Script for the genotyping using the reference-based approach with the BWA aligner. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Generate BWA index file from S. rexii genome assembly bwa index [genome assembly.fa]

# Align the RADseq reads using BWA, then use SAMtools to generate # the BAM file

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Chapter 5: Building genetic map bwa aln –t [no. threads] –n [n value] –k [k value] \ [genome index] [RADseq reads.fq.gz] > temp.sai bwa samse [genome index] temp.sai [RADseq reads.fq.gz] \ | samtools view -Sb \ | samtools sort -O bam -o [Aligned RADseq reads.bam]

# ref_map.pl analysis to analyse the generated BAM files ref_map.pl \ -p [S. grandis parent reads.bam] -p [S. rexii parent reads.bam] \ -r [BC individual 1 reads.bam] \ -r [BC individual 2 reads.bam] \ -r [BC individual 3 reads.bam] \ … -r [BC individual 199 reads.bam] \ -r [BC individual 200 reads.bam] \ -o [Output directory] \ –T [no. threads] –m 3 -A BC1 -S -b 1

# Genotype calling. Only retain markers with less than 20% missing data # (-r 160) genotypes -b 1 -P . -r 160 -t BC1 -o joinmap

5.2.6 Genotyping of RAD-Seq data – Reference-based approach using Stampy aligner The overall analysis process was the same as with the BWA approach described in the previous section, except for that the aligner used here was Stampy v1.0.29 (Lunter and Goodson, 2011). Stampy aligner was chosen due to its different alignment algorithm used (hash-based) comparing to that of BWA (Burrows-Wheeler transform), and the alignment results generated from the two aligners were suggested to be complimentary to each others in terms of alignment speed and sensitivity (Lunter and Goodson, 2011). In brief, Stampy was used to align the preprocessed RAD-Seq reads to the S. rexii draft genome. SAMtools v1.7 was used to convert the alignment into BAM files. The Stacks v1.47 script ref_map.pl was used to reconstruct the genetic loci information, and script genotypes for the genotyping and the generation of the locus genotype files. For the calculation of MapA, the reads were mapped to the preliminary S. rexii genome assembly under default parameters (Table 3.4). For the calculation of MapB, the reads were aligned to the filtered assembly, also under default parameters (Table 3.16, S. rexii ). The alignment files were converted into BAM format using SAMtools v1.7 (Li et al., 2009), followed by the Stacks script ref_map.pl and genotypes analysis as described in the previous BWA mapping section. In addition, the mapping statistics were accessed using SAMStats v1.5.1 (Lassmann et al., 2011), which calculates the number of mismatches per read across the 200 BC individuals (Appendix 5.1 b). The detailed commands used are given in Box 5.5.

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Chapter 5: Building genetic map Box 5.5 Script for the genotyping using the reference-based approach with the Stampy aligner. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Build the genome index file (.stidx) stampy.py -G [output genome file name] [genome assembly.fasta] # Build the genome hash table (.sthash) stampy.py -g [output genome file name] -H [output hash table name]

# Perform Stampy alignment and generate the BAM file stampy.py -g [genome file] -h [hash table] -M [RADseq reads.fq.gz] \ | samtools view -Sb \ | samtools sort -O bam -T tmp -o [Aligned RAD reads.bam]

# Genotype calling. Only retain markers with less than 20% missing data # (-r 160) genotypes -b 1 -P . -r 160 -t BC1 -o joinmap

5.2.7 Combining the genotype locus file and calculation of the genetic map For both MapA and MapB, the markers recovered from all three different approaches (i.e. de novo approach, reference-based approach using BWA, and reference- based approach using Stampy) were combined to test the effect on the number of markers when combining the approaches. To do so, a prefix with capital letters was first added to the genotype locus files to distinguish the markers recovered in the different approaches: “DN” represented markers recovered from the de novo approach, “BW” for the BWA approach, and “ST” for the Stampy approach. The genotype locus files from all three approaches were then concatenated, and filtered by keeping only the markers with parental genotypes of [aa bb], which indicates that the genotype of the parents at these markers were homozygous and thus appropriate to be used for genetic mapping. The concatenated genotype locus file was then had its’ header and footer information updated to reflect the actual property of the combined dataset (Box 5.6), i.e. file name, cross type, number of individuals, number of markers information, and the list of all individuals. The updated genotype locus file was used for genetic map calculation.

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Chapter 5: Building genetic map Box 5.6 Script for filtering and combining the markers generated from three approaches, i.e. de novo , BWA and Stampy. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Add prefix to the genotype locus files generated from each approach sed ‘s/^/DN/’ [original_denovo_loc.loc ] > [denovo_loc.loc] sed ‘s/^/BW/’ [original_BWA_loc.loc] > [BWA_loc.loc] sed ‘s/^/ST/’ [original_Stampy_loc.loc ] > [Stampy_loc.loc]

# Filter the markers and keeps the one with a parental genotype of aa bb. # Then remove the string from the file as they will not be # recognized by the software JoinMap 4.1 grep “aaxbb” [denovo_loc.loc] [BWA_loc.loc] [Stampy_loc.loc] | sed ‘s///’ > [Combined_file.loc]

# Update the header information (add following lines to Combined_file.loc) # The number of markers can be calculated using wc command name = Combined_file.loc popt = BC1 nloc = [no. markers] nind = [no. individuals]

# Append the footer information tail –n 202 [any of the three loc file] >> Combined_file.loc

All genetic map calculations were performed in JoinMap 4.1 (Van Ooijen, 2006; Van Ooijen and Jansen, 2013). The Stacks output file (genotype locus file; .loc file) was loaded into the program and the markers were first filtered. Markers showing missing data in more than 20% of the BC individuals (i.e. less than 160 out of 200 BC genotyped individuals) were removed. Markers that showed severe segregation distortion (P < 0.0005) were also removed (the related information is indicated in the ‘Locus Genotype Frequency’ tab). The JoinMap diagnostic Similarity of Loci , which checks the pairs of markers showing identical genotypes, was then used to remove loci with similar segregation patterns under default settings. For the calculation of MapA, the linkage groups were first identified based on the LOD scores calculated using the Grouping Tree function in JoinMap, with a minimum LOD threshold of 4 and a maximum of 7. The regression mapping algorithm was selected, and the Haldane’s mapping function was used for the calculation of the map distance (Haldane, 1919). The quality of the map was checked by Chi-square values (values should be <5) and nearest neighbour fit values (N.N.fit; no outstanding values) of each marker. If markers with outlying values were observed, those markers were excluded and the map recalculated. This process was iterated until no more markers showed outlying Chi-square or N.N.fit values. The final linkage map was visualized using MapChart v2.2 (Voorrips, 2002). The numbering of the linkage groups was then determined by the length of the calculated maps, which the longest linkage group denoted LG1, followed by the second longest linkage group as LG2,

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Chapter 5: Building genetic map and so on. The flow chart of the analysis and calculation of MapA is summarised in Figure 5.4. For the calculation of MapB, the same settings as described above were used. However, additional settings were tested to construct a map with a ‘less-stringent’ marker filtering strategy (Figure 5.5). As listed in Table 5.5, MapB-1 was calculated with exactly the same filtering strategy as described above for MapA. For MapB-2, the filtering strategy allowed markers with higher proportions of missing data (<30%) to be kept, and also includes markers with slightly more significant segregation distortion (markers with Chi- square value  0.0001 removed). MapB-3 had the exactly the same filtering strategy as MapB-2, but markers that were mapped in MapB-1 but not in MapB-2 (excluded due to Chi- square contribution > 5) were forcibly added in (Figure 5.5). The order of the linkage groups of MapB-2 and MapB-3 followed that of MapB-1, rather than being assigned a new linkage group order based on the map length. The remaining steps of the calculation were the same as described above for MapA. The final maps were visualised using MapChart v2.30 (Voorrips, 2002).

Table 5.5 Different marker filtering strategies for the calculation of the diverse MapBs MapB-1 MapB-2 MapB-3 Marker removal Remove marker with Remove marker with Remove marker with threshold for missing >20% missing data >30% missing data >30% missing data genotype%

Threshold for removing segregation Remove  0.0005 Remove  0.0001 Remove  0.0001 distorted markers* Forcibly include Additional markers mapped in N/A N/A modification MapB-1 but not in MapB -2 * The value indicates the Chi-square test value of the deviation of segregation ratio from the expected 1:1 ratio. The lower the value the more significant the distortion.

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Figure 5.4 Flow chart of data analysis and the calculation of the MapA series. Grey rectangles – data file, White rectangles – analysis steps, Red stars – map calculation.

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Figure 5.5 Flow chart of data analysis and the calculation of the MapB series. Grey rectangles – data file, White rectangles – analysis steps, Red stars – map calculation.

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Chapter 5: Building genetic map 5.2.8 Synteny analysis of the genetic maps Synteny analyses were carried out between MapB-1 and MapB-2, MapB-3 and the combined approach-MapA. For the synteny analyses between MapB-1, MapB-2 and MapB- 3, the synteny relationships were visualised using MapChart v2.30 via the function ‘Show homologs’ (Voorrips, 2002). By enabling this function, the software draws lines among markers that share the same name, thus allowing a visual inspection of the differences in marker order and distances between two genetic maps. For the synteny analysis between MapB-1 and MapA, since the markers from the two maps were generated from different Stacks analyses, the marker names assigned by Stacks were different. Thus, the marker synteny had to be compared based on the marker sequences. To do so, the information of the mapped markers was first extracted from the ‘catalog.tags.tsv’ file, an intermediate file produced during Stacks analyses described previously. The information extracted included marker name and locality (Genome scaffold name, strand, and position), and sequence (Catchen et al., 2011). For markers recovered by the de novo approach, no marker locality information was available. The recovered information of the marker sequences and names was then used to produce a marker sequence-fasta file, with each fasta entry representing one genetic marker with the name of the entry identical to the name of the marker. blastn of the BLAST+ package v2.7.1+ (Camacho et al., 2009) was used to search for identical markers that was shared between two maps. This was done by using the ‘marker sequence-fasta file’ of the MapB-1 as BLAST query, and the ‘marker sequence-fasta file’ of the combined approach- MapA as BLAST database. Hits identified by the blastn search indicated the markers that were shared between the two genetic maps. The names of these shared markers were homogenised in the corresponding genetic map file (.map file; JoinMap4.1 manual). For example, if marker ‘A’ mapped in MapA, and marker ‘B’ mapped in MapB, were found to have the same sequences, they were renamed to ‘Shared_marker_1’ in the .map files of both MapA and MapB. Finally, the synteny relationship between two maps was visualised using MapChart v2.30 as described above. The detail commands used are summarised in Box5.7, including the commands for retrieving the marker sequencing information, transforming of the file to fasta format, blast search, and for the modification of marker names in the .map files.

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Chapter 5: Building genetic map Box 5.7 Script used for the synteny analysis between genetic maps. Text in bold with brackets [Text] indicate the input files and output file names to be specified. # Retrieve marker information from Stacks output files awk ‘$3== [MARKER_NAME] ’ batch_1.catalog.tags.tsv >> [TAGS.INFO.OUTPUT]

# Transform the output file into fasta format awk ‘{print “>”$3“,”$10}’ [TAGS.INFO.OUTPUT] | tr , ‘\n’ > [MARKER.fasta]

# Example of blastn search, using markers from MapB as query and MapA as # database makeblastdb -in [MAPA_MARKER.fasta] -dbtype nucl blastn -task megablast \ -query [MAPB_MARKER.fasta] -db [MAPA_MARKER.fasta ] \ -outfmt “6 qseqid sseqid qseq sseq” -max_target_seqs 1 \ -out [OUTPUT_FILE.tsv]

# Example of changing marker name into ‘Shared_marker_N’ in the # map1.map file. The final output was written into map1.synteny.map file sed 's/ [MARKER_NAME_1] /Shared_marker_1/' map1.map | sed ‘s/ [MARKER_NAME_2] /Shared_marker_2/’ | sed ‘s/ [MARKER_NAME_3] /Shared_marker_3/’ | … sed ‘s/ [MARKER_NAME_N] /Shared_marker_N/’ > map1.synteny.map

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Chapter 5: Building genetic map 5.3 Results 5.3.1 Quality check and preprocessing of the RAD-Seq reads In total, 386,334,623 reads (19,703,065,773 bp) were obtained from all three lanes of the RAD-Sequencing (Table 5.6). On average, 1,630,104 reads per library were obtained (containing 386,334,623 bp). However, in reality the read count of each individual BC plant varied greatly, with the highest read count of 14,685,141 (Figure 5.6; BC individual qualifier HV), and the lowest of 10,706 reads (Figure 5.6; BC individual qualifier DL). The read quality was good in general, with most positions having average quality scores above 35 except for position 30 that showed a drop in average quality to 25 (Figure 5.7 a). The per sequence GC content graph showed a severe GC content bias in all sequenced libraries, with most libraries showing a pattern of multiple peaks of GC distribution (Figure 5.7 b). Further examination of the reads giving rise to these peaks indicated that these were highly represented reads derived from organellar genomes. The per base sequence N content graph showed N bases were called between position 40 to 50 bp in 29 libraries (Figure 5.7 c). The sequence duplication level showed a large proportion of overrepresented reads with over 5,000 or 10,000 duplicates in the data (Figure 5.7 d). After preprocessing, 147,913,800 reads were kept (38.2% of the original reads), with a total of 7,395,690,000 base pairs (Table 5.6). The sample with the highest read count had 7,261,353 reads (BC individual, qualifier HV), and that with the fewest had 3,457 reads (BC individual, qualifier CO). The mean quality score of all libraries was good, with all positions showing a quality score above 30 (Figure 5.7 a). The per base GC content graph had improved greatly, with the peaks contributed by the organellar reads disappearing after reads derived from these non-nuclear-genomes had been removed (Figure 5.7 b). Some bias and peaks were still observed, but further examination of the identity of these peaks revealed that they were mostly transposable elements in the nuclear genome, and were thus kept as they represent a part of the target genome to be mapped. The per base N content graph indicated that the N bases were no longer detected in the data (Figure 5.7 c). The sequence duplication level graph indicated that the highly represented sequences (possibly derived from the organellar genomes) had been removed by the preprocessing procedure, with some reads showing mostly >10 to < 500 duplicates being kept (Figure 5.7 d). The metrics of all libraries before and after preprocessing are summarised in Appendix 5.1 a.

Table 5.6 Number of reads before and after preprocessing Before After % retained preprocessing preprocessing Total read cou nt 386,334,623 147,913,800 38.3 Total base pa irs (bp) 19,703,065,773 7,395,690,000 37.5

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Figure 5.6 Read count obtained from all libraries sequenced, ordered from the lowest to the highest.

Before preprocessing After preprocessing

FastQC: Mean Quality Scores FastQC: Mean Quality Scores

(a) Phred Scores Phred Scores

Mean quality score quality Mean Position (bp) Position (bp)

FastQC: Per Sequence GC Content FastQC: Per Sequence GC Content

(b) Percentage Percentage GC content GC Per sequence Per sequence % GC % GC

FastQC: Per Base N Content FastQC: Per Base N Content

Count Count - - (c) Percentage N Percentage Percentage N Percentage

Per base N content Per base Position in Read (bp) Position in Read (bp)

FastQC: Sequence Duplication Levels FastQC: Sequence Duplication Levels

(d) brary % of Li % of % of Library % of Sequence Duplication level Duplication Sequence Duplication Level Sequence Duplication Level

Figure 5.7 FastQC quality check result of all 237 libraries of RAD-Seq data, before and after preprocessing, with each line representing one sequencing library (one BC individual). (a) Mean quality score. (b) Per sequence GC content. (c) Per base N content. (d) Sequence duplication level.

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Chapter 5: Building genetic map 5.3.2 MapA calculation – de novo approach Three major parameters (m, M, and N) were optimised (Table 5.7). m = 3 and m = 4 gave highest number of usable markers (664 and 585 markers respectively). These two parameter values for m were chosen for the next step of optimisation (optimisation of within individual distance, M), where M = 1 and M = 2 gave the best results. These M values were chosen again for the optimisation of the last parameter (N), in which the final parameters were decided as (m=3, M=1, N=1) and the number of markers recovered (705 markers) is more than the default setting (Table 5.7; 702 markers). A correlation was observed between the read count per individual and the number of marker recovered. With lower read count in an individual, the fewer markers can be recovered (Figure 5.8). Libraries with over one million reads usually had missing genotypes (i.e. undetermined genotype) in less than 20% of all the markers recovered (Figure 5.8). On the other hand, with lower than one million reads, the percentage of missing genotype increased almost exponentially. In individuals with lower than 500,000 reads, the percentage of missing genotype can increase to 60% to 70% (Figure 5.8). This indicated that including individuals with low read counts increased the proportion of markers with high levels of missing data that had to be filtered out, thus leading to a decrease in the number of mappable markers. Thus, as a trade-off between the number of individuals used for genetic map building and the number of recoverable markers, 200 BC individuals with the highest read counts were chosen for the calculation of the genetic maps for all the following analyses (i.e. MapA and MapB); i.e. the remaining 33 BC individuals with low read counts were excluded from the map calculation (see Appendix 5.1 b for the list selected individuals).

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Chapter 5: Building genetic map Table 5.7 Parameter optimisation for the de novo approach calculation of the MapA (data based on 50 BC plants) Stacks parameters Usable markers Minimum stack Within individual Mismatch allowed recovered* depth ( -m) distance ( -M) for 2ndry reads ( -N) default (2) default (2) default (M+2=4) 702 Optimi sation of minimum stack depth ( -m) 1 2 M+2 281 3 2 M+2 664 4 2 M+2 585 5 2 M+2 507 9 2 M+2 314 10 2 M+2 298 Optimi sation of within individual distance ( -M) 3 1 M+2 698 3 2 M+2 664 3 3 M+2 639 3 4 M+2 614 3 5 M+2 597 4 1 M+2 623 4 2 M+2 585 4 3 M+2 563 4 4 M+2 536 4 5 M+2 518 Optimi sation of mismatch allowed for merging secondary reads ( -N) 3 1 M+2 698 3 1 M+1 702 3 1 M 705 3 1 0 673 3 2 M+1 666 3 2 M 668 4 1 M+2 623 4 1 M+1 626 4 1 M 637 4 1 0 633 4 2 M+1 588 4 2 M 592 * Markers genotyped in at least 40 out of the 50 BC individuals used for optimisation, i.e. 80% of the individual. Bold text indicates the best results achieved in each step of the optimisation. The parameter showing the overall highest marker counts is highlighted in grey

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Figure 5.8 Correlation between read count per individual and proportion of missing genotypes among markers. Each dot represents the library of one BC individual of the 50 chosen for the initial mapping experiments.

The de novo approach MapA was calculated under the optimised parameters using the data of 200 BC individuals. In total 1,361 markers were recovered, but only 62 markers remained after removing markers with more than 20% missing data and markers showing strong segregation distortion, with most of the markers removed due to a high proportion of missing data (Table 5.8; >20% missing data). Eventually, 10 linkage groups were identified, with a total span of 716 cM with 55 mapped markers (Figure 5.9 a). The longest linkage group (LG1) was 120.1 cM and the shortest (LG10) 16.5 cM. The average marker interval was 13 cM (Table 5.11). To increase the number of markers recovered and improve the final map density, more individuals with lower read counts were removed, leaving 150 BC individuals with higher read counts. The recalculated linkage map (Table 5.8; see Appendix 5.1 b for the removed individuals) recovered 1,359 markers. Of these, 198 markers were kept after filtering (Table 5.8), and finally 16 linkage groups were identified, which is identical to the haploid chromosome number of the S. rexii and S. grandis (n=16). The map had a total span of 1,119.8 cM with 183 mapped markers (Figure 5.9 b). The longest linkage group (LG1) was 120.1 cM, and the shortest (LG16) 8.5 cM. The average marker interval was 6.1 cM (Table 5.9).

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Chapter 5: Building genetic map Table 5.8 Statistics of the de novo approach MapA calculation De novo MapA De novo MapA

200 BC plants 150 BC plants No. of BC plant used 200 150

Total number of reads (after preprocessing) 144,479,900 136,102,282

No. reads used for analysis 130,186,134 122,384,682

No. read used (%) 90.1 89.9

Stacks analysis

Total number of marker recovered 1,361 1,359

Mean coverage of marker (×) 13.6 14.8

Marker filtering No. marker remained after missing genotype 121 311 filtering* No. marker remained after segregation distortion 62 198 filtering No. markers remained after identical marker 62 198 filtering Final map statistics

No. of linkage group recovered 10 16

No. of mapped markers 55 183

Total map distance (cM) 716.0 1,119.8

Average distance between markers (cM) 13.0 6.1 * More than 20% missing genotype

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Chapter 5: Building genetic map Table 5.9 Linkage group statistic summary of de novo approach MapA De novo MapA – 200 BC individuals Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 10 120.1 13.3 LG2 9 91.5 11.4 LG3 3 84.6 42.3 LG4 8 84.0 12.0 LG5 5 75.0 18.8 LG6 5 68.0 17.0 LG7 4 60.5 20.2 LG8 4 58.8 19.6 LG9 4 56.4 18.8 LG10 3 16.5 8.3 TOTAL 55 715.4 13.2 De novo MapA – 150 BC individuals Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 32 120.1 3.9 LG2 14 116.0 8.9 LG3 17 104.3 6.5 LG4 9 95.9 12.0 LG5 18 85.8 5.0 LG6 15 85.4 6.1 LG7 12 73.2 6.7 LG8 10 72.3 8.0 LG9 10 72.2 8.0 LG10 13 70.8 5.9 LG11 9 59.4 7.4 LG12 7 58.3 9.7 LG13 8 52.5 7.5 LG14 3 27.0 13.5 LG15 4 17.0 5.7 LG16 2 8.4 8.4 TOTAL 183 1,118.6 6.1

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(a) LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8

D2_20750 D2_24775 D2_13484 D2_15096 D2_23614 D2_23960 D2_4855 D2_636 9.2 8.7 D2_6660 15.1 D2_4236 12.4 14.7 17.9 19.2 D2_17704 11.8 D2_17157 D2_4709 6.6 D2_13444 D2_18980 D2_15350 D2_12778 17.1 6.9 8.4 13.1 D2_12334 D2_17441 23.0 20.6 57.6 7.3 44.0 D2_878 D2_14045 6.8 D2_5556 8.5 D2_18788 D2_9555 D2_17553 D2_573 10.6 D2_13523 17.9 27.8 22.9 D2_3242 D2_31139 30.0 18.5 D2_8508 34.8 10.6 14.3 D2_30534 21480 D2_8360 D2_14227 D2_22038 23814 13.3 27.1 11.1 8.1 D2_383 D2_7492 D2_7925 D2_9452 6.5 D2_16871 9.6 6.7 13.7 D2_16907 D2_838 D2_18006 D2_25237 5.4 D2_13496 10.0 D2_22595 15.1 D2_9132

LG9 LG10

D2_11601 D2_13712 12.2 10.0 D2_14963 D2_20130 6.5 9.3 D2_5422 D2_18118

35.0

D2_12692 (b) LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8

DN33146 DN19787 4.9 DN9132 6.3 DN23447 3.7 DN23850 DN24478 4.8 DN27582 DN11207 DN5882 DN4747 DN11066 DN25237 DN9452 DN25967 5.8 5.7 3.1 DN20349 8.7 DN29912 6.6 DN7925 DN22038 11.8 12.1 DN16907 5.9 12.8 5.0 DN22595 DN15096 DN24957 6.8 DN9544 2.7 DN10262 5.0 11.6 DN20314 6.7 DN4566 15.7 6.3 4.6 2.9 DN24123 15.1 DN150 3.0 DN12455 DN31139 10.0 DN4838 8.4 DN24744 2.7 DN636 DN464 DN2923 3.4 DN28257 4.9 17.3 DN33494 DN9164 6.7 DN19525 8.9 10.1 DN18846 DN8360 2.4 9.3 5.8 DN26692 14.7 1.1 DN16871 3.9 DN1414 DN8508 4.2 DN17839 DN15832 DN35637 2.7 7.9 9.1 DN1100 DN11936 1.3 6.1 3.8 DN5556 10.9 DN27288 DN2130 DN11601 2.0 DN19877 5.4 6.3 DN21061 5.9 DN12777 0.8 DN15589 5.5 3.3 DN17441 DN2815 DN13444 DN13523 DN1740 5.3 2.4 7.5 4.8 DN12369 5.7 DN17268 2.7 DN7492 4.8 DN20130 DN27707 DN15656 DN18788 5.3 2.2 3.3 19.9 4.9 5.2 DN20752 DN32712 DN18006 4.7 DN18118 DN12778 2.8 DN20744 6.4 DN15350 2.1 4.2 2.5 6.1 14.4 4.2 DN4236 DN1137 DN13496 DN2915 DN34645 3.8 DN33540 DN12334 2.6 5.7 3.6 4.8 DN3242 5.4 DN23614 DN33801 11.5 DN32427 DN13754 10.5 2.2 DN14045 DN17157 3.3 8.0 5.2 DN9742 2.4 5.3 DN12328 DN9898 4.3 DN22920 5.9 DN23360 DN5804 11.1 21.6 DN23424 13.5 DN1228 2.6 DN5058 5.4 11.4 DN1983 DN2891 DN14227 9.2 5.9 7.5 DN27302 6.0 DN13484 DN15448 DN24775 4.7 DN9903 DN12692 DN383 9.9 DN21480 7.2 8.4 6.9 DN573 DN24266 DN838 DN12813 6.3 DN24951 9.5 7.1 2.1 DN1454 DN10887 DN20625 3.3 8.4 2.7 DN6660 DN24104 2.9 DN18980 2.2 DN26868 3.0 DN1933 6.5 DN8550 DN20750 DN32412 LG9 LG10 LG11 LG12 LG13 LG14 LG15 LG16

DN17704 DN9459 DN3162 DN13594 DN30534 DN13147 DN1210 DN19410 6.3 DN21172 9.8 8.5 14.6 10.2 DN33256 6.7 DN23960 DN1489 19.1 17.3 DN2081 DN17271 6.6 22.5 3.5 DN14172 3.2 DN25888 DN15099 DN21368 3.4 5.3 5.4 5.7 DN17553 DN11868 DN9498 DN1535 DN21278 DN8262 9.8 5.3 DN23151 5.7 9.2 10.4 DN32854 DN15981 10.7 DN9569 DN4273 DN878 10.4 4.9 7.7 3.4 DN16869 DN32097 DN14788 DN15447 DN9555 5.0 7.4 3.6 DN4709 8.2 DN18604 DN28507 12.2 DN35066 4.4 DN14806 3.4 DN33181 9.9 DN12138 8.9 DN5811 4.9 DN220 4.9 DN3221 18.9 3.0 DN13712 4.2 4.7 3.5 10.4 DN30777 DN33243 DN17160 DN15341 3.9 DN1895 3.0 DN14963 DN13243 9.8 2.2 DN5422 DN23814 4.2 DN3359

Figure 5.9 De novo approach MapA. (a) Map calculated based on 200 BC individuals. The marker name prefix ‘D2’ stands for de novo map calculated based on 200 individuals. (b) Map calculated based on 150 BC individuals. The marker interval distance is shown on the left (cM), and the marker name is shown on the right of each linkage.

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Chapter 5: Building genetic map 5.3.3 MapA calculation – reference-based approach using BWA and Stampy aligners The preprocessed RAD-Seq reads were mapped to the S. rexii SOAPdenovo2 assembly using BWA. In total, about 90 million reads originating from the 200 BC individuals plus parent plants were mapped, representing about 62.5% of the total input reads (Table 5.10). The Stacks analysis of the BAM files recovered a total of 3,751 markers. Amongst these, 414 markers were kept after filtering out markers with a high proportion of missing data or showing strong segregation distortion. Finally, 317 markers were mapped across 16 linkage groups, with a total span of 1,468.6 cM (Table 5.10, Figure 5.10). The longest linkage group (LG1) was 129.1 cM, and the (LG16) 15.0 cM. The average marker interval was 4.6 cM (Table 5.11).

Table 5.10 Statistics of the reference-based BWA approach MapA calculation (data based on 200 BC plants) BWA-MapA No. of BC plant used 200 plants Total number of reads (after preprocessing) 144,479,900 No. mapped reads 90,233,330 Mapped read (%) 62.5 Stacks analysis Total number of marker recovered 3,751 Mean coverage of marker (×) 23.3 Marker filtering No. marker remained after missing genotype filtering 699 No. marker remained after segregation distortion filtering 414 No. markers remained after identical marker filtering 414 Statistics of the final map No. of linkage group recovered 16 No. of mapped markers 317 Total map distance (cM) 1,468.6 Average distance between markers (cM) 4.6

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Chapter 5: Building genetic map Table 5.11 Linkage group statistic summary of reference-based BWA approach MapA (data based on 200 BC plants) Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 45 129.1 2.9 LG2 21 128.9 6.4 LG3 17 115.6 7.2 LG4 29 115.0 4.1 LG5 22 112.4 5.4 LG6 21 111.3 5.6 LG7 23 96.8 4.4 LG8 17 95.7 6.0 LG9 26 95.1 3.8 LG10 10 94.7 10.5 LG11 14 93.7 7.2 LG12 23 92.2 4.2 LG13 26 87.4 3.5 LG14 15 69.8 5.0 LG15 4 16.1 5.4 LG16 4 15.0 5.0 TOTAL 317 1,468.8 4.6

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LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8

BW4699 BW12159 BW13402 6.3 BW12019 4.2 BW12018 2.9 2.3 BW16439 2.7 BW7640 2.0 BW16422 BW8101 BW13670 3.4 BW12036 BW18666 4.3 BW7448 3.0 BW18928 7.6 BW17206 1.4 BW7871 BW13134 2.8 BW5107 4.0 BW11676 7.1 BW17852 BW12419 7.9 BW6974 BW7652 3.2 BW6061 BW11931 1.5 BW3872 BW18357 8.8 BW19004 6.2 BW3459 BW6971 9.0 BW7650 8.3 BW16934 8.4 BW8864 9.2 3.6 BW14432 13.0 BW12191 4.3 BW13908 BW7644 2.9 BW12283 7.9 BW3602 BW6347 2.6 7.5 3.6 BW12709 3.3 BW9817 10.1 1.2 BW13905 BW13801 3.8 BW10092 8.2 BW13316 12.4 BW13099 37.4 5.2 BW16056 BW18777 6.4 2.9 BW13904 13.7 BW4639 2.6 BW15453 6.3 5.0 BW15819 BW14097 5.7 2.4 BW8950 5.2 BW18778 7.2 2.1 BW17205 BW8739 BW7893 1.8 BW15455 2.9 BW5820 BW8718 4.6 2.0 BW10524 4.3 5.3 BW18783 18.6 3.2 BW5990 BW9535 2.6 BW17076 2.3 BW16944 2.6 BW13313 BW8710 4.6 5.2 2.7 BW7134 3.3 10.7 BW8959 6.7 BW13809 BW9902 1.9 BW5841 3.1 BW5010 BW9839 2.9 7.1 2.5 BW8555 BW7131 2.5 11.2 BW10915 4.8 BW17155 BW7732 1.3 BW13599 5.3 BW9114 BW16956 6.0 3.6 1.8 BW9151 BW10293 5.4 5.5 BW6652 6.8 2.3 BW13693 3.8 BW9901 2.2 BW6957 BW8635 7.1 BW14655 5.0 2.1 BW4836 BW10296 4.5 5.4 BW17754 7.4 BW12302 3.6 BW10601 3.2 BW13835 4.7 BW10764 2.9 BW17851 1.5 1.6 BW9222 2.4 BW17528 4.3 BW9703 2.7 2.0 BW4707 14.3 BW12518 1.4 4.8 BW10866 2.1 BW3767 3.0 BW7710 BW12354 3.0 BW17529 BW12361 2.9 1.5 BW10114 5.2 BW9061 3.0 3.4 BW17378 3.7 1.4 BW17172 2.5 BW14081 4.0 BW19112 10.4 BW14350 BW5707 2.4 1.6 BW15610 BW1762 1.5 BW9383 9.7 BW13574 2.4 1.2 BW13024 BW4538 2.4 4.1 BW14378 1.6 BW16959 2.4 1.2 BW11020 BW15286 2.0 BW12786 2.0 BW11420 2.7 BW17173 BW11088 2.5 3.3 BW11736 3.7 4.5 BW12782 1.6 0.6 BW10216 BW10630 17.4 3.3 BW11418 2.5 BW18188 BW10749 0.5 3.7 8.4 BW408 BW8528 3.1 5.7 BW16544 4.3 BW9016 2.3 BW7718 4.0 BW10337 2.8 BW7679 BW11440 1.0 9.7 BW17355 BW3333 10.4 2.2 BW8035 4.1 8.3 BW3941 2.8 BW15207 2.7 BW10336 BW16397 BW10891 1.1 11.4 BW14910 BW17964 5.3 3.9 BW7842 4.6 BW3716 10.0 BW12584 2.0 BW12214 6.4 BW17572 2.2 10.1 BW7072 BW8215 12.4 BW11802 BW16403 BW13230 2.2 BW9925 BW14725 2.1 BW8517 6.5 BW2130 BW19010 1.7 BW17200 17.9 BW12699 BW14628 BW5802 5.0 BW8477 1.6 BW18898 12.9 5.2 BW9920 BW3858 0.9 BW14760 BW14942 1.0 BW14521 BW13749 2.8 BW16417 3.8 BW8391 4.8 2.8 BW7272 4.1 BW6990 4.7 BW6482 9.5 BW9717 BW5426 BW6046 BW6641

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BW12754 BW6588 BW7925 BW10818 BW11591 17.6 BW10250 3.1 BW13264 BW17458 6.7 2.8 BW5474 BW10941 BW9893 0.8 BW7676 BW8632 1.6 BW11333 20.6 1.1 BW14266 2.2 BW11216 BW18399 BW9999 BW17109 BW11684 BW14357 BW19128 9.7 7.7 1.0 4.5 BW14273 2.8 BW5384 BW18400 5.1 6.8 BW17106 BW16325 2.3 BW15870 1.0 5.2 BW12176 6.2 BW9373 4.7 BW15938 7.9 BW14828 5.3 BW12454 1.8 BW14702 5.2 BW13346 1.9 BW18696 2.9 BW11977 3.2 3.0 33.1 1.3 BW6195 BW19041 5.2 BW16718 43.6 5.3 BW12240 BW17071 1.5 BW11981 2.1 1.1 BW5834 BW5512 7.8 BW10461 BW12505 3.9 BW18437 4.0 BW9870 4.3 BW10032 2.7 0.9 9.1 BW12399 1.6 BW8394 BW13519 3.9 BW12072 3.8 BW3667 10.4 8.0 BW12266 BW4668 1.0 BW5520 BW7876 3.8 BW17112 5.4 BW14696 2.9 4.4 4.5 8.4 BW11895 1.5 BW12403 1.2 BW14901 BW18295 BW17473 9.1 BW19189 5.0 2.8 3.8 BW17232 4.5 1.4 BW6529 1.9 BW17642 2.0 BW13829 BW11070 BW8051 1.1 5.7 9.4 BW11130 3.2 0.7 BW10623 BW16738 3.5 BW13826 BW7384 BW15427 1.5 4.6 3.9 BW7420 4.0 1.3 BW6202 BW15354 7.5 BW19193 7.6 BW9148 4.3 4.6 BW19008 1.0 BW9419 6.8 BW7505 7.4 BW9588 2.2 BW6297 7.6 BW11648 4.0 4.7 BW16867 0.9 BW6994 BW15710 BW2273 4.6 BW18918 1.3 BW9133 11.3 5.0 BW5828 0.9 BW8286 BW15709 2.5 1.2 1.5 BW9428 BW6370 5.2 BW6449 BW6993 BW7488 2.7 6.1 6.9 2.6 3.0 BW9294 BW11831 BW16246 BW15444 6.2 BW4333 3.5 BW9295 1.6 7.3 BW16603 2.4 BW10932 9.3 BW9849 3.1 BW7170 2.4 BW15766 4.7 BW18551 3.4 BW10352 BW7003 6.6 BW18067 BW10351 BW11458 BW15107

Figure 5.10 Reference-based BWA approach MapA. The marker interval distance is shown on the left (cM), and the marker name is shown on the right of each linkage group.

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Chapter 5: Building genetic map A second reference-based approach map was constructed by mapping the RAD-Seq data to the S. rexii SOAPdenovo2 preliminary genome assembly using the software Stampy. Among the 144 million preprocessed reads, about 134 million were mapped representing 93.4% of the total reads (Table 5.12). From these, a total of 9,185 markers were recovered. However, a large proportion of these markers were removed by filtering and only 503 markers remained. Among these, 16 linkage groups were identified with 338 mapped markers with a total span of 1,567.4 cM (Table 5.12; Figure 5.11). The longest linkage group (LG1) was 154.9 cM, and the shortest (LG16) 21.8 cM, The average marker interval was 4.6 cM (Table 5.13).

Table 5.12 Statistics of the reference-based Stampy approach MapA calculation (data based on 200 BC plants) Stampy-MapA No. of BC plant used 200 plants Total number of reads (after preprocessing) 144,479,900 No. mapped reads 134,888,035 Mapped read (%) 93.4 Stacks analysis Total number of marker recovered 9,185 Mean coverage of marker (×) 22.2 Marker filtering No. marker remained after missing genotype filtering 853 No. marker remained after segregation distortion filtering 503 No. markers remained after identical marker filtering 503 Statistics of the final map No. of linkage group recovered 16 No. of mapped markers 338 Total map distance (cM) 1,567.4 Average distance between markers (cM) 4.6

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Chapter 5: Building genetic map Table 5.13 Linkage group statistic summary of reference-based Stampy approach MapA (data based on 200 BC plants) Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 24 154.9 6.7 LG2 32 145.3 4.7 LG3 21 131.3 6.6 LG4 31 114.9 3.8 LG5 27 109.1 4.2 LG6 19 107.2 6.0 LG7 13 100.3 8.4 LG8 35 99.7 2.9 LG9 21 99.3 5.0 LG10 17 98.4 6.2 LG11 24 95.1 4.1 LG12 22 92.5 4.4 LG13 26 86.6 3.5 LG14 17 70.3 4.4 LG15 4 39.9 13.3 LG16 5 21.8 5.5 TOTAL 338 1,566.6 4.6

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ST10842 ST18530 3.7 ST8049 ST13681 2.2 ST7682 ST23821 3.4 ST21779 ST20872 3.5 ST19542 ST33067 1.6 ST23085 ST7401 6.8 ST31488 ST25742 1.0 ST28651 7.3 14.8 ST20958 3.6 ST13246 ST31003 1.0 ST19428 ST20664 2.6 5.1 6.1 ST12406 ST14958 0.8 ST23559 2.7 3.1 ST21468 0.7 4.3 ST8191 ST32380 ST26288 ST20056 ST25571 ST5990 7.4 2.2 ST33533 0.6 4.3 ST20667 ST25200 ST12872 ST18744 ST10116 3.8 ST947 2.9 2.5 8.3 ST21004 ST10920 0.6 12.5 ST13516 1.1 ST11629 ST25706 ST21048 9.5 ST16140 3.4 ST22045 7.7 15.1 1.4 11.0 ST11577 18.3 2.9 ST30240 ST12126 ST20488 5.8 ST30065 4.9 ST28142 9.0 ST7291 1.1 ST18241 10.7 ST11572 0.9 ST22625 ST16769 ST33477 6.0 23.5 ST15609 2.4 ST15252 5.9 2.2 ST32747 11.4 ST24265 2.4 1.4 ST28933 8.7 ST33161 19.6 ST23658 2.5 2.8 ST14521 ST18034 1.5 ST25368 9.6 ST24268 2.9 1.9 1.4 ST30241 4.3 ST33155 7.9 2.8 ST7681 ST11862 ST7050 2.1 ST29401 ST13317 ST30326 1.9 2.0 1.6 ST5796 10.5 ST15272 17.6 1.4 5.2 4.3 ST24506 ST17603 ST19077 ST9502 ST24121 ST30207 2.5 2.0 3.2 14.2 ST10961 9.5 3.3 3.3 8.5 ST11859 ST18445 ST16858 ST9195 1.6 ST21334 1.8 7.3 3.5 ST18201 7.0 ST9203 1.3 2.1 7.6 ST32454 ST17609 ST14659 ST8656 ST10700 1.8 ST33713 3.0 7.4 3.9 ST31328 2.6 ST26634 2.4 2.0 6.2 ST30319 ST30912 ST15558 ST30459 ST11806 2.7 1.7 ST18482 2.2 5.1 2.7 4.6 ST12947 3.9 1.8 ST28818 2.8 ST7986 ST7696 ST9052 2.0 2.0 ST15993 3.2 ST30913 4.6 2.8 ST18225 2.2 ST10676 3.8 ST6113 2.7 ST23160 ST21349 ST23696 1.0 2.0 ST30592 8.3 ST25076 3.9 1.4 ST5569 2.9 ST30123 1.7 ST15371 3.0 ST9477 2.9 ST29854 ST9512 0.8 13.9 ST30593 6.2 ST17128 2.6 ST32120 3.3 ST20377 3.6 ST17464 5.5 1.8 4.3 ST27765 5.6 ST9291 3.5 2.0 ST26095 ST28524 6.1 ST26805 2.1 ST18980 ST29825 ST18891 8.8 5.1 6.5 ST23164 2.3 ST29086 11.4 1.6 1.1 ST23067 ST20169 ST15462 4.8 ST17628 ST27126 ST27412 5.0 2.8 10.6 ST5495 6.3 ST9954 4.4 2.4 1.7 ST11743 ST9828 ST14231 ST12285 ST27123 ST25160 6.9 2.0 4.5 ST15105 3.0 ST13559 7.4 4.2 4.4 ST14440 ST31676 ST15605 ST17255 7.2 ST21230 10.1 3.4 5.3 ST20507 ST13201 2.5 3.1 7.0 ST22027 1.6 ST13878 6.6 ST21534 12.5 ST21202 1.5 ST28855 ST20035 6.8 4.9 ST27598 5.2 ST14371 3.6 5.9 ST19984 8.1 ST29804 6.8 ST6872 ST30328 8.2 ST19451 3.7 ST17230 6.7 4.3 ST10066 12.9 ST9913 3.2 ST10658 ST32971 ST8190 4.1 4.8 ST5957 9.7 ST16071 20.0 ST10663 11.4 ST32972 ST20800 4.7 ST3532 2.4 ST15854 ST16590 4.8 ST26045 3.4 ST26575 ST17711 3.9 ST9891 ST23960 ST17712 8.4 ST8798 ST26456 ST9888 ST10943

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ST22124 ST13356 ST10435 17.4 ST17535 ST24827 ST19895 2.9 ST8892 ST33756 7.1 ST30791 ST14655 ST22763 0.7 ST12868 ST24785 11.4 ST24628 20.7 ST16901 ST32461 8.1 2.2 ST19224 6.9 ST17132 ST9137 6.6 ST24630 9.8 ST24925 2.2 ST32462 31.7 4.4 2.7 ST15976 ST30119 ST8172 ST25611 ST14819 4.6 ST24933 6.7 1.8 ST9529 ST29166 3.8 17.0 ST14215 8.7 ST30118 ST5616 14.0 ST21157 ST14811 5.4 ST20984 2.4 4.1 ST27943 7.7 2.0 6.3 ST30055 ST18925 5.6 ST30114 4.1 ST25695 ST7647 ST27012 5.1 ST23220 0.8 1.5 ST20585 28.9 3.0 5.9 ST21642 14.7 4.8 ST21525 2.9 ST22098 ST20452 ST17921 0.7 2.2 ST20584 4.8 3.4 ST16804 8.3 ST27248 ST19618 2.7 7.1 ST22696 ST29106 ST21435 1.9 1.0 ST20590 ST33604 5.5 2.7 ST29850 11.7 3.7 17.6 3.4 ST33863 ST30368 ST7344 0.7 ST26079 ST32525 2.7 5.2 ST16049 2.6 9.3 4.5 ST12347 0.9 ST16219 ST27549 ST27075 1.3 2.9 ST32291 2.0 ST10743 7.4 ST23539 4.4 3.0 1.1 ST29907 ST33539 7.4 ST19071 ST18423 0.8 ST18217 1.4 4.7 ST13254 3.7 0.9 ST29435 ST29676 3.0 ST16325 ST19006 1.3 ST10151 1.1 4.1 ST32273 2.7 2.8 ST12349 1.5 ST31137 3.9 ST28675 ST7111 0.9 ST16057 1.5 3.3 ST14840 3.3 1.6 0.9 ST26936 3.9 ST9495 ST33383 ST7112 ST11599 1.2 3.5 ST24107 4.1 0.8 1.0 ST32718 7.0 1.0 ST15024 ST27027 1.1 ST5929 ST14001 3.4 ST33867 5.9 1.1 3.8 ST24044 7.6 1.4 ST25260 ST14758 1.7 ST32666 ST11598 ST10330 4.5 0.9 1.6 ST27585 1.6 ST22814 2.3 ST20343 0.6 ST20971 ST8716 ST3348 2.5 1.7 2.0 ST12485 ST9009 2.8 ST10476 1.3 ST23168 2.5 ST21443 2.9 1.7 ST12528 ST14775 5.2 ST20346 2.6 ST14055 2.4 ST19371 3.7 7.5 8.1 ST27588 ST13746 ST21013 3.0 ST27687 10.9 ST19877 2.6 ST11620 ST16828 ST15594 8.9 ST32763 ST18655 ST31842

Figure 5.11 Reference-based Stampy approach MapA. The marker interval distance is shown on the left (cM), and the marker name is shown on the right of each linkage group.

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Chapter 5: Building genetic map The alignment percentage of the RAD-Seq reads of the two parents (S. rexii and S. grandis ) was also examined. For the S. rexii data, both BWA and Stampy achieved over 90% of alignment coverage (Table 5.14). BWA aligned 1,230,052 reads and Stampy aligned 1,289,073 reads, corresponding to a 91.7% and 96.1% alignment coverage respectively. On the other hand, BWA struggled to align the S. grandis reads to the S. rexii genome assembly, with only 755,201 reads aligned (56.3%). This was improved when the software Stampy was used, resulting in 2,176,451 S. grandis reads aligned (91.3%) (Table 5.14).

Table 5.14 Comparisons of BWA and Stampy alignment percentages. The RAD-Seq reads of S. grandis and S. rexii were aligned to the S. rexii SOAPdenovo2 genome assembly S. rexii S. grandis Aligner Aligned reads / Aligned reads % Aligned reads / Aligned reads % BWA 1,230,052 / 91.7% 755,201 / 56.3% Stampy 1,289,073 / 96.1% 2,176,451 / 91.3%

The mismatch distribution of the alignments between BWA and Stampy were compared (Figure 5.12). The default BWA alignment settings only allowed up to 3 bp mismatches per 51 bp read (Figure 5.12 a), while the Stampy default settings allowed more than 5 bp mismatches per 51 bp read (Figure 5.12 b).

(a) No. of alignedreads

No. of mismatch No. of mismatch No. of mismatch No. of mismatch No. of mismatch (b) No. ofaligned reads

No. of mismatch No. of mismatch No. of mismatch No. of mismatch No. of mismatch

Figure 5.12 Mismatch distributions of BWA and Stampy alignments, with the X-axis showing the number of mismatches per read, and the y-axis showing the number of reads. (a) Mismatch distribution of BWA alignment. (b) Mismatch distribution of Stampy alignment. MAPQ: Mapping quality score.

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Chapter 5: Building genetic map Finally, the de novo approach and the reference-based Stampy approach were compared in terms of the read count per individual to the proportion of missing genotype, i.e. markers that failed to be genotyped. As shown previously in the de novo approach, the proportion of missing genotypes increased nearly exponentially when the read count per individual is below one million (Figure 5.13 blue dots). However, when the same data was analysed using the reference-based approach, more markers were recovered and the proportion of missing genotypes remained below 10% and rarely to below 20%, even when the number of read counts per individual was below 500,000 (Figure 5.13 red dots).

Figure 5.13 Correlation of proportion of missing genotypes to the number of reads per individual, where each dot represents one BC individual. Blue dots: data analysed using the de novo approach, as shown in Figure 5.8. Red dots: data analysed using the reference-based Stampy approach. Data for 50 BC individuals used in initial analyses are shown.

5.3.4 MapA calculation – combined approaches To maximise the number of recovered markers and to increase the resolution of the genetic map, the genotype locus files generated from all three approaches (i.e. de novo , BWA and Stampy) were combined. The recalculated genetic map included 14,297 markers in the combined locus genotype file. When markers with more than 20% missing data were excluded, 1,673 markers were left after filtering, and when markers showing strong segregation distortion were also removed, 979 markers remained (Table 5.15). Among these, 180 BWA – Stampy marker pairs showed identical segregation patterns across the 200 BC individuals, and were also found to originate from the same locus in the S. rexii genome assembly and shared the same sequences (Appendix 5.2). The Stampy counterpart of these duplicated markers was excluded, which left 799 markers for the genetic map calculation (Table 5.15). On the other hand, no de novo approach-generated markers were found sharing identical segregation patterns to other markers. Eventually, 16 linkage groups were identified

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Chapter 5: Building genetic map with 599 mapped markers (Table 5.15, Figure 5.14). The total span of the map was 1,578.2 cM. The longest linkage group (LG1) was 148.0 cM, and the shortest (LG16) 23.7 cM. The average marker interval was 2.6 cM (Table 5.16).

Table 5.15 Statistics of the MapA-combined approaches map calculation (data based on 200 BC plants) Combined MapA Total no. of markers generated from all three approaches 14,297 Marker filtering No. marker remained after missing genotype filtering 1,673 No. marker remained after segregation distortion filtering 979 No. markers remained after identical marker filtering 799 Statistics of the final map No. of linkage group recovered 16 No. of mapped markers 599 Total map distance (cM) 1,578.2 Average distance between markers (cM) 2.6

Table 5.16 Linkage group statistic summary of combined approach MapA (data based on 200 BC plants) Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 44 148.0 3.4 LG2 83 139.6 1.7 LG3 54 128.6 2.4 LG4 35 125.0 3.7 LG5 43 120.9 2.9 LG6 54 113.7 2.1 LG7 38 113.6 3.1 LG8 34 105.0 3.2 LG9 20 104.9 5.5 LG10 38 97.5 2.6 LG11 31 90.9 3.0 LG12 41 87.5 2.2 LG13 35 72.8 2.1 LG14 34 64.3 1.9 LG15 7 41.3 6.9 LG16 8 23.7 3.4 TOTAL 599 1,577.3 2.6

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ST20872 BW4699 DN20750 BW12159 DN18980 12.7 ST20664 3.8 ST20667 2.2 1.1 BW12019 0.8 ST8191 3.3 BW12018 ST20665 0.1 BW13402 BW8101 2.4 DN6660 BW15107 ST13681 1.6 BW7640 ST12801 BW10351 ST23821 0.4 3.5 BW10352 0.2 BW16439 2.9 BW13670 3.0 BW9294 3.5 ST28896 6.0 0.5 ST19542 1.1 BW7170 1.7 BW16422 2.4 BW7652 5.9 BW17852 BW12036 2.7 DN9452 ST31488 0.3 BW7650 3.1 ST21468 ST33401 BW18928 0.4 BW9295 0.1 ST13246 BW7871 3.7 ST12820 BW12419 1.4 BW13134 4.0 BW9428 3.3 DN24775 ST26045 0.3 DN22038 9.0 BW7644 0.7 ST33533 1.8 ST11577 ST19451 0.5 1.9 BW7448 ST20800 4.1 ST10920 0.9 7.9 ST16769 7.1 BW6974 BW9133 ST27598 7.1 BW18357 BW18471 ST32597 7.9 DN15096 7.0 6.2 BW9817 1.2 BW13904 BW11648 BW12699 7.9 DN17157 BW10092 0.1 6.9 BW4639 0.2 0.6 BW12191 0.5 BW6971 ST19984 DN9459 9.2 BW14432 4.6 BW15453 0.7 5.9 ST7291 3.1 ST21004 3.9 ST24265 DN31139 BW8517 1.8 DN15350 0.1 BW15455 0.5 4.3 0.5 ST14862 0.6 BW18777 BW13905 0.1 BW12460 ST11743 0.3 ST22045 9.5 25.9 ST27126 0.9 7.1 5.4 BW8739 3.5 BW18778 BW13908 5.3 ST21534 10.5 BW7072 0.4 BW12709 4.2 BW16944 1.2 0.1 BW9535 ST33155 27.6 BW17205 3.7 ST15605 0.1 ST23067 0.4 DN12334 0.4 BW13599 1.3 4.6 BW19000 4.7 DN25967 11.6 ST24066 4.7 BW7384 6.4 BW14910 4.1 BW16056 BW5841 3.4 4.6 DN12778 0.5 BW18783 2.1 6.0 BW13809 1.8 ST17628 3.8 BW17355 2.0 ST15252 ST3756 1.4 1.0 DN5556 10.8 BW8959 ST15272 2.1 0.1 ST30207 BW17155 BW11070 6.2 ST30592 0.5 BW8950 ST24477 2.2 2.0 ST7071 BW4510 11.4 ST18693 0.1 3.9 BW17473 4.6 ST16140 2.5 1.3 ST23388 2.8 11.5 BW11717 DN18788 0.2 1.4 DN11601 1.2 ST20377 BW9468 2.0 1.7 BW10915 BW13693 0.8 0.9 BW7134 4.5 4.5 BW6957 4.3 ST30123 2.0 DN17441 1.4 BW6652 BW4708 1.7 1.2 ST10948 BW7131 2.4 1.1 ST9052 5.2 0.1 BW10601 1.4 ST18201 ST7417 2.3 BW17112 2.1 ST17690 ST17603 BW10293 3.6 1.7 ST9195 0.1 1.0 BW9901 2.1 ST31328 3.8 BW6489 0.4 BW10341 0.2 0.7 1.0 BW17200 2.4 ST16916 0.3 ST17609 ST11806 1.1 BW12072 2.1 ST15609 2.3 BW17754 1.7 0.3 ST30319 0.4 BW9902 3.0 BW10296 BW7104 3.4 BW9870 1.1 2.7 ST7696 0.3 1.5 ST29525 BW9151 3.4 2.9 BW17528 ST30912 BW6505 0.1 2.0 BW7732 1.7 BW9703 1.6 0.9 BW3716 BW5358 ST8656 ST24506 0.5 0.2 DN13523 ST10700 1.6 0.8 ST12982 3.2 BW12361 1.8 0.9 BW16403 BW5834 DN14963 11.8 0.1 ST30913 1.5 ST24121 1.7 0.7 ST9203 3.5 0.1 ST29854 1.3 DN16871 ST9502 BW4836 3.5 BW17529 1.8 0.1 BW13835 2.5 0.8 ST15747 7.2 BW16959 0.1 BW3941 BW16718 ST18482 3.4 BW14350 1.3 0.9 BW10866 ST18621 0.9 0.9 ST26634 5.3 BW12782 0.7 BW9016 ST15371 ST25368 BW19112 0.1 ST17128 0.7 0.2 BW17378 0.4 0.8 0.6 BW15207 0.1 BW5707 BW10216 1.2 ST7682 ST33713 2.0 BW11736 4.3 3.0 ST30638 0.4 0.9 2.0 BW7718 ST12947 2.0 ST9291 0.1 ST17464 0.8 ST18241 0.4 BW12354 0.7 ST20169 0.5 ST23649 1.0 2.2 DN14227 2.2 BW16544 3.4 BW11020 0.7 DN23614 2.0 ST21334 1.1 ST28524 BW3115 0.9 1.3 BW10630 1.2 ST29086 3.9 1.0 ST27412 BW15610 0.1 BW10941 ST18744 0.3 BW9222 3.9 DN8360 ST4618 0.6 1.4 BW1762 2.0 ST9954 0.6 0.7 ST25160 BW11684 2.2 ST15734 1.5 2.0 BW14378 BW11420 0.6 0.1 ST5569 2.0 2.6 ST29963 1.3 0.3 BW10114 BW16325 BW8555 1.8 ST9828 BW11418 0.2 4.0 BW15286 0.3 1.2 2.1 1.4 ST28651 ST14521 0.8 ST8043 ST17294 0.9 0.4 ST32120 1.6 BW17964 2.2 1.5 ST19595 0.6 ST23559 BW7893 6.4 BW8035 ST20723 0.3 21.9 2.0 ST13878 3.2 6.1 BW10337 ST17685 0.9 2.8 BW12584 ST13559 ST7200 0.4 BW11333 15.5 DN13712 2.6 BW8215 7.9 1.8 BW10336 0.1 0.6 BW13230 BW11802 BW4707 1.3 ST19428 0.2 ST947 5.6 BW2130 0.3 ST17682 1.8 3.7 DN383 ST8157 3.5 ST7416 1.0 DN4236 BW3872 0.1 BW8477 BW12214 0.1 5.7 BW18217 ST8585 2.8 BW9920 2.8 BW13264 ST5990 1.4 BW8528 ST14460 DN12692 1.9 2.2 BW9406 0.4 0.4 ST16946 2.7 BW14702 3.3 BW3858 ST16952 0.4 BW8404 8.7 2.1 BW14760 ST21779 3.8 ST5957 BW9925 3.3 ST14231 9.7 3.7 1.2 ST25814 ST32747 DN25237 0.9 ST10116 0.1 1.8 DN22595 BW5038 ST3532 0.3 BW14628 29.2 1.0 ST26776 ST18530 ST26575 8.0 ST25571 0.6 BW10818 7.3 0.6 BW14521 BW13749 1.4 BW16417 ST28855 BW6588 1.7 ST10842 1.0 BW8391 1.3 ST14211 BW3578 2.8 ST6872 2.5 BW7272 2.2 ST25627 1.5 BW6990 0.8 ST10658 4.3 0.1 BW6482 3.5 DN9132 2.7 BW9717 7.2 BW5426 ST8798 ST9888 BW6046 BW6641 LG8LG8 LG9LG9 LG10LG10 LG11LG11 LG12LG12 LG13LG13 LG14LG14

BW11458 BW10032 ST31842 ST17132 BW18067 BW8898 BW3459 2.2 0.1 BW5512 BW18551 ST15162 BW6347 5.2 2.2 ST8172 ST16828 2.1 ST25179 ST24827 3.8 1.9 2.2 BW3667 4.9 ST6736 BW14417 2.6 BW17206 BW13099 DN16907 9.2 0.3 ST5616 4.4 BW4333 BW14712 0.4 BW18666 11.1 DN23814 BW11591 5.0 2.8 DN30534 4.1 0.8 BW7488 ST6693 0.1 BW11931 0.4 ST24628 0.8 DN8508 1.3 BW14696 6.8 3.0 DN14045 0.2 BW4309 4.3 ST20507 7.1 BW14097 20.5 BW17458 1.7 ST22098 1.2 BW14357 0.6 1.6 BW15709 6.3 ST12834 BW11676 0.5 ST24630 BW9893 6.5 ST22696 4.9 8.4 9.3 0.3 2.8 BW13829 3.4 ST11417 BW14266 BW15710 2.7 BW19189 0.1 ST20035 DN19410 ST9137 4.5 1.0 2.0 ST33867 7.7 ST14819 ST24925 ST12528 0.4 ST33863 5.8 BW8864 10.4 0.2 1.5 0.1 BW19193 BW8718 BW14273 BW7505 3.2 BW8051 9.0 BW3602 13.5 0.9 5.7 0.4 2.3 BW13826 0.2 DN9555 6.7 ST32718 1.7 DN17553 ST5495 5.0 4.3 BW12176 0.3 ST24103 8.7 ST25611 ST14811 0.3 BW18536 2.1 1.0 BW15427 BW15819 0.9 4.5 BW13346 ST18877 3.9 0.2 BW8710 7.4 1.2 ST26936 0.1 BW19008 ST27765 0.2 BW12240 BW11014 3.8 3.4 5.2 ST16804 2.9 BW10461 ST17921 1.1 BW5520 3.4 ST29676 2.0 BW5820 1.0 ST32273 0.5 28.0 0.1 BW9839 10.9 BW12399 BW8394 3.4 BW16867 2.2 BW13313 0.4 BW7876 2.0 0.3 DN878 2.3 DN13444 0.2 ST28675 0.7 BW13316 BW12266 BW4668 1.1 8.6 ST13254 3.2 2.2 BW16956 2.5 BW12505 1.1 BW5828 2.4 ST23164 ST7647 0.2 BW6202 0.3 1.2 BW13519 ST23539 4.5 4.9 ST16049 ST10151 ST21642 3.6 BW5568 5.7 BW5010 BW11895 4.5 0.7 1.2 8.4 DN15981 0.5 5.4 BW11088 ST10743 0.2 BW17071 0.1 ST15024 ST15558 2.7 ST29106 0.8 0.1 0.1 BW18437 8.0 BW10749 BW6529 0.3 ST27248 0.5 BW8810 BW9114 1.0 BW17232 1.7 0.7 3.8 ST32525 0.1 ST7111 BW4538 0.3 ST16219 1.2 0.9 DN4709 BW8635 7.5 BW7418 1.6 1.5 BW10623 ST20590 BW11981 0.8 9.4 BW7710 1.1 1.1 ST29907 1.7 ST25260 ST14659 2.7 BW11130 0.7 BW6994 ST11599 ST20584 BW11977 1.7 2.1 ST5929 0.5 1.6 BW14901 1.7 BW13130 BW3767 3.0 ST16325 0.1 BW8286 ST20585 2.2 0.7 1.2 ST7112 1.1 0.9 BW16738 ST29435 0.2 ST9009 BW5539 1.8 BW17172 0.4 BW9588 BW9419 1.6 ST27943 2.0 3.5 0.7 BW14081 0.9 BW17642 4.5 ST14775 1.4 ST30240 BW7420 BW6993 1.0 0.4 4.2 1.1 ST32666 0.3 BW18696 ST33025 0.2 BW15938 0.5 BW13024 ST22625 ST8716 0.3 2.2 1.6 BW16246 0.2 ST33383 0.5 ST20971 2.7 BW9373 BW18399 1.3 ST30241 BW12403 6.9 0.1 3.7 ST28497 6.2 BW18918 0.8 0.5 ST15976 ST32461 3.2 2.4 BW17173 ST23168 2.0 DN13484 3.2 7.4 ST13746 4.4 ST27027 2.2 BW11216 BW18400 0.8 ST28933 ST14055 2.3 BW15444 0.8 BW11582 2.5 ST20343 0.4 BW5474 ST32462 3.1 BW7679 1.5 BW12198 BW10932 0.9 DN4855 3.1 BW6370 1.8 BW10250 BW8632 2.0 ST11629 2.7 ST21013 ST18728 13.5 3.7 BW8681 1.6 BW7676 BW16397 1.0 ST26705 BW11831 17.4 DN636 ST14958 6.1 DN23960 0.4 BW7925 ST33067 0.4 ST7523 ST18655 BW12754 BW10891 ST22124

LG15LG15 LG16LG16

ST29166 ST30119 6.6 9.6 ST18925 2.4 BW19041 0.1 ST21525 13.0 5.0 BW12454 BW11431 0.3 BW17106 1.8 ST19618 6.4 ST30114 ST30118 BW17109 19.1 ST27549 0.1 BW15693 0.7 DN1210

Figure 5.14 Combined approach-MapA. The marker interval distance is shown on the left (cM), and the marker name is shown on the right of each linkage group.

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Chapter 5: Building genetic map 5.3.5 MapB calculation – de novo approach optimisation Four de novo analysis-parameters were optimised for the calculation of MapB (Table 5.17). The optimisation results of m, M and N were the same as for the MapA-de novo approach, with m=3, M=1 and N=1 giving the highest number of markers (Table 5.17). The number of usable markers (genotyped in 40 out of 50 BC individuals) increased together with larger n values, but the number of mapped markers actually decreased when attempting to reconstruct the genetic map (Table 5.17; Appendix 5.3 a). The maximum markers density was achieved when n=1, with 362 markers mapped. On the other hand, when the n value increased to 8 and 16, only 341 and 337 markers were mapped respectively (Table 5.17; Appendix 5.3 b and c). Thus, the smallest n value of 1 was chosen, and the parameter setting of m=3, M=1, N=1, and n=1 to analyse and genotype the data of 200 BC individuals gave a total of 1,349 markers. The generated locus genotype file was kept for the calculation of the combined approach map.

Table 5.17 Summary of de novo analysis optimisation of MapB (based on 50 BC plants) Stacks parameters Mismatch Within Mismatch Usable markers Minimum stack allowed for individual allowed for recovered * depth (-m) building distance (-M) 2ndry reads (-N) catalog ( -n) default (2) default (2) default default (1) 531 (M+2=4) Optimi sation of minimum stack depth ( -m) 1 2 M+2 1 55 2 (default) 2 M+2 1 531 3 2 M+2 1 531 4 2 M+2 1 519 5 2 M+2 1 479 6 2 M+2 1 411 8 2 M+2 1 316 10 2 M+2 1 241 Optimi sation of within individual distance ( -M) 2 (default) 1 M+2 1 538 2 (default) 2 (default) M+2 1 531 2 (default) 3 M+2 1 468 2 (default) 4 M+2 1 442 2 (default) 5 M+2 1 423 3 1 M+2 1 579 3 2 (default) M+2 1 531 3 3 M+2 1 502 3 4 M+2 1 476 3 5 M+2 1 458 4 1 M+2 1 556 4 2 ( default) M+2 1 519 4 3 M+2 1 490 4 4 M+2 1 464 4 5 M+2 1 448

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Chapter 5: Building genetic map Table 5.17 continued Stacks parameters Mismatch Within Mismatch Usable markers Minimum stack allowed for individual allowed for recovered * depth (-m) building distance (-M) 2ndry reads (-N) catalog ( -n) default (2) default (2) default default (1) 531 (M+2=4) Optimi sation of mismatch allowed for merging secondary reads ( -N) 2 (default) 1 M+2 1 538 2 (default) 1 M+1 1 545 2 (default) 1 M 1 561 3 1 M+2 1 579 3 1 M+1 1 590 3 1 M 1 620 4 1 M+2 1 556 4 1 M+1 1 562 4 1 M 1 592 Optimi sation of mismatch allowed for building catalog ( -n) Usable No. marker No. of No. linkage m M N n markers after mapped group formed recovered* filtering† marker 3 1 M 1 620 486 15 362 3 1 M 8 665 461 15 341 3 1 M 16 694 456 15 337 * markers genotyped in at least 40 out of the 50 BC individuals used for optimisation † remove markers with strong segregation distortion and similar segregation pattern Bold text indicates the best results achieved in each step of the optimisation. The parameter showing the overall highest marker counts is highlighted in grey

5.3.6 MapB calculation – reference-based approach using BWA and Stampy aligners For the approach using the BWA aligner, two major BWA aln parameters were tested and optimised using the data of 50 BC individuals (Table 5.18). Different values of the maximum edited distance (-n) were first tested. The higher the value of -n, the more usable markers were recovered: the high maximum edit distance of 12 gave the highest mapping percentage of 76% of input reads (about 69 million reads), and recovered 1,943 usable markers (Table 5.18). The maximum edit distance in seed (-k) was then tested. Again, the higher the k value the higher the mapping percentage and the number of usable markers recovered. The best result was achieved when using the setting n=12 and k=3, which generated 83% mapping coverage (about 75 million mapped reads) with 2,074 usable markers (Table 5.18). An attempt was made to optimise an additional parameter, the seed length (-l). However, this leads to dramatic increases of the computational time required for the BWA

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Chapter 5: Building genetic map mapping: for instant, with the setting of n=12, k=3, and l=6, our server only managed to process the reads from two BC individuals per day, i.e. about 7 million read per 24 hour. This analysis was performed on the local RBGE server ‘Galvatron’, which uses AMD Opteron 6176 SE processors. This was ran using 20 CPUs, suggesting that it would require about 480 CPU hours to align 7 million reads. With a total amount of 90 million reads from 50 BC individuals, it would take at least 6,171 CPU hours (~13 days) to test just one parameter value. If for example, four parameter values are to be tested on 50 BC individuals, it would take more than 50 days for just the mapping step (excluding the time required for genetic map calculation). And for the final mapping of approximately 148 million reads from 200 BC individuals, it would take another 10,149 CPU hours (~21 days) for just the mapping step. The total amount of time required for a proper optimisation plus the actual mapping of 200 BC individuals (>71 days), together with the time required for genetic map calculation, exceeds our available computational and time resources. Thus, the computational time becomes a limitation factor of the analysis, and we decided to proceed the analysis with the two optimised parameters (n and k). To confirm that the optimised BWA parameters can improve the actual mapping results (i.e. increase the number of mapped markers), genetic maps calculated using the analysis results of the 50 BC individuals were compared with the map calculated using the default settings for these BC individuals (Table 5.18, last row; Appendix 5.4 a). The analysis with default BWA settings resulted in 1,273 markers on 15 linkage groups, and the analysis with optimised parameters of n=12 k=3 resulted in 1,537 mapped markers (Table 5.18, Final mapping results), 264 more than in the default analysis (Table 5.18, Appendix 5.4 b). This suggested that the optimisation does improve the mapping results. Thus, the parameter setting of n=12 k=3 was applied to the analysis of 200 BC individuals, which gave a total of 3,790 markers. The generated locus genotype file was kept for the calculation of the combined approach map.

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Chapter 5: Building genetic map Table 5.18 Optimisation of the BWA parameters for the reference-based approach of the MapB calculation (data based on 50 BC plants) BWA parameters No. reads mapped Usable markers Maximum edit Maximum edit (% mapped) recovered* distance ( -n) distance in seed ( -k) default (3) default (2) 59,952,269 (66%) 1,716 Optimi sation of maximum edit distance ( -n) 3 (default) 2 59,952,269 (66%) 1,716 4 2 63,759,042 (70%) 1,809 6 2 67,263,341 (74%) 1,888 12 2 69,166,302 (76%) 1,943 Optimi sa tion of mismatch allowed within seed ( -k) 12 1 57,110,681 (63%) 1,538 12 2 (default) 69,166,302 (76%) 1,943 12 3 75,653,709 (83%) 2,074 Final mapping results No. reads Usable No. marker No. linkage No. of n k mapped markers after group mapped (% mapped) recovered * filtering† formed marker 3 2 59,952,269 1,716 1,306 15 1,273 (default) (default) (66%) 75,653,709 12 3 2,074 1,572 15 1,537 (83%) * Markers genotyped in at least 40 out of the 50 BC individuals used for optimisation † Remove markers with strong segregation distortion and similar segregation pattern

For the reference-based approach using the Stampy aligner, the aligner was tested using the data of 50 BC individuals under default parameter settings. In total about 91% of the reads (about 83 million) were aligned to the genome, producing 2,115 usable markers (Table 5.19). Among these, 1,594 markers could be mapped on 16 linkage groups (Table 5.19, Appendix 5.5). Since the default Stampy settings already gave the best results among all three approaches tested (Table 5.20), the default settings were applied for the analysis of 200 BC individuals, which resulted in a total number of 4,043 recovered markers. The generated locus genotype file was kept for the calculation of the combined approach map.

Table 5.19 Analysis result of reference-based approach using Stampy (data based on 50 BC plants) No. No. reads Usable No. marker No. of linkage Parameter mapped markers after mapped group (% mapped) recovered * filtering† marker formed 83,508,405 Default 2,115 1,644 16 1,594 (91%) * Markers genotyped in at least 40 out of the 50 BC individuals used for optimisation † Remove markers with strong segregation distortion and similar segregation pattern

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Chapter 5: Building genetic map Table 5.20 Statistics of number of markers generated from de novo , BWA and Stampy approaches for the calculation of MapB. Calculation based on 50 BC individuals Usable markers Mapped Approach Parameters used recovered* markers m=3 M=1 De-novo approach 620 362 N=1 n=1 Reference-based approach n=12 k=3 2,074 1,537 (BWA) Reference-based approach Default 2,115 1,594 (Stampy) * Markers genotyped in at least 40 out of the 50 BC individuals used for optimisation † Remove markers with strong segregation distortion and similar segregation pattern

5.3.7 MapB calculation – combined approaches The genotype locus files generated from de novo , BWA, and Stampy approaches were combined, giving a total 9,173 markers before filtering. For the calculation of MapB-1, a total of 801 among the 9,173 recovered markers were kept after filtering out markers with excessive missing data (Table 5.21; genotyped in less than 160 among the 200 BC individuals). Among these, 553 were kept after removing markers showing strong segregation distortion (Table 5.21). Finally, 17 linkage groups were identified, with 377 mapped markers and a total span of 1,144.2 cM (Table 5.21; Figure 5.15). The longest linkage group (LG1) was 136.0 cM, and the shortest (LG16) 10.7 cM. The average marker interval was 3.1 cM (Table 5.22). For the calculation of MapB-2, a lower threshold (i.e. < 30%) of missing data was allowed, which left 1,572 markers after removing markers that were genotyped in less than 140 BC individuals (Table 5.21). Among these, 1,233 markers were kept after removing markers showing strong segregation distortion (Table 5.21). Finally 16 linkage groups were identified, with 836 mapped markers and a total map distance of 1,322.5 cM (Table 5.21; Figure 5.16). The longest linkage group (LG1) was 133.3 cM, and the shortest one (LG13) 51.1 cM, with an average marker interval of 1.6 cM (Table 5.23).

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Chapter 5: Building genetic map Table 5.21 Statistics summary of MapB-1 and MapB-2 (data based on 200 BC plants) MapB-1 MapB-2 Total no. of markers generated from all three 9,173 9,173 approaches Marker filtering No. marker remained after missing genotype filtering* 801 1,572 No. marker remained after segregation distortion 553 1,233 filtering† Stats of the final map constructed No. of linkage group recovered 17 16 No. of mapped markers 377 836 Total map distance (cM) 1,144.2 1,322.5 Average distance between markers (cM) 3.1 1.6 * MapB-1: Remove markers with <20% missing data, MapB-2: Remove markers with <30% missing data † MapB-1: Remove markers with Chi-square value  0.0005, MapB-2: Remove markers with Chi-square value  0.0001

Table 5.22 Linkage group statistic summary of MapB-1 (data based on 200 BC plants) Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 45 136.0 3.1 LG2 13 102.7 8.6 LG3 52 94.7 1.9 LG4 33 79.7 2.5 LG5 20 78.6 4.1 LG6 20 77.0 4.1 LG7 15 72.5 5.2 LG8 17 72.4 4.5 LG9 40 71.8 1.8 LG10 44 71.3 1.7 LG11 16 60.5 4.0 LG12 12 51.1 4.6 LG13 13 50.9 4.2 LG14 16 49.4 3.3 LG15 12 35.2 3.2 LG16 5 10.7 2.7 LG17 2 27.5 27.5 TOTAL 375 1,142.2 3.1

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MapB-1-LG1 MapB-1-LG2 MapB-1-LG3 MapB-1-LG4 MapB-1-LG5 MapB-1-LG6 MapB-1-LG7 MapB-1-LG8 MapB-1-LG9

DN10356 1.6 BW11519 BW15862 0.7 DN8661 ST3945 0.6 BW9466 BW5993 BW7556 0.4 BW17203 0.5 BW3504 BW14068 BW1571 0.3 2.1 1.7 DN19274 BW3437 1.7 ST10770 BW5992 3.1 0.4 BW6409 ST6880 0.2 BW14493 BW4934 6.2 0.1 0.1 0.6 ST17972 BW15861 3.2 ST8582 BW7503 ST5668 0.7 BW12297 BW11310 0.4 BW4722 2.1 BW11518 0.9 BW4660 BW7933 10.8 0.1 15.9 3.6 13.8 1.4 ST13074 10.3 5.1 BW2103 DN16882 1.3 5.8 BW8973 BW6804 ST7788 BW2472 BW14817 DN10587 1.4 BW11097 BW3649 ST16789 0.2 13.7 0.3 0.1 BW5677 BW7795 BW7785 ST4190 0.4 BW4238 2.9 0.3 4.7 BW4421 DN11719 BW13439 ST15203 3.3 ST9599 BW9533 BW9482 3.2 BW15126 35.3 BW9159 1.6 BW416 1.7 BW8572 ST9760 ST10790 0.1 1.5 ST2435 10.7 6.8 21.2 BW372 1.0 BW3957 9.4 BW8465 ST17144 0.2 BW2115 5.8 1.6 BW4368 BW440 1.2 BW8185 1.7 5.3 BW12519 BW8152 BW12005 ST530 0.9 ST10013 2.9 0.1 0.1 0.9 BW11033 BW9781 ST13621 0.5 BW1738 4.1 5.6 15.1 0.7 BW11032 3.8 2.6 BW12627 3.3 BW13589 BW8785 BW10925 ST18466 BW16292 0.7 BW2198 BW7715 0.3 BW15824 2.7 15.0 BW8694 BW7710 0.6 ST16384 9.3 8.2 BW4426 ST18326 BW16168 8.7 0.5 BW14446 BW9941 14.0 5.1 0.2 3.2 ST9651 0.5 BW4661 BW11672 0.1 4.6 BW11431 BW8912 0.2 BW8473 7.9 BW8078 BW645 2.1 BW14008 0.1 BW16573 BW5095 BW15806 2.9 BW14864 0.6 DN14130 5.1 0.2 DN20121 5.0 BW11425 0.2 BW13980 1.7 ST9112 ST6200 0.2 BW3170 4.0 ST7404 BW6464 BW8504 0.1 BW7977 2.4 BW5408 ST13686 0.3 DN17943 ST8680 3.2 2.1 ST13570 0.1 BW3112 BW11523 8.4 8.4 BW7590 1.1 2.6 ST4995 0.2 6.3 10.3 ST16650 6.8 BW13350 ST13079 DN13340 0.8 2.4 BW4336 DN21158 BW1619 0.3 4.5 ST1170 BW990 0.1 BW12546 0.2 BW13921 BW2142 DN3070 0.1 BW13076 2.7 BW2384 0.3 2.7 1.8 DN12843 0.3 BW14695 ST16649 0.3 BW12502 ST14586 BW12869 8.1 0.1 BW7227 3.6 ST2748 7.6 0.6 BW5419 3.2 ST13198 BW11625 0.5 BW8997 33.1 0.8 BW3073 0.4 BW17102 0.2 ST3843 32.8 1.0 ST10249 0.8 1.2 BW9363 ST7981 ST6138 BW16235 7.5 ST635 24.0 0.7 BW8998 0.4 1.0 0.2 BW528 0.1 0.2 ST19176 2.1 BW12547 ST10254 6.8 0.1 ST15392 1.3 0.4 BW16937 0.1 BW8365 11.0 0.1 BW9003 0.1 BW13607 0.3 BW7416 BW9287 0.9 BW1718 0.2 2.9 BW12334 0.1 2.4 ST1986 BW3347 ST17776 0.9 BW9270 1.9 ST13405 0.1 DN16842 4.3 ST9976 BW8748 1.0 BW15170 BW6249 1.3 BW11806 2.4 DN4522 0.2 BW4665 0.1 BW8686 0.1 ST17196 1.3 BW8229 0.1 0.5 0.7 ST7289 BW8479 BW3925 1.6 BW11530 BW12320 4.3 DN6523 0.2 0.3 0.1 BW6365 8.8 BW1361 0.1 0.1 ST4505 1.2 ST13089 ST13960 0.1 BW15681 0.5 0.9 BW12479 3.5 BW16864 19.5 BW734 0.9 BW13054 0.1 BW12237 0.9 5.2 0.1 DN18035 BW4284 0.1 ST876 BW4950 0.8 BW1919 ST2206 BW8741 0.1 3.2 BW928 0.3 5.9 BW11986 ST5883 1.8 ST5874 0.5 ST11928 BW10523 0.3 10.1 ST12884 BW5127 1.4 BW5121 0.7 BW992 0.4 DN23069 BW11351 DN19762 1.3 BW4298 ST4949 BW2205 1.5 BW9507 1.5 BW15087 2.5 1.1 BW8075 0.1 DN24241 BW15086 ST17106 0.1 6.1 ST14261 BW12584 BW7283 1.9 BW6886 7.8 ST8320 12.8 ST7878 BW3066 15.6 BW14989 BW10567 BW15169

BW7768

33.9

BW3317

9.4

BW11387

MapB-1-LG10 MapB-1-LG11 MapB-1-LG12 MapB-1-LG13 MapB-1-LG14 MapB-1-LG15 MapB-1-LG16 MapB-1-LG17

BW10375 ST11764 0.8 ST1501 BW7510 BW9692 ST11037 0.2 BW3683 BW3399 BW10063 ST14416 BW12722 BW10393 2.1 ST8589 0.4 ST17555 BW15489 1.0 DN20564 3.4 BW9980 BW6800 6.2 0.4 7.9 1.4 6.6 7.5 1.2 BW5388 9.1 BW4251 BW14594 ST16541 BW3623 0.5 ST6054 ST5152 1.7 0.1 BW11111 6.9 1.4 BW6627 BW16705 BW5278 BW12630 4.0 1.1 1.8 DN13854 2.9 BW8331 4.2 BW12744 0.2 BW6993 1.7 BW4275 ST4915 BW4832 2.4 BW12235 BW9580 2.4 ST16686 0.1 9.0 2.3 0.2 27.5 ST10912 BW3051 BW12849 BW14726 3.0 2.6 10.8 3.1 ST2586 BW2259 4.2 3.8 ST8341 BW7665 ST3780 3.0 0.5 BW16895 ST19124 1.3 BW8883 0.3 DN21010 ST8592 3.2 ST12954 0.7 2.9 BW3948 BW3567 5.4 BW2029 BW7513 0.6 BW13110 1.0 BW13150 BW16847 0.3 BW14360 1.6 2.4 BW3331 ST3823 ST4105 0.7 ST16274 11.7 0.3 0.3 BW3573 BW356 BW8677 6.4 15.5 14.7 2.8 ST422 10.4 1.1 BW4682 BW9602 1.8 BW8792 18.1 5.8 1.4 BW986 ST978 ST9771 BW3762 0.2 0.5 BW14177 ST16056 1.2 BW1597 BW822 0.3 BW12304 4.0 0.2 BW14567 4.4 BW14183 ST1429 BW 1213 BW6884 0.2 3.3 8.4 1.5 BW16184 BW9882 BW2949 3.0 BW14570 0.7 1.1 BW2355 0.8 ST10064 BW8832 ST3393 1.2 1.0 ST17373 BW15324 3.7 DN8485 BW6818 BW2953 3.1 9.4 4.0 4.2 5.1 ST17604 BW15531 BW3044 0.1 BW13198 BW5742 BW9051 2.1 ST14939 0.2 ST6585 0.1 ST13362 BW11765 BW9203 BW15240 BW8656 12.5 8.5 DN6875

BW2126 1.2 BW14979 BW16038 3.4 ST10678 0.2 BW9384 5.0 BW11403 2.1 BW6961

Figure 5.15 MapB-1. Marker interval distances are shown on the left (cM), and the marker names are shown on the right of each linkage group.

182

Chapter 5: Building genetic map Table 5.23 Linkage group statistic summary of MapB-2 (data based on 200 BC plants) Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 89 133.3 1.5 LG2 53 97.6 1.9 LG3 108 109.9 1.0 LG4 50 85.4 1.7 LG5 43 85.1 2.0 LG6 46 82.2 1.8 LG7 32 83.8 2.7 LG8 48 69.2 1.5 LG9 80 75.2 1.0 LG10 62 88.9 1.5 LG11 55 71.1 1.3 LG12 47 85.0 1.8 LG13 38 51.1 1.4 LG14 36 78.0 2.2 LG15 36 66.3 1.9 LG16 13 57.3 4.8 TOTAL 836 1,319.5 1.6

183

MapB-2-LG1 MapB-2-LG2 MapB-2-LG3 MapB-2-LG4 MapB-2-LG5 MapB-2-LG6 MapB-2-LG7 MapB-2-LG8

BW4130 BW5756 DN10356 6.2 DN14557 5.4 BW16450 0.5 BW8973 0.1 ST13074 0.8 BW11519 0.5 DN8661 0.7 0.6 BW11518 0.3 BW11515 0.7 BW7503 ST8582 1.8 BW14493 BW10222 0.3 DN19274 BW4837 ST5573 BW8823 BW5992 1.8 BW3888 ST18074 1.7 2.9 ST411 DN19325 DN25060 1.3 BW8736 2.0 2.4 BW7556 0.2 BW1455 BW17203 ST17739 0.2 BW1571 BW5994 BW14562 0.4 BW15648 DN3887 1.1 0.6 1.8 0.3 BW8826 DN10587 BW4124 BW2103 0.4 9.3 5.5 1.7 0.7 DN5573 0.1 BW5677 10.3 0.1 ST5264 9.0 10.0 ST7662 0.5 11.1 0.5 BW6745 2.8 BW4421 DN11719 2.6 BW4589 ST3945 0.1 BW6692 0.7 ST6880 2.5 ST17144 BW15126 0.7 BW9158 0.1 0.2 BW4660 BW3437 0.9 BW13948 6.8 BW8257 ST14816 1.6 BW9159 2.3 BW1757 0.2 3.9 0.1 BW4934 BW12297 BW14068 0.7 ST14581 DN31266 ST2435 0.3 0.3 BW6409 5.4 0.4 0.3 1.1 ST5668 6.5 0.2 2.0 BW4722 BW15611 BW7581 7.1 BW2115 ST17972 BW15861 0.9 1.8 0.4 0.1 1.1 BW9502 ST10808 DN24089 BW12218 1.0 BW11033 8.4 BW3504 0.2 2.6 BW13439 ST15203 0.1 1.5 BW10819 0.3 BW12978 3.0 BW4426 BW456 1.0 BW12519 BW7795 4.2 0.1 DN3050 1.1 DN16882 0.1 BW13654 4.6 ST9651 BW8473 0.4 ST14185 BW12520 2.2 BW10234 0.2 ST17829 BW6467 0.3 0.4 BW2472 0.5 BW11310 0.2 BW4907 8.1 1.9 BW11032 0.1 BW7796 2.3 BW15734 0.1 BW15862 0.8 0.1 0.2 BW12775 0.5 ST16840 DN26800 ST7678 BW13589 0.1 ST8896 0.1 BW8185 0.8 BW11320 0.4 0.2 0.8 1.2 ST314 BW7933 BW11019 BW6708 DN13276 0.1 DN26070 2.0 DN22186 1.4 BW4575 0.3 0.2 7.1 0.2 0.7 3.6 3.6 0.3 BW11097 ST8731 BW3170 DN726 BW1519 BW7094 3.9 BW9781 BW8180 0.5 2.9 3.2 0.4 0.4 11.7 1.0 BW7785 BW11728 BW5599 0.1 BW13904 10.2 ST17929 DN19561 0.3 1.4 6.0 BW10925 0.4 BW6804 ST7788 1.7 ST9599 3.4 ST13317 BW11729 0.5 ST16650 BW9941 0.3 BW15824 0.4 BW11629 1.1 5.7 BW8321 2.9 BW10100 ST11494 0.3 ST17533 BW15470 ST8194 5.3 DN27399 ST10013 1.2 DN34750 1.3 1.9 BW16555 0.1 ST6925 BW6056 1.5 DN10738 3.2 BW5095 1.6 ST18326 BW16168 BW8785 10.4 DN4675 3.8 BW15193 4.9 5.0 0.8 1.7 BW416 BW3649 BW2145 0.6 BW8504 BW4661 BW645 3.9 BW3845 0.2 0.2 2.5 BW11993 ST13608 0.2 0.3 ST13686 1.8 5.5 BW372 ST4190 BW14951 0.6 ST1680 3.1 BW15194 0.5 1.4 2.3 BW15806 BW11523 5.3 BW8152 1.4 DN297 0.5 BW990 BW11992 0.2 BW3612 3.1 BW7715 0.2 1.0 BW49 13.6 ST15959 BW14099 ST13079 2.2 0.7 BW16672 0.1 BW9533 0.4 ST1170 1.1 BW8078 0.1 BW7710 DN2721 1.5 BW2142 ST6200 9.1 DN27733 1.3 1.5 BW3957 3.0 ST16649 2.0 0.7 DN20121 0.8 0.1 BW11431 0.5 BW5408 0.4 BW7914 7.9 ST18466 BW16292 0.1 BW8572 ST9760 0.6 BW2384 1.8 BW12203 BW7977 2.2 2.7 BW11425 1.1 0.6 0.5 BW3112 1.9 DN22278 BW14695 1.6 BW12205 ST9112 0.1 ST9029 ST12965 13.0 0.3 BW10219 ST11606 0.5 2.2 ST4995 1.7 BW8845 2.8 BW14425 BW12005 ST2748 1.3 BW17100 0.6 0.3 BW11428 0.9 1.3 0.5 1.9 0.4 2.4 BW4336 ST10081 BW8912 ST13621 ST18089 BW4974 ST5720 0.2 BW11059 8.0 BW8168 1.8 0.1 0.3 1.3 0.1 BW13076 3.2 BW14864 4.3 BW4368 0.1 BW6733 BW15495 0.2 ST13570 0.4 BW4197 ST3274 1.3 0.5 2.7 BW13077 BW8927 2.9 BW12569 0.1 ST7712 ST16410 DN1131 0.2 BW2575 1.0 ST4828 BW2845 0.3 0.6 DN6720 1.5 BW12570 0.3 ST19176 BW14471 0.1 BW1924 1.9 0.3 ST18797 7.4 DN21158 BW1619 0.1 ST11112 BW9759 1.4 4.7 ST14245 0.1 BW5121 0.5 ST6138 0.2 BW5430 0.3 BW16580 0.1 0.2 0.3 ST13198 BW11625 0.5 BW4033 ST5874 1.0 BW5354 BW5419 0.7 BW992 BW9363 6.7 0.1 0.2 BW12869 0.3 ST8402 BW9507 0.2 BW6861 BW13350 0.6 BW5662 17.7 0.1 0.4 0.8 ST14586 0.9 0.3 BW7346 DN10373 0.4 BW5637 ST1580 0.2 DN5157 0.1 0.1 0.1 3.9 DN32837 BW12454 0.1 BW7416 BW14841 ST16817 0.3 BW1355 0.6 DN18035 10.9 6.9 0.8 2.3 BW3347 DN23937 ST14114 0.1 BW14363 ST1019 0.1 DN6173 0.2 0.1 BW12479 1.0 ST18480 BW2854 0.3 BW6365 BW10269 0.2 ST5916 1.2 BW854 0.1 BW12502 0.2 0.1 0.1 ST3843 0.3 BW6249 BW1560 ST11653 BW15681 ST17776 1.2 BW8235 ST15101 BW13345 0.4 ST7289 0.2 0.2 0.3 0.8 ST15392 BW13607 0.1 ST14936 BW13195 DN6251 2.6 BW12320 ST13960 DN23734 2.4 DN6661 DN14752 BW17102 3.6 0.2 BW2305 0.1 0.1 0.2 BW528 0.1 DN4753 BW3495 BW12237 ST9660 0.4 BW7360 BW16474 ST18678 0.1 0.1 2.5 DN9055 0.1 0.9 0.1 1.0 ST635 ST4014 0.2 BW8479 DN9742 0.4 BW9727 ST11076 0.6 BW6970 3.0 BW4151 0.6 0.1 BW15170 0.3 BW2582 BW13724 ST17747 0.5 BW16235 0.5 DN10582 BW3496 10.2 1.1 ST17196 0.2 ST2967 DN8177 BW15655 0.3 BW12546 0.5 0.6 BW928 4.0 8.1 0.9 ST10593 BW1919 0.2 BW1991 BW14545 DN7794 0.6 DN3070 0.9 0.2 DN4522 0.1 2.0 1.7 ST2206 0.3 1.1 BW2878 0.1 BW13531 1.1 BW11530 BW6136 BW1361 0.6 DN16842 BW4665 0.7 7.1 BW8741 0.1 BW4299 BW14828 5.0 ST13089 BW10523 0.1 0.4 3.3 BW13054 BW5157 7.3 ST4949 BW4298 DN19006 ST11928 0.7 1.0 0.4 BW4950 0.1 BW14876 BW15087 0.3 BW11060 BW9270 2.4 0.5 ST5883 BW14337 0.7 BW10417 0.1 BW15086 0.7 BW11957 1.2 DN24241 ST17106 0.2 BW11951 BW5127 DN19762 1.0 DN23069 6.3 BW7309 1.0 BW12547 3.9 0.2 ST7981 BW1369 DN7661 0.6 DN2208 0.4 0.4 BW16937 1.2 DN24248 0.3 BW3073 BW7768 2.9 BW8365 1.4 BW15310 0.1 ST1986 0.5 BW12584 0.9 BW1718 7.3 ST14261 0.2 BW8748 5.1 ST11001 0.4 ST9976 27.5 0.8 BW9658 0.8 BW10373 BW6886 ST7878 0.8 ST11761 BW5856 3.5 BW8686 BW15935 0.1 DN12274 BW3066 1.4 ST876 DN8958 1.5 BW734 3.0 ST4505 ST18365 0.5 4.7 BW4565 3.7 BW3925 BW10567 0.4 BW11986 0.8 ST2369 0.9 ST12884 BW11351 BW3546 2.3 BW12034 7.4 BW15169 0.2 BW13863 BW7599 2.2 BW9262 5.1 BW15027 DN14977 ST10539 BW3317 BW2205 BW11389 BW9264 DN21863 BW11448 BW7285 DN33268 BW8075 BW7283 ST8320 DN9393 BW14989

MapB-2-LG9 MapB-2-LG10 MapB-2-LG11 MapB-2-LG12 MapB-2-LG13 MapB-2-LG14 MapB-2-LG15 MapB-2-LG16

BW9466 ST10770 BW9963 0.4 BW14811 4.9 BW14817 7.9 ST16789 1.7 0.4 BW3903 2.1 BW8642 0.9 BW12884 3.2 BW10502 1.9 DN18414 ST13510 0.7 ST15922 DN3603 BW73 BW3399 0.4 DN4578 ST14880 0.2 BW10505 BW10375 BW690 BW9066 ST11764 BW12630 BW3683 0.2 ST825 BW10041 BW4238 ST1501 BW7574 ST18696 0.9 8.0 3.2 BW6800 BW13310 0.6 ST14254 BW1179 ST10033 BW8804 4.2 ST17684 BW7242 2.2 BW10050 9.1 2.6 0.2 BW4251 BW14801 0.2 3.0 BW7411 DN27932 0.9 BW7665 BW15605 2.3 DN19803 2.2 ST11442 3.3 2.1 BW4832 ST5523 1.0 BW5049 ST1462 0.6 BW328 DN17461 1.0 0.4 BW5900 0.6 17.1 0.1 BW3830 1.1 BW14715 0.9 BW8465 BW1243 2.4 DN22819 BW11111 5.3 3.8 0.3 0.8 1.6 BW12849 BW15662 0.3 ST10191 ST16793 1.9 BW9980 9.2 BW7518 0.2 ST9250 BW8106 1.8 0.1 0.3 0.7 ST1798 0.6 ST16072 ST17754 0.9 BW14081 0.1 BW9482 0.6 ST6054 0.9 ST19371 BW9757 1.1 17.4 0.2 0.1 BW1550 0.8 BW8051 2.0 ST10790 BW5278 0.9 ST6261 ST19164 BW16929 1.5 0.6 0.4 DN18166 0.3 0.8 8.8 BW12908 0.3 BW1736 BW9916 0.3 BW4275 ST4915 1.0 ST3780 1.4 0.3 0.1 BW3431 1.1 ST14716 0.9 ST14633 BW10910 0.6 BW5388 0.1 BW15674 1.6 BW13110 0.1 0.4 ST3938 0.6 DN24905 6.7 ST668 BW440 0.4 BW8331 0.4 0.4 BW3498 ST4018 2.9 BW7449 0.1 0.3 0.3 ST8592 BW12992 ST530 0.1 BW9580 0.2 ST18556 7.6 BW7165 3.6 ST2093 BW1020 0.5 0.1 BW7513 2.7 BW17122 0.2 2.6 BW1738 0.3 ST10912 0.1 DN17319 ST12508 1.9 BW1813 BW10393 0.2 0.6 ST10761 BW9457 1.9 0.3 BW10381 3.6 0.2 BW12627 0.6 ST3979 0.1 ST135 BW11029 BW6758 1.8 0.5 DN9150 0.1 0.3 0.5 DN20908 0.8 4.5 0.3 BW14446 ST2586 0.7 BW112 1.8 BW8601 ST13115 BW11555 BW8835 ST10067 1.6 ST9515 0.3 1.0 BW16721 0.3 0.2 ST16384 BW2259 0.4 DN5016 0.1 ST9793 2.0 BW14625 2.0 DN35940 0.1 0.2 BW8349 0.4 BW244 3.6 BW2198 1.9 ST19124 BW16895 4.3 BW2678 1.0 BW3762 0.2 BW10063 0.1 BW10362 0.1 BW12336 0.3 ST11037 BW9692 BW8694 1.2 DN21010 0.1 1.2 BW3051 0.1 DN19773 5.7 0.2 9.6 ST9771 1.7 BW15489 ST17555 4.1 BW5009 BW11672 0.1 BW2029 0.6 BW13785 DN19093 0.5 0.1 2.0 BW15236 1.1 BW8754 1.9 BW12235 0.1 BW16573 2.6 BW677 1.0 BW13664 ST742 1.4 0.5 0.2 BW7311 0.5 0.1 ST9981 1.1 DN13854 0.8 DN3011 BW356 ST3219 BW620 0.7 0.3 0.7 DN15833 1.8 1.1 4.5 ST5152 BW6627 17.1 0.6 BW13005 ST422 0.1 BW1676 2.1 BW6884 1.8 1.1 BW12304 0.8 BW12744 BW14594 0.8 BW13980 BW14360 0.2 BW13150 0.8 BW2949 1.8 0.1 0.8 4.0 BW10467 0.2 ST16686 ST16541 BW15444 0.2 ST16274 0.6 1.0 DN22296 0.4 BW7549 0.3 1.7 BW14403 3.3 BW14726 ST4081 1.4 BW4682 1.3 0.6 BW3331 ST3823 0.1 BW2954 0.4 1.2 BW6818 0.6 ST8341 10.3 BW8820 0.3 DN3078 BW8792 1.9 BW8883 ST3393 1.5 BW10294 0.8 0.4 BW11571 2.2 1.5 BW7980 BW16847 0.1 ST15693 6.1 BW8791 6.3 DN23227 BW2953 2.9 0.5 BW1463 0.2 0.2 1.3 ST12954 0.9 BW16846 5.7 0.8 BW4373 BW986 0.2 BW3948 1.0 ST672 0.8 ST3879 0.4 BW7434 0.3 BW3567 BW8677 3.2 BW4943 ST16056 BW14177 0.2 BW10539 1.0 BW562 4.3 0.6 0.5 BW16184 0.5 0.5 DN3751 BW7210 0.2 DN1181 BW2355 0.3 ST11946 0.7 BW15240 BW11421 1.8 0.1 1.5 1.7 BW3379 9.2 ST4105 ST8241 BW15212 1.4 DN8485 0.1 2.2 BW2661 0.2 BW15241 0.3 3.5 0.2 DN23548 BW2669 BW3573 7.0 ST5680 0.1 BW14567 2.4 DN7229 1.1 BW13198 6.5 0.1 0.1 ST10064 9.5 0.1 ST7404 0.5 BW14183 2.1 BW9882 ST14939 0.3 0.8 BW8832 0.1 BW9602 ST11680 0.4 BW6464 BW14570 BW16009 BW7045 1.9 ST978 0.4 1.4 BW5014 DN3791 3.7 BW1599 1.4 BW7245 ST17604 BW15531 0.1 DN30669 2.6 0.2 BW822 2.5 BW1213 6.9 ST9521 0.4 DN17943 BW9051 1.2 BW2100 0.1 1.3 BW11566 0.1 1.0 ST1429 BW8356 ST8680 2.9 DN7146 0.4 ST2417 2.3 0.1 ST13131 0.5 BW7590 ST13362 0.1 BW14241 BW9203 BW1597 4.0 0.9 0.4 ST9947 BW8726 DN13340 0.1 BW11765 0.5 BW3044 ST18151 0.8 0.5 DN13074 6.8 0.1 BW7585 1.5 ST7585 0.1 0.5 BW13251 17.2 0.8 BW13921 BW6621 0.4 ST17373 BW15324 0.4 0.2 DN21742 DN13538 DN12843 3.0 1.3 0.1 BW7227 ST11042 5.5 ST3503 6.6 3.0 1.5 BW3043 BW8997 0.1 BW8656 1.4 2.0 DN6875 DN14093 BW4310 0.1 ST10249 8.4 ST345 BW8998 0.1 DN24883 BW803 1.0 ST8995 1.2 DN24758 1.0 BW9387 6.5 3.4 ST10254 0.7 BW2126 0.8 BW8652 0.8 BW9003 BW14979 0.3 0.8 BW12334 0.5 DN15489 0.7 ST13405 ST16980 2.0 4.1 0.1 DN30053 1.5 BW14977 1.0 BW11806 2.6 BW11403 0.2 BW8229 BW6961 0.8 ST19091 BW16859 BW17180 0.1 BW16853 DN6523 BW16864 BW7631 BW4282 ST4928 BW4284 ST12866 DN24602 BW579 Figure 5.16 MapB-2. Marker interval distances are shown on the left (cM), and the marker names are shown on the right of each linkage group.

184

Chapter 5: Building genetic map In the synteny analysis carried out between MapB-1 and MapB-2, a total of 17 markers were identified to be mapped in MapB-1, but not in MapB-2 (Table 5.24). These markers were located on MapB-1 LG1, LG2, LG8, LG10, LG11, LG14 and LG16. They were often not mapped in MapB-2 due to high mean Chi square values, or were sometimes not grouped during linkage group identification (Table 5.24). Interestingly, the MapB-1 LG16 was not identified in MapB-2 at all, while the MapB-1 LG17 was found to correspond to MapB-2 LG16 (Figure 5.17). In addition to these unmapped markers, inversions of local marker order were found in all linkage groups (Figure 5.17, red open boxes) except for LG2, LG7, LG14, LG16 and LG17. No markers were found to be mapped on different linkage groups between MapB-1 and MapB-2.

Table 5.24 List of markers that were mapped in MapB-1 but not in MapB-2 Marker LG Reason of removal in MapB-2 name BW5993 1 High mean Chi square contribution 6.558 BW11387 1 High mean Chi square contribution 13.489 BW9287 2 High mean Chi square contribution 8.256 BW14008 8 High mean Chi square contribution 5.686 DN14130 8 High mean Chi square contribution 6.568 ST10678 10 Discarded in round 3 mapping BW9384 10 Discarded in round 3 mapping BW7510 11 High mean Chi square contribution 9.596 ST78589 11 High mean Chi square contribution 8.841 BW16038 11 High mean Chi square contribution 22.178 BW5742 14 High mean Chi square contribution 5.395 ST6585 14 High mean Chi square contribution 7.285 ST14416 16 Excluded when grouping BW3623 16 Excluded when grouping BW16705 16 Excluded when grouping BW6993 16 Excluded when grouping BW12722 16 Excluded when grouping

185

MapB%1%LG1 MapB%2%LG1 MapB%1%LG2 MapB%2%LG2 MapB%1%LG3 MapB%2%LG3 MapB%1%LG4 MapB%2%LG4 MapB%1%LG5 MapB%2%LG5 MapB%1%LG6 MapB%2%LG6 MapB%1%LG7 MapB%2%LG7 MapB%1%LG8 MapB%2%LG8

BW 4130 BW 5756 DN10356 DN14557 BW 16450 BW 8973 ST13074 BW 11519 DN8661 BW 11518 BW 11515 BW7503 ST8582 BW 10222 BW 14493 DN10356 BW4837 ST5573 BW 5992 DN19274 BW8823 BW 11519 ST18074 DN19325 BW 3888 BW 15862 ST411 DN8661 ST3945 ST17739 BW 8736 BW5993 BW 5994 BW7556 BW 7556 DN25060 BW 1455 BW 17203 BW17203 BW 3504 BW 14068 BW1571 BW1571 DN19274 BW 3437 BW15648 DN3887 BW5992 DN10587 BW 14562 BW 6409 ST6880 BW8826 BW 14493 BW 4934 BW2103 BW 5677 BW 4124 ST17972 BW15861 DN5573 ST8582 BW7503 ST5668 ST7662 BW11310 BW4421 DN11719 ST5264 BW 12297 BW6745 BW 11518 BW6692 BW 4722 ST17144 BW15126 BW 4589 ST3945 BW 4660 BW7933 ST6880 ST13074 BW4660 DN16882 BW 8257 BW 9158 BW 3437 BW 2103 BW 14068 BW13948 ST14816 BW 8973 BW1757 BW12297 BW 2472 DN31266 BW 9159 BW 4934 BW 6804 ST7788 BW 4722 ST14581 DN10587 BW6409 BW 11097 BW3649 BW 7581 ST2435 ST5668 BW9502 ST10808 BW15611 BW5677 BW 7795 ST17972 BW15861 BW 7785 ST4190 BW 12218 BW 2115 BW 10819 DN24089 BW4421 DN11719 BW13439 ST15203 BW3504 ST9599 BW9533 BW 4426 BW 11033 BW13439 ST15203 DN16882 BW12978 BW 456 BW 9159 BW 7795 BW 416 BW8572 ST9760 BW15126 BW 12519 DN3050 BW 2472 BW13654 ST9651 BW8473 ST2435 BW 10234 ST17829 BW 372 BW3957 ST17144 ST14185 BW12520 BW6467 BW 12775 BW11310 BW 4907 BW 2115 BW 7796 BW15734 BW4368 ST7678 BW 11032 BW15862 ST314 ST16840 DN26800 BW 12519 ST8896 BW 8185 BW8185 BW12005 BW 6708 BW 13589 BW11320 BW 8152 BW 11097 BW7933 BW 11019 BW 11033 ST10013 DN26070 DN22186 ST13621 DN726 DN13276 BW 9781 BW4575 BW 7785 ST8731 BW 3170 BW 11032 BW 7094 BW9781 BW 13904 BW 1519 BW8180 ST9599 BW11728 BW 5599 BW 13589 BW 8785 DN19561 BW 9941 ST17929 BW 10925 BW10925 BW 7715 BW6804 ST7788 ST18466 BW16292 BW 8321 ST13317 BW11729 ST16650 BW 15824 ST8194 BW 15824 BW11629 BW 16555 BW10100 ST11494 ST17533 BW15470 ST10013 BW 7710 BW 5095 DN27399 DN4675 DN34750 BW 416 ST6925 BW6056 BW4426 DN10738 ST18326 BW16168 BW 8785 BW9941 BW 8504 ST18326 BW16168 BW645 BW15193 BW 372 BW 2145 BW 4661 BW3649 ST9651 BW11993 ST13608 BW 4661 ST13686 BW3845 BW 8152 BW 14951 BW 8078 BW 645 BW 11431 BW 8912 BW14008 ST4190 BW8473 ST1680 BW 15806 BW11523 BW15194 BW 990 BW5095 BW 11992 BW 15806 BW 14864 BW 16672 DN297 DN20121 ST15959 BW14099 BW 11425 BW7715 DN14130 ST1170 BW 49 BW 3612 ST6200 ST13079 DN2721 BW9533 ST9112 ST6200 BW7710 BW3170 ST16649 BW 2142 BW 8078 BW 5408 ST13686 DN27733 ST18466 BW16292 BW3957 BW8504 BW 7977 BW 5408 BW11431 BW 2384 BW 12203 DN20121 BW7914 BW8572 ST9760 ST13570 BW 3112 BW 3112 BW 11523 BW11425 BW 14695 BW 12205 BW 7977 ST9029 DN22278 ST16650 BW 13350 ST4995 ST4995 ST13079 ST12965 BW 14425 ST2748 BW10219 ST11606 ST9112 BW8845 BW12005 ST1170 BW990 BW 12546 BW 4336 BW 4336 DN21158 BW1619 BW11428 BW 8912 ST18089 BW2142 BW4974 ST5720 BW 17100 BW 13076 ST10081 ST13621 BW2384 DN3070 BW 13076 BW8168 BW 14864 BW 6733 BW 15495 BW 11059 ST14586 BW12869 BW4197 BW4368 BW14695 ST16649 BW 12502 BW 13077 ST3274 BW 8927 ST7712 ST16410 DN1131 ST13570 ST13198 BW11625 ST4828 BW12569 ST2748 BW 5419 DN6720 BW2845 ST19176 BW 14471 BW 2575 ST3843 ST18797 BW12570 BW3073 BW 17102 ST11112 BW 9759 BW 9363 DN21158 BW1619 BW 5121 ST6138 ST6138 BW 1924 BW16580 ST14245 ST7981 BW 16235 ST635 ST13198 BW11625 ST5874 BW 5354 BW 5430 BW992 BW4033 ST19176 BW 12547 BW 528 BW 12869 BW9363 BW 9507 BW 6861 BW 5419 BW5662 ST8402 BW16937 BW 8365 ST15392 ST14586 DN10373 BW 5637 BW 13350 BW 13607 DN5157 BW7346 BW7416 BW9287 BW 1718 DN32837 BW 7416 BW14841 ST16817 ST1580 BW 3347 DN18035 BW12454 ST17776 ST1986 BW 3347 BW 14363 ST1019 BW 1355 BW 15170 BW 9270 BW12479 DN23937 ST14114 DN16842 ST9976 BW8748 ST18480 BW 2854 BW 6249 ST5916 BW 854 DN6173 ST17196 DN4522 BW6365 BW10269 BW4665 BW 8686 ST3843 BW6249 BW15681 ST17776 BW 8235 BW 12502 ST7289 ST7289 BW 1560 ST11653 BW8479 BW 3925 BW 11530 DN6251 BW12320 DN6661 ST15101 BW13345 ST15392 BW13607 BW 6365 ST14936 BW13195 BW 1361 BW 2305 BW12320 ST13960 BW15681 DN23734 ST4505 ST13089 ST13960 BW 7360 DN14752 BW17102 BW 528 BW 12479 DN4753 BW3495 DN9055 BW12237 BW12237 ST9660 BW 734 BW 13054 DN9742 BW16474 ST18678 ST635 DN18035 BW6970 ST4014 BW 4151 BW8741 BW 8479 ST876 BW 4950 BW 1919 ST2206 ST17747 BW9727 ST11076 BW 15170 BW 928 DN10582 BW3496 ST5874 BW2582 BW13724 BW 11986 ST5883 BW 15655 BW 16235 ST17196 ST11928 BW10523 BW928 BW5121 ST2967 DN8177 ST12884 BW 5127 BW1919 BW14545 DN7794 BW 12546 ST10593 BW 992 DN4522 DN23069 BW 1991 BW 11351 DN19762 ST2206 BW4298 ST4949 BW 2878 DN3070 BW 11530 BW6136 BW 1361 BW9507 DN16842 BW 4665 BW 2205 BW15087 BW 4299 BW 13531 ST13089 BW10523 DN24241 BW 8741 BW 8075 BW15086 ST17106 ST4949 BW 4298 BW 14828 BW 13054 ST11928 ST14261 BW12584 BW 5157 BW 7283 BW 15087 DN19006 BW 4950 BW9270 BW6886 BW 14876 ST8320 BW 15086 BW 11060 ST5883 BW14337 ST7878 BW 10417 ST17106 BW 11957 BW 5127 BW3066 DN24241 BW 7309 BW 14989 BW 11951 DN19762 BW10567 DN23069 BW 1369 BW 12547 BW15169 ST7981 BW 16937 DN2208 DN7661 BW 3073 BW 7768 DN24248 BW 15310 BW7768 BW 8365 BW 12584 ST1986 ST14261 BW 1718 ST11001 BW 8748 BW 9658 ST9976 BW 6886 ST7878 BW 10373 BW 5856 ST11761 BW 15935 BW 8686 BW 3066 DN12274 DN8958 ST876 ST18365 BW 734 BW 4565 ST4505 BW 10567 BW 3925 BW3317 ST2369 BW 11986 BW 3546 ST12884 BW11351 BW 15169 BW 12034 BW 7599 BW 13863 BW15027 DN14977 BW 9262 BW11387 BW 3317 ST10539 BW 11389 BW 2205 BW 9264 DN21863 BW 11448 BW 7285 DN33268 BW 8075 BW 7283 ST8320 DN9393 BW 14989

MapB%1%LG9 MapB%2%LG9 MapB%1%LG10 MapB%2%LG10 MapB%1%LG11 MapB%2%LG11 MapB%1%LG12 MapB%2%LG12 MapB%1%LG13 MapB%2%LG13 MapB%1%LG14 MapB%2%LG14 MapB%1%LG15 MapB%2%LG15 MapB%1%LG16 MapB%2%LG16++++MapB%1%LG17+

BW9466 ST10770 BW9963 BW14811 BW14817 ST16789 BW3903 BW8642 BW12884 BW10502 ST13510 DN18414 BW 73 ST15922 DN3603 BW 3399 BW 10375 DN4578 ST14880 BW10505 BW 690 ST11764 BW 12630 BW3683 BW9066 BW 10375 ST825 ST1501 BW 7574 ST18696 BW10041 BW4238 ST11764 BW 6800 BW 1179 ST10033 BW 8804 ST17684 BW13310 BW 9466 ST14254 ST1501 BW7510 BW 4251 BW9692 ST11037 BW 7242 ST14416 BW12722 DN27932 BW 7665 BW 3683 BW15605 BW 3399 BW 10063 BW14801 ST10770 BW7411 DN20564 ST8589 BW 4832 ST17555 BW15489 DN19803 ST1462 BW 328 DN17461 ST5523 BW5049 BW 9980 BW 6800 BW 3830 BW 5900 BW 1243 DN22819 BW11111 BW14715 BW8465 BW 5388 BW 4251 BW 12849 BW3623 BW 9980 BW 7518 ST9250 BW8106 BW 15662 BW14594 ST16541 ST10191 ST16793 ST6054 ST16072 ST5152 BW16705 ST6054 ST1798 BW 11111 ST19371 ST17754 BW 6627 BW14081 BW9482 BW 5278 BW12630 BW 1550 BW 9757 BW 14817 BW 5278 ST6261 DN13854 BW8051 BW6993 ST10790 BW 8331 DN18166 ST19164 BW16929 BW12744 ST16789 BW 9916 BW4275 ST4915 BW4275 ST4915 BW 4832 BW 12235 BW12908 BW1736 BW 9580 BW 3431 ST3780 ST16686 BW 4238 BW 5388 BW15674 ST14716 ST14633 BW10910 ST10912 BW 3051 BW 12849 BW 13110 BW14726 BW 9482 BW 8331 ST3938 BW3498 ST4018 DN24905 ST668 BW440 ST2586 BW 2259 BW 7449 ST8341 BW 9580 BW7665 ST8592 ST18556 BW 12992 ST10790 ST530 BW16895 ST19124 ST3780 BW 7165 ST2093 ST10912 ST8592 BW 7513 BW 8883 DN17319 ST12954 BW 17122 BW 8465 BW1738 DN21010 ST12508 BW 3567 BW1813 ST3979 BW7513 ST10761 BW 9457 BW 3948 ST135 BW 13110 BW 10381 BW 440 BW12627 BW 2029 BW 11029 BW 16847 BW6758 ST2586 DN9150 BW 13150 BW112 DN20908 ST530 BW14446 BW 14360 BW 8601 ST4105 ST13115 BW11555 BW 2259 ST9515 BW3331 ST3823 DN5016 BW 16721 BW 1738 ST16384 ST16274 ST9793 BW 3573 BW14625 ST19124 BW16895 BW 8349 BW2678 BW 244 BW 12627 BW2198 BW 356 BW 3762 BW10063 DN21010 BW 12336 BW3051 ST11037 BW9692 BW 2198 BW8694 ST422 DN19773 BW5009 BW 2029 ST9771 BW13785 BW15489 ST17555 BW 8694 BW11672 BW 4682 DN19093 BW9602 BW12235 BW 677 BW 15236 BW13664 BW 8754 ST16384 BW16573 BW 8792 ST742 DN13854 BW 356 BW 7311 ST3219 ST9981 ST978 BW 14446 DN3011 BW 986 BW 620 BW6627 ST422 ST9771 DN15833 BW1676 BW 3762 ST5152 BW 822 BW 11672 BW13005 BW14177 ST16056 BW 6884 BW1597 BW14594 BW 14360 BW12304 BW 12304 BW13150 BW 12744 BW 16573 BW13980 BW 14567 BW 2949 ST16541 ST16274 BW 10467 DN22296 BW 6884 ST16686 BW 13980 BW15444 BW 14183 ST1429 BW1213 BW 7549 BW 4682 BW 14403 BW 9882 BW3331 ST3823 BW 14726 ST7404 BW6464 ST4081 BW 14570 BW16184 BW 2949 BW 2954 DN3078 BW8792 BW 6818 BW8883 ST8341 DN17943 ST8680 BW8820 BW 2355 ST10064 BW8832 ST3393 ST3393 BW16847 BW 8791 BW 11571 DN23227 ST17373 BW15324 BW 7980 BW 7590 ST15693 DN8485 BW6818 BW 2953 BW 2953 BW16846 DN13340 BW 986 BW 1463 BW3948 ST12954 BW4373 ST17604 BW15531 BW3044 ST3879 BW 13198 ST672 BW5742 BW3567 BW 13921 ST16056 BW14177 BW10539 BW 7434 BW4943 BW 9051 ST14939 BW 562 ST6585 DN3751 BW 2355 BW 16184 BW 9203 ST11946 BW 11421 DN12843 DN1181 ST13362 BW11765 BW 15240 BW 15240 ST4105 DN8485 BW 3379 BW2661 BW 7227 BW15212 BW 8656 BW 15241 BW3573 BW 14567 DN23548 BW2669 DN7229 BW 8997 ST5680 DN6875 BW 13198 ST10249 BW 14183 ST10064 BW9882 ST7404 BW 8832 ST14939 BW 9602 BW 8998 BW 14570 BW16009 ST978 BW6464 BW 2126 BW5014 DN3791 BW 7045 BW 1599 ST10254 ST17604 BW15531 BW16038 DN30669 BW822 BW7245 BW 14979 ST9521 BW 9051 BW 1213 BW2100 BW11566 BW 9003 DN17943 ST10678 BW 8356 BW 12334 DN7146 ST1429 ST2417 ST13131 ST8680 BW 9384 BW 1597 ST13362 BW 14241 BW9203 ST13405 BW7590 ST9947 BW8726 BW 11765 BW 3044 ST18151 BW 11806 DN13340 BW 11403 ST7585 DN13074 BW 8229 BW7585 BW 6961 BW 6621 BW 13251 ST17373 BW15324 DN6523 BW13921 DN21742 DN13538 BW 16864 DN12843 ST11042 ST3503 BW 4284 BW7227 BW 8656 BW 3043 BW8997 BW 4310 DN6875 DN14093 ST345 ST10249 DN24883 BW 803 BW8998 BW 9387 ST8995 DN24758 BW 2126 ST10254 BW8652 BW 14979 BW9003 DN15489 BW12334 ST16980 ST13405 BW 14977 DN30053 BW 11403 BW11806 BW 6961 BW8229 BW 17180 ST19091 BW16859 BW16853 DN6523 BW16864 BW7631 BW4282 ST4928 BW4284 ST12866 DN24602 BW579

Figure 5.17 Synteny analysis between MapB-1 and MapB-2. Green highlight: markers mapped in MapB-1 but not MapB-2. Red open boxes: local marker rearrangement. Marker names are shown on the right of each linkage group.

186

Chapter 5: Building genetic map The 17 markers identified in Table 5.25 were forcibly included for the calculation of MapB-3, and were kept regardless of their fitness, i.e. high mean Chi square and low N.Nfit values. The result was that MapB-3 had a total of 853 mapped markers distributed across 16 linkage groups (Table 5.25, Figure 5.18). The map spanned 1,389.9 cM. The longest linkage group (LG1) was 134.4 cM and the shortest (LG13) 51.3 cM, with an average marker interval of 1.6 cM (Table 5.26).

Table 5.25 Statistics of MapB-1, MapB-2 and MapB-3 MapB-1 MapB-2 MapB-3 Total no. of markers generated from all three 9,173 9,173 9,173 approaches Marker filtering No. marker remained after missing genotype 801 1,572 1,572 filtering* No. marker remained after segregation 553 1,233 1,233 distortion filtering Statistics of the final map constructed No. of linkage group recovered 17 16 16 No. of mapped markers 377 836 853 Total map distance (cM) 1,144.2 1,322.5 1,389.9 Average distance between markers (cM) 3.0 1.6 1.6 * MapB-1: <20% missing, MapB-2: <30% missing

187

Chapter 5: Building genetic map Table 5.26 Linkage group statistic summary of MapB-3 Total distance Average marker interval Linkage group No. marker (cM) (cM) LG1 91 134.4 1.5 LG2 54 97.8 1.8 LG3 108 109.9 1.0 LG4 50 85.4 1.7 LG5 43 85.1 2.0 LG6 46 82.2 1.8 LG7 32 83.8 2.7 LG8 50 69.7 1.4 LG9 80 75.2 1.0 LG10 64 90.2 1.4 LG11 58 73.0 1.3 LG12 47 85.0 1.8 LG13 38 51.1 1.4 LG14 38 78.2 2.1 LG15 41 13 0.7 3.2 LG16 13 57.3 4.8 TOTAL 853 1,389.9 1.6

188

MapB-3-LG1 MapB-3-LG2 MapB-3-LG3 MapB-3-LG4 MapB-3-LG5 MapB-3-LG6 MapB-3-LG7 MapB-3-LG8

BW 4130 BW 5756 DN10356 6.2 DN14557 5.4 BW 16450 0.5 BW 8973 0.1 ST13074 0.8 0.5 BW 11519 0.7 DN8661 0.6 BW 11518 0.3 BW 11515 0.7 BW7503 ST8582 BW10222 1.8 BW 14493 0.3 BW6056 ST6925 BW5992 DN19274 BW4837 ST5573 1.8 DN5573 1.5 BW5993 BW 3888 ST18074 2.9 BW6745 1.6 BW5994 DN25060 1.3 ST17739 BW8736 BW7556 0.2 BW1455 BW 17203 0.2 3.0 BW10100 ST11494 1.5 DN19325 1.8 BW 14562 0.4 BW 15648 DN3887 0.7 0.3 BW11310 0.5 DN10587 5.5 BW 4124 BW 2103 1.2 9.3 1.7 ST8731 BW5677 10.4 0.1 ST5264 9.0 10.0 ST7662 1.0 0.1 0.5 11.1 ST16840 BW4421 2.6 BW 4589 BW 6692 0.9 2.7 0.7 ST3945 0.1 0.3 ST13317 BW11729 DN11719 BW 9158 0.1 0.2 BW 4660 0.1 1.6 BW3437 BW 1757 BW14068 0.3 BW11728 2.5 BW15126 ST17144 ST14816 BW 9159 2.3 0.2 BW 12297 0.1 BW4934 5.4 BW4722 0.2 DN24089 6.8 BW8257 1.1 ST2435 0.3 0.3 BW 6409 0.3 ST5668 6.5 0.2 2.0 BW9502 ST10808 BW7933 3.9 DN31266 7.0 0.4 BW 2115 ST17972 BW15861 0.1 0.2 0.1 1.1 BW10819 BW13654 BW7581 1.0 BW 11033 8.4 BW13439 ST15203 BW 3504 0.2 2.0 BW456 0.1 1.5 DN16882 BW12978 BW12218 1.0 BW 12519 BW7795 DN3050 1.1 0.2 2.4 4.2 0.1 BW2472 0.3 ST14581 BW4426 0.4 ST14185 BW12520 2.2 BW10234 0.2 ST17829 0.4 BW 6467 0.3 3.0 1.9 BW 15734 BW12775 0.5 BW15611 0.1 ST9651 8.1 ST7678 BW 11032 0.1 BW7796 2.3 0.1 BW 15862 0.8 0.2 0.1 0.1 BW 8185 0.4 ST314 0.6 BW13948 4.6 BW8473 BW6708 BW 13589 ST8896 0.8 BW 11320 0.5 1.2 0.1 2.0 DN22186 0.3 BW11097 ST6880 0.2 BW4907 DN726 0.7 DN13276 DN26070 1.4 BW 4575 1.0 0.3 3.6 3.6 0.3 BW7785 BW1571 0.1 DN26800 3.1 BW13904 0.4 BW 1519 BW7094 3.9 BW 9781 BW 8180 0.5 0.8 0.4 11.7 1.0 ST9599 ST411 BW11019 0.2 BW9941 10.2 ST17929 DN19561 BW 10925 BW6804 ST7788 0.4 7.0 6.0 0.4 1.7 BW8321 BW8826 BW3170 BW5095 0.3 BW 15824 BW 11629 1.1 1.2 3.0 5.6 5.3 0.4 BW16555 3.0 BW8823 BW5599 ST8194 DN27399 ST10013 1.2 DN34750 1.3 1.4 1.6 1.9 DN4675 BW416 5.1 BW3649 ST4190 0.5 ST16650 3.4 BW8504 ST18326 BW16168 BW8785 10.4 3.8 BW 15193 4.9 1.7 BW 645 BW372 0.1 DN297 0.3 ST17533 BW15470 0.8 BW49 0.2 BW 4661 0.3 3.9 BW 3845 0.9 ST13686 1.8 5.5 BW8152 BW9533 1.4 DN10738 1.5 BW11993 ST13608 0.6 ST1680 3.1 BW 15194 1.1 1.7 BW15806 BW 11523 5.3 BW16672 BW3957 0.9 BW2145 BW11992 0.2 BW 3612 3.1 BW 7715 0.2 1.8 0.2 1.6 ST15959 BW14099 ST13079 2.2 0.7 DN2721 BW8572 ST9760 BW14951 BW9287 1.1 BW 8078 13.6 0.1 BW 7710 0.1 1.4 1.1 0.7 ST6200 9.1 DN27733 1.3 ST18466 BW16292 2.3 DN22278 BW990 2.3 BW2142 DN20121 0.8 0.1 BW 11431 0.5 0.5 BW5408 0.4 BW 7914 7.9 6.9 BW12005 ST1170 1.5 BW12203 BW 7977 2.2 2.7 BW 11425 0.2 0.5 0.1 BW3112 1.9 ST9029 0.4 ST13621 3.2 ST16649 1.4 BW12205 ST9112 ST12965 0.5 2.2 ST4995 1.7 BW 8845 2.8 BW14425 5.6 BW4368 0.7 BW2384 1.5 BW10219 0.4 BW 17100 0.6 0.3 BW 11428 0.9 1.5 1.3 2.4 BW4336 ST10081 BW8912 BW14008 0.6 BW14695 ST11606 0.2 BW 11059 0.1 8.0 3.2 BW 8168 1.8 0.1 0.2 0.1 BW13076 BW 4197 1.3 BW14864 DN14130 ST2748 BW4974 ST5720 0.2 ST13570 2.7 0.4 ST3274 4.2 0.4 1.8 BW13077 1.0 ST4828 BW8927 BW12569 ST18089 BW15495 0.2 BW 2575 0.6 BW 2845 3.0 0.3 1.2 0.1 DN6720 0.3 ST18797 1.4 BW12570 BW6733 0.5 ST16410 DN1131 BW 1924 1.9 7.4 DN21158 BW 1619 0.1 0.2 0.3 ST11112 BW9759 1.4 BW 16580 4.8 ST14245 0.2 ST7712 0.3 BW14471 BW 5430 0.3 0.7 ST13198 BW11625 0.5 BW 992 0.1 BW4033 0.4 ST19176 0.1 ST6138 BW 5419 0.3 BW 9363 6.6 0.2 0.6 BW12869 BW 5662 17.7 ST8402 0.2 BW5121 0.6 BW5354 0.4 BW 13350 0.9 0.9 0.8 ST14586 DN5157 0.4 BW7346 0.2 ST5874 BW6861 0.1 ST1580 3.9 0.2 0.1 0.2 DN32837 DN18035 BW12454 BW9507 BW5637 0.3 BW 1355 2.3 0.6 10.9 6.9 0.1 0.5 BW3347 0.1 BW 12479 DN23937 ST14114 DN10373 BW7416 BW14841 ST16817 0.1 DN6173 0.2 0.1 0.1 0.1 ST18480 BW2854 0.3 BW 6365 BW10269 BW15681 DN23734 0.7 ST1019 BW 12502 0.2 0.1 0.2 0.1 0.4 ST3843 0.3 ST7289 BW 6249 BW1560 ST11653 0.1 ST17776 0.9 BW854 ST15101 BW13345 0.2 2.6 0.3 0.8 ST15392 BW13607 0.1 ST14936 BW13195 DN6251 BW2305 BW12320 0.1 ST5916 BW14363 1.3 BW8235 DN14752 BW 17102 0.1 3.6 0.2 0.1 0.2 BW528 DN4753 BW 3495 DN9055 ST13960 0.2 1.2 0.1 BW16474 ST18678 0.1 0.1 2.5 BW12237 2.4 DN6661 1.0 ST635 BW 6970 ST4014 BW4151 0.1 0.4 BW9727 ST11076 0.6 3.0 0.2 ST9660 BW8479 0.4 BW7360 0.1 BW15170 DN10582 BW 3496 BW2582 DN9742 0.5 BW 16235 1.1 0.5 0.3 0.9 ST17196 0.6 BW 928 8.1 0.3 BW 12546 0.5 4.0 0.2 BW13724 ST2967 0.6 ST17747 0.6 ST10593 0.2 DN4522 BW 1919 10.3 DN3070 0.9 0.1 0.1 DN8177 BW1991 BW15655 0.1 1.1 BW11530 1.7 BW 6136 ST2206 0.4 0.8 BW1361 DN16842 BW4665 BW14545 DN7794 0.7 BW 13531 5.0 ST13089 7.1 BW 10523 0.6 BW8741 2.0 BW2878 0.4 BW 14828 1.1 3.3 BW13054 ST11928 0.1 BW5157 BW4299 1.0 DN19006 0.4 0.6 0.1 BW4950 BW 9270 BW14876 ST4949 BW4298 0.3 BW 11060 0.5 0.2 7.3 0.7 ST5883 BW 14337 0.7 BW10417 2.5 BW15087 BW 11957 BW5127 0.2 BW 11951 1.2 DN24241 6.3 BW15086 1.0 DN19762 1.0 DN23069 ST17106 0.2 BW 12547 3.8 ST7981 BW7309 0.4 DN7661 0.7 BW16937 BW1369 DN2208 1.2 DN24248 0.4 BW3073 BW7768 2.9 BW 8365 0.6 0.1 0.8 BW15310 ST1986 BW12584 0.9 BW 1718 0.4 0.2 8.3 ST14261 0.4 BW 8748 4.5 ST11001 0.8 ST9976 27.8 BW9658 0.8 BW 10373 BW6886 ST7878 0.8 ST11761 BW5856 3.5 BW 8686 BW15935 0.1 DN12274 1.4 BW3066 1.5 ST876 DN8958 3.0 BW 734 ST18365 0.5 ST4505 4.4 BW4565 3.7 BW 3925 0.7 BW10567 0.4 BW 11986 ST2369 0.9 ST12884 BW11351 BW3546 2.3 BW 12034 7.9 0.2 BW15169 2.2 BW 13863 BW7599 5.1 BW 9262 0.4 BW15027 DN14977 ST10539 BW3317 BW 2205 BW11389 BW 9264 BW11387 DN21863 BW 11448 BW 7285 DN33268 BW 8075 BW 7283 ST8320 DN9393 BW 14989

MapB-3-LG9 MapB-3-LG10 MapB-3-LG11 MapB-3-LG12 MapB-3-LG13 MapB-3-LG14 MapB-3-LG15 MapB-3-LG16

BW9466 ST10770 BW9963 0.4 BW14811 4.9 7.9 BW14817 1.7 ST16789 0.4 BW3903 2.1 BW8642 0.9 BW12884 3.2 BW10502 1.9 DN18414 0.7 ST13510 DN3603 BW 73 BW 3399 0.4 BW 10375 ST15922 BW10505 ST14880 BW 690 0.2 ST11764 DN4578 0.2 BW9066 BW3683 ST825 ST1501 BW7574 0.9 BW4238 ST18696 BW 6800 BW 1179 BW12630 4.2 3.2 0.6 ST14254 8.1 ST17684 0.2 BW 4251 BW7242 BW10050 DN27932 ST10033 2.6 BW 6993 0.2 3.0 BW7411 9.3 BW15605 2.1 BW 4832 2.3 DN19803 2.9 ST11442 1.0 BW8804 3.3 BW5049 17.1 ST1462 0.8 DN17461 1.0 0.4 BW5900 BW 16705 0.9 ST8589 0.1 BW 3830 1.7 BW8465 0.8 BW 1243 0.9 1.6 BW11111 5.3 BW 12849 3.8 BW 3623 0.3 BW 9980 1.5 BW7510 1.8 BW15662 ST16793 1.9 0.2 ST9250 BW8106 ST16072 0.1 0.3 0.6 ST6054 0.4 BW328 DN22819 0.6 ST17754 6.2 0.1 BW9482 0.9 ST19371 BW 9757 17.4 0.2 BW 5278 3.5 BW3431 0.9 0.8 2.0 ST10790 0.6 ST6261 0.8 ST19164 BW16929 BW 12722 ST14416 0.6 BW 9916 BW7665 0.3 8.8 0.3 BW1736 0.1 6.0 BW4275 ST4915 1.0 ST3780 0.3 BW 5388 ST3938 1.1 ST14716 BW10910 0.6 0.7 BW15674 1.6 BW 13110 0.1 0.4 BW 8331 BW7518 0.6 DN24905 BW440 0.4 0.5 0.4 BW3498 ST4018 2.9 BW 7449 0.1 0.3 BW 9580 0.5 DN18166 BW12992 ST530 0.1 0.2 ST18556 7.6 BW 7165 3.6 BW1020 0.5 0.3 ST10912 0.6 ST1798 BW1550 2.7 BW17122 2.6 0.2 BW1738 0.1 DN17319 ST12508 1.9 BW10393 0.6 ST3979 0.5 ST8592 1.9 0.3 BW10381 0.2 BW12627 0.1 ST135 BW 11029 1.8 ST2586 0.1 BW7513 0.1 0.3 0.5 DN20908 4.5 0.3 BW14446 0.6 BW112 1.8 BW 8601 BW8835 ST10067 1.6 BW 2259 ST10761 BW 9457 0.3 1.0 BW16721 0.2 ST16384 0.2 0.5 DN5016 0.1 ST9793 2.0 DN35940 0.1 ST19124 BW16895 DN9150 0.4 2.0 BW244 BW2198 1.9 0.7 BW2678 1.0 BW 3762 0.2 0.1 BW10362 0.1 DN21010 ST9515 BW8349 0.3 ST11037 BW 9692 BW8694 1.2 1.0 1.2 BW3051 0.1 DN19773 5.7 0.2 BW 2029 3.3 BW16038 1.7 BW15489 ST17555 BW11672 0.1 0.6 BW13785 DN19093 0.5 0.1 2.6 BW 677 7.2 BW12336 1.1 BW8754 0.1 BW16573 1.0 BW13664 ST742 1.4 0.5 BW 356 3.4 ST9771 0.1 ST9981 0.8 DN3011 0.5 ST3219 BW 620 0.3 1.2 BW15236 1.8 1.1 4.5 17.1 0.6 BW13005 ST422 0.6 BW1676 2.1 BW 6884 ST5152 1.1 DN15833 0.8 0.8 BW13980 4.0 BW 14360 0.8 BW13150 0.8 BW 2949 1.9 BW12744 53.7 0.8 ST16274 BW7311 0.2 ST16686 BW15444 0.2 0.4 1.0 DN22296 0.4 BW 7549 0.4 1.7 1.4 BW 4682 0.3 BW12304 BW3331 ST3823 0.1 3.2 BW14726 0.4 ST4081 0.6 BW 2954 0.3 DN3078 BW8792 1.1 BW10467 1.9 BW8883 0.6 1.6 ST8341 0.8 BW8820 0.9 ST3393 BW10294 6.2 BW 8791 BW14403 6.3 DN23227 2.2 1.4 BW7980 0.1 ST15693 2.8 0.7 0.2 BW 2953 0.8 BW 986 BW6818 0.2 BW3948 1.4 ST12954 5.7 BW4373 0.8 0.8 BW11571 0.4 1.0 ST672 BW8677 3.2 ST16056 BW14177 0.5 BW10539 1.0 3.8 BW7434 0.6 BW4943 0.5 BW1463 0.5 BW 562 BW7210 0.2 DN1181 1.8 BW 2355 0.2 ST11946 0.7 BW 15240 BW11421 0.1 1.5 DN8485 ST3879 9.2 ST8241 BW15212 1.3 0.2 2.2 BW2661 0.2 BW 15241 3.5 0.2 BW 14567 0.2 BW16184 DN7229 1.1 6.8 7.0 0.1 ST5680 2.4 BW 13198 0.4 BW 14183 0.2 BW2669 DN23548 2.1 BW9882 0.1 ST7404 0.2 ST14939 BW9602 ST11680 0.4 BW 14570 BW3379 0.1 BW16009 0.4 BW6464 0.4 0.8 BW 7045 2.3 BW1599 ST17604 BW15531 ST10064 BW 8832 3.7 DN30669 1.4 BW7245 2.5 1.5 2.4 ST9521 0.4 BW 9051 BW5014 DN3791 6.9 BW2100 BW 10041 DN17943 0.9 0.1 0.1 BW8356 0.1 DN7146 1.2 BW1213 ST2417 2.2 BW 13310 ST8680 3.1 2.0 BW1597 0.5 0.4 ST13362 BW11765 0.4 ST1429 BW9203 2.1 BW 14801 0.9 BW7590 0.1 BW14241 0.7 ST5523 DN13340 1.4 ST7585 ST18151 6.5 0.8 0.1 BW 6621 0.8 BW3044 1.0 BW 14715 0.1 BW7585 0.2 0.6 DN13074 17.2 0.4 ST10191 0.8 BW13921 DN21742 ST17373 BW15324 3.1 0.3 BW13251 DN13538 0.9 BW 14081 0.4 DN12843 ST11042 1.8 3.8 1.1 1.4 ST3503 BW5742 BW 8051 0.1 BW7227 0.1 BW 8656 4.3 0.1 1.5 3.0 BW3043 ST6585 BW 12908 BW8997 7.3 DN6875 2.2 3.3 1.3 2.0 DN14093 BW4310 1.0 ST14633 ST10249 0.1 ST10678 ST345 0.1 2.3 BW803 6.7 ST668 BW8998 BW 9384 1.0 0.1 ST8995 6.5 1.2 DN24758 DN24883 ST2093 1.1 BW 9387 0.1 3.4 ST10254 BW 1813 0.6 BW 2126 BW8652 3.7 0.8 BW9003 0.7 BW 6758 0.3 BW 14979 0.7 ST13115 BW11555 BW12334 0.9 0.4 0.7 0.5 DN15489 BW 14625 2.0 ST13405 3.6 DN30053 4.1 ST16980 BW 10063 0.1 1.6 BW 14977 1.0 BW11806 4.1 BW 5009 2.4 BW 11403 0.2 BW8229 BW 12235 BW 6961 1.8 0.8 ST19091 BW16859 1.2 DN13854 0.1 BW 17180 BW16853 0.6 BW 6627 DN6523 BW 16864 1.9 BW 14594 0.1 BW7631 ST16541 BW4282 ST4928 10.3 BW4284 BW 16847 ST12866 0.9 BW 16846 DN24602 0.2 BW 3567 BW579 0.6 DN3751 1.7 0.3 ST4105 BW 3573 9.6 ST978 0.2 BW 822 1.2 BW 11566 0.2 4.0 ST13131 ST9947 BW8726

Figure 5.18 MapB-3. Marker interval distances are shown on the left (cM), and the marker names are shown on the right of each linkage group.

189

Chapter 5: Building genetic map Close examination of the 17 markers forcibly added in the MapB-3 confirmed that they were mapped in the correct linkage groups as they were in MapB-1 (Table 5.29). However these markers had the high Chi-square values (>5), except for BW11387 in MapB- 3 LG1, BW14008 in LG8, ST10678 and BW9382 in LG10, and the group of five markers in LG15 (Table 5.27). In addition, inclusion of these markers lowered the fitness of the surrounding markers in MapB-3 LG2, LG8, and LG15. For example, in MapB-3 LG2 the marker BW49 had a higher Chi square value of 6.117 than originally where it was below 5 in MapB-2 (Table 5.27). The same is the case in LG8, with the markers BW14008 and DN14130 that were well mapped in MapB-2, but now had a higher mean Chi square value of 5.222 in MapB-3 (Table 5.27).

Table 5.27 Markers added in the calculation of MapB-3 Chi Marker LG in LG in square Note name MapB-1 MapB-3 value* BW5993 1 1 5.97 BW11387 1 1 1.195 BW49 mean Chi square value BW9287 2 2 8.256 bec ame 6.117 BW14008 8 8 4.921 BW8823 mean Chi square value DN14130 8 8 6.547 became 5.222 ST10678 10 10 3.412 BW9384 10 10 3.594 BW7510 11 11 7.221 ST78589 11 11 6.464 BW16038 11 11 23.665 BW5742 14 14 5.2 ST6585 14 14 5.512 ST14416 16 15 0.143 BW3623 16 15 0.345 MapB-1 LG16 is linked to MapB-2 LG15; BW16705 16 15 0.239 MapB-1 LG17 is linked to MapB-2 BW6993 16 15 1.15 LG16 BW12722 16 15 0.146 * Indicates the Chi square value in MapB-3

190

Chapter 5: Building genetic map 5.3.8 Genetic map synteny analyses For the comparisons between MapB-1 and MapB-3, an inversion of the marker order was observed in all but LG7, LG14, LG15, and LG16 (Figure 5.19). Interestingly, MapB-1 LG15 and MapB-1 LG16 were found to be linked together in MapB-3 as LG16, although with a relatively long marker interval of 54 cM (Figure 5.19). In addition, the two markers from MapB-1 LG17 were found to be mapped in MapB-3 LG16 (Figure 5.19). The relationship of corresponding linkage groups between MapB-1 and MapB-3 is summarised in Table 5.28.

Table 5.28 Relationship of linkage groups between MapB-1 and MapB-3 Linkage group in MapB-1 Corresponding linkage group in MapB-3 MapB -1_LG1 MapB -3_LG1 MapB -1_LG2 MapB -3_LG2 MapB -1_LG3 MapB -3_LG3 MapB -1_LG4 MapB -3_LG4 MapB -1_LG5 MapB -3_LG5 MapB -1_LG6 MapB -3_LG6 MapB -1_LG7 MapB -3_LG7 MapB -1_LG8 MapB -3_LG8 MapB -1_LG9 MapB -3_LG9 MapB -1_LG10 MapB -3_LG10 MapB -1_LG11 MapB -3_LG11 MapB -1_LG12 MapB -3_LG12 MapB -1_LG13 MapB -3_LG13 MapB -1_LG14 MapB -3_LG14 MapB -1_LG15 MapB -3_LG15 MapB -1_LG16 MapB -3_LG15 MapB -1_LG17 MapB -1_LG16

191

Figure 5.19 Result of the synteny analysis between MapB-1 and MapB-3. Marker names are shown on the right of each linkage group. Red rectangles: inversion of marker order.

192

Chapter 5: Building genetic map For the comparison between MapB-1 and the combined approach-MapA, a total of 223 MapB-1 markers were found present in MapA (Figure 5.20). Inversion of marker orders were found in 10 linkage groups, on MapB-1 LG3 (MapA LG2), LG4 (MapA LG7), LG5 (MapA LG4), LG6 (MapA LG11), LG8 (MapA LG10), LG9 (MapA LG12), LG10 (MapA LG6), LG12 (MapA LG14), LG13 (MapA LG13), and LG14 (MapA LG9) (Figure 5.20). Markers in MapB-1 LG16 were not found in MapA at all, and the MapB-1 LG17 were found to be associated with MapA LG16 (Figure 5.20, Table 5.29).

Table 5.29 Relationship of the linkage groups between MapB-1 and combined approach- MapA Linkage group in MapB-1 Corresponding linkage group in MapA MapB -1_LG1 MapA_LG3 MapB -1_LG2 MapA_LG1 MapB -1_LG3 MapA_LG2 MapB -1_LG4 MapA_LG7 MapB -1_LG5 MapA_LG4 MapB -1_LG6 MapA_LG11 MapB -1_LG7 MapA_LG8 MapB -1_LG8 MapA_LG10 MapB -1_LG9 MapA_LG12 MapB -1_LG10 MapA_LG6 MapB -1_LG11 MapA_LG5 MapB -1_LG12 MapA_LG14 MapB -1_LG13 MapA_LG13 MapB -1_LG14 MapA_LG9 MapB -1_LG15 MapA_LG15 MapB -1_LG16 N/A MapB -1_LG17 MapA_LG16

193

MapB-1_LG1 MapA_LG3 MapB-1_LG2 MapA_LG1 MapB-1_LG3 MapA_LG2 MapB-1_LG4 MapA_LG7 MapB-1_LG5 MapA_LG4 MapB-1_LG6 MapA_LG11

ST20872 BW4699 Shared_marker_33 BW12159 Shared_marker_35 BW15107 Shared_marker_33 ST20664 BW10351 Shared_marker_34 ST20667 Shared_marker_1 Shared_marker_35 BW12019 BW9294 Shared_marker_36 ST8191 Shared_marker_106 BW7170 Shared_marker_37 BW12018 Shared_marker_34 ST3945 Shared_marker_107 ST18728 DN9452 Shared_marker_38 Shared_marker_39 ST21468 Shared_marker_1 BW7556 ST26045 Shared_marker_42 BW3437 BW17203 Shared_marker_93 Shared_marker_108 BW10932 Shared_marker_2 Shared_marker_40 BW12419 BW5992 Shared_marker_36 Shared_marker_71 Shared_marker_109 Shared_marker_110 BW15444 BW9428 Shared_marker_41 BW7640 Shared_marker_38 Shared_marker_72 Shared_marker_111 DN13484 Shared_marker_4 Shared_marker_42 ST27598 BW16439 Shared_marker_112 BW12403 Shared_marker_6 BW9159 ST33533 ST28896 Shared_marker_113 Shared_marker_108 Shared_marker_2 BW11648 ST2435 BW12699 Shared_marker_3 BW16422 Shared_marker_114 Shared_marker_115 Shared_marker_106 ST19984 BW2115 Shared_marker_73 ST16769 Shared_marker_95 Shared_marker_4 Shared_marker_5 BW12036 Shared_marker_93 Shared_marker_94 Shared_marker_111 DN31139 Shared_marker_43 BW9817 ST33401 BW18928 Shared_marker_107 Shared_marker_6 BW12460 Shared_marker_44 BW12191 BW13134 Shared_marker_96 Shared_marker_109 BW6994 Shared_marker_7 ST21534 Shared_marker_45 Shared_marker_71 ST11577 Shared_marker_95 Shared_marker_113 Shared_marker_8 Shared_marker_8 Shared_marker_46 ST10013 ST20800 BW6974 Shared_marker_96 BW6529 Shared_marker_9 Shared_marker_9 BW15824 ST11743 Shared_marker_45 ST10743 Shared_marker_10 ST17628 Shared_marker_47 Shared_marker_48 BW8785 Shared_marker_73 BW10925 BW7072 Shared_marker_43 Shared_marker_116 Shared_marker_112 Shared_marker_11 Shared_marker_11 Shared_marker_49 Shared_marker_74 Shared_marker_97 ST24265 BW18778 Shared_marker_117 BW6202 ST16650 BW17473 BW8078 Shared_marker_75 Shared_marker_98 BW9941 Shared_marker_44 ST33155 BW4668 ST1170 BW990 ST20377 DN20121 Shared_marker_76 BW17355 Shared_marker_46 DN25967 BW645 Shared_marker_118 Shared_marker_114 Shared_marker_12 ST30123 BW5095 ST9112 Shared_marker_77 ST30592 BW17205 BW18783 Shared_marker_119 ST17921 BW10461 Shared_marker_13 Shared_marker_14 BW17112 BW7977 Shared_marker_78 Shared_marker_97 ST16140 ST24066 BW12240 ST2748 BW6489 Shared_marker_25 ST13570 Shared_marker_79 Shared_marker_98 BW9468 BW13809 Shared_marker_116 Shared_marker_15 BW12072 Shared_marker_50 Shared_marker_80 Shared_marker_99 ST10948 ST30207 Shared_marker_47 Shared_marker_117 Shared_marker_16 BW9870 BW12546 Shared_marker_81 Shared_marker_82 ST17690 Shared_marker_120 Shared_marker_121 BW2142 Shared_marker_49 BW8959 ST15272 Shared_marker_118 Shared_marker_17 BW5358 ST8656 BW10341 BW18471 ST32597 DN3070 BW13693 Shared_marker_83 Shared_marker_84 Shared_marker_18 BW5834 ST15609 Shared_marker_119 Shared_marker_51 BW4708 ST3843 Shared_marker_19 Shared_marker_13 Shared_marker_52 Shared_marker_85 BW9151 BW9363 BW14266 Shared_marker_26 ST7417 ST18693 Shared_marker_20 Shared_marker_12 Shared_marker_25 Shared_marker_53 Shared_marker_86 ST24506 BW9893 BW17200 BW10915 Shared_marker_21 ST25368 BW15453 Shared_marker_54 Shared_marker_87 Shared_marker_100 Shared_marker_52 Shared_marker_74 Shared_marker_22 ST7682 BW15455 BW12547 Shared_marker_88 BW9270 BW4836 BW9287 ST29525 Shared_marker_75 BW8479 Shared_marker_17 ST27126 BW8365 BW3347 Shared_marker_100 ST18482 BW3716 ST31328 Shared_marker_122 Shared_marker_120 Shared_marker_23 DN23614 BW16944 Shared_marker_55 Shared_marker_89 Shared_marker_101 BW19112 Shared_marker_53 Shared_marker_77 DN8508 BW12237 BW10941 ST18744 BW13599 Shared_marker_56 Shared_marker_90 Shared_marker_102 ST33713 DN16871 Shared_marker_78 BW8741 BW11684 BW5841 Shared_marker_57 Shared_marker_58 Shared_marker_91 BW12479 BW12354 BW3941 BW9703 ST5874 BW16325 ST3756 BW8686 Shared_marker_92 DN18035 Shared_marker_101 BW1919 ST2206 BW9016 Shared_marker_51 Shared_marker_80 BW5121 Shared_marker_20 ST24477 Shared_marker_59 BW13054 Shared_marker_103 Shared_marker_105 BW10216 ST29854 DN23069 Shared_marker_16 DN11601 Shared_marker_60 BW4950 Shared_marker_104 Shared_marker_105 Shared_marker_104 Shared_marker_27 Shared_marker_28 ST17464 Shared_marker_83 BW9507 BW11333 BW6957 Shared_marker_61 ST5883 BW992 BW8555 Shared_marker_29 Shared_marker_50 BW12782 DN24241 ST19428 ST9052 Shared_marker_62 BW5127 ST14521 Shared_marker_30 Shared_marker_31 ST27412 BW15610 BW5707 ST14261 BW12584 DN4236 ST9195 Shared_marker_63 DN19762 BW7893 Shared_marker_122 Shared_marker_54 Shared_marker_81 BW6886 Shared_marker_21 ST11806 Shared_marker_64 Shared_marker_103 DN16907 BW10114 BW16544 ST7878 Shared_marker_19 BW7104 Shared_marker_65 ST17294 ST29086 Shared_marker_24 ST21779 BW6505 Shared_marker_66 ST20723 Shared_marker_87 BW10567 ST32747 Shared_marker_26 Shared_marker_67 Shared_marker_55 ST29963 BW15169 BW5038 ST24121 Shared_marker_68 Shared_marker_32 BW4707 ST8043 ST18530 BW13835 Shared_marker_69 ST7416 Shared_marker_92 Shared_marker_24 BW10866 ST18621 Shared_marker_70 BW9920 Shared_marker_91 BW6588 BW17378 ST947 Shared_marker_57 Shared_marker_85 ST10842 ST30638 BW14760 ST8157 ST23649 ST25814 Shared_marker_89 BW3115 DN22595 ST4618 Shared_marker_61 BW11420 Shared_marker_63 BW3872 BW11418 BW3317 BW16417 ST28855 ST5990 Shared_marker_27 BW3578 BW8391 BW10337 Shared_marker_29 Shared_marker_59 BW10336 Shared_marker_64 BW11387 Shared_marker_30 BW7272 BW12214 ST25627 DN12692 Shared_marker_66 ST16952 ST10658 BW9925 BW6482 ST10116 DN9132 BW14628 BW9717 Shared_marker_32 BW5426 Shared_marker_67 Shared_marker_68 BW6046 Shared_marker_70

MapB-1_LG7 MapA_LG8 MapB-1_LG8 MapA_LG10 MapB-1_LG9 MapA_LG12 MapB-1_LG10 MapA_LG6 MapB-1_LG11 MapA_LG5 MapB-1_LG12 MapA_LG14

BW10375 BW9466 ST11764 ST10770 Shared_marker_158 Shared_marker_194 BW14817 DN20564 BW13749 BW11582 BW10891 ST16789 BW11458 ST13746 BW1571 ST31842 BW9980 Shared_marker_158 ST18655 Shared_marker_142 BW7510 ST28497 BW14068 ST33067 Shared_marker_131 BW18067 Shared_marker_159 ST3532 BW7652 BW3683 ST7523 BW9482 ST8589 BW16246 Shared_marker_132 BW18551 ST6054 DN25237 BW4722 Shared_marker_133 DN23960 ST10790 BW5278 ST5957 BW3858 ST14775 BW8465 ST16828 DN16882 Shared_marker_134 ST26705 Shared_marker_160 ST14460 BW8528 BW7650 Shared_marker_194 Shared_marker_195 ST14958 Shared_marker_143 ST6736 Shared_marker_187 BW2472 Shared_marker_135 ST21013 Shared_marker_161 Shared_marker_159 ST12820 ST9009 Shared_marker_124 Shared_marker_144 BW4333 Shared_marker_195 Shared_marker_196 Shared_marker_123 Shared_marker_136 BW12198 Shared_marker_162 BW2130 BW13130 ST11629 Shared_marker_145 BW7488 BW3051 ST25260 Shared_marker_124 Shared_marker_137 Shared_marker_138 ST14055 ST2586 BW2259 Shared_marker_161 BW7679 BW12627 DN14045 Shared_marker_188 BW7644 DN4709 ST9599 Shared_marker_139 Shared_marker_131 BW16895 ST19124 ST13878 BW8883 ST28933 BW15709 ST8592 Shared_marker_125 BW4368 ST20971 Shared_marker_146 Shared_marker_163 Shared_marker_160 Shared_marker_197 BW8810 ST15024 BW17173 BW8694 BW15710 BW7513 BW372 BW12005 ST32666 Shared_marker_164 ST9828 Shared_marker_198 BW5568 ST30241 Shared_marker_147 ST12528 ST10920 BW8152 ST13621 BW14081 Shared_marker_165 BW14378 Shared_marker_199 Shared_marker_200 BW5828 Shared_marker_123 BW13024 Shared_marker_148 Shared_marker_142 Shared_marker_198 ST7112 Shared_marker_166 Shared_marker_163 Shared_marker_126 Shared_marker_127 ST30240 Shared_marker_149 ST32718 DN15096 Shared_marker_199 ST5929 BW356 ST28524 BW17172 BW18536 BW4639 BW7710 Shared_marker_150 ST422 ST20169 ST29676 Shared_marker_125 Shared_marker_151 ST26936 ST9771 Shared_marker_187 Shared_marker_133 ST7111 Shared_marker_167 BW11736 Shared_marker_197 ST7404 BW6464 BW5520 BW12304 ST14862 Shared_marker_128 BW14008 Shared_marker_132 Shared_marker_168 Shared_marker_165 BW15427 ST14659 BW8635 DN17943 ST8680 BW8394 ST1429 BW1213 BW8739 Shared_marker_129 DN14130 BW11088 BW986 Shared_marker_167 DN17553 Shared_marker_134 Shared_marker_152 DN13444 Shared_marker_189 BW9535 Shared_marker_201 BW8051 BW12505 Shared_marker_169 Shared_marker_170 Shared_marker_168 Shared_marker_139 Shared_marker_153 Shared_marker_190 Shared_marker_191 BW19000 ST33863 Shared_marker_126 Shared_marker_171 ST30913 Shared_marker_154 Shared_marker_150 Shared_marker_192 DN12778 BW19189 Shared_marker_136 Shared_marker_172 DN13523 ST15558 BW17071 Shared_marker_193 DN5556 BW9839 ST16804 Shared_marker_155 Shared_marker_173 ST30912 ST22696 BW5010 Shared_marker_156 ST27248 ST7071 Shared_marker_188 Shared_marker_202 Shared_marker_137 Shared_marker_174 BW17528 Shared_marker_15 ST23164 BW8997 Shared_marker_147 BW11717 ST14811 Shared_marker_175 Shared_marker_169 Shared_marker_201 BW13316 ST10249 Shared_marker_143 DN17441 DN30534 DN9555 Shared_marker_176 Shared_marker_177 ST17609 BW13313 BW8998 Shared_marker_145 BW10601 BW8718 Shared_marker_178 Shared_marker_172 BW16038 ST5616 Shared_marker_128 BW16738 Shared_marker_146 BW9901 ST14819 ST10254 Shared_marker_179 Shared_marker_180 ST17603 BW3667 Shared_marker_129 BW9003 Shared_marker_149 ST16916 ST11417 Shared_marker_181 Shared_marker_171 ST8172 BW15819 BW18696 ST33025 BW9902 ST24630 BW12334 Shared_marker_182 Shared_marker_173 Shared_marker_202 Shared_marker_157 BW9373 BW7732 ST17132 Shared_marker_140 BW14097 Shared_marker_183 Shared_marker_174 Shared_marker_130 ST5495 ST24628 BW11806 Shared_marker_151 ST12982 BW10032 Shared_marker_141 Shared_marker_184 Shared_marker_176 BW3602 BW8229 BW11216 DN23814 ST10678 ST15252 DN6523 BW5474 Shared_marker_189 BW13099 Shared_marker_156 BW9384 Shared_marker_178 BW16864 Shared_marker_185 DN12334 Shared_marker_192 BW8864 BW4284 BW7676 ST26634 Shared_marker_152 Shared_marker_186 BW12709 ST22045 Shared_marker_190 DN19410 ST20035 Shared_marker_154 BW7718 ST12947 BW11676 ST24827 BW12754 Shared_marker_179 BW14432 DN14227 ST20507 ST22124 Shared_marker_140 Shared_marker_181 BW10630 BW11931 BW18357 BW1762 BW3459 BW7448 ST5569 BW18666 DN24775 BW15286 Shared_marker_183 ST13246 ST32120 Shared_marker_130 Shared_marker_184 BW17852 BW12584 ST19542 BW13230 Shared_marker_185 DN383 ST23821 Shared_marker_193 Shared_marker_186 BW9406 BW8101 BW8404 ST14231

MapB-1_LG13 MapA_LG13 MapB-1_LG14 MapA_LG9 MapB-1_LG15 MapA_LG15 MapB-1_LG16 MapA_LG16 MapB-1_LG17

BW8898 BW9692 ST11037 Shared_marker_220 Shared_marker_203 ST15162 BW14357 BW10063 ST14416 BW12722 ST30119 BW10393 ST17555 BW15489 Shared_marker_221 Shared_marker_204 ST25179 ST27549 Shared_marker_205 Shared_marker_203 BW14594 ST16541 ST5152 BW3623 Shared_marker_205 ST9137 BW6627 BW16705 Shared_marker_224 ST6693 Shared_marker_213 Shared_marker_220 BW6993 ST21525 BW4832 Shared_marker_204 ST16686 Shared_marker_221 Shared_marker_206 BW12454 ST12834 BW14726 BW17106 ST3780 Shared_marker_206 ST8341 BW3567 ST19618 ST30114 Shared_marker_207 ST33867 Shared_marker_214 BW19193 ST25611 BW16847 BW11431 ST30118 BW17109 BW13826 ST4105 ST24103 BW3573 Shared_marker_224 ST18877 Shared_marker_215 BW11014 Shared_marker_222 BW3762 Shared_marker_207 Shared_marker_222 Shared_marker_223 Shared_marker_208 BW7876 BW1597 Shared_marker_209 ST13254 ST29166 Shared_marker_210 BW13519 ST23539 Shared_marker_211 DN15981 Shared_marker_216 Shared_marker_217 BW13198 BW18437 Shared_marker_218 ST14939 Shared_marker_208 Shared_marker_219 Shared_marker_213 Shared_marker_212 ST20590 Shared_marker_209 ST20584 Shared_marker_211 ST7647 Shared_marker_210 Shared_marker_214 ST27943 ST29106 BW15938 BW17232 BW18399 ST32461 BW18400 BW7418 Shared_marker_212 Shared_marker_215 BW8632 ST16325 BW9588 BW7420 ST33383 Shared_marker_157

Shared_marker_218 ST20343 BW6370 BW8681 Shared_marker_216

Figure 5.20 Result of the synteny analysis between MapB-1 and the combined approach-MapA. Marker names are shown on the right of each linkage group. Red open boxes: inversion of marker order.

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Chapter 5: Building genetic map 5.4 Discussion 5.4.1 RAD-Seq and data preprocessing The RAD-Seq read quality check results revealed multiple peaks in the per sequence GC content graph of all the libraries sequenced. Further investigation revealed that the observed GC distribution biases were derived from reads of organellar genome origin, which can be removed by mapping the reads to organellar genome assemblies. However, this leads to over 60% of reads lost after preprocessing (Table 5.6). One possible cause is a high content of chloroplasts and mitochondria in the leaf tissues used for DNA extraction. For instance, in Arabidopsis mesophyll cell it was estimated to have up to approximately 100,000 copies of plastid genomes per cell (200 chloroplasts with 500 copies of plastid DNA per chrloplast; Fujie et al., 1994; Pyke and Keech, 1994; Sakamoto et al., 2008). Hence, the organellar genomic DNA was likely to co-precipitate with the nuclear genomic DNA in our plant tissues, and was sequenced and present in the data. Since the aim of this project is the mapping of plant nuclear genome, these organellar genome-derived reads did not significantly contribute to this project and may be considered wasted. One possible improvement for future work in this area could be to keep the plant materials in a dark room prior to DNA extraction to reduce the reproduction of chloroplasts, thus reduce the chloroplast DNA content as well as secondary metabolites (Triboush et al., 1998; Waters and Langdale, 2009). Another bias observed in the RAD-Seq data was the uneven read counts across the sequenced libraries (Figure 5.6). As shown in this study, libraries with lower read counts had much higher proportions of missing data (Figure 5.8), confirming previous studies (Catchen et al., 2011; Davey et al., 2012). Another possible consequence of insufficient sequencing depths could be genotyping errors, resulting in heterozygous loci being interpreted as homozygous, due to the insufficient depth of one of the allele or other (Davey et al., 2012). In this study, it was attempted to screen out correct and informative markers through various marker quality filtering steps (e.g. exclusion of individuals with very low read counts, removing markers with high proportions of missing data, removal of markers with excessive coverage; Miller et al., 2007; Catchen et al., 2011; Amores et al., 2011). The resultant genetic map here suggested that these attempts were successful in constructing 16 linkage groups with a high number of mapped markers and appropriate fitness values (i.e. Chi-sqaure and N.N.fit values).

5.4.2 Comparison between RAD-Seq data analysis approaches In the MapA series, genetic maps were calculated based on 200 BC individuals using de novo approach, reference-based approach using the BWA aligner, and reference- based approach using the Stampy aligner. De novo approach generated 1,361 total markers, which is the least comparing to the other two reference-based approaches (3,751 and 9,185

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Chapter 5: Building genetic map markers for BWA and Stampy approaches, respectively). This could possibly be explained by the fact that reference-based approaches are better at recovering genotyping data from individuals with low read count (Shafer et al., 2016; Fountain et al., 2016). This is supported by the observation that in de novo approach, genotypes were difficult to be recovered from individuals with less than 750 million read counts, resulting in a high proportion of 20% to 70% missing genotypes (Figure 5.13). On the contrary, the reference-based approach using the Stampy aligner recovered more genotyping data, including individuals with less than 500 million read counts (Figure 5.13). The de novo approach map was also the least dense (Table 5.30) compared to other maps, and only 10 linkage groups were reconstructed instead of the expected 16. The overall results of less markers and linkage groups recovered suggested that the de novo approach alone was insufficient to generate a good genetic map from our dataset. The two reference-based approaches also differed in their performance in terms of sequence alignment. Both aligners performed similarly when aligning S. rexii RAD reads to the S. rexii reference genome (Table 5.14; ~90% total reads). However, BWA struggled to align S. grandis RAD reads to the reference under default parameters, which resulted in only 56.3% of the total reads mapped (Table 5.14). In comparison, Stampy aligned 91.3% S. grandis RAD reads to the S. rexii reference genome, more than 1.5 times higher than BWA (Table 5.14). This may explain the overall lower alignment percentage of BWA (62.5% total reads) compared to Stampy (93.4% total reads) in the BWA approach MapA (Table 5.30). Since all BC individuals carry at minimum half of the S. grandis chromosomes, the generated RAD reads reflected this property and were thus difficult to map to the reference genome using BWA. The difference in BWA and Stampy alignment percentage also correlated to the number of markers recovered. In MapA, the BWA approach recovered 3,751 markers and the Stampy approach 9,185 markers (Table 5.30). However, most of the 9,185 markers recovered in the Stampy approach were filtered out, and only 853 markers remained after removing markers with >20% missing data (9.2% of total markers). This suggested that many of the recovered markers were possibly errors, such as sequencing errors or contaminant sequences, which were only present in a few individuals but not in other BC individuals (Catchen et al., 2011). This is possibly due to the fact that Stampy allows more mismatches during the alignment process (Lunter and Goodson, 2011), and more RAD reads with sequencing errors were aligned and mis-judged as informative loci during Stacks analyses. These loci were eventually removed during marker filtering, as they do not constantly appear in all the BC individuals (i.e. high proportion of missing data), or do not follow the expected segregation ratio (segregation distortion). In the end, both BWA and Stampy approaches recovered 16 linkage groups and about 300 markers (Table 5.30). Both maps spanned around 1,500 cM long, and the average marker intervals are both 4.6 cM (Table 5.30). This result suggests that while the two

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Chapter 5: Building genetic map aligners have very different alignment percentages, the number of markers recovered and the final genetic mapping is similar. In the calculation of the MapA series, the combined approach was found to generate the genetic map with highest resolution and longest map distance (Table 5.30). As shown in the MapA calculation, the combined approach-MapA had 599 mapped markers spanning 1,578.2 cM, with an average marker interval of 2.6 cM, nearly twice as dense as the BWA or Stampy maps alone (Table 5.30; marker interval 4.6 cM in the maps of both approaches). Some de novo assembled markers were also mapped, suggesting that de novo approach may contribute to recovering markers outside the currently available genome assembly (Wang et al., 2013). Thus, the combined approach is valuable in recovering as many markers as possible, and this approach was taken for the calculation of the MapB series. In MapB series maps, different marker filtering strategies were applied for the calculation of MapB-1, MapB-2, and MapB-3. The markers used for constructing MapB-1 was processed under stringent filtering, while the markers used for MapB-2 and MapB-3 were processed with a more relaxed filtering allowing higher proportion of missing data and segregation distortion (Table 5.5). This resulted in difference in number of mapped markers of each map, with in MapB-2 and MapB-3 more than twice the number of markers were mapped (836 markers and 853 markers, respectively) than in MapB-1 (377 markers). The low number of markers recovered in MapB-1 was most likely due to a high proportion of missing data (Table 5.30; before filtering 9,173 markers, after filtering 801 markers). Hence, by lowering the threshold of filtering markers with missing data, the genetic map density was improved in MapB-2 and MapB-3. However, using markers with excessive amounts of missing data (>20% missing) has been shown to result in ~50% chances to misplace the marker order or produce false linkages in simulation data (Hackett and Broadfoot, 2003). This suggests that the result of MapB-2 and MapB-3 should be treated carefully, and lowering the threshold for missing data is not a permanent solution to increase the map density as it also increases the chance of constructing incorrect genetic maps. Instead, resequencing of the samples with lower depth of coverage would be the optimal way to improve the map quality.

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Table 5.30 Summary of the statistics of the main results of the genetic map reconstruction in this chapter

De novo BWA- Stampy- Combined- MapB-1* MapB-2* MapB-3* MapA MapA MapA MapA No. of BC plant used 200 plants 200 plants 200 plants 200 plants 200 plants 200 plants 200 plants Total number of read (after preprocessing) 144,479,900 144,479,900 144, 479,900 - - - - No. analysed / mapped reads 130,186,134 90,233,330 134,888,035 - - - - No. analysed / mapped reads (%) 90.1 62.5 93.4 - - - - Stacks analysis Total number of marker recovered 1,361 3,751 9,185 - 9,173 9,173 9,173 Mean coverage of marke r (×) 13.6 23.3 22.2 - - - - Marker filtering No. marker kept after missing genotype 121 699 853 1,673 801 1,572 1,572 filtering No. marker kept after segregation distortion 62 414 503 979 553 1,233 1,233 filtering No. markers kept after identical marker 62 414 503 799 553 1,233 1,233 filtering Final map statistics No. of linkage group recovered 10 16 16 16 17 16 16 No. of mapped markers 55 317 338 599 377 836 853 Total map distance (cM) 716.0 1,468.6 1,567.4 1,578.2 1,144.2 1,322.5 1,389.9 Average distance between markers (cM) 13.0 4.6 4.6 2.6 3.0 1.6 1.6 * - MapB-1, MapB-2 and MapB-3 were all calculated based on combined approach

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Chapter 5: Building genetic maps 5.4.3 Comparison between MapA and MapB maps The genetic maps (MapA and MapB) calculated in this study show different advantages: the combined approach-MapA had the longest total distance with 1,578.2 cM; the MapB-3 had the highest number of 853 mapped markers and highest resolution (average marker interval = 1.6 cM); the MapB-1 may have the most reliable marker order and sequences as it was constructed under the most stringent conditions (i.e. usage of the filtered S. rexii genome and the stringent marker filtering strategy). A major difference between the three maps concerned the number of linkage groups they recovered. Combined approach-MapA and MapB-3 both had 16 linkage groups, consistent with 16 pairs of chromosomes of Streptocarpus (Figure 5.14, Figure 5.18). On the other hand MapB-1 showed 17 linkage groups, with a small LG17 that contained only two markers (Figure 5.15, Table 5.22). The synteny analysis between MapB-1 and MapB-3 showed that MapB-1 LG17 actually corresponded to MapB-3 LG16, and MapB-1 LG16 was linked to LG15 (Figure 5.19, Table 5.28). The same analysis between MapB-1 and combined approach-MapA suggested that LG17 corresponded to MapA LG16, while the markers on MapB-1 LG16 could not be identified in combined approach-MapA (Figure 20, Table 5.29). It can be speculated that the stringent marker filtering criteria of MapB-1 filtered out too many markers, and those which linked the LG17 to other linkage groups. Such linkages were supported in combined approach-MapA and MapB-3, suggesting that these two maps may be better to illustrate the actual linkage groupings in this genome area. A second difference observed among the diverse maps concerned the number of markers recovered. Comparing between MapB-1 and combined approach-MapA, MapB-1 only had 377 markers mapped while in combined approach-MapA 599 markers were mapped (Table 5.30). As these two maps were constructed using the marker filtering strategies of same stringency (i.e. removing markers with >20% missing data and removing markers showing strong segregation distortion), it is likely that the difference came from (1) the S. rexii reference genome used, which in combined approach-MapA is the unfiltered genome assembly that was used as reference, and in MapB-1 the contaminant-filtered genome assembly was used. (2) In combined approach-MapA the default BWA mapping parameters were used, while in MapB-1 the de novo and BWA mapping parameters were further optimised. As shown in this study that the optimisation of the de novo analysis and BWA alignment parameters were proven to improve the map marker density (Table 5.17 and Table 5.18, respectively), a more possible explanation is that some of the markers recovered in combined approach-MapA are actually contaminant sequences which inflated the marker density of combined approach-MapA. An examination of the combined approach-MapA markers could be done, by performing BLAST searches of the marker sequences against the nucleotide database (nt) of NCBI to identify possible contaminant sequences.

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Chapter 5: Building genetic maps The third difference observed concerned the order of markers. Inversions of marker orders were observed in most linkage groups between MapB-1 and MapB-3, and between MapB-1 and combined approach-MapA (Figure 5.19 and Figure 5.20). This is possibly a result of the genetic mapping algorithm chosen here, i.e. regression mapping. In regression mapping, the order of markers is determined by minimising the sum of the squared deviation of the distance between two adjacent markers (Van Ooijen and Jansen, 2013). In other words, the marker order that generates the shortest linkage group is favoured, implying that the determination of marker order may change every time a new marker is added (Van Ooijen and Jansen, 2013). It is thus unsurprising that the three maps discussed here show marker inversions. However, it should be noted that marker order in MapB-3 might be the least reliable among the three maps due to its’ less-stringent marker filtering and forced addition of some markers for map calculation: in MapB-3 the forcibly added markers on LG1, LG2, LG8, LG11, and LG14 showed >5 Chi-square goodness-of-fit values (Table 5.27), implying that the quality of the map was lower (Van Ooijen and Jansen, 2013). Moreover, it is known that inclusion of markers with excessive amounts of missing data (>20% missing) during map calculation leads to incorrect map ordering and overestimation of map distances (Hackett and Broadfoot, 2003), which is the case of MapB-3 as it included markers with up to 30% missing data in its’ calculation. On the other hand, there was no marker found to be grouped incongruently in the synteny analyses among the three genetic maps discussed here (i.e. grouped in different linkage groups between two maps analysed). This implies that the overall marker grouping is highly reliable.

5.4.4 Difficulties in reconstructing linkage groups LG15 and LG16 Amongst all the genetic maps calculated, there were always one or two linkage groups that were difficult to reconstruct. These are the LG15 and LG16 of all the MapA maps and in MapB-1, which were always shorter than 50 cM and contained fewer than 10 markers. One possible explanation for this poor mapping results is the evolutionary distance between S. rexii and S. grandis (Nishii et al., 2015). Recombination suppression is known to occur between species that have undergone chromosomal rearrangements, and for recombination to happen the two genomes should show similarities in gene order or chromosome homology (Jackson, 2011; Ren et al., 2018). Even though S. rexii and S. grandis are from sister clades, the BWA show poor alignment percentage when aligning S. grandis RAD-Seq reads to the S. rexii genome (Table 5.14). This suggests that the genome sequences between the two species share high proportion of heterologous sequences, which may contributed to lesser frequency of recombination hence the chromosomes can only be mapped partially.

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Chapter 5: Building genetic maps On the other hand, MapB-2 and MapB-3 both showed better resolved LG15 and LG16 with longer than 50 cM genetic distance and contained more than 10 markers (Table 5.23, Table 5.26). The difference between MapB-2, MapB-3 and the MapA, MapB-1 is that the former two maps allowed higher proportion of missing genotypes (up to 30% missing). It can thus be speculated that the markers on LG15 and LG16 are actually presented in our RAD-Seq data, but were not mapped in MapA and MapB-1 due to high proportion of missing genotypes. The most plausible reason for having missing genotypes is the low read counts in many of the libraries sequenced (Appendix 5.1 a; Catchen et al., 2011; Davey et al., 2012). (Hackett and Broadfoot, 2003). By performing additional RAD-Seq experiments, it can be possible to increase the read counts per libraries, and in turn improve the genotyping result and the number of markers recovered to increase the resolution of LG15 and LG16.

5.4.5 Comparison of the Streptocarpus genetic map to other Gesneriaceae maps Currently, there are three genetic maps available for species in the Gesneriaceae family, for the New World genus Rhytidophyllum (Alexandre et al., 2015), the Asian genus Primulina (Feng et al., 2016), and the African genus Streptocarpus (this study). Using MapB-1 as the representative Streptocarpus genetic map (for it was constructed under the most stringent strategy), the statistics of these genetic maps can be compared (Table 5.31). The Rhytidophyllum map was built using a Genotyping-by-Sequencing approach (GbS), and resulted in 559 mapped markers across 16 linkage groups (Alexandre et al., 2015). The Primulina map was built using a SNP massARRAY derived from an Expressed Sequence Tags (EST-SNP massARRAY) genotyping method, with 215 markers in 18 linkage groups (Feng et al., 2016). While the current Streptocarpus MapB-1 does not have the highest number of markers, it is the densest map with the average marker interval of 3.0 cM (Table 5.31). As these three genera are geographically and phylogenetically widely separated (Möller et al., 2009; Weber et al., 2013), comparative studies or synteny analyses of the three genetic maps may provide interesting evolutionary insights.

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Chapter 5: Building genetic maps Table 5.31 Genetic maps of the Gesneriaceae family Total Average Genotyping No. No. Taxon map marker Reference method LG markers distance interval Streptocarpus 1 RAD-Seq 17 377 1,144.2 3.0 This study Alexandre et Rhitidophyllum 2 GbS 16 559 1,650.6 3.4 al., 2015 EST-SNP Feng et al., Primulina 3 18 215 3,774.7 17.6 massARRAY 2016 1 – Streptocarpus grandis and S. rexii , 2 – Rhitidophyllum auriculatum and R. rupicola , 3 – Primulina eburnea

5.4.6 Conclusion In this chapter, the first genetic map of the genus Streptocarpus was constructed using a RAD-Seq genotyping method. Several genetic maps were constructed throughout the study, with their information content may be complementary to each other. In particular, de novo and reference-based map-building approaches were compared, and the combined approach was found to generate the genetic map of highest map density. Further improvement of the map resolution can be made through more sequencing experiments in future studies to improve the number of marker recovered. Nevertheless, these genetic maps provide the basis for the QTL mapping in Chapter 6.

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Chapter 6: QTL mapping Chapter 6 Studies of marker-trait association – morphological variations and QTL mapping in the Streptocarpus backcross population

6.1 Introduction 6.1.1 Quantitative trait loci (QTL) and binary trait loci (BTL) mapping overview Most morphological traits, such as height and body weight, show continuous variation in phenotypic values. These traits are often regulated by multiple genes with smaller effects, in combination with interactions with environmental factors. The genetic regions associated with these traits are called quantitative trait loci (QTL). On the other hand, Mendelian traits are regulated by a single or a few genes, and segregation follows a discrete pattern, often binary, according to Mendelian laws. The genetic loci associated with these traits are called Mendelian loci or binary trait loci (BTL) (Lynch and Walsh, 1998; Mauricio, 2001; Coffman et al., 2005). QTL or BTL mapping (abbreviated henceforth as QTL mapping for simplicity) describes the process of locating these loci on a genetic map. This is achieved through collecting genotype and phenotype data from an experimental population, and analysing their correlations (Lynch and Walsh, 1998; Broman and Sen, 2009). Identifying the causative loci could address questions such as how much phenotypic variation is due to genetic variation, and how much does each one of the loci contribute to the difference in phenotypic values observed. The loci information also narrows down the candidate region aiding isolation of the causative genes (Lynch and Walsh, 1998; Broman and Sen, 2009). QTL mapping of important agricultural traits has helped the selective breeding process to improve crops (Causse et al., 2002; Lanceras et al., 2004; Wang et al., 2016). QTL mapping was adopted in evolutionary biology to identify genetic regions related to important morphological changes related to fitness or ecological importance (Bradshaw et al., 1998; Gailing, 2008; Wessinger et al., 2014; Alexandre et al., 2015; Feng et al., 2018). Taking QTL studies of Gesneriaceae for example, in the genus Rhytidophyllum , a QTL study identified the loci regulating floral shape and nectar volume in relation to the formation of their hummingbird-specific pollination syndrome (Alexandre et al., 2015). QTLs for floral and leaf shape traits were identified for the genus Primulina to study the differentiation of two ecologically distinct sister species that grow sympatrically but have different morphologies and occupy contrasting microhabitats (Feng et al., 2018). This study carries out QTL mapping of morphological variation in the genus Streptocarpus. The diverse morphological characters of this genus are well documented and preliminary knowledge about their genetic inheritance is available (Reviewed in Chapter 1;

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Chapter 6: QTL mapping Oehlkers, 1938; Lawrence et al., 1939; Oehlkers, 1942; Lawrence, 1947; Lawrence and Sturgess, 1957; Lawrence, 1957; 1958; Oehlkers, 1966). In particular, the rosulate / unifoliate trait, and some of the floral pigmentation traits were suggested to be inherited in Mendelian fashion implying that loci with major effects may be found (Oehlkers, 1938; 1942; Lawrence, 1957). QTL mapping will aid the identification of these loci and shed light on the genetic basis of these interesting traits, which may ultimately enhance our understanding on how this highly diverse genus has evolved. In terms of methodology, QTL mapping starts with constructing a genetic map and collecting phenotype data from the mapping population to study the trait segregation (Sehgal et al., 2016). For a Mendelian trait, the segregation ratio observed within the population should follow Mendelian laws of segregation; for a quantitative trait, the distribution of the phenotype should be statistically tested for their distribution (i.e. parametric or nonparametric), as this will affect the selection of the QTL model in later analyses (Broman and Sen, 2009). Phenotypic correlations evaluate how tightly two traits tend to co-segregate, suggesting pleiotropic effects (Lynch and Walsh, 1998). With the genetic maps and phenotype data at hand, QTL mapping can be performed. Commonly used mapping approaches include standard interval mapping (SIM; Lander and Botstein, 1989) and composite interval mapping (CIM; Zeng, 1994). SIM performs QTL model fitting along the intervals between two genotyped markers and tests the result using a maximum-likelihood method. CIM is based on SIM but incorporates ‘cofactors’ in the analysis, a group of markers which show significant association with the trait. This reduces the genetic background noise hence improves the power of QTL detection, distinguishing closely linked QTLs (Broman and Sen, 2009). After identifying the genetic loci, QTL models can be fitted to estimate (1) the proportion of phenotypic variance explained by the loci, (2) the effect size of each loci, and (3) the interaction between loci (Broman and Sen, 2009). In addition, effect plots of the identified loci can be graphed for direct comparison of the average phenotypic values between different genotypes. For example, if ‘marker A’ was found to be a potential QTL in a BC population, the average phenotypic value of individuals with homozygous genotype at ‘marker A’ should be statistically different from that of the individuals with heterozygous genotype (Broman and Sen, 2009). On the contrary, if effect plots show no difference between the two genotypes, then the identified QTL may be a false signal. A general workflow of QTL mapping is summarised in Figure 6.1.

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Figure 6.1 General workflow of QTL mapping using SIM and CIM methods

6.1.2 Morphological differences between S. rexii and S. grandis and their hybrids The mapping population and the genetic maps constructed in Chapter 5 represented the basis for QTL mapping, as the two species used to construct the BC population show contrasting phenotypes. The morphologies of S. rexii and S. grandis were briefly described in Chapter 1 and illustrated (Figure 1.5). Here a more detailed background on their morphological, developmental, and genetic differences are provided. Streptocarpus rexii is a perennial plant with an excentric rosulate growth form, and has open-tube type flowers with pollination chambers and a purple anthocyanin stripe pigmentation in the corolla (Jong and Burtt, 1975; Möller et al., 2018). During embryogenesis, the S. rexii embryo does not develop a shoot apical meristem (SAM) between the cotyledons (Jong, 1970; Mantegazza et al., 2007). The seedlings also lack a SAM and the cotyledons develop unequally in size (anisocotyly) due to the activity of a basal meristem (BM) at the proximal end of the lamina of the macrocotyledon (Jong, 1970; Mantegazza et al., 2007; Nishii and Nagata, 2007). In anisocotylous seedlings at around 21 to 35 days-after-sown (DAS), the groove meristem (GM) first emerges as a group of densely staining cells between the two cotyledons at the base of the macrocotyledon. With further development, the GM is organised and possesses a tunica-corpus-like meristem structure, and is located at the groove at the junction of lamina and petiolode of the macrocotyledon (Nishii and Nagata, 2007). The formation of the first phyllomorph occurs at around 65 DAS (Nishii et al., 2010a), as the GM transforms into the bulge stage (i.e. forming a bulge of small meristematic cells), followed by the dome-shaped GM stage (i.e. a bulge partly covered with trichomes), followed by the formation of the first phyllomorph (Nishii and Nagata, 2007). Streptocarpus grandis is a monocarpic plant (i.e. dies after flowering and fruiting) with a unifoliate growth form, and has open-tube type flowers with broad cylindrical tubes

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Chapter 6: QTL mapping that have purple pigmentation blotches and yellow spots (Möller et al., 2018; Figure 1.5 c and d). The embryo and early seedling stages of S. grandis are similar to those of S. rexii (Jong, 1970). The onset of anisocotyly begins at about 16 DAS and becomes apparent at 30 DAS, when fan-shaped BMs can be observed at the proximal end of the macrocotyledon (Jong, 1970; Imaichi et al., 2000). At about the same time, the formation of a GM is observed on the petiolode of macrocotyledons and is distinguished from the surrounding tissue by smaller cell sizes. The GM increases gradually in size with the enlarging macrocotyledon and petiolode, and a tunica-corpus-like meristem structure is established (Imaichi et al., 2000). The inflorescence meristem later initiates from the GM with multiple inflorescence primordia arising in acropetal order (Jong, 1970, 1978; Imaichi et al., 2000). Occasionally an additional phyllomorph may form at the base of the first inflorescence towards the end of the flowering season, termed ‘accessory phyllomorph’, ‘subtending phyllomorph’ or ‘supplementary phyllomorph’ (Oehlkers 1956; Jong, 1978; Dubuc-Lebreux, 1978; Nishii et al., 2012a). This development was documented in several unifoliate species including S. grandis , S. wendlandi, S. michelmorei and S. goetzei , and external hormone treatment with gibberellin can enhance the production of more accessory phyllomorphs (Dubuc-Lebreux, 1978; Nishii et al., 2012a). It is unknown whether the accessory phyllomorph originates from the GM or from a separate blastogen (Jong, 1978). Hybrid plants of crosses S. grandis × S. rexii all show a rosulate growth form, and the (S. grandis × S. rexii ) × S. grandis backcross (BC) population were reported to segregate into a Mendelian ratio of rosulate to unifoliate ratio of 3:1 (Oehlkers, 1938, 1942). It was suggested that the rosulate phenotype is regulated by an early acting locus (E) and a late acting locus (L); the early locus producing a rosulate to unifoliate 1:1 ratio in six-months-old BC plants, and the late locus producing a rosulate to unifoliate 3:1 ratio in nine-months-old BC plants (Oehlkers 1942). Streptocarpus rexii is hypothesised to carry the dominant alleles at both loci (E/E and L/L), and S. grandis the recessive alleles (e/e and l/l; Oehlkers, 1938; 1942). However, there can be great variations in the time the rosulate phenotype appears, and it may take longer time observe the 3:1 ratio (Oehlkers 1942). On the other hand, more recent studies using other rosulate × unifoliate crossing combinations, including S. rexii × S. wittei and S. rexii × S. dunnii , suggest the distinction between rosulate and unifoliate may not be clear in the BC population, and ambiguous phenotypes can be found (Harrison, 2002; Harrison et al., 2005). For instance, some backcross individuals may have two macrocotyledons, and if the phenotype was not scored at an early stage the second macrocotyledon may result in the plant to be scored as a rosulate phenotype. The formation of an accessory phyllomorph can also cause confusion, as it is morphologically similar to a phyllomorph in rosulates but the trait is actually inherited from the unifoliate parent, and it was suggested that they should not be scored as a rosulate phenotype (Harrison, 2002). At least six morphological types were observed in BC populations previously, including single

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Chapter 6: QTL mapping leaf unifoliates, plants with two macrocotyledons, plants with one main leaf with some very small additional leaves, plants with two main leaves and some small ones, plants with more than two main leaves but not fully rosulate, and fully rosulate plants (Harrison, 2002). In addition to the rosulate and unifoliate phenotypes, other traits were described for S. grandis × S. rexii F2 populations, but not BC populations (Oehlkers, 1942). A Mendelian segregation 3:1 ratio was observed for the inflorescence colour, midrib colour, and absence / presence of striped pigmentation in the flower (Oehlkers, 1942). Other traits such as presence or absence of yellow spots were studied in other Streptocarpus hybrids, and were reviewed in Chapter 1 (Table 1.2 and Table 1.3).

6.1.3 Objectives of this chapter Overall, the S. grandis × S. rexii BC population can be used to study the genetic inheritance of Streptocarpus morphological traits, and QTL mapping can shed light on the underlying genetics of these traits. In this chapter, the vegetative and floral characters were studied for the parental lineages and the BC population, including the growth habit, floral dimension traits, flowering time, and flower pigmentation patterns. The segregation and correlation patterns between the phenotypes were investigated, and QTL mapping of the measured traits performed using the genetic maps constructed in Chapter 5. In particular, the main focus was the mapping of the rosulate / unifoliate loci, with SIM and CIM of four different scoring methods performed on three different genetic maps (i.e. MapA, MapB-1, and MapB-3) to retrieve as much information as possible. For other traits measured, SIM analyses were performed using the main genetic map MapB-1 as this map represent the most stringently filtered genetic map (i.e. based on contaminant-free genome assembly and with strigent marker filtering strategy). Finally, we identified the genome scaffolds that fallen within the QTL found that are associated with rosulate / unifoliate trait, and performed genome annotation on the scaffolds in search of candidate genes.

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Chapter 6: QTL mapping 6.2 Materials and methods A flowchart summarising the whole analysis process carried out in this chapter can be found in Appendix 6.1.

6.2.1 Plant materials The (S. grandis × S. rexii ) × S. grandis backcross population used for phenotyping and QTL mapping consisted of 233 plants and was the same as described in section 5.2.1 (Figure 6.2). For the phenotype scoring of the parental materials, four plants of S. rexii (accession 20150819*A) were used, which were propagated from a single plant using leaf cuttings and thus have the same qualifier *A. For S. grandis , eight S. grandis BC plants (accession 20150821) and one S. grandis F1 plant (accession 20151810) were available at the time of this experiment and were all used for phenotype scoring. For the S. grandis × S. rexii F1 hybrid, three leaf-cutting-propagated plants of the original F1 lineage (accession 20071108*J) were used (Table 6.1). All plant materials were sown, propagated and maintained in the living research collection at the Royal Botanic Garden Edinburgh.

Table 6.1 List of parental and backcross materials used in the study No. Taxon Accession Qualifier Date sown Note plants Streptocarpus rexii 20150819 A 17.01.2015 4 Used for genome sequencing Streptocarpus grandis BC 20150821 A, B, C, 17.01.2015 8 Used for H, I, K, genome M, O sequencing Streptocarpus grandis F1 20151810 O 27.07.2015 1

Streptocarpus grandis F1 × 20071108 J 27.08.2007 3 S. rexii

(S. grandis × S. rexii ) × 20150825 A - IS 17.01.2015 233 Used for S. grandis BC genetic mapping

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Figure 6.2 Streptocarpus materials used in the study. (a) S. rexii . (b) S. grandis . (c) S. grandis × S. rexii F1. (d) Example of a rosulate (S. grandis × S. rexii ) × S. grandis BC plant. (e) Example of a unifoliate BC plant. Bars = 10 cm.

6.2.3 Morphology scoring of the parental plants Floral and vegetative characters of the parental materials were measured. For the scoring of floral characters, fresh flowers were collected from S. rexii (4 plants, totally 17 flowers), S. grandis BC (8 plants, totally 12 flowers), S. grandis F1 (1 plant, 10 flowers), and S. grandis  rexii F1 hybrids (Table 6.3; 3 plants, totally 17 flowers). Photos of the collected flowers were taken including side view, top view, front five, and dissected view (removal of the adaxial side of the corolla) using a Canon G12 camera (Canon Inc., Tokyo, Japan) (Figure 6.3 a, b, c, d). All images taken have a scale ruler for standardisation. The images were analysed in ImageJ v1.48 (Schneider et al., 2012), and the each character measured as summarised in Table 6.4 and Figure 6.3. The list of traits measured was based on previous studies (Lawrence, 1957; Oehlkers, 1967; Harrison et al., 1999; Chou, 2008). For measuring the quantitative floral traits the scale in the photos was used, and the Straight line tool in ImageJ used for length measurements. For binary traits, visual inspection was made directly on the photo. The pigmentation traits were scored using the dissected flower photos (Figure 6.3 d). Statistical tests were carried out for quantitative traits in R v3.3.0 (R Development Core Team, 2008): The Wilcoxon-rank-sum test (Package ‘wilcox.test’ in R; Bauer, 1972) was used for comparing the quantitative data between S. grandis F1 and S. grandis BC , and between S. rexii and S. grandis (S. grandis BC and S. grandis F1 ); and Dunn’s post-hoc test (Package ‘dunn.test’ in R; Dunn, 1964) was used for three-ways comparisons among S. rexii , S. grandis , and the F1 hybrid.

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Chapter 6: QTL mapping Table 6.3 Number of plants and flowers collected for the trait measurement in parental materials No. flowers Taxon Accession No. plants collected Streptocarpus rexii 2015 0819 4 17 Streptocarpus grandis BC 20150821 8 12 Streptocarpus grandis F1 20151810 1 10 Streptocarpus grandis × S. rexii F1 20071108 3 17

(Next page) Figure 6.3 Illustration of the floral pictures taken using a flower of S. rexii as example. (a) Flower side view. (b) Flower top view. (c) Flower face view. (d) Flower dissected view. The dorsal corolla tube was dissected off, and the pistil removed. (e)(f)(g)(h) Schematic illustration of the flower photos, with the traits measured in each photo indicated with dotted lines. (e) Side view. (f) Top view. (g) Face view. (h) Dissected view. The numbers assigned to each trait correspond to Table 6. (1) Corolla length. (2) Undilated tube length. (3) Dilated tube length. (4) Undilated tube height. (5) Dilated tube height. (6) Undilated tube width. (7) Dilated tube width. (8) Corolla face height. (9) Tube opening height, outer. (10) Tube opening height, inner. (11) Corolla face width. (12) Tube opening width, outer. (13) Tube opening width, inner. (14) Pistil length. (15) Ovary length. (17) Calyx length. (18) Stamen length. (19) Filament length, attached part. (21) Ventral tube length. (22) Ventral lobe length. (23) Dorsal tube length. (24) Dorsal lobe length. Bar = 2.5 cm.

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Chapter 6: QTL mapping Figure 6.3 Illustration of the floral pictures taken using a flower of S. rexii as example. Full legent given on prevopus page.

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Chapter 6: QTL mapping Table 6.4 List of characters measured in the parental S. rexii , S. grandis and F1 materials Trait Data Trait name Trait description No. type* I. Flower dimensions 1 Corolla length Q Length of whole corolla (corolla tube + lobe)

2 Undilated tube length Q Length of undilated part of corolla tube Length of dilated part of corolla tube 3 Dilated tube length Q (trait 1 – trait 2) 4 Undilated tube height Q Height of undilated part of corolla tube

5 Dilated tube height Q Height of dilated part of corolla tube

6 Undilated tube width Q Width of undilated part of corolla tube

7 Dilated tube width Q Width of dilated part of corolla tube

8 Corolla face height Q Height of front facing corolla

9 Tube opening height (outer) Q Height of corolla tube entrance†

10 Tube opening height (inner) Q Height of corolla tube entrance

11 Corolla face width Q Width of front facing corolla

12 Tube opening width (outer) Q Width of corolla tube ‡

13 Tube opening width (inner) Q Width of corolla tube entrance

14 Pistil length Q Length of pistil

15 Ovary length Q Length of ovary (purple part of the pistil)

16 Style length Q Length of style (trait 14 – trait 15)

17 Calyx length Q Length of calyx Length of whole stamen 18 Stamen length Q (includes filament and anther) Length of part of filaments that is fused to the 19 Filament length (attached) Q corolla tube Total filament length minus the fused part 20 Filament length (free) Q (tra it 18 – trait 19) 21 Ventral tube length Q Length of ventral tube

22 Ventral lobe length Q Length of ventral lobe

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Chapter 6: QTL mapping Table 6.4 continued Trait Data Trait name Trait description No. type* 23 Dorsal tube length Q Length of dorsal tube

24 Dorsal lobe length Q Length of dorsal lobe II. Other floral traits 25 Flowering time Q Time of first flower, unit: days after sowing Presence or absence of the pigmentation on 26 Lateral lobe pigmentation B lateral lobe Presence or absence of the pigmentation on 27 Ventral lobe pigmentation B ventral lobe 28 Yellow spot B Presence or absence of the yellow spot III. Vegetative traits 29 Rosulate/unifoliate scoring B Rosulate or unifoliate

30 Two macrocotyledons B With or without two macrocotyledons Time to first leaf initiation, unit: days after 31 Days to 1 st leaf Q sowing * Q – Quantitative data, B – binary data. † the height from the joint between two dorsal lobe to the line between the two joints of the lateral lobe and ventral lobe. ‡ The width between the two joints of the dorsal lobe and the lateral lobe.

6.2.4 Morphology scoring and examination of trait distribution in the BC mapping population The morphology of 233 plants of the BC mapping population was assessed as described previously as the parental material in section 6.2.3. For floral characters, photos of two to three flowers per BC individuals were taken (Figure 6.3) . The vegetative habit of the BC plants was observed by eye once every week and photos of each plant taken once every month from 15 April 2015 to 30 May 2016. As reported above and also observed in the present work, categorising the phyllomorphs in some BC plants was challenging due to their variability in occurrence (such as the morphologies described in Harrison 2002). As a result, we classified any additional phyllomorph observed (i.e. any newly produced ones in addition to the two cotyledons) into six types (Table 6.5, Figure 6.4). To aid the categorisation, the weekly visual observations and monthly photo records were used.

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Chapter 6: QTL mapping Table 6.5 Description of the types of additional phyllomorphs observed in the (S. grandis F1 × S. rexii ) × S. grandis BC backcross population Type Name Description of the type 1 True Additional phyllomorphs originating from the position of the phyllomorphs GM at the base of the preceding, usually cotyledonary, phyllomorph; the additional phyllomorphs were sessile, i.e. did not have an elongated stalk (Figure 6.4 a). 2 Accessory Subtending a series of acropetally forming inflorescences and phyllomorphs its petiolode was attached to the base of the preceding, usually cotyledonary, phyllomorph in the position of the GM; the petiolode usually has an elongated stalk (Figure 6.4 b). 3 Bract-like Produced from a “node” in the position of the GM at the base phyllomorphs of a late developing inflorescence, and is usually located near the base of the cotyledonary phyllomorph and is sessile (Figure 6.4 c). 4 Ambiguous Originating from the base of the cotyledonary phyllomorph in phyllomorphs position of the GM but were presumed to have been buried underground during repotting and under-developed (Figure 6.4 d). 5 Adventitious Produced along the base of the acropetally formed row of phyllomorphs inflorescences and did not originated from the position of the GM (Figure 6.4 e). 6 Paired accessory This morphology is similar to the type 2 accessory phyllomorphs phyllomorphs, but possessed two opposite phyllomorphs both bearing inflorescences in acropetal succession (Figure 6.4 f).

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Figure 6.4 Examples of vegetative phenotypes observed in the BC population. These pictures were taken after removing the plants from the pots and before pressing them into herbarium specimen, thus the morphology of each phyllomorph can be more accurately captured. (a) Type 1, true phyllomorph. (b) Type 2, accessory phyllomorph. (c) Type 3, bract-like phyllomorph. (d) Type 4, ambiguous phyllomorph. The leaf buds were buried under soil after repotting and were typically under-developed, i.e. less than 5 mm. (e) Type 5, adventitious phyllomorph, produced along the row of inflorescences. (f) Type 6, paired accessory phyllomorphs. (g) Unifoliate. Red arrow heads and red lines indicate the additional phyllomorphs observed which is the main distinguishing feature. Bars = 2 cm.

Because of the difficulties in categorising some plants in the BC population due to the type of additional phyllomorphs they produced, four different scoring methods were devised used differing in the categorisation of these ambiguous phenotypes (Table 6.6):

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Chapter 6: QTL mapping Method 1 – Plants with type 1 phyllomorphs were scored as rosulate. Those with only type 2, 3, 4, 5 and/or 6 and true unifoliates were all scored as unifoliate. Method 2 – Plants with type 1 and/or type 2 phyllomorphs were scored as rosulate. Plants with only type 3, 4, 5 and/or 6 and true unifoliates were scored as unifoliate. Method 3 – Plants with type 1 phyllomorphs were scored as rosulate. Plants with only type 2, 3, 4, 5 and/or 6 were scored as unknown. Only plants without any additional phyllomorphs were scored as unifoliate. Method 4 – Plants with type 1, 2, 3, 4 or 6 phyllomorphs were scored as rosulate. Those plants with type 5 phyllomorphs were scored as unknown, and plants without any additional phyllomorphs were scored as unifoliate.

Table 6.6 Scoring methods for the QTL analysis of the vegetative habit trait for the S. grandis F1 × S. rexii ) × S. grandis BC backcross population. Primary Type Method 1 Method 2 Method 3 Method 4 phyllomorph 1 True R R R R 2 Accessory U R ? R 3 Bract -like U U ? R 4 Ambiguou s U U ? R 5 Adventitious U U ? ? 6 Paired accessory U U ? R 7 Unifoliate U U U U

Eventually, all BC plants were processed into herbarium voucher specimens to preserve their morphology. Prior to pressing, photos of each plant were taken with particular focus on the basal part, which was easier after the plants were removed from the pots.

6.2.5 Phenotypic distribution, segregation ratio, and phenotypic correlation in the BC population The distribution of the different phenotypes observed in the BC population was visualised using R v3.3.0. The normality of the distribution (i.e. whether the data fits a normal distribution or not) was checked using Shapiro-Wilk test in R (function ‘shapiro.test’; Royston, 1982). The segregation ratio of binary traits (traits 26, 27, 28, 29, 30) were examined by Chi-square tests using the QuickCalc Chi-square test function (GraphPad Software, Inc. Accessed 5 March 2019. Available at https://www.graphpad.com/quickcalcs/chisquared1.cfm). For phenotypic correlations, Spearman correlation coefficients were calculated between each pair of quantitative traits, using the ‘cor.test’ function in R (Hollander and Wolfe, 1973; Best and Roberts, 1975). The Spearman correlation was chosen instead of Pearson’s correlation, as some of the measured traits showed non normal distributions and some were binary. Thus, the data themselves did

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Chapter 6: QTL mapping not meet the assumptions of Pearson’s correlation (i.e. the data should be continuous and follow a normal distribution; Hollander and Wolfe, 1973). The results of the correlation tests were visualised in R using the function ‘pairs’. The commands used are summarised in Box 6.1.

Box 6.1 Commands used for the phenotypic correlation analysis and visualisation of the results ## Phenotpyic correlation ## Ref : ## http://stackoverflow.com/questions/31709982/how-to-plot-in-r-a- ## correlogram-on-top-of-a-correlation-matrix ## http://stat.ethz.ch/R-manual/R-devel/library/graphics/html/pairs.html

## 1. Load data data=read.csv(" [INPUT_FILE.csv] ")

## 2. Set heatmap color range ## from left to right: ## negative correlation, no correlation, positive correlation colorRange <- c('green3', 'white', 'red3') myColorRampFunc <- colorRamp(colorRange)

## 3. Set panel.smooth for printing scattered plot ## Copy and paste the whole command block below panel.smooth<-function (x, y, col = "grey", bg = NA, pch = 18, cex = 0.8, col.smooth = "black", span = 2/3, iter = 3, ...) { points(x, y, pch = pch, col = col, bg = bg, cex = cex) ok <- is.finite(x) & is.finite(y) if (any(ok)) lines(stats::lowess(x[ok], y[ok], f = span, iter = iter), col = col.smooth, ...) }

## 4. Set panel.hist for histogram ## Copy and paste the whole command block below panel.hist <- function(x, ...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col = "white", ...) }

## 5. Set panel.cor.value for printing the correlation coefficient ## and print the degree of correlation in heatmap

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Chapter 6: QTL mapping ## Copy and paste the whole command block below panel.cor.value <- function(w, z, digits = 2, cex.cor, ...) { ## Heat map part ################ correlation <- round(cor(w, z,method="spearman",use="pairwise"),2) col <- rgb( myColorRampFunc( (1+correlation)/2 )/255 ) ## Also, square the value to avoid visual bias due to "area vs diameter" radius <- sqrt(abs(correlation)) radians <- seq(0, 2*pi, len=50) ## 50 is arbitrary x <- radius * cos(radians) * 10 y <- radius * sin(radians) * 10 ## ‘*10’ is to fill the whole square same as above x <- c(x, tail(x,n=1)) y <- c(y, tail(y,n=1)) ## make them full loops par(new=TRUE) plot(0, type='n', xlim=c(-1,1), ylim=c(-1,1), axes=FALSE, asp=1) polygon(x, y, border=col, col=col) ################ ## Correlation coefficient part ################ usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) # correlation coefficient r <- cor(w, z, method="spearman",use = "pairwise") ## I added ‘use=“pairwise”’ to deal with missing value txt <- format(c(r, 0.123456789), digits = digits)[1] txt <- paste("r= ", txt, sep = "") text(0.5, 0.6, txt) # For P-value calculation p <- cor.test(w, z, method="spearman")$p.value txt2 <- format(c(p, 0.123456789), digits = digits)[1] txt2 <- paste("p= ", txt2, sep = "") if(p<0.01) txt2 <- paste("p ", "<0.01", sep = "") text(0.5, 0.4, txt2) ################ }

## 6. Plot pdf('Correlation plot.pdf',width=20,height=20) pairs(data, lower.panel = panel.smooth, diag.panel = panel.hist, upper.panel = panel.cor.value, gap = 0.5, text.panel = NULL) dev.off()

6.2.6 QTL mapping QTL mapping was performed using the R package ‘qtl’ v1.39-5 (Broman and Sen, 2009) in R v3.3.0. The genetic maps reported in Chapter 5 were used for the analysis, in specific the combined approach-MapA, MapB-1, and MapB-3. The analysis focused particularly on the mapping of the rosulate / unifoliate loci, which was performed on all three genetic maps using both SIM and CIM methods (Table 6.7). On the other hand, the mapping

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Chapter 6: QTL mapping of all other traits was solely performed on MapB-1 using the SIM method, as this map is composed by the most stringently filtered markers and hence is the most accurate genetic map (Table 6.7).

Table 6.7 Details of the genetic maps used for QTL mapping Combined- MapB-1 MapB-3 MapA Genetic map statistics No. of linkage group recovered 16 17 16 No. of mapped markers 599 377 853 Total map distance (cM) 1,578.2 1,144.2 1,389.9 Average distance between markers 2.6 3.0 1.6 (cM) Usage in QTL mapping Used for rosulate / unifoliate trait yes yes yes mapping Used for quantitative trait mapping no yes no Used for other binary trait mapping no yes no

The function ‘calc.genoprob’ of the ‘qtl’ package was used to calculate the underlying genotype at every 1 cM using the Haldane map function (Haldane, 1919). The function ‘scanone’ was then used for the SIM method to calculate the likelihood that the genetic regions were associated with the trait variations, and the results were visualized as a LOD curve. The model selection for the SIM analysis was based on the type of distribution of the measured traits (described in section 6.2.3). For quantitative traits showing normal distributions, extension of the Haley-Knott regression method was used (Feenstra et al., 2006); for quantitative traits showing nonparametric distribution, the model “np” was selected (Kruglyak and Lander, 1995); for binary traits, including the rosulate / unifoliate trait, the model ‘binary’ was selected (Xu and Atchley, 1996; Broman, 2003). For CIM for the rosulate / unifoliate trait, the function ‘cim’ was used (Broman and Sen, 2009). The genome-wide LOD threshold was determined in 5,000 permutation tests, i.e. option ‘n.perm’ in the function ‘scanone’, and the value corresponding to 0.05 false discovery rate was chosen as the LOD threshold (Broman and Sen, 2009). LOD curves showing ‘peaks’ (a LOD score higher than the obtained threshold) were examined and their Bayes confidence intervals calculated using the ‘qtl’ package function ‘lodint’ (Manichaikul et al., 2006). The percentage of phenotypic variance explained was calculated using the ‘fitqtl’ function. The effect plots of the measured loci were generated using the ‘effectplot’ function. All commands and functions used are summarised in Box 6.2.

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Chapter 6: QTL mapping Box 6.2 Commands used for QTL mapping using the ‘qtl’ package in R ## 1. Install package install.packages("qtl") library(qtl)

## 2. Load input data. The missing genotype are denoted as – or NA data=read.cross(format="csv",file=" [R/QTL_INPUT_FILE.csv] ",na.strings=c(" -","NA"),genotypes=c("b","h"),estimate.map=FALSE,convertXdata=FALSE)

## 3. QTL mapping # 3.1 Generation of missing genotype using HMM model with 1 cM iteration data = calc.genoprob(data, step=1, stepwidth="fixed")

# 3.2 SIM with the traits listed in column 1 in the input file # First line: for binary traits # Second line: for quantitative traits # Third line: for nonparametric traits morph.bin = scanone(data,pheno.col=1,model="binary") morph.ehk = scanone(data,pheno.col=1,model="normal",method=”ehk”) morph.np = scanone(data,pheno.col=1,model="np")

# 3.3 CIM Cim.bin=cim(data,pheno.col=1)

# 3.4 Check LOD distribution of all LGs plot(morph.bin) plot(morph.ehk) plot(morph.np)

# 3.5 Permutation test to calculate LOD threshold, with 5,000 permutations morph.perm.bin = scanone(data,pheno.col=1,model="binary",n.perm=5000) morph.perm.ehk = scanone(data,pheno.col=1,model="normal",method=”ehk”,n.perm=5000) morph.perm.np = scanone(data,pheno.col=1,model="binary",n.perm=5000)

# 3.6 Obtain the LOD threshold corresponding to 0.05 false discovery rate quantile(morph.perm.bin,0.95) quantile(morph.perm.ehk,0.95) quantile(morph.perm.np,0.95)

# 3.7 Calculate Bayes confidence interval based on the LOD threshold bayesint(morph.bin, [LG] ,[LOD_THRESHOLD] ,expandtomarkers=TRUE) bayesint(morph.ehk, [LG] ,[LOD_THRESHOLD] ,expandtomarkers=TRUE) bayesint(morph.np, [LG] ,[LOD_THRESHOLD] ,expandtomarkers=TRUE)

## 4. Fitting QTL models and calculated the variance explained% # Example for binary trait with one locus detected qc= [LG_OF_DETECTED_LOCI] qp= [POSITION_OF_MARKER] qtl=makeqtl(data,qc,qp,what="prob") lod=fitqtl(data,pheno.col= [TRAIT_COLUMN] ,qtl,formula=y~Q1,model="binary") summary(lod)

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# Example for quantitative trait with five loci detected qc= [LG_OF_DETECTED_LOCI] qp= [POSITION_OF_MARKER] qtl=makeqtl(data,qc,qp,what="prob") lod=fitqtl(data,pheno.col= [TRAIT_COLUMN] ,qtl,formula=y~Q1*Q2*Q3*Q4*Q5 ,model=”normal”,method="ehk") summary(lod)

# Example for nonparametric trait qc= [LG_OF_DETECTED_LOCI] qp= [POSITION_OF_MARKER] lod=fitqtl(data,pheno.col= [TRAIT_COLUMN] ,qtl,formula=y~Q1*Q2*Q3*Q4*Q5 ,model=”np”) summary(lod)

## 5. Effect plot of a specific marker (within the detected QTLs) effectplot(data,pheno.col= [TRAIT_COLUMN] ,mname=” [MARKER_NAME] ”)

6.2.7 Genome annotation for the rosulate / unifoliate loci To search for candidate genes related to the rosulate / unifoliate trait, the BTL regions identified from the above section were examined. Genetic markers which fell within the BTL regions (from both SIM and CIM mapping results of all three maps) were listed, and their corresponding genome scaffolds retrieved (the genome scaffolds where the marker sequences were derived from). The retrieved sequences were annotated using the web-based pipeline MEGANTE (Numa and Itoh, 2014 Release 2018-02), with Nicotiana tabacum chosen as reference for the gene prediction and annotation. The retrieved sequences were all from reference-based approach markers whereas no genome information was available for de novo -approach-derived markers.

6.2.8 Scaffold to scaffold alignments To identified the relationships between genome scaffolds (i.e. whether the scaffold from one genome assembly can be align to a scaffold from another genome assembly), scaffold-to-scaffold alignment was carried out using the D-GENIES web-tool under default settings (Cabanettes and Klopp, 2018). Three different alignments were performed: (1) using scaffolds of MapA as query sequence (which came from the preliminary SOAPdenovo2 S. rexii assembly), and scaffolds of MapB-1 / MapB-3 as target sequence (which was the filtered ABySS2 S. rexii assembly); (2) using scaffolds of MapA as query sequence, and the S. grandis genome assembly (the filtered ABySS2 S. grandis assembly) as target sequence; (3) using scaffolds of MapB-1 / MapB-3 as query sequence, and the S. grandis genome assembly as target sequence. Alignment (1) was used to identify shared-sequences between the two S. rexii genome assemblies; alignment (2) and (3) were used to identify the corresponding allelic sequences from the S. grandis genome assembly.

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Chapter 6: QTL mapping 6.3 Results 6.3.1 Morphological variation between S. rexii , S. grandis , and their F1 hybrid The two S. grandis lineages used (i.e. S. grandis F1 and S. grandis BC ) showed no significant difference between the two lineages in most of the 25 quantitative traits measured, except for 7 traits: corolla length (P < 0.01), corolla tube length (P < 0.01), corolla face width (P < 0.01), ventral and dorsal tube length (P < 0.01), pistil length (P < 0.01), ovary length (P < 0.01), and flowering time (P < 0.01) (Appendix 6.2). In general, the flower of S. grandis F1 lineage was 0.2 – 0.7 cm longer and wider than the flower of S. grandis BC (Appendix 6.2). In terms of flowering time, the recorded flowering time of S. grandis F1 lineage is earlier (on average 265 DAS) than that of the S. grandis BC lineage (on average 377 DAS; Appendix 6.2). Statistical comparisons were carried out on the three parental lineages, i.e. S. rexii , S. grandis F1 , and F1 hybrid (Table 6.8 and Figure 6.7). Amongst all the floral dimension traits measured, S. rexii usually showed the larger trait values (Table 6.8; Figure 6.7, red boxes) while S. grandis usually had the lowest trait values (Figure 6.7, blue boxes). The F1 hybrid values usually fell between those of S. rexii and S. grandis (Figure 6.7, purple boxes), and sometimes more closely resembled S. rexii (e.g. corolla length and dilated tube length; Figure 6.7 a and c). The differences between S. rexii and S. grandis were statistically significant for most of the traits, except for ‘undilated tube height’ (Appendix 6.3). In general, the S. rexii flower was larger than or was similar to the S. grandis flower in most the floral traits measured. Three way comparisons were also conducted for the three parents (Appendix 6.4). The ‘undilated tube height’ was identified to be not statistically different among all three parents (Appendix 6.4; Trait 4), while in several other traits, the trait value of the F1 hybrid was more similar to that of S. rexii (Appendix 6.4; Trait 1, 3, 6, 8, 9, 10, 16, 17, 21, 22, 24). In terms of flowering time, S. rexii flowered on average at 237 days after sowing (DAS), which is earlier than S. grandis (329 DAS; Table 6.8). For the F1 lineage, because the plants used in this study originated from leaf cuttings of a plant originally sown and grown in 2007 (accession 20071108), the ‘flowering time (DAS)’ data were not available. Pigmentation on the lateral lobes was observed in all three lineages (Figure 6.8 a). Pigmentation on the ventral lobe was observed in S. rexii and the F1 hybrid, but not in S. grandis (Figure 6.8 b). A yellow spot was observed in S. grandis , but not in S. rexii or the F1 hybrid (Figure 6.8 c). Plants with two macrocotyledons were not observed among the parental lineage materials.

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(a) (b) (c) (d) Corolla length (cm) Undialated tube length (cm) Dialated tube length (cm) Undialated tube height (cm) 4 5 6 7 8 0.4 0.5 0.6 0.7 1.5 2.0 2.5 3.0 3.5 1.5 2.0 2.5 3.0

S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

(e) (f) (g) (h) Dialated tube height (cm) Undialated tube width (cm) Dialated tube width (cm) Corolla face height (cm) 2 3 4 5 6 0.4 0.5 0.6 0.7 0.6 0.7 0.8 0.9 1.0 1.1 1.2 0.6 0.7 0.8 0.9 1.0 1.1 1.2

S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

(i) Outer tube opening width (cm) (j) Inner tube opening width (cm) (k) Corolla face width (cm) (l) Outer tube opening width (cm) 1.0 1.5 2.0 2.5 2 3 4 5 6 7 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0

S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

(m) Inner tube opening width (cm) (n) Pistil length (cm) (o) Ovary length (cm) (p) Style length (cm) 1.0 1.5 2.0 2.5 1.5 2.0 2.5 0.6 0.8 1.0 1.2 1.4 2.0 2.5 3.0 3.5 4.0 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

(q) Calyx length (cm) (r) Stamen length (cm) (s) Filament length (fused) (cm) (t) Filament length (free) (cm) 2.0 2.5 3.0 3.5 0.3 0.4 0.5 0.6 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.4 1.6 1.8 2.0 2.2 2.4 2.6

S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

(u) (v) (w) (x) Ventral tube length (cm) Ventral lobe length (cm) Dorsal tube length (cm) Dorsal lobe length (cm) 2.5 3.0 3.5 4.0 4.5 5.0 3.0 3.5 4.0 4.5 5.0 5.5 6.0 0.6 0.8 1.0 1.2 1.4 1.6 0.4 0.6 0.8 1.0 1.2 1.4 1.6 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1 S. rexii S. grandis F1

Figure 6.7 Box plots of floral quantitative traits measured in the parental lineages. Red: S. rexii . Blue: S. grandis . Purple: F1 hybrid. Unit = cm. (a) Corolla length. (b) Undilated tube length. (c) Dilated tube length. (d) Undilated tube height. (e) Dilated tube height. (f) Undilated tube width. (g) Dilated tube width. (h) Corolla face height. (i) Tube opening height, outer. (j) Tube opening height, inner. (k) Corolla face width. (l) Tube opening width, outer. (m) Tube opening width, inner. (n) Pistil length. (o) Ovary length. (p) Style length. (q) Calyx length. (r) Stamen length. (s) Filament length, attached part. (t) Filament length, free part. (u) Ventral tube length. (v) Ventral lobe length. (w) Dorsal tube length. (x) Dorsal lobe length.

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Table 6.8 Summary of results of the morphometric measurements and flowering time of the parental lineages Undilated Undilated Dilated tube Undilated Dilated tube Dilated tube Species N Corolla length tube tube length length tube height height width width S. rexii 17 6.79 ± 0.60 2.65 ± 0 .42 2.74 ± 0.46 0.49 ± 0.08 1.01 ± 0.11 0.44 ± 0.05 1.03 ± 0.10 S. grandis* 22 4.13 ± 0.46 1.58 ± 0.10 1.86 ± 0.38 0.52 ± 0.09 0.71 ± 0.06 0.55 ± 0.09 0.74 ± 0.13 F1 hybrid 17 6.35 ± - 0.46 2.19 ± 0.26 2.90 ± 0.62 0.53 ± 0.07 0.95 ± 0.05 0.49 ± 0.04 0.90 ± 0.08

Tube Tube Corolla Tube opening Tube opening Corolla opening opening Pistil Ovary Style Species face height (outer) height (inner) face width width width length length length height (outer) (inner) S. rexii 4.29 ± 0.86 1.94 ± 0.49 1.51 ± 0.34 5.06 ± 0.91 2.73 ± 0.67 1.88 ± 0.41 3.91 ± 0.10 2.60 ± 0.12 1.31 ± 0.14 S. grandis* 1.97 ± 0.44 1.08 ± 0.24 0.93 ± 0.21 2.29 ± 0.52 1.29 ± 0.22 0.88 ± 0.16 2.49 ± 0.23 1.71 ± 0.31 0.79 ± 0.15 F1 hybrid 3.86 ± 0.49 1.73 ± 0.21 1.42 ± 0.17 4.36 ± 0.50 1.91 ± 0 .20 1.34 ± 0.18 3.77 ± 0.12 2.47 ± 0.08 1.30 ± 0.07

Filament Filament Calyx Stamen Ventral Ventral Dorsal tube Dorsal lobe Flowering Species length length length length tube length lobe length length length time (DAS) (attached) (free) S. rexii 0.56 ± 0 .07 3.44 ± 0.10 2.55 ± 0.08 0.88 ± 0.07 5.39 ± 0.51 1.39 ± 0.17 4.62 ± 0.33 1.13 ± 0.22 237 ± 0.00 S. grandis* 0.45 ± 0.11 2.13 ± 0.12 1.59 ± 0.11 0.53 ± 0.08 3.44 ± 0.40 0.69 ± 0.10 2.74 ± 0.24 0.62 ± 0.08 329 ± 77.08 F1 hybrid 0.51 ± 0.06 3.05 ± 0.11 2.28 ± 0.11 0.77 ± 0.11 5.08 ± 0.47 1.26 ± 0.07 4.04 ± 0.22 1.04 ± 0.14 -

Lateral lobe Ventral lobe Rosulate / Two Species Yellow spot 1st leaf time pigmentation pigmentation Unifoliate macrocotyledons S. rexii present present absent Rosulate N/A 65 DAS S. grandis* absent absent present Unifoliate N/A N/A F1 hybrid present present absent Rosulate N/A 65 DAS * The values for S. grandis are averages taken from both lineages. S. grandis F1 and S. grandis BC

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Figure 6.8 Floral pigmentation of the parental lineages. (a) Pigmentation on the lateral lobes. (b) Pigmentation on the ventral lobe. (c) Yellow spot on the ventral side of the corolla tube. Green arrows: pigmentation on the lateral and ventral corolla. Orange arrow: yellow spot on the ventral corolla. Bars = 2.5 cm.

6.3.2 Segregation of morphological variations in the backcross population The morphology of the 200 BC plants were measured and the segregation patterns were examined (Appendix 6.5, 6.6, 6.7). In terms of the segregation of the vegetative habits, when scoring using Method 1 (i.e. score all ambiguous morphologies as unifoliate) the ratio of rosulate to unifoliate was 107:93 (Table 6.9). The ratio did not fit the expected 3:1 ratio (Chi-square test: P < 0.0001), but conformed to a 1:1 ratio (Chi-square test: P = 0.3222),. Method 2 (i.e. score rosulate and accessory phyllomorphs as rosulate, and others as unifoliate) scoring gave a ratio of rosulate to unifoliate of 142:58, which fitted the expected 3:1 ratio (Chi-square test: P = 0.1914,). Method 3 (i.e. score all ambiguous morphologies as unknown) scoring gave a ratio of 123:25, which deviated from the expected 3:1 ratio (Chi- square test: P = 0.0227) but conformed to a 4:1ratio (Chi-square test: P = 0.3445). The scoring of Method 4 (i.e. score plants with any phyllomorphs produced from the GM as rosulate, and others as unifoliate or unknown) gave a ratio of 154:44, which best fitted a 3:1

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Chapter 6: QTL mapping ratio (Chi-square test: P = 0.3667). In addition, the ratio of plants with presence:absence of accessory phyllomorphs in the BC population was 104:77, which slightly deviated from the Mendelian ratio of 1:1 (Chi-square test: P = 0.0448).

Table 6.9 Result of rosulate / unifoliate trait scoring in the BC population using four different methods Rosulate Unifoliate R:U P-value N Unknown (R) (U) ratio (Chi-square test) Method 1 200 107 93 0 1:1 0.3222 Method 2 200 142 58 0 3:1 0.1914 Method 3 200 123 25 52 4:1 0. 3445 Method 4 200 154 44 2 3:1 0.3667

Among the 200 BC plants used for genetic mapping, 14 plants did not produce flowers and thus their floral data were not available (Appendix 6.5; i.e. qualifier G, AH, AS, BQ, BY, CE, DJ, DO, DZ, FR, GF, IF, IJ, IQ). The distributions of the measured quantitative traits indicated that 14 showed a normal distribution and another 12 skewed nonparametric distributions (Table 6.10 and Appendix 6.6, 6.7). The flower pigmentation patterns among the BC plants showed great variation and a gradient of pigmentation intensity and stripiness (Figure 6.9). The variation could roughly be categorised into nearly absence of stripes on the corolla floor (Figure 6.9 a), a pattern that resembled S. grandis flowers, with two short double stripes in the throat (Figure 6.9 b), a pattern somewhat more similar to the F1 plant flowers with seven stripes (Figure 6.9 c), and a very intensive pigmentation blotch covering the entire corolla floor (Figure 6.9 d). The least-pigmented phenotype (Figure 6.9 a) and the most intensely pigmented phenotype (Figure 6.9 d) were only observed in the BC population but not in the parental materials (Figure 6.8) and represented new phenotypes. There was some variation in the two more- intensively pigmented phenotypes (i.e. Figure 6.9 c and d) that made their categorisation sometimes difficult. In terms of segregation patterns, the presence and absence of pigmentation on the lateral lobe segregated as 161:25, a non-Mendelian ratio (i.e. Figure 6.9 a are scored as absence; Figure 6.9 b, c, d are scored as presence). The presence and absence of the ventral lobe pigmentation stripe was 102:85 (i.e. Figure 6.9 a and b are scored as absence; Figure 6.9 c and d are scored as presence), which fitted a Mendelian ratio of 1:1 (Chi square test: P = 0.2138). The yellow spot on the ventral lobe observed in the backcross population (Figure 6.10 a) was sometimes more intensive in colour compared to the S. grandis parent (Figure 6.10 b). However, in the case where the purple pigmentation in the corolla tube was very intensive (i.e. Figure 6.9 d), the purple pigmentation may have covered the area where the yellow spot was located, making the yellow spot trait indeterminable. For these plants the

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Chapter 6: QTL mapping yellow spot trait was scored as unknown (Appendix 6.5). The ratio of presence and absence of the yellow spot was 76:110, and deviated from a 1:1 ratio (Chi square test: P = 0.0127). Two macrocotyledons were rarely encountered in a seedling, only in 6 plants among the 200 BC plants observed (Appendix 6.5).

Table 6.10 Summary of the results of the morphology scoring for the BC population Trait Trait (trait unit) Average value Note No. 1 Corolla length (cm) 4.57 ± 0.56 Normal distribution 2 Undilated tube length (cm) 1.46 ± 0.19 Normal distribution 3 Dilated tube length (cm) 3.28 ± 0.43 Normal distribution 4 Undilated tube height (cm) 0.52 ± 0.07 Non-normal distribution 5 Dilated tube height (cm) 0.80 ± 0.09 Normal distribution 6 Undilated tube width (cm) 0.56 ± 0.08 Non-normal distribution 7 Dilated tube width (cm) 0.90 ± 0.11 Non-normal distribution 8 Corolla face height (cm) 2.65 ± 0.47 Normal distribution 9 Tube opening height (outer) (cm) 1.20 ± 0.20 Non-normal distribution 10 Tube opening height (inner) (cm) 0.97 ± 0.18 Non-normal distribution 11 Corolla face width (cm) 2.99 ± 0.46 Normal distribution 12 Tube opening width (outer) (cm) 1.50 ± 0.22 Non-normal distribution 13 Tube opening width (inner) (cm) 1.01 ± 0.17 Non-normal distribution 14 Pistil length (cm) 3.03 ± 0.25 Normal distribution 15 Ovary length (cm) 1.99 ± 0.20 Normal distribution 16 Style length (cm) 1.03 ± 0.13 Non-normal distribution 17 Calyx length (cm) 0.54 ± 0.13 Non-normal distribution 18 Stamen length (cm) 2.70 ± 0.25 Normal distribution 19 Filament length (attached) (cm) 2.00 ± 0.21 Normal distribution 20 Filament length (detached) (cm) 0.70 ± 0.11 Non-normal distribution 21 Ventral tube length (cm) 3.76 ± 0.46 Normal distribution 22 Ventral lobe length (cm) 0.99 ± 0.16 Normal distribution 23 Dorsal tube length (cm) 3.07 ± 0.33 Normal distribution 24 Dorsal lobe length (cm) 0.91 ± 0.16 Normal distribution 25 Flowering time (DAS) 247 ± 77.36 Non-normal distribution

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Chapter 6: QTL mapping Table 6.10 continued Trait Trait (trait unit) Average value Note No. 26 Lateral lobe pigmentation present:absent = 161:25 Non-Mendelian ratio 27 Ventral lobe pigmentation present:absent = 102:85 Mendelian ratio = 1:1 28 Yellow spot present:absent = 76:110 Non-Mendelian ratio 30 Accessory phyllomorph present:absent = 104:77 Non-Mendelian ratio 31 Two macrocotyledons present:absent = 6:193 Non-Mendelian ratio

Figure 6.9 Examples of the floral pigmentation patterns observed in the BC population. Flowers were dissected by cutting between the lateral and dorsal corolla lobes on both sides. The lower parts of the corolla including the lateral and ventral lobes are shown. (a) Flower lacking stripe pigmentation on both lateral and ventral lobes. (b) Flower with four short lateral stripes on the three lobes, lacking the middle stripes. (c) Flower with seven long stripe pigmentation on the lateral and ventral lobes. (d) Flower with seven intensive stripe pigmentation showing as a blotch. Bar = 2.5 cm.

Figure 6.10 Examples of the yellow spot phenotype observed in (a) BC plants, and in (b) parental lineages. Bar = 2.5 cm.

Analysis of the correlations between the measured traits indicated that all floral dimension traits were significantly positively correlated with each other. The correlation of ‘style length’ and ‘filament length, detached’ was less significant (Figure 6.11; traits 16 and

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Chapter 6: QTL mapping 20). On the other hand, the ‘flowering time’ (trait 25) was negatively correlated with floral dimensions (i.e. the later the plant flowered the smaller the flower size). At the same time ‘flowering time’ was positively correlated with all four vegetative habit scoring methods (traits 29-32; i.e. early flowering plants were more likely to be rosulate). The two floral pigmentation traits, ‘lateral’ and ‘ventral pigmentation’ (traits 26 and 27), were positively correlated to each other. But the two traits were negatively correlated with the ‘yellow spot’ (trait 28). The ‘ventral pigmentation’ trait was also positively correlated to ‘tube opening height’ (traits 9 and 10). In terms of vegetative traits, the four vegetative habit scoring methods were all positively correlated with each other (Figure 6.11; traits 29 - 32). The presence of ‘two macrocotyledons’ (trait 34) was positively correlated only with Method 1 scoring, but not with other traits. In addition, various correlations were found between the vegetative traits and floral dimensions, such as a positive correlation between scoring Method 4 and ‘tube opening height, outer’ trait (trait 9).

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Figure 6.11 Pairwise correlation comparisons of the measured traits in the BC population. The graph has three parts: the upper triangle (coloured correlations), the diagonal (with histograms), and the lower triangle (scatter plots and trend lines). The colour scale at the bottom left of the graph shows the degree of correlation, with positive correlations in red, and negative correlations in green. The upper triangle shows the degree of correlation, the Spearman correlation coefficient (r), and P-values. Asterisks indicate the degree of significance of the correlation (* P ≤ 0.05, ** P ≤ 0.01). Diagonal line is composed of histograms of the distribution of each of the trait values measured in the BC population. Lower triangle shows scattered plots of the measured trait values (grey dots) and polynomial regression lines (black).

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Chapter 6: QTL mapping 6.3.3 Mapping of the rosulate / unifoliate loci In the OTL analysis using MapA, BTL signals were detected on LG1, LG7, and LG9 (Table 6.11; Figure 6.12 a). The maximum LOD score of the detected loci ranged from 3.24 (Method 4, LG9) to 6.28 (Method 2, LG9) while the calculated LOD threshold was between 3.05 and 3.10. Among the detected loci, the one on LG9 was detected in the analysis of all four scoring methods (Figure 6.12 d). Mapping of scoring Method 2 and 4 detected a signal on LG1 (Figure 6.12 b), and only the mapping of Method 4 scoring detected a signal on LG7 (Figure 6.12 c). The confidence intervals varied from the smallest of 12.9 cM (Method 4, LG1) to as large as the whole linkage group (148.04 cM; Method 2, LG1). The highest percentage of variance explained was obtained in the mapping using scoring Method 4 (24.77%, Table 6.11). The CIM results showed similar LOD curves to those of SIM (Figure 6.13), except for the mapping of scoring Method 4 where only the locus on LG7 was detected (Figure 6.13 d). The BTL signals found in CIM were identical with those found by SIM and no additional BTL region was discovered (Figure 6.13). The effect plots of the detected loci are summarised in Appendix 6.8 a.

Figure 6.12 LOD curves of the rosulate / unifoliate SIM results using MapA. (a) LOD curve by standard interval mapping of all linkage groups. (b) LOD curve of LG1. (c) LOD curve of LG7. (d) LOD curve of LG9. Red: LOD score of scoring Method 1. Blue: LOD score of scoring Method 2. Green: LOD score of scoring Method 3. Yellow: LOD score of scoring Method 4. Black horizontal lines: LOD score thresholds.

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Figure 6.13 LOD curves of the rosulate / unifoliate CIM results using MapA. (a) Scoring Method 1. (b) Scoring Method 2. (c) Scoring Method 3. (d) Scoring Method 4. Red lines: CIM LOD curves. Blue lines: SIM LOD curves.

Slightly different results were obtained in the SIM using MapB-1, with one additional BTL site found on LG10 (Table 6.12). Overall, BTL signals were detected in LG2, LG4, LG10, and LG14 (Figure 6.14 a), equivalent to MapA LG1, LG7, LG6, and LG9, respectively. The LOD scores obtained in MapB-1 mapping were lower than those of MapA, with the lowest value of 3.17 (Method 4, LG2) and highest of 4.94 (Method 2, LG14). The

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Chapter 6: QTL mapping LOD thresholds obtained were the same as the mapping in MapA, ranging from 3.05 to 3.10 (Table 6.12). The BTL loci found in LG14 (equivalent to MapA LG9) were again detected in the mapping of all four scoring methods (Figure 6.14 e). Scoring Method 4 also detected the loci on LG2 (equivalent to MapA LG1; Figure 6.14 b) and LG4 (equivalent to MapA LG7; Figure 6.14 c), and the additional locus on LG10 (equivalent to MapA LG6; Figure 6.14 d) which was not found previously. The size of the confidence intervals varied from 8.65 cM (Method 4, LG10) to 49.01 cM (Method 4, LG14). The highest percentage of variance explained was obtained in the mapping using scoring Method 4, and was higher than the result when using MapA (38.66%; Table 6.12). The CIM results of the MapB-1 mapping showed similar LOD curves to SIM and the same BTL regions were identified (Figure 6.15). The CIM of scoring Method 1, 2, and 3 received the maximum LOD scores of 3.98, 5.12, and 4.75 respectively, higher than the SIM results (Figure 6.15 a). But CIM of scoring Method 4 detected no BTL at all (Figure 6.15 d). The effect plots of the detected loci are summarised in Appendix 6.8 b.

Figure 6.14 LOD curves of the rosulate / unifoliate SIM results of the MapB-1. (a) LOD curves by SIM of all linkage groups. (b) LOD curves of the LG2. (c) LOD curves of the LG4. (d) LOD curves of the LG10. (e) LOD curves of the LG14. Red: LOD curve of Method 1. Blue: LOD curve Method 2. Green: LOD curve of Method 3. Yellow: LOD curve of Method 4. Black horizontal lines: LOD score threshold.

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Figure 6.15 LOD curves of the rosulate / unifoliate CIM results of the MapB-1. (a) Scoring Method 1. (b) Scoring Method 2. (c) Scoring Method 3. (d) Scoring Method 4. Red lines: CIM LOD curves. Blue lines: SIM LOD curves.

The mapping results using MapB-3 were more similar to those of MapA, with BTL signals detected on LG2, LG4, and LG14 (Table 6.13; Figure 6.16 a). The signal in LG14 (equivalent to MapA LG9) was again detected in all four scoring methods (Figure 6.16 d). The signal in LG2 was detected in both scoring Method 2 and 4 (Figure 6.16 b), and the

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Chapter 6: QTL mapping signal in LG4 was only detected in scoring Method 4 (Figure 6.16 c). The LOD score obtained was similar to MapB-1 and lower than that of the mapping in MapA, with the lowest LOD score of 3.33 (Method 4, LG14) and highest LOD score of 5.22 (Method 2, LG14). The size of the confidence intervals ranged from 16.06 cM (Method 2, LG2) to 53.09 cM (Method 4, LG14). The highest percentage of variance explained was obtained in the mapping using scoring Method 4, but the value was lower than in the results of both MapA and MapB-1 (Table 6.13; 19.72%). The CIM results showed a slightly different pattern (Figure 6.17), and in scoring Method 1 an additional locus was found on LG1 (corresponding to MapA LG3) with a maximum LOD score of 4.19 (Figure 6.17 a). This region gave a very narrow confidence interval of 6.93 cM, and together with the LG14 locus detected in SIM they explained 17.15% of the trait variance (Table 6.13). No additional locus was found in other CIM results (Figure 6.17). The effect plots of all the detected loci are summarised in Appendix 6.8 c.

Figure 6.16 LOD curves of the rosulate / unifoliate mapping results of the MapB-3. (a) LOD score by standard interval mapping of all linkage groups. (b) LOD score of the LG2. (c) LOD score of the LG4. (d) LOD score of the LG14. Red: LOD score of Method 1. Blue: LOD score Method 2. Green: LOD score of Method 3. Yellow: LOD score of Method 4. Black horizontal lines: LOD score threshold.

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Figure 6.17 LOD curves of the rosulate / unifoliate CIM results of the MapB-3. (a) Scoring Method 1. (b) Scoring Method 2. (c) Scoring Method 3. (d) Scoring Method 4. Red lines: CIM LOD curves. Blue lines: SIM LOD curves.

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Table 6.11 Mapping of the rosulate / unifoliate loci on MapA Marker LG Marker position LOD threshold Max LOD score Bayes CI* CI* size Variance name (cM) (cM) (cM) explained † (%) Method 1 c9.loc86 9 86 3.06 4.77 77.12 – 104.94 27.81 10.68 Method 2 ST16952 1 131 3.06 3.73 0.00 – 148.04 148.04 19.56 c9.loc88 9 88 3.06 6.28 80.56 – 101.20 20.63 Method 3 BW18918 9 85 3.10 6.20 63.54 – 91.18 27.64 17.55 Method 4 ST16952 1 131 3.05 4.27 126.43 – 139.33 12.90 24.77 ST8585 7 114 3.05 4.48 61.75 – 113.64 51.89 c9.loc90 9 90 3.05 3.24 21.89 – 104.94 83.04 * CI: confidence interval. † Showing the combined variance explained of all the loci and inter-loci interactions

Table 6.12 Mapping of the rosulate / unifoliate loci on MapB-1 Marker LG Marker position LOD threshold Max LOD score Bayes CI* CI* size Variance name (cM) (cM) (cM) explained † (%) Method 1 C14.loc48 14 48 3.06 3.38 19.19 – 49.45 30.25 7.72 Method 2 C14.loc49 14 49 3.06 4.94 30.87 – 49.45 18.58 11.05 Method 3 BW5742 14 49.3 3.10 4.62 10.99 – 49.45 38.46 13.68 Method 4 C2.loc95 2 95 3.05 3.17 65.12 – 102.78 37.66 38.66 C4.loc47 4 47 3.05 3.38 28.66 – 70.23 41 .57 C10.loc66 10 66 3.05 3.41 60.66 – 69.32 8.65 ST6585 14 49.45 3.05 3.23 0.44 – 49.01 49.01

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Table 6.13 Mapping of the rosulate / unifoliate loci on MapB-3 Marker LG Marker position LOD threshold Max LOD score Bayes CI* CI* size Variance name (cM) (cM) (cM) explained † (%) Method 1 BW5121 1 1 4.19 3.06 4.19 64.64 – 71.57 6.93 17.15 BW1599 14 59.99 3.06 4.07 57.70 – 78.20 20.49 Method 2 DN2208 2 91.61 3.06 3.77 81.82 – 97.88 16.06 16.84 C14.loc76 14 76 3.06 5.22 59.99 – 78.20 18.20 Metho d 3 ST6585 14 74.93 3.10 4.74 39.45 – 78.20 38.74 13.95 Method 4 C2.loc92 2 92 3.05 3.57 67.47 – 97.88 30.41 19.72 C4.loc53 4 53 3.05 3.47 34.54 – 53.00 18.45 ST6585 14 74.93 3.05 3.33 25.11 – 78.20 53.09 1 The marker BW5121 on LG1 was found in CIM results

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Chapter 6: QTL mapping 6.3.4 Mapping of the floral and other vegetative trait loci Since MapB-1 was constructed under the most stringent filtering condition and the above mapping analysis showed an overall similar pattern in all three genetic maps, the mapping of other traits was performed solely on MapB-1. Table 6.14, Figure 6.18 and Figure 6.19 summarise the standard interval mapping results. In terms of floral characters, most had 1 to 4 QTLs associated with dimension traits. Exceptions were the ‘Dilated tube height’, ‘Style length’, and ‘Filament length (detached)’, which no QTL was found (Table 6.14). The QTLs of corolla-length related traits (i.e. corolla length, dilated and undilated tube length) were only found on LG2, with small effect sizes between 9.29% and 12.47% variance explained. The QTLs of tube height and tube width (i.e. dilated and undilated tubes) were found on LG1, LG2, and LG14, with small effects between 7.7% and 19.36% variance explained. The QTL for corolla face-related traits (i.e. corolla face height, tube opening height, corolla face width, and tube opening width) were found in LG2, LG3, LG6, and LG9, again with low proportions of variance explained between 5.47% and 18.53%. QTLs of pistil length-related traits (i.e. pistil and ovary length) located differently from those of the corolla-related traits, which were found on LG2, LG6, LG7, LG9, and LG14. In particular, the four loci identified for ‘Pistil length’ explained 39.85% of the phenotypic variance, which was highest among the floral dimension traits examined. QTLs of stamen-related traits (i.e. stamen and filament length) were found on LG2, LG7, and LG12. Finally, the QTLs of tube and lobe length-related traits (i.e. length of dorsal and ventral tube / lobe) were found on LG2, LG6, LG8, LG9, and LG12. Overall, the floral dimension-related QTLs were distributed across 9 linkage groups, including LG1, LG2, LG3, LG6, LG7, LG8, LG9, LG12, and LG14. In particular, QTLs on LG2 and LG14 were detected for multiple traits: LG2 locus was reported among 20 floral dimension traits, and LG14 locus was reported in 4 floral dimension traits (Table 6.15). The Bayes confidence interval of the detected QTLs ranged from 15.56 cM (LG2 locus, Table 6.14) up to 72.48 cM (LG7 locus for the stamen length; Table 6.14). However, the percentage of trait variance explained was low in most of the traits mapped (i.e. ~5% - 25%; Table 6.14). Two exceptions were the QTLs for ‘Pistil length’ and ‘Dorsal lobe length’, with the detected QTLs explaining 39.85% and 30.79% of the variance, respectively (Table 6.14). The mapping of the ‘Flowering time’ trait identified 3 QTLs with major effects that explained 50.88% of the trait variance (Table 6.14). The 3 QTLs were located on LG1, LG2 and LG14, with the locus on LG2 showing a high LOD score of 14.24. The region overlapped with other floral dimension traits (Figure 6.18). In particular, the confidence regions of the LG2 and LG14 QTLs were relatively specific, with a size of 15.56 cM and 13.23 cM respectively. On the other hand, the confidence interval on LG1 was less specific (75.43 cM).

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Chapter 6: QTL mapping QTLs related to the pigmentation traits were detected on the LG3, LG9 and LG10 (Table 6.14). For the pigmentation on the lateral lobes, two BTLs on LG3 and LG10 were identified with the highest LOD score of 7.29 and 6.36. The two loci have relatively specific confidence interval size of 28.56 cM and 6.15 cM, respectively, and contributed to 29.92% of the trait variance (Table 6.14). The locus on LG3 showed a very high LOD score of 37.21 and a specific confidence interval size of 18.28 cM (Table 6.14). This locus also contributed to high proportion of 59.94% of the trait variance, the highest percentage found in this study (Table 6.14). Finally, the LG3 and LG10 loci with an additional LG9 locus were found correlated to the yellow spot trait (Table 6.14). The LG10 locus showed a particularly high LOD score of 11.53, and a specific confidence interval size of 8.4 cM. The three loci combined contributed 45.58% of the trait variance.

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Figure 6.18 Summary of the QTL / BTL confidence intervals identified in the MapB-1.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Full legend given on page 250.

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Figure 6.19 LOD curves and effect plots of the standard interval mapping on MapB-1. Red horizontal lines indicates the LOD threshold.

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Table 6.14 Standard interval mapping results of morphological traits on MapB-1 Trait QTL LG QTL position LOD Max LOD Bayes CI* CI size Variance marker (cM) threshold score (cM) (cM) explained† (%) Corolla length C2.loc98 2 98.00 2.76 5.44 87.22 – 102.7 8 15.56 12.30 Undilated tube length BW7768 2 102.78 2.89 5.38 87.22 – 102.78 15.56 12.47 Dilated tube length C2.loc96 2 96.00 2.72 4.09 65.12 – 102.78 37.66 9.29 Undilated tube height BW5993 1 0.00 2.64 2.89 0.00 – 53.10 53.10 7.7 Dilated tube hei ght N/A N/A N/A 2.78 2.74 N/A N/A N/A Undilated tube width BW5993 1 0.00 2.64 3.25 0.00 – 34.36 34.36 19.36 BW7768 2 102.78 2.64 2.85 65.12 – 102.78 37.66 ST11037 14 0.02 2.64 2.79 0.00 – 30.87 30.87 Dilated tube width C2.loc102 2 102.00 2.6 7 4.14 87.22 – 102.78 15.56 12.45 BW15489 14 0.44 2.67 3.85 0.00 – 19.19 19.19 Corolla face height C2.loc97 2 97.00 2.66 4.67 65.12 – 102.78 37.66 17.86 C6.loc13 6 13.00 2.66 3.00 3.70 – 37.82 34.12 Tube opening height BW7768 2 102.78 2.71 4.69 87.22 – 102.78 15.56 16.26 (outer) C3.loc70 3 70.00 2.71 3.96 21.56 – 84.35 62.79 Tube opening height BW7768 2 102.78 2.65 3.12 87.22 – 102.78 15.56 12.59 (inner) DN20121 3 46.50 2.65 3.74 21.56 – 84.35 62.79 Corolla face width C2.loc9 8 2 98.00 2.72 4.25 65.12 – 102.78 37.66 18.53 C9.loc5 9 5.00 2.72 3.23 0.00 – 53.33 53.33 Tube opening width (outer) C2.loc97 2 97.00 2.64 3.30 58.33 – 102.78 44.45 6.50 Tube opening width (inner) C2.loc100 2 100.00 2.66 3.22 50.70 – 102.78 52. 08 5.47 Pistil length C2.loc100 2 100.00 2.64 9.46 87.22 – 102.78 15.56 39.85 BW416 7 16.41 2.64 2.99 6.19 – 37.55 31.36 BW440 9 32.36 2.64 3.60 14.46 – 63.89 49.43 C14.loc26 14 26.00 2.64 3.60 0.44 – 36.68 36.24

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Table 6.14 continued Trait QTL LG QTL position LOD Max LOD Bayes CI* CI* size Variance marker (cM) threshold score (cM) (cM) explained† (%) Ovary length C2.loc99 2 99.00 2.74 8.15 87.22 – 102.78 15.56 26.94 C6.loc20 6 20.00 2.74 2.84 3.70 – 37.82 34.12 C14.loc6 14 6.00 2.74 3.51 0.00 – 30.87 30.87 Style length N/A N/A N/A 2.72 2.11 N/A N/A N/A Calyx length C2.loc100 2 100.00 2.70 4.61 65.12 – 102.78 37.66 12.19 Stamen length C2.loc100 2 100.00 2.77 7.21 87.22 – 102.78 15.56 25.13 BW416 7 16.41 2.77 3.11 0.00 – 72.48 72.48 Filament length (attached) C2.loc101 2 101.00 2.75 6.17 87.22 – 102.78 15.56 26.76 C7.loc17 7 17.00 2.75 3.24 6.19 – 72.48 66.29 BW13150 12 22.06 2.75 2.84 0.00 – 41.77 41.77 Filament length (free ) N/A N/A N/A 2.75 2.6 7 N/A N/A N/A Ventral tube length C2.loc99 2 99.00 2.74 3.21 65.12 – 102.78 37.66 21.56 Ventral lobe length C2.loc96 2 96.00 2.70 6.72 87.22 – 102.78 15.56 22.29 C8.loc16 8 16.00 2.70 3.64 0.00 – 38.66 38.66 BW7227 9 53.31 2.70 2.83 0.25 – 62.61 62.36 Dorsal tube length C2.loc100 2 100.00 2.78 4.56 87.22 –102.78 15.56 17.04 BW3948 12 21.54 2.78 2.91 0.00 – 41.77 41.77 Dorsal lobe length C2.loc100 2 100.00 2.71 3.84 65.12 – 102.78 37.66 30.79 C6.loc11 6 11.00 2.71 3.00 3.70 – 30.00 26.3 C9.loc45 9 45.00 2.71 4.00 35.74 – 53.33 17.59 C12.loc21 12 21.00 2.71 3.24 0.00 – 41.77 41.77 Flowering time C1.loc81 1 81.00 2.76 3.21 60.62 – 136.05 75.43 50.88 C2.loc97 2 97.00 2.76 14.24 87.22 – 102.78 15.56 C14.loc49 14 49.00 2.76 3.88 36.68 – 49.45 13.23 Lateral lobe pigmentation BW14493 3 4.17 2.78 7.29 0.00 – 28.56 28.56 29.92 BW2259 10 12.74 2.78 6.36 9.62 – 15.77 6.15 Ventral lobe pigmentation C3.loc3 3 3.00 2.79 37.21 1.58 – 19.86 18.28 59.94

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Table 6.14 continued Trait QTL LG QTL position LOD Max LOD Bayes CI* CI* size Variance marker (cM) threshold score (cM) (cM) explained† (%) Yellow spot DN10356 3 0.00 2.66 4.70 0.00 – 19.86 19.86 45.58 C9.loc8 9 8.00 2.66 2.82 0.00 – 34.74 34.74 BW5388 10 5.54 2.66 11.53 4.31 – 12.71 8.4 Accessory phyllomorph N/A N/A N/A 2.84 2.65 N/A N/A N/A Two macrocotyledons N/A N/A N/A 2.44 2.09 N/A N/A N/A * CI: confidence interval † Showing the combined variance explained of all the loci and inter-loci interactions

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Chapter 6: QTL mapping Table 6.15 Summary of the QTLs / BTLs by linkage group Linkage group Trait Bayes CI (cM) LG1 Undilated tube height 0.00 – 53.10 Undilated tube width 0.00 – 34.36 Flowering time 60.62 – 136.05 LG2 Corolla length 87.22 – 102.7 8 Undilated tube length 87.22 – 102.78 Dilated tube length 65.12 – 102.78 Undilated tube width 65.12 – 102.78 Dilated tube width 87.22 – 102.78 Corolla face height 65.12 – 102.78 Tube opening height (outer) 87.22 – 102.78 Tube opening height (inner) 87.22 – 102.78 Corolla face width 65.12 – 102.78 Tube opening width (outer) 58.33 – 102.78 Tube opening width (inner) 50.70 – 102.78 Pistil length 87.22 – 102.78 Ovary length 87.22 – 102.78 Calyx length 65.12 – 102.78 Sta men length 87.22 – 102.78 Filament length (attached) 87.22 – 102.78 Ventral tube length 65.12 – 102.78 Ventral lobe length 87.22 – 102.78 Dorsal tube length 87.22 – 102.78 Dorsal lobe length 65.12 – 102.78 Flowering time 87.22 – 102.78 Rosulate / unifoliate (Method 4) 65.12 – 102.78 LG3 Tube opening height (outer) 21.56 – 84.35 Tube opening height (inner) 21.56 – 84.35 Lateral lobe pigmentation 0.00 – 28.56 Ventral lobe pigmentation 1.58 – 19.86 Yellow spot 0.00 – 19.86 LG4 Rosulate / unifoliate (Method 4) 28.66 – 70.23 LG5 N/A N/A LG6 Corolla face height 3.70 – 37.82 Ovary length 3.70 – 37.82 Dorsal lobe length 3.70 – 30.00 LG7 Pistil length 6.19 – 37.55 Stamen length 0.00 – 72.48 Filam ent length (attached) 6.19 – 72.48 LG8 Ventral lobe length 0.00 – 38.66 LG9 Corolla face width 0.00 – 53.33 Pistil length 14.46 – 63.89 Ventral lobe length 0.25 – 62.61 Dorsal lobe length 35.74 – 53.33 Yellow spot 0.00 – 34.7 4

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Chapter 6: QTL mapping Table 6.15 continued Linkage group Trait Bayes CI (cM) LG10 Lateral lobe pigmentation 9.62 – 15.77 Yellow spot 4.31 – 12.71 Rosulate / unifoliate (Method 4) 60.66 – 69.32 LG11 N/A N/A LG12 Filament length (attached) 0.00 – 41.77 Dorsal tube length 0.00 – 41.77 Dorsal lobe length 0.00 – 41.77 LG13 N/A N/A LG14 Undilated tube width 0.00 – 30.87 Dilated tube width 0.00 – 19.19 Pistil length 0.44 – 36.68 Ovary length 0.00 – 30.87 Flowering time 36.68 – 49.45 Rosulate / unifoliate (Method 1) 19.19 – 49.45 Rosulate / unifoliate (Method 2) 30.87 – 49.45 Rosulate / unifoliate (Method 3) 10.99 – 49.45 Rosulate / unifoliate (Method 4) 0.44 – 49.01 LG15 N/A N/A LG16 N/A N/A LG17 N/A N/A

6.3.5 Genome annotation for the rosulate / unifoliate genetic regions using three genetic maps In the mapping results of MapA, three genetic regions were associated with the rosulate / unifoliate trait (Table 6.16; LG1, LG7 and LG9). Among the identified regions, the confidence intervals (CI) on LG1 and LG7 mapped by scoring Method 4 and the LG9 locus mapped by scoring Method 2 were the most specific (Table 6.16). These three regions were chosen for genome annotation. 5, 7, and 23 markers fell within the confidence intervals for LG1, LG9, and LG7 respectively (Table 6.17). These corresponded to 3, 18, and 6 scaffolds from the preliminary S. rexii genome assembly respectively, with a total size of 1,341,715 bp (Table 6.17). The functional annotation of these scaffolds is summarised in Table 6.18.

Table 6.16 BTL regions identified for rosulate / unifoliate trait on MapA Scoring LG Bayes CI (cM) CI size (cM) Used for genome annotation method 1 Method 2 0.00 – 148.04 148.04 1 Method 4 126.43 – 139.33 12.90 ✓ 7 Method 4 61.75 – 113.64 51.89 ✓ 9 Method 1 77.12 – 104.94 27.82 9 Method 2 80.56 – 101.20 20.64 ✓ 9 Method 3 63.54 – 91.18 27.64 9 Method 4 21.89 – 104.94 83.05

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Chapter 6: QTL mapping Table 6.17 List of genome scaffolds within the rosulate / unifoliate BTL regions identified on MapA. CI: confidence interval. No. markers Corresponding genome Total length LG Bayes CI (cM) in CI scaffolds of scaffold (bp) scaffold2920, scaffold22461, 1 126.43 – 139.33 5 164,489 bp scaffold1363 scaffold25327, scaffold14550, scaffold24553, scaffold81701, scaffold110, scaffold21844, scaffold29891, scaffold6967, 7 61.75 – 113.64 23 scaffold3166, scaffold12762, 815,681 bp scaffold6352, scaffold13454, scaffold7066, scaffold11456, scaffold17737, scaffold27909, scaffold11568, scaffold11935 scaffold16151, scaffold96243, 9 80.56 – 101.20 7 scaffold4937, scaffold28029, 361,545 bp scaffold14085, scaffold19363 Total 85.43 cM 35 27 scaffolds 1,341,715 bp

Table 6.18 Summary of functional annotation results of the genome scaffolds identified on MapA Genome Scaffold LG Functional genes annotated* scaffold length Cytokinin riboside 5'-monophosphate 1 scaffold2920 32,519 bp phosphoribohydrolase Additional 5 hypothetical proteins Hydroxyproline-rich glycoprotein family protein putative 1 scaffold22461 18,084 bp Auxin-responsive protein IAA8 Additional 2 hypothetical proteins Mutant phytoene synthase RNA polymerase II transcription factor B subunit 2 Acyl-activating enzyme 11 Polyamine oxidase Microsomal glutathione S-transferase 3 Harpin inducing protein 1 scaffold1363 113,886 bp Yellow stripe-like protein 5 Protein GRIP Plant UBX domain-containing protein 11 Prolyl 4-hydroxylase 1 Peptide methionine sulfoxide reductase A5 Additional 8 hypothetical proteins Casein kinase II subunit beta 60S ribosomal protein L37a 7 scaffold25327 58,538 bp Formyltetrahydrofolate deformylase RING-H2 finger protein ATL46 Additional 4 hypothetical proteins Cysteine synthase 7 scaffold14550 81,686 bp Additional 5 hypothetical proteins

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Chapter 6: QTL mapping Table 6.18 continued Genome Scaffold LG Functional genes annotated* scaffold length 7 scaffold24553 46,317 bp 7 hypothetical proteins UPF0176 protein 7 scaffold81701 32,980 bp Additional 3 hypothetical proteins 7 scaffold110 10,516 bp Ethylene response factor 1 Chlororespiratory reduction 21 7 scaffold21844 43,263 bp Calmodulin binding heat shock protein Additional 2 hypothetical proteins Ferric reductase 7 scaffold29891 43,786 bp Additional 3 hypothetical proteins Aspartic proteinase-like protein 2 7 scaffold6967 28,045 bp Additional 1 hypothetical protein 7 scaffold3166 245,361 bp Mevalonate kinase U11/U12 small nuclear ribonucleoprotein 35 kDa protein Mitochondrial Rho GTPase E3 ubiquitin-protein ligase Phosphatidylinositol 4-kinase gamma 3 Ankyrin repeat/KH domain protein (DUF1442) Terpene cyclase/mutase family member Pentatricopeptide repeat-containing protein mitochondrial Beclin 1 protein Helicase with zinc finger protein Additional 15 hypothetical proteins 7 scaffold12762 55,147 bp Cation calcium exchanger 5-like Ubiquitin-fold modifier 1 Probable E3 ubiquitin-protein ligase LUL4 E3 ubiquitin-protein ligase ATL6 Additional 6 hypothetical proteins 7 scaffold6352 92,231 bp DNA-directed RNA polymerase subunit Additional 5 hypothetical proteins 7 scaffold13454 9,419 bp N/A 7 scaffold7066 68,392 bp DNA mismatch repair protein MutS Additional 2 hypothetical proteins 7 scaffold11456 65,480 bp Regulatory protein NPR1 Additional 6 hypothetical proteins 7 scaffold17737 44,571 bp AR781, similar to yeast pheromone receptor Pentatricopeptide repeat protein Additional 3 hypothetical proteins 7 scaffold27909 32,519 bp Cytokinin riboside 5'-monophosphate phosphoribohydrolase Additional 5 hypothetical proteins 7 scaffold11568 43,716 bp Plasminogen activator inhibitor 1 RNA-binding protein Additional 2 hypothetical proteins 7 scaffold11935 17,008 bp 2 hypothetical proteins

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Chapter 6: QTL mapping Table 6.18 continued Genome Scaffold LG Functional genes annotated* scaffold length 9 scaffold16151 73,297 bp Hydroxyproline O-galactosyltransferase HPGT2 Disease resistance protein-like Reticulon-like protein B8 Suppressor of G2 allele of SKP1 Putative septum site-determining protein MinD Additional 7 hypothetical proteins 9 scaffold96243 1,395 bp N/A 9 scaffold4937 35,130 bp DExH-box ATP-dependent RNA helicase DExH10 Auxin response factor 9 scaffold28029 118,704 bp Transmembrane 9 superfamily member Metal-dependent phosphohydrolase Receptor-like protein kinase HERK 1 Additional 12 hypothetical proteins Cytokinin oxidase 3 Eukaryotic translation initiation factor 3 subunit E Mitochondrial glycoprotein 9 scaffold14085 98,260 bp Zeatin O-glucosyltransferase-like Kinesin KP1 Insulin-degrading enzyme-like 1 peroxisomal Additional 5 hypothetical proteins Receptor-like protein kinase HSL1 Calmodulin-binding receptor-like cytoplasmic kinase 3 9 scaffold19363 34,759 bp Proline-rich receptor-like protein kinase PERK1 Protein SPEAR3 Additional 3 hypothetical proteins * Hypothetical genes are uncharacterised genes and with unknown functions

The same mapping procedure on MapB-1 and MapB-3 showed that for MapB-1, four genetic regions were identified on LG2, LG4, LG10 and LG14 for the rosulate / unifoliate trait (Table 6.19). These regions were used for marker check and genome annotation, and the linkage groups corresponded to 4, 14, 3, 4 scaffolds respectively, with a total of 1,840,643 bp of sequence (Table 6.20). On the other hand, four genetic regions were identified on MapB-3, LG1, LG2, LG4, and LG14 (Table 6.21). In particular, the 6.93 cM region on LG1 detected by CIM spanned 39 markers and 12 genome scaffolds (Table 6.22). For the other three regions, nine of their corresponding genome scaffolds had been found in the results of MapB-1 already (Table 6.22; scaffolds marked with an asterisk were identified in MapB-1). Finally, the scaffolds identified in both MapB-1 and MapB-3 were used for functional annotation (Table 6.23). The relationship between all the retrieved scaffolds are summarised in Table 6.24.

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Chapter 6: QTL mapping Table 6.19 BTL regions identified for rosulate / unifoliate trait on MapB-1 Scoring LG Bayes CI (cM) CI size (cM) Used for genome annotation method 2 Method 4 65.12 – 102.78 37.66 ✓ 4 Method 4 28.66 – 70.23 41.57 ✓ 10 Method 4 60.66 – 69.32 8.65 ✓ 14 Method 1 19.19 – 49.45 30.25 14 Method 2 30.87 – 49.45 18.58 ✓ 14 Method 3 10.99 – 49.45 38.46 14 Method 4 0.44 – 49.01 49.01

Table 6.20 List of genome scaffolds fall within the rosulate / unifoliate BTL regions identified on MapB-1 Bayes CI No. markers Corresponding genome Total length LG (cM) in CI scaffolds of scaffold (bp) 4621390, 4602510, 2 65.12 – 102.78 7 437,759 bp 4628119, 4619676 4621038, 4628495, 4606711, 4596587, 4602628, 4626269, 4 28.66 – 70.23 23 4626131, 4625029, 750,999 bp 4598249, 4303143, 4626813, 4628153, 4624979, 4626266 4628071, 4621448, 10 60.66 – 69.32 4 353,203 bp 4624923 4621635, 4583468, 14 30.87 – 49.45 6 298,682 bp 4628222, 4609125 Total 106. 46 cM 40 25 scaffolds 1,840,643 bp

Table 6.21 BTLs identified for rosulate / unifoliate trait on MapB-3 Scoring LG Bayes CI (cM) CI size (cM) Used for genome annotation method 1 Method 1 64.64 – 71.57 6.93 ✓ 2 Method 2 81.82 – 97.88 16.06 ✓ 2 Method 4 67.47 – 97.88 30.41 4 Method 4 34.54 – 53.00 18.45 ✓ 14 Method 1 57.70 – 78.20 20.49 14 Method 2 59.99 – 78.20 18.20 ✓ 14 Method 3 39.45 – 78.20 38.74 14 Method 4 25.11 – 78.20 53.09

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Chapter 6: QTL mapping Table 6.22 Candidate genome scaffolds identified in MapB-3 Bayes CI No. markers Corresponding genome Total length LG (cM) in CI scaffolds of scaffold (bp) 4628601, 4618086, 4629712, 4605369, 4621557, 4619330, 4628439, 4605598, 4627533, 4620874, 4592339, 4626926, 1 64.64 – 71.57 39 1,666,522 bp 4586234, 4603630, 4621018, 4605598, 4628027, 4622699, 4618924, 4596325, 4628211, 4625786, 4621691, 4618583 4628119*, 4619231, 2 81.82 – 97.88 5 340,454 bp 4582452, 4619676* 4621038*, 4628495*, 4 34.54 – 53.00 7 4627304, 4606711*, 361,184 bp 4596587* 4583468*, 4620652, 14 59.99 – 78.20 9 4628222*, 4609125*, 399,859 bp 4602532 Total 59.64 cM 60 38 scaffolds 2,768,019 bp * Repeated genome scaffolds that were also identified in the result of MapB-1 (Table 6.S)

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Chapter 6: QTL mapping Table 6.23 Summary of functional annotation results of the genome scaffolds identified on MapB-1 and MapB-3 Genome Scaffold LG Functional genes annotated* scaffold length COMPASS-like H3K4 histone methylase component WDR5A 1 4628601 129,642 bp Chaperone protein ClpB1 Additional 13 hypothetical proteins Reverse transcriptase-related family protein 1 4618086 62,244 bp DUF4228 domain protein Additional 6 hypothetical proteins Non-specific lipid transfer protein GPI-anchored 2-like isoform 1 4629712 140,813 bp Mitochondrial transcription termination factor family protein Additional 19 hypothetical proteins 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase 1 4605369 52,719 bp Cholinephosphate cytidylyltransferase Additional 2 hypothetical proteins Integral membrane protein like 1 4621557 32,784 bp Additional 5 hypoth etical proteins MA3 domain-containing protein 1 4619330 86,919 bp Tetratricopeptide repeat (TPR)-containing protein Additional 11 hypothetical proteins Adenosine 5'-phosphosulfate reductase 8 Mitochondrial dicarboxylate/tricarboxylate transporter DTC 1 4628439 121,713 bp ARM repeat superfamily protein Additional 12 hypothetical proteins Transcription factor Pur-alpha 1 1 4605598 68,158 bp Pentatricopeptide repeat-containing protein At5g55840 Additional 7 hypothetical proteins DNA replication complex GINS protein SLD5 1 4627533 54,234 bp Additional 11 hypothetical proteins Leucine-rich repeat (LRR) family protein Prolyl carboxypeptidase like protein 1 4620874 128,869 bp Kinesin-like protein NACK1 WAT1-related protein At5g64700 Additional 11 hypothetical proteins 1 4592339 15,962 bp 1 hypothetical protein Mevalonate kinase U11/U12 small nuclear ribonucleoprotein 35 kDa protein 1 4626926 56,617 bp Protein BRASSINOSTEROID INSENSITIVE 1 Additional 8 hypothetical proteins 1 4586234 44,456 bp tRNA pseudourid ine synthase A 1 4603630 18,303 bp 2 hypothetical proteins Putative E3 ubiquitin-protein ligase HERC1 1 4621018 69,478 bp Additional 6 hypothetical proteins Transcription factor Pur-alpha 1 1 4605598 68,158 bp Pentatricopeptide repeat-containing protein At5g55840 Additional 7 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length 1 4628027 189,799 bp Ankyrin repeat domain-containing protein EMB506 Protein NRT1/ PTR FAMILY 8.1-like Alpha-mannosidase At3g26720 Type IV inositol polyphosphate 5-phosphatase 9 Probable mitochondrial adenine nucleotide transporter BTL3 50S ribosomal protein L29 LRR receptor-like kinase BZIP transcription factor, putative (DUF630 and DUF632) Tyrosine transaminase Additional 15 hypothetical proteins 1 4622699 57,786 bp TCP20 protein Peptidylprolyl isomerase Additional 8 hypothetical proteins 1 4618924 37,328 bp Polyprotein Heavy metal-associated isoprenylated protein 1 Protease Do-like 9 Additional 3 hypothetical proteins 1 4596325 27,567 bp 4 hypothetical proteins 1 4628211 84,038 bp Subtilisin-like serine protease Bifunctional inhibitor/lipid-transfer protein/seed storage 2S-albumin superfamily protein AT-hook motif nuclear-localized protein 15 Additional 14 hypothetical pr oteins 1 4625786 53,980 bp Polyprotein 40S ribosomal protein SA Microtubule associated protein Additional 9 hypothetical proteins 1 4621691 24,664 bp 7 hypothetical proteins 1 4618583 40,291 bp BTB/POZ domain-containing protein At1g03010 isoform X1 Addi tional 4 hypothetical proteins 2 4621390 58,262 bp Zinc finger BED domain-containing protein RICESLEEPER 1 Alkaline alpha-galactosidase Additional 9 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length 2 4602510 165,194 bp C2 calcium/lipid-binding plant phosphoribosyltransferase family R2R3 MYB Lim domain protein Bifunctional protein FolD 4, chloroplastic Digalactosyldiacylglycerol synthase 2, chloroplastic RRNA adenine N(6)-methyltransferase SET domain-containing protein LysM domain containing protein Peroxisome biogenesis protein 16 UTP:RNA uridylyltransferase 1 Probable carboxylesterase 9 Protein NRT1/ PTR FAMILY 8.1-like Pentatricopeptide repeat (PPR) superfamily protein Cysteine-rich repeat secretory protein 15 Protein YABBY 5 Scarecrow-like protein 6 isoform X1 Phosphatidate cytidylyltransferase SPX domain-containing protein 3 BHLH transcription factor Additional 8 hypothetical proteins 2 4619676 52,194 bp Putative serine/threonine-protein kinase Rad53 ABC transporter D family member 1 Additional 7 hypothetical proteins 2 4619231 107,848 bp Zinc finger (C3HC4-type RING finger) family protein Protein RMD5 homolog Phosphoenolpyruvate carboxykinase (ATP) EPIDERMAL PATTERNING FACTOR-like protein 2 Thioredoxin superfamily protein ACT domain-containing protein ACR4 NADH-ubiquinone oxidoreductase 18 kDa subunit Telomere repeat-binding factor 1 Remorin family protein Mitogen-activated protein kinase kinase kinase npk1 Nicotinate phosphoribosyltransferase 2 Transcription factor APETALA2 Pollen receptor-like kinase 3 Transmembrane protein, putative (DUF247) Additional 8 hypothetical proteins Auxin-responsive protein IAA8 Hydroxyproline-rich glycoprotein family protein, putative 2 4582452 18,303 bp Enolase (DUF1399) Additional 2 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length UDP-glycosyltransferase 79B30 Ribosomal protein L11 methyltransferase BnMAP4K alpha1 4 4621038 112,522 bp Serine/threonine-protein kinase WNK-like protein Protein DETOXIFICATION 16 Additional 8 hypothetical proteins Ankyrin repeat protein SKIP35 Cysteine synthase COMPASS-like H3K4 histone methylase component 4 4628495 81,446 bp WDR5A BAG-associated GRAM protein 1 Addition al 5 hypothetical proteins E3 ubiquitin-protein ligase UPL6 4 4606711 43,402 bp Inositol transporter 4 Additional 9 hypothetical proteins UPF0176 protein UDP-glucose 4-epimerase GEPI48 4 4596587 33,990 bp SNARE-interacting protein KEULE Additional 2 hypo thetical proteins 4 4602628 10,463 bp 1 hypothetical protein Mechanosensitive ion channel protein 1, mitochondrial UPF0496 protein 4 Probable serine/threonine-protein kinase At1g01540 4 4626269 97,206 bp Ferric reductase Protein phosphatase 2C 16 Calmodulin binding protein Additional 8 hypothetical proteins Cation/calcium exchanger 4 Signal peptidase complex subunit 3B Ubiquitin-fold modifier 1 4 4626131 67,802 bp Probable E3 ubiquitin-protein ligase LUL4 E3 ubiquitin-protein ligase ATL6 Additional 9 hyp othetical proteins Aspartic peptidase A1 family, Aspartic peptidase domain protein 4-coumarate:coenzyme A ligase 4 4625029 75,423 bp Polyketide cyclase/dehydrase and lipid transport superfamily Truncated xanthoxin dehydrogenase Additional 4 hypothetical p roteins DNA mismatch repair protein MSH2 4 4598249 39,938 bp Addition al 2 hypothetical proteins Cytokinin riboside 5'-monophosphate phosphoribohydrolase 4 4303143 31,880 bp Additional 5 hypothetical proteins 4 4626813 17,364 bp 1 hypothetical protein 4 4628153 20,308 bp 2 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length Pentatricopeptide repeat protein AR781, similar to yeast pheromone receptor 4 4624979 38,803 bp Serine carboxypeptidase II-3 Additional 3 hypothetical proteins Proline-, glutamic acid/leucine-rich protein IMP dehydrogenase 4 4626266 80,452 bp Glucuronoxylan methyltransferase Additional 7 hypothetical proteins Haloacid dehalogenase-like hydrolase domain-containing protein Dynamin-related protein 1E Malate dehydrogenase (oxaloacetate-decarboxylating) Chloroplast chaperonin 10 E3 ubiquitin-protein ligase ATL4 4 4627304 89,824 bp Formyltetrahydrofolate deformylase Eukaryotic translation initiation factor 4G-like isoform X1 60S ribosomal protein L37a Histone H3 K4-specific methyltransferase SET7/9 family protein Casein kinase II subunit beta Additional 5 hypothetical proteins Glycine hydroxymethyltransferase Adenosylhomocysteinase Rapid alkalinisation factor 1 Selenium binding protein Pentatricopeptide repeat-containing protein Putative SPINDLY protein 10 4628071 95,141 bp MazG nucleotide pyrophosphohydrolase domain protein Transcription factor RF2a Electron transfer flavoprotein-ubiquinone oxidoreductase Strictosidine synthase Protein ENHANCED DISEASE RESISTANCE 2 Additional 11 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length Mannose-1-phosphate guanylyltransferase Calmodulin binding protein Succinate dehydrogenase subunit 7A, mitochondrial Nucleobase-ascorbate transporter 6 Pollen receptor-like kinase 3 NHL domain protein Pyruvate decarboxylase 2 Deleted in split hand/splt foot protein 1 Bidirectional sugar transporter SWEET 10 4621448 188,400 bp Serine/threonine-protein kinase HT1 S locus glycoprotein E3 ubiquitin-protein ligase SIS3 DUF1645 family protein Serine/threonine-protein kinase ATM isoform X1 Histone deacetylase Chloride channel protein Pollen receptor-like kinase 3 Plastid division protein CDP1, chloroplastic Additional 9 hypothetical proteins Protein LIGHT-DEPENDENT SHORT HYPOCOTYLS 3 Malate dehydrogenase Cryptochrome 2 Inorganic phosphate transporter 10 4624923 69,662 bp Ethylene-responsive transcription factor 1B RING-H2 finger protein ATL16 AT-hook motif nuclear-localized protein 28 Ethylene receptor Additional 6 hypothetical proteins MYB transcription factor 77 Outer membrane OMP85 family protein Probable inactive receptor-like protein kinase At3g56050 14 4621635 80,310 bp Phosphoribosylanthranilate isomerase CTD small phosphatase-like protein Serine/threonine-protein kinase-like protein CCR1 Additional 5 hypothetical proteins Keratin-associated protein, putative (DUF819) Putative septum site-determining protein MinD 14 4583468 26,938 bp Suppressor of G2 allele of SKP1 Reticulon-like protein B8 Additional 3 hypothetical proteins

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Chapter 6: QTL mapping Table 6.23 continued Genome Scaffold LG Functional genes annotated* scaffold length Protein FLC EXPRESSOR Receptor-like protein kinase HERK 1 Metal-dependent phosphohydrolase Putative clathrin assembly protein Transmembrane 9 superfamily member Histone-lysine N-methyltransferase ASHR3 ATP-dependent DNA helicase Eukaryotic translation initiation factor isoform 4G-1 Protein MULTIPOLAR SPINDLE 1 14 4628222 163,556 bp Signal recognition particle receptor subunit alpha-like protein Transmembrane 9 superfamily member Eukaryotic translation initiation factor 5A Leucine-rich repeat receptor protein kinase EMS1 Photosynthetic NDH subunit of lumenal location 5, chloroplastic Glycerol 3-phosphate permease Additional 19 hypothetical proteins Auxin response factor 14 4609125 27,878 bp Additional 2 hypothetical proteins Lipase class 3-like Polypyrimidine tract-binding protein 1 Mitochondrial carrier protein MTM1 14 4620652 90,616 bp Inorganic phosphate transporter Hydroxyproline O-galactosyltransferase HPGT2 Disease resistance protein-like Additional 8 hypothetical proteins Cytokinin dehydrogenase Eukaryotic translation initiation factor 3 subunit E Mitochondrial glycoprotein 14 4602532 90,871 bp Zeatin O-glucosyltransferase-like Kinesin KP1 Additional 6 hypothetical proteins * Hypothetical genes are uncharacterised genes and with unknown functions

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Chapter 6: QTL mapping Table 6.24 Genome-to-genome alignment between identified scaffolds and S. grandis genome Corresponding scaffolds Scaffolds identified in Corresponding scaffolds in S. grandis genome MapB-1 and MapB-3 identified in MapA assembly 4628601 N/A 4294169 4618086 N/A 4315545 4629712 N/A 4321228 4605369 N/A 4283118 4621557 N/A 4356665 4619330 N/A 4350554 4628439 N/A 4284933 4605598 N/A 4345973 4627533 N/A 4355442 4620874 N/A 4277864 4592339 N/A 4352733 4626926 N/A 4353756 4586234 N/A 4278727 4603630 N/A 4350841 4621018 N/A 4294905 4605598 N/A 4345973 4628027 N/A 4349035 4622699 N/A 435 2460 4618924 N/A 4343558 4596325 N/A 4361608 4628211 N/A 4289226 4625786 N/A 4348323 4621691 N/A 4354299 4618583 N/A 4310861 4621390 N/A 4350128 4602510 N/A 4356806 4628119 N/A 4355104 4619676 N/A 4348241 4619231 Scaffold2920 4349104 4582452 Sc affold22461 4349104 4621038 N/A 4353991 4628495 Scaffold14550 4349065 4606711 Scaffold24553 4319817 4596587 Scaffold81701 4358164 4602628 Scaffold110 4284635, 4287149 4626269 Scaffold29891, Scaffold11568 4348201 4626131 Scaffold16762 4303565 462502 9 Scaffold6967 4349078 4598249 Scaffold7066 4355834 4303143 Scaffold27909 4357159 4626813 Scaffold13454 4361635 4628153 Scaffold11935, Scaffold11456 4323079 4624979 Scaffold17713 4356839 4626266 Scaffold6352 4287335 4627304 Scaffold25327 4319817 46 28071 N/A 4320697

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Chapter 6: QTL mapping Table 6.24 continued Corresponding scaffolds Scaffolds identified in Corresponding scaffolds in S. grandis genome MapB-1 and MapB-3 identified in MapA assembly 4621448 N/A 4359247 4624923 N/A 4352617 4621635 N/A 4318672 4583468 Scaf fold16151 4287149 Scaffold28029, Scaffold3166, 4628222 4359040 Scaffold1363 4609125 Scaffold4937 4352832 4620652 N/A 4352732 4602532 Scaffold21844, Scaffold14085 4345871 N/A Scaffold19363 4352075 N/A Scaffold96243 4352667

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Chapter 6: QTL mapping 6.4 Discussion 6.4.1 Genetic architecture of the rosulate and unifoliate growth form Genetic mapping of the rosulate / unifoliate loci was carried out using four different scoring methods on three genetic maps. The result identified up to five genetic loci, on LG1, LG2, LG4, LG10, LG14 of MapB-1 and MapB-3, and on LG1, LG7, LG9 of MapA (corresponds to MapB LG2, LG4 and LG14, respectively). In particular, the loci on MapB LG14 and LG2 were consistently detected in most of the mapping results. These identified loci contributed small to medium proportion of variance (7.72% - 38.66%), indicating that a substantial amount of phenotype was not explained. Overall, these results suggest that the determination of rosulate and unifoliate trait might be controlled by several genes. However, several key issues remained to be addressed, including the ambiguous growth forms observed, the skewed non-Mendelian segregation ratio, the multiple loci found and differences between QTL mapping results of various scoring methods, and the verification of the early and late loci hypothesis. For ease of following the discussion, the linkage group numbering below follows the MapB-1 system. Since there were some ambiguous growth forms in our BC mapping population, the distinction between rosulate and unifoliate phenotype was not always clear (Figure 6.4). Some of the observed phenotypes did not represent S. rexii or S. grandis , thus it was difficult to distinguish between rosulate and unifoliate categories. Similar complications were encountered by Harrison (2002) who described ‘a spectrum‘ of rosulate / unifoliate phenotypes in the crosses between S. rexii and S. dunnii (unifoliate) and S. rexii and S. wittei (unifoliate). It is common for inter- and intraspecific hybrids to exhibit novel phenotypes that differ from both parental lineages (Rieseberg et al., 1999). They could be a result of additive allele effects or epistasis interactions of the novel allele combinations obtained through hybridisation, which leads to unusual phenotypes than observed in the parents (Dittrich-Reed and Fitzpatrick, 2012). It is possible that the diverse growth forms observed in the BC population were novel phenotypes originating from the combination of S. rexii and S. grandis genetic backgrounds. The diverse growth forms complicated the scoring process, and either a Mendelian 3:1 ratio or non-Mendelian ratio was obtained depending on the scoring scheme chosen (Table 6.9). Deviations from the expected 3:1 ratio for a trait inherited by two dominant loci has previously being reported by Harrison (2002), who recorded a rosulate:unifoliate ratio of 7.92:1 in the BC population of (S. rexii × S. wittei ) × S. wittei (N = 116). The segregation ratio may be greatly affected by the decision whether or not to score the presence of accessory phyllomorph as rosulate (Harrison, 2002). S. grandis is capable of producing accessory phyllomorphs (Jong, 1970; Nishii et al., 2012a), and it is possible for the BC individuals to inherit this trait from S. grandis and falsely be scored as rosulate. In this study, four different scoring methods were employed that differed in the placement of plants with

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Chapter 6: QTL mapping accessory phyllomorphs and other ambiguous phenotypes. When scoring accessory phyllomorphs as rosulate (i.e. Method 2, and Method 4 if the accessory phyllomorph is originated from the groove meristem), the expected 3:1 ratio (for 2 dominant loci) of rosulate:unifoliate was obtained; when scoring accessory phyllomorphs as unknown or unifoliate (i.e. Method 1 and Method 3) the ratio of rosulate to unifoliate = 1:1 (for 1 dominant locus) and 5:1 ratio (non-Mendelian) was obtained, respectively (Table 6.9). Thus, the decision on whether the accessory phyllomorph is scored as rosulate or not affected the resulting segregation ratios. However, it remained unknown whether the accessory phyllomorphs found in the BC plants represented the ‘late developing rosulates’ described by Oehlkers (1938; 1942). Oehlkers (1938; 1942) recorded a rosulate to unifoliate ratio of 3:1 at the flowering season of the BC plants. This is partly overlapped with the time the accessory phyllomorphs emerged, i.e. after flowering (Jong, 1978; Nishii et al., 2012a). The development of the accessory phyllomorph in S. grandis was first properly documented by Jong (1970) who named it ‘subtending phyllomorph’ in his PhD thesis. Later, Nishii et al. (2012) published this distinctive development for several unifoliates. It is possible that Oehlkers (1938; 1942) overlooked the capability of S. grandis to produce additional phyllomorphs, and subsequently scored all BC plants with accessory phyllomorphs as rosulate for their similar appearance to rosulates in his study. This hypothesis can be partly supported by our Method 2 scoring result, where plants with accessory phyllomorphs were scored as rosulate and a 3:1 Mendelian segregation ratio was obtained, suggesting 2 dominant loci. The fact that scoring Method 1, where only truly rosulates were scored as rosulates and the remaining as unifoliates, resulted in a 1:1 segregation ratio, indicative of a single dominant locus (for true and early rosulateness), further suggests that it is possible to separate the two loci and that the accessory phyllomorphs may represent the late acting rosulate locus. Different rosulate / unifoliate loci were mapped among the 12 mapping attempts (i.e. four scoring methods × three genetic maps). In total, five genetic loci were found associated to the growth form variation and the two loci on LG14 and LG2 were consistently detected. The LG14 locus was detected in all 12 mapping results; the LG2 locus was found in the mapping using Method 2 and Method 4 scoring methods (Table 6.11, Table 6.12 and Table 6.13). This result suggests that the two loci may have major effects on determining the rosulate phenotype and are thus frequently detected. In particular, the locus on LG14 is probably associated with the production of true phyllomorphs (type 1 phyllomorphs in Figure 6.4 a), as this locus was detected in both the strictest scoring (Method 1; only counts true rosulates) as well as the less-strict scorings (Method 2 and Method 4; counting true rosulates, accessory phyllomorphs, and other additional phyllomorphs associated with flowering). On the other hand, the locus on LG2 is possibly associated with accessory

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Chapter 6: QTL mapping phyllomorphs (type 2 phyllomorphs in Figure 6.4 b), as it was only detected when using the less-strict scoring method (Method 2 and Method 4) but not the strict scoring Method 1. Mapping using Method 4 scoring always detected 1 to 2 loci in addition to the loci on LG2 and LG 14 described above, and it detected the highest number of loci compared to other scoring methods (Table 6.11, Table 6.12 and Table 6.13). One possible explanation is that the multiple loci detected using Method 4 scoring are additional genes related to the novel phenotypes of phyllomorph formation. For example, as Method 4 scoring considered any phyllomorph related to the groove meristem as rosulate, it is possible that it detected additionally linked phenotypes, such as modifier loci involving in regulating length of petiolode, a common feature of accessory phyllomorphs (Figure 6.4 b and c). On the other hand, mapping using Method 3 scoring always gave the wider confidence intervals, and was only able to detect the LG14 locus. This is probably due to the high proportion of ‘unknown’ phenotype scored in Method 3 (52 unknowns) that were excluded from analysis and thus a major part of the genetic information was therefore discarded. It is known that scoring errors and missing data in phenotyping can reduce the detection of genetic associations (Edwards et al., 2005; Broman and Sen, 2009). In addition, one locus on LG1 was uniquely mapped in the CIM result on MapB-3 but was not detected in other mapping analyses (Figure 6.17 a). The validity of this locus may require further study as it was not consistently found in other mapping scenarios, and maybe therefore an artefact. Also, MapB-3 was built using the least stringent marker-filtering strategy, that can increase the chances of incorrect marker order and distance information of the map (Hackett and Broadfoot, 2003). In his original work Oehlkers (1938, 1942) speculated that the rosulate / unifoliate trait is regulated by an early and a late acting dominant genetic locus, where the rosulate to unifoliate ratio of a BC population is expected to be 1:1 at six months after sowing, and 3:1 at around nine months after sowing. There may be great variation in the timing at which point the rosulate trait appears, and it may take a longer time to observe the 3:1 ratio in a backcross population, though exactly how long was not specified in the original study (Oehlkers, 1942). However, our mapping results suggested that more than two loci may be associated with the rosulate / unifoliate trait. Thus, it is possible that the number of phyllomorphs is regulated by a more complicated genetic mechanism than previously hypothesised. However, there may be two particular loci that are associated with time of development. As previously discussed, the LG14 locus could be associated with the regulation of type 1 (Figure 6.4 a) phyllomorph development, which is usually observed prior to plant flowering and may represent phyllomorph production at earlier developmental stages. The LG2 locus are possibly associated with type 2 (Figure 6.4 b) phyllomorph development, which is always produced together with the inflorescence and may represent the phyllomorphs produced at later developmental stages.

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Chapter 6: QTL mapping Strangely, the effect plots of almost all the detected loci suggest that the homozygous genotype (b; carrying only S. grandis alleles) was associated with a rosulate phenotype while the heterozygous genotype (h; carrying one S. rexii allele and one S. grandis allele) was associated with a unifoliate phenotype (Appendix 6.8). The exceptions were the LG10 locus identified in MapB-1 Method 4 mapping (Appendix 6.8 b), and the LG1 locus identified in MapB-3 Method 1 mapping (Appendix 6.8 c). At the same time, most of the effect plots suggested that the difference of the average phenotypic values between the two genotypes were quite small, usually between 0.3 – 0.4 (Appendix 6.8). One observation was that the genetic regions identified for LG4, LG10 and LG14 were all quite poorly resolved and had relatively lower marker density (Figure 6.18). It is possible that the available markers in these regions were still quite distant from the actual causative locus. This may have affected the calculation of the conditional genotype probabilities (i.e. the calc.genoprob function in r/qtl), that the genotype of the causative loci may have been incorrectly estimated (Broman and Sen, 2009). As can be observed in the LG10 and LG1 loci, the two BTL regions identified had a higher marker density and a narrower confidence interval was obtained. Thus, increasing the marker density of the LG2, LG4 and LG14 BTL regions and reanalysis of the effect plots may give a more accurate picture of the effect of the potentially causative loci.

6.4.2 Candidate genes identified for the rosulate and unifoliate growth form Several plant developmental and hormone-related genes were identified in the annotated genome scaffolds and the results are summarised by linkage groups (Table 6.25). In terms of developmental proteins and genes, the COMPASS-like H3K4 histone methylase component WDR5A protein (LG1 and LG4) regulates the methylation of histone and is related to the suppression of FLOWERING LOCUS T gene, where the knockout mutant causes accelerated floral transition in A. thaliana (Jiang et al., 2011). The ankyrin repeat domain-containing EMB506 gene (LG1) is essential for embryogenesis and vegetative development, especially for the transition of radial symmetry to bilateral symmetry at the early heart stage (Despres et al., 2001). Mosaic emb506 Arabidopsis plants exhibit defect leaf morphologies, including elimination of one cotyledon, altered shaped cotyledon, addition of cotyledon number (possibly related to a complete bifurcation of one of the cotyledons), and similar phenotypes can be observed in the true leaves (Latvala-Kilby and Kilby, 2006). The SPEAR3 protein (LG14), or TIE1, are associated with transcription factors TOPLESS protein and the mutation results in abnormal leaf growth in Arabidopsis (Tao et al., 2013). Some of the developmental proteins and genes are related to the genes previously studied in Streptocarpus . For instance, SET domain-containing proteins (LG2) regulate gene expression through histone modification (Ng et al., 2007), and some of the gene members

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Chapter 6: QTL mapping such as the CURLY LEAF (CLF ) functions to repress meristem identity genes including AGAMOUS (AG ) and SHOOTMERISTEMLESS (STM ) (Goodrich et al., 1997; Ng et al., 2007). In Streptocarpus , the orthologs of STM are expressed in groove and basal meristems and are related to meristem activity (Harrison et al., 2005; Mantegazza et al., 2009). YABBY proteins (LG2) regulate the development of adaxial and abaxial polarity and the expression can be detected from embryo stage (Siegfried et al., 1999; Stahle et al., 2009). In Streptocarpus , the orthologous gene SrGRAMILIFOLIA (SrGRAM ) is expressed in the basal meristem (Tononi et al., 2010). Finally, the LIGHT-DEPENDENT SHORT HYPOCOTYL genes (LG10; identical to ORGAN BOUNDARY (OBO ) genes) are expressed at the junction between shoot apical meristem and lateral organs. It may act as the transcription factor for several meristem related genes such as CUP-SHAPED COTYLEDON (CUC ), LATERAL ORGAN BOUNDARIES (LOB ), or ASYMMETRIC LEAVES (AS ) (Cho and Zambryski, 2011). Overexpression of OBO1 gene leads to disrupt phyllotaxy and multiple shoot apex (Cho and Zambryski, 2011). Interestingly, one member of the gene family LSH1 is involved in light sensing which the knockout mutation exhibits hypersensitive to red, far-red and blue lights, resulted in shorter hypocotyl (Zhao et al., 2004). In S. rexii light was also found to be an important factor for early seedling and anisocotylous development (Nishii et al., 2012b). Seedling grown under blue light condition shows normal anisocotyly development, while seedling grown under red light show no basal meristem activity and remained with two microcotyledons, with a small proportion (32%) of plants showing leaf with elongated petiole emerged between the two cotyledons (Nishii et al., 2012b). In Streptocarpus , gibberellin and cytokinin are related to the establishment of anisocotyly and production of additional phyllomorphs (Rosenblum and Basile, 1984; Mantegazza et al., 2009; Nishii et al., 2012a; 2014; Chen et al., 2017). Gibberellin and cytokinin metabolic proteins and genes were also found in the annotated genome scaffolds. For gibberellin, the scarecrow-like proteins (LG1) promotes gibberellin signalling by counteract the signalling repressor DELLA protein (Zhang et al., 2011), while the SPINDLY protein negatively regulates the signalling pathway (Silverstone et al., 2006). The Scarecrow-like protein 6 and its homologs, also known as the LOST MERISTEM genes, are crucial for meristem maintenance in both Arabidopsis and Petunia (Stuurman et al., 2002; Engstrom et al., 2011). For cytokinin, the LONELY GUY (LOG ; LG2 and LG4) gene encodes a cytokinin activating enzyme for the biosynthesis of biologically active cytokinin molecules, and is directly involved in shoot apical meristem maintenance (Kuroha et al., 2009). On the other hand, cytokinin oxidase (CKX; LG14) degrades biologically active cytokinin and overexpression leads to reduced shoot apical meristem size and reduced leaf number in Arabidopsis (Schmülling et al., 2003). The zeatin-O-glucosyltransferase protein (ZOG; LG14) does not degrade cytokinin but instead converts it into a non-active form for storage, which can be reactivated by other enzymes (Martin et al., 2001). The protease Do-

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Chapter 6: QTL mapping like 9 (LG1) is a ATP-independent serine protease involved in cytokinin and light-signalling pathway through degrading the ARABIDOPSIS RESPONSE REGULATOR 4 (ARR4) protein. It is also involved in seedling development that the mutation deg9 shows the phenotype of elongated hypocotyl under red light (Chi et al., 2016). The plant hormone auxin are involved in shoot apical dominance and lateral organ differentiation (Azizi et al., 2015). The identified genes AUXIN/INDOLE-3-ACETIC ACID (IAA ; LG2) are involved in auxin signalling pathway by encoding for short-lived transcriptional repressor, which is inhibit in the presence of auxin (Overvoorde et al., 2005). The Auxin Response Factor proteins (ARF; LG14) are transcription factors that target auxin- related downstream genes (Liscum and Reed, 2002; Li et al., 2016). Finally, TCP20 (LG1) is a transcription factor that induces expression of LIPOXYGENASE2 (LOX2 ), a gene involved in jasmonate signalling pathway for leaf development and is down regulated by the miRNA JAGGED AND WAVY (JAW) (Danisman et al., 2012). However, the currently detected BTL regions found in the present study still ranged around 10 cM to 30 cM, and the corresponding genome assemblies are still fragmented that all markers were traced back to different scaffolds (i.e. only partial and fragmented genome sequences are available within the BTL regions). Thus, it is likely that more candidate genes can be found in the gaps between scaffolds, and the identity of the causative rosulate / unifoliate gene still remains elusive.

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Chapter 6: QTL mapping Table 6.25 List of developmental and hormone-related genes identified in genome annotation Linkage Protein name / gene Description Reference group name (italic) LG1 COMPASS-like H3K4 Regulates histone methylation Jiang et al., 2011 (MapA histone methylase that is related to floral LG3) component WDR5A transition through repressing FLOWERING LOCUS T Ankyrin repeat domain- Related to the embryogenesis, Despres et al., 2001; containing protein chlorophyll biogenesis and leaf Latvala-Kilby and EMB506 development of cotyledon, true Kilby, 2006 leaf, and cauline leaves TCP20 protein Involved in jasmonate Danisman et al., signalling pathway and leaf 2012 development Protease Do-like 9 Involved in cytokinin and Chi et al., 2016 light -signalling pathways LG2 Cytokinin riboside 5'- Involved in cytokinin Kuroha et al., 2009 (MapA monophosphate biosynthesis LG1) phosphoribohydrolase (LONELY GUY ) Auxin-responsive protein Involved in auxin signalling Overvoorde et al., IAA8 2005; Liscum and Reed, 2002 SET domain-containing Protein family that consist of Ng et al., 2007 protein genes such as CURLY LEAF that regulates meristem related gene Protein YABBY 5 Promotes adaxial cell identity Siegfried et al., and regulates the initiation of 1999; Stahle et al., embryonic shoot apical 2009 meristem (SAM) development Scarecrow-like protein 6 Involved in gibberellin Zhang et al., 2011 isoform X1 signalling LG4 Cytokinin riboside 5'- Cytokinin-activating enzyme Kuroha et al., 2009 (MapA monophosphate that hydrolise cytokinin LG7) phosphoribohydrolase riboside 5'-monophosphate (LONELY GUY ) and release bioactive cytokinin COMPASS-like H3K4 Regulates histone Jiang et al., 2011 histone methylase methylation that is related to component WDR5A floral transition through repressing FLOWERING LOCUS T Ankyrin repeat protein Related to the Despres et al., SKIP35 embryogenesis, chlorophyll 2001; Latvala- biogenesis and leaf Kilby and Kilby, development of cotyledon, 2006 true lea f, and cauline leaves

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Chapter 6: QTL mapping Table 6.25 continued Linkage Protein name / gene Description Reference group name (italic) LG10 SPINDLY protein Involved in gibberellin signalling Silverstone et al., (MapA 2006 LG6) LIGHT-DEPENDENT Expressed between SAM and Cho and SHORT HYPOCOTYLS 3 lateral organs and may act as Zambryski, 2011 (LSH3 ) transcription factor for several meristem related genes LG14 Auxin response factor Involved in auxin signalling Li et al., 2016 (MapA (ARF) LG9) Cytokinin oxidase 3 Involved in cytokinin Schmülling et al., degredation 2003 Zeatin O- Inactivation of cytokinin Martin et al., 2001 glucosyltransferase (ZOG) molecule Protein SPEAR3 (TIE1) Transcription factors related to Tao et al., 2013 leaf development

6.4.3 Genetic architectures of the floral traits and other vegetative traits The genetics of floral dimensions, floral pigmentation, and flowering time traits were studied in Streptocarpus here. Most of the traits measured between the two S. grandis lineages (S. grandis F1 and S. grandis BC ) show little to no differences, yet the flowering time recorded in S. grandis F1 (265 DAS) is considerably shorter than that of S. grandis BC (377 DAS). This is possibly due to the different sowing time of the two lineages, which the S. grandis F1 was sown in July 2015 and S. grandis BC in January 2015 (Table 6.1). The difference in growing season may resulted in the variations in flowering time observed (M Möller personal communication). Between S. rexii and S. grandis , dominance effects were observed for several traits, including corolla length, dilated tube length, corolla face height, tube opening height (outer), tube opening height (inner), style length, ventral lobe length, and dorsal tube length. In these traits the average phenotypic value of F1 was statistically similar to that S. rexii , implying that the S. rexii carries the dominant alleles for the QTL of these traits (Appendix 6.4). Dominance effects of the S. grandis alleles were observed in the trait ‘undilated tube width’ and ‘calyx length’, where the average phenotypic value of the F1 was more similar to the S. grandis value (Appendix 6.4; Trait 6 and 17). The three parental lineages show no statistical variation in the trait ‘undilated tube height’ (Appendix 6.4; Trait 4). In the BC population, the segregation of the floral dimension traits were found to deviate from normal distributions (Table 6.10 and Appendix 6.6). Traits regulated by multiple QTLs with similar effect sizes usually segregated approximating a normal distribution (Lynch and Walsh, 1998). On the other hand, skewed, dichotomous, or even spike distributions of the trait can be the result of the presence of major-effect loci (Lynch and Walsh, 1998).

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Chapter 6: QTL mapping The segregation of floral pigmentation showed a gradual pattern, from near absence of stripes on the corolla tube floor to densely purple colour pigmentation (Figure 6.9). Both of the extreme phenotypes (Figure 6.9 a and d) were not observed in the parental lineages, and may be a result of transgressive segregation (Rieseberg et al., 1999). In this study, the floral pigmentation was classified into binary traits (presence / absence), including lateral lobe pigmentation, ventral lobe pigmentation, and yellow spot (Figure 6.8). The segregation of the presence and absence of ventral lobe pigmentation was found to conform to a Mendelian 1:1 ratio, suggesting the presence of one major-effect genetic locus (Lawrence and Sturgess, 1957). However, the segregation of the yellow spot trait did not follow a 1:1 ratio as previously observed (Lawrence and Sturgess, 1957; Oehlkers, 1966). It is possible that the yellow spot trait cannot be correctly scored in plants where the flower exhibited a densely purple pigmented phenotype that masked the yellow spot (Figure 6.9 d). Thus, the number of ‘yellow spot absence’ individuals could be overestimated. In order to score the presence of yellow pigmentation correctly, a possible solution for future study is to use chromatography techniques to separate different pigments from petal extracts (Tatsuzaka and Hosokawa, 2015). Evidence of significant phenotypic correlations was found for most of the measured floral traits (Figure 6.11). Strong correlations were found among the floral dimension traits (Figure 6.11, trait 1 - 24), suggesting that the overall flower size changes in a synchronised fashion, i.e. the flower were usually larger or smaller as a whole, rather than larger in some parts and smaller in others. Correlation results in the co-localisation of QTLs of more than 20 floral traits on LG2 (Figure 6.18) suggested the presence of a pleiotropic effect where a single gene is regulating multiple phenotypes (Lynch and Walsh, 1998). Pleiotropic effects are known for several genes regulating floral organ size, such as AINTEGUMENTA and the auxin-related ARGOS genes (Weiss et al., 2005). Almost all floral dimension traits were negatively correlated to flowering time (Figure 6.11, trait 25). In other words, the later a plant flowers the smaller the flower is. The negative correlation between the floral dimension traits and flowering time trait may also be explained by their co-localised QTL on LG2 (Figure 6.18), and the effect plot showing that homozygous (b) genotype at the flowering time locus resulted in later flowering (Appendix 6.9 x), in contradiction to other traits with the homozygous genotype (thus genetically more similar to S. grandis which has smaller flowers) leading to smaller floral organs (Appendix 6.9). The rest of the traits, including floral pigmentation, rosulate / unifoliate, accessory phyllomorph, and two macrocotyledons, showed less apparent correlation patterns (Figure 6.11, trait 26 - 34). The three pigmentation traits were all found correlated to the tube opening height (Figure 6.10, trait 26 - 28). In particular, lateral lobe and ventral lobe pigmentation were significantly positively correlated to the tube opening height and (though not significantly) to tube opening width (Figure 6.11, trait 12 - 13). This implies that the

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Chapter 6: QTL mapping wider the corolla tube opening is, the more likely the flower is pigmented. This can be explained by the co-localisation of their effective loci on LG3, which do not overlap but are genetically linked (Figure 6.18). Variations in corolla tube opening are usually associated with sizes of different pollinators (Hilliard and Burtt, 1971), and pigmentation patterns on flowers such as stripes are often considered as nectar guides for the pollinators (Leonard and Papaj, 2011). The co-localisation of these two effective loci on the same linkage group suggest that the traits are more likely to cosegregate, which may contribute to the pollination syndrome of larger flowers with more distinct nectar guides (Lynch and Walsh, 1998). In addition, flowering time was found to be negatively correlated to all four rosulate / unifoliate scoring results (Figure 6.11; traits 29 - 32), suggesting that the later the plant flowers the more likely that the plant is rosulate. Their co-localised effective loci on LG14 may have contributed to the phenotypic correlation observed (Figure 6.18). Small to medium-sized loci were detected for most of the traits, which explained about 10% to 25% of the phenotype variance (Table 6.14). On the other hand, loci with major-effect that explain more than 30% of the phenotype variance were found for pistil length, dorsal lobe length, flowering time, and the three pigmentation traits (Table 6.14). In particular, the loci identified for pistil length, flowering time, ventral lobe pigmentation and yellow spot contributed to 39.85%, 50.88%, 59.94%, and 45.58% of the variance, respectively. Very high LOD scores were obtained in the LOD curves of flowering time, ventral lobe pigmentation and yellow spot, with the LOD value of 14.24, 37.21, and 11.53, respectively (Table 6.14). The identification of one major effect locus and the 1:1 segregation ratio of the ventral lobe pigmentation trait supports the previous Mendelian inheritance observation (Lawrence and Sturgess, 1957). Overall, these results suggests that major effect loci of these traits are tightly linked to our genetic markers, and further study of the corresponding genome regions may help identify the causative genes. Interestingly, no genetic regions were found associated with the accessory phyllomorph and two macrocotyledons traits (Table 6.14). In particular, the presence of two macrocotyledons was only recorded in six of the BC individuals and is unlikely to provide sufficient linkage information to identify the effective loci. One possibility is that these two traits have low heritability, which a large proportion of the phenotype is not determined by genetic variance but instead by environmental factors or gene × environment interaction (Lynch and Walsh, 1998). The efficiency of QTL mapping is strongly influenced by the heritability of the trait studied, and the power to detect the QTL was found proportional to heritability, sample size and marker density (Li et al., 2010; Viana et al., 2016). These two traits are not commonly observed in the parental lineages and their genetic inheritance has not been documented before, i.e. whether a two macrocotyledons parents will lead to two macrocotyledon offspring, and whether selfing of S. grandis with accessory phyllomorph can

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Chapter 6: QTL mapping and produce offspring with accessory phyllomorphs. Further study of these two traits is required to understand their genetic inheritance.

6.4.4 Conclusion Five loci were identified to be associated with the rosulate / unifoliate growth forms. The loci on LG14 and LG2 were consistently found in most of the mapping analysis and may represent major loci regulating the formation of additional phyllomorphs. The LG14 locus could be associated with true phyllomorph development at earlier developmental stages, and the LG2 locus could be related to accessory phyllomorph development at later stages when plant is flowering. On the other hand, the identification of multiple loci and the novel phyllomorph morphologies observed suggested that the regulation of the growth form may be more complicated than previously hypothesised with more than two genes being involved. While the identity of the candidate ‘rosulate’ gene remained inconclusive, this study narrowed down the genetic regions for further investigation and several developmental related genes were annotated. In addition, the corresponding S. grandis genome scaffolds were retrieved. These resources provide the foundation for further fine mapping or resequencing study to pin down the exact location of the rosulate / unifoliate loci and to identify their sequences. The genetic architecture of the floral traits was studied and several small to medium size QTLs were identified for most of the traits. Phenotypic correlations were found between many floral dimension traits, and were likely due to the co-localisation of QTL or pleiotropic effects; for example the LG2 locus was found associated with more than 20 floral dimension traits. Major effect loci were identified for pistil length, dorsal lobe length, flowering time, and the pigmentation traits. In particular, the ventral lobe pigmentation trait was found to follow a Mendelian 1:1 segregation ratio and a single major effect locus was identified on LG3. Overall, the results add to our knowledge towards the genetic basis of Streptocarpus morphological characters. Their inheritance pattern, how the phenotypic traits were correlated, and the approximate location of the regulatory loci were uncovered. Further studies on the identified genetic regions to identify the causative genes would greatly enhance our understanding about the molecular regulation of these characters.

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Chapter 7: Discussion and conclusions Chapter 7 Discussion and conclusions

This study revisits the classic genetic inheritance observation of the rosulate / unifoliate growth forms as well as other floral characters in the genus Streptocarpus using modern NGS technologies. The main outcomes of this study includes establishing the procedures for generating next generation sequencing data, and constructing the genomic and genetic resources for this non-model Streptocarpus . Analysis pipelines were set up and documented in detail for future reference and downstream applications. The results, including nucleic acid extraction protocols, genome and transcriptome assemblies, genetic maps, and QTL / BTL mapping approaches, revealed the genetic architectures of several morphological traits and offer the basis for future genomic research using Streptocarpus as study material.

7.1 Streptocarpus as a model system for developmental studies The regulation of SAM development is a fundamental research topic for plant developmental biology as it concerns the formation of new lateral organs and self- perpetuation of the meristem (Laux et al., 1996; Lenhard et al., 2002; Nishii et al., 2010b). Developmental studies of model plant species greatly expanded our knowledge on how plant growth is regulated and what genetic network or hormones are involved. For example, previous work in A. thaliana revealed that the SAM activity is regulated by meristem identity genes including WUSCHEL (WUS ), CLAVATA (CLV ), and SHOOTMERISTEMLESS (STM ), and the establishment of hormone gradients such as auxin, gibberellin, and cytokinin (reviewed in Soyars et al., 2016). On the other hand, studies using non-model species provide important insight into special biological features that are not present in model systems. Some of these species, referred to as non-model model organisms, possess unconventional and often unique properties that can be utilised to answer critical biological questions, including how major evolutionary processes were achieved (Russell et al., 2017). The genus Streptocarpus is a highly suitable system to be developed for the studying of unconventional meristem regulation in plants for several reasons. First, species in the genus exhibit a highly unconventional development lacking a SAM in the embryo stage, which greatly deviates from most other angiosperm species (Imaichi et al., 2000; Mantegazza et al., 2007; Nishii et al., 2010b; 2016). Second, the genus consists of at least three distinct basic growth forms: caulescent, rosulate, and unifoliate (for latest classification see Nishii et al., 2015). By utilising the opportunity that viable hybrids between the growth forms can be produced between rosulates and unifoliates, it is possible to carry out genetic studies and identify developmental genes related to differences in rosulate and unifoliate

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Chapter 7: Discussion and conclusions vegetative habit (Chen et al. 2017). Third, an extensive and well-resolved phylogeny exists for Streptocarpus as well as chromosome counts (e.g. Jong and Möller, 2000; Nishii et al., 2015). Fourth, developmental processes have been studied in at least 8 species of different growth forms (e.g. Jong, 1970; Jong and Burtt, 1975; Imaichi et al., 2007; Nishii and Nagata, 2007; Nishii et al., 2017). This knowledge provided the opportunity for choosing appropriate study material. Fifth, the cultivation method of Streptocarpus is well established during its development as an ornamental horticultural plant, and they can be mass propagated sexually and asexually in conventional temperature-controlled glasshouses and it is straight forward to produce inbred lines for genetic studies or genome sequencing. Sixth, the wet lab molecular techniques are well established, especially for S. rexii . This includes protocols for tissue sectioning, SEM, DNA and RNA extraction, RNA in situ hybridisation, and RT-PCR (see example in Nishii et al., 2017). These techniques have been applied to study several meristem related gene expression, such as STM (Harrison et al., 2005; Mantegazza et al., 2009; Nishii et al., 2017), WUS (Mantegazza et al., 2009), and ASSYMETRIC LEAVES1 / ROUGH SHEATH 2 / PHANTASTICA (ARP; Nishii et al., 2010a). All this invaluable knowledge helped establishing Streptocarpus as a study system by enabling the relatively rapid setting up of investigations using this material. As for this study, it presents optimised NGS workflows and provides genomic, transcriptomic, and genetic resources of Streptocarpus . The NGS workflow, including the preparation of nucleic acid samples and detail documentation of bioinformatics analysis pipelines and parameters, will be beneficial for future NGS works of Streptocarpus . The draft genomes of S. rexii and S. grandis can serve as the reference sequence for future sequencing experiments, designing new markers, or for gene identification. The transcriptomes of the two species provides gene sequence information which is useful for candidate gene isolation. The genetic maps and the QTL mapping revealed the genetic architectures of several morphological traits, including floral dimension, floral pigmentation, flowering time, and growth form variations. These traits are important in terms of the evolution of pollination syndromes or vegetative habits, and may also be interesting for their ornamental values that may potentially be utilised through marker assisted selection to facilitate the generation of new cultivars (Kole and Abbott, 2008). The mapping results narrowed down the genetic region to be screened for morphological trait-related genes, which can ultimately help resolving the molecular mechanisms that shape the morphological diversities exhibited in this genus. In conclusion, this work will be a key step for establishing Streptocarpus as a model system for studying plant meristem regulation and growth form evolution, and in a broader sense provide useful genomic resources for the Gesneriaceae family. This study can also be an example on the methodological approaches for establishing genomic resources for non- model plant organisms.

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Chapter 7: Discussion and conclusions 7.2 Directions for future studies In this study, the genetic loci identified for the rosulate / unifoliate growth form trait were not specific enough to generate a short list of candidate genes for functional verification. One possible reason is the limitation of the population size, which in current study a modest number of 200 BC individuals were used, and by increasing the population size the sensitivity and accuracy of QTL mapping can potentially be improved (Vales et al., 2005; Li et al., 2006; Raghavan and Collard, 2012). However, the resolution in QTL mapping may still be dependent on the species and mapping population used even when similar number of individual and mapping algorithm were taken. For example, QTL mapping in Primulina sp. used 201 F2 individuals for composite interval mapping (similar to the 200 BC individuals in current study) and achieved to narrow down the confidence interval to 0.5 cM to 2 cM (Feng et al., 2018). On the other hand, QTL mapping in Rhytidophyllum sp. used 177 F2 individuals for standard interval mapping, and obtained QTL confidence interval ranging from 10 cM to 120 cM (Alexandre et al,. 2015). It is therefore important to consider the limitation on our current mapping population and QTL mapping strategy, and whether increasing the population size can greatly enhance the mapping result or not. This is partly related to the intrinsic limitations of the QTL / BTL mapping methodology: firstly, only the genetic variation segregating between the two parents can be tested (Borevitz and Nordborg, 2003), and secondly, the resolution of the mapping relies on the recombination events that occurred within the mapping population (Balasubramanian et al., 2009). Both factors limited the number of recombination events recorded, thus thus reduce the resolution of the map. To overcome these limitations, an alternative approach is perhaps through fine-mapping method using a different mapping population and develop new markers to increase the marker density within the current BTL loci (Cockram et al., 2015; Calderon et al., 2016). In this approach advanced inbred lines (AILs) are typically used, such as recombinant inbred lines (RILs), near-isogenic lines (NILs) or Multiparent Advanced Generation Inter-Cross (Cavanagh et al., 2008; Gonzales and Palmer, 2014; Schneeberger, 2014). So far, we have generated a BC2 progeny by backcrossing BC individuals to the S. grandis parent, and in the future we can construct NILs by continuously backcrossing the progenies. Another alternative is the NGS-based bulk-segregant analysis (Schneeberger, 2014): by pooling the genomic DNA of individuals with the same phenotype (either rosulate or unifoliate) and performing whole genome resequencing on the pooled DNA, the output data are expected to cover the genome regions outside the original RAD-Seq markers, thus more genetic variations can be observed and the SNPs genotype frequency can be compared between the two phenotypes to narrow down the candidate regions (Schneeberger, 2014). This method was used to identify the Hairy gene responsible for trichome development in A. majus using a NIL (9 th backcross generation) population (Tan, 2018).

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Chapter 7: Discussion and conclusions Another important aspect for future study is to improve the contiguity of the genome assembly. A high contiguity genome is crucial for candidate gene isolation, as the mapped loci on the genetic linkage map will eventually be integrated with the physical map (genome sequence) so that the genome sequence corresponding to the loci can be examined for differences at SNPs level (Yang et al., 2004; Zhou et al., 2015). In the current Streptocarpus genome assembly, multiple genome scaffolds were found inside the rosulate / unifoliate loci and the sequences between these scaffolds (gaps) remained unknown. This can be problematic as the physical distance (bp) of the gaps can be too large for candidate gene screening, or the actual causative genes may be located in these gaps and are yet to be discovered. To improve the genome contiguity we may incorporate new genome sequencing data based on mate-pair library or long read sequencing such as PacBio and Nanopore (Jiao and Schneeberger, 2017; Li and Harkess, 2018). In particular, the latest Nanopore device PromethION is expected to generate 50 Gbp of data per flow cell and produce the longest read among currently available long-range sequencing technologies, and with modified protocols up to 882 Kbp reads can be achieved (Loose, 2017; Jain et al., 2018). In the most ideal case this would suggest an approximately 50× depth of coverage of long read data for the Streptocarpus genome (~1 Gbp). While these technologies are mostly been tested in human at current stage, applications on plant materials has been proven successful (Michael et al., 2018). All sequencing technologies mentioned above have just been made available at the Edinburgh Genomics facility, which can be beneficial for the near future work. In addition, a genome can be further improved by anchoring the assembled scaffolds to the genetic map to achieve chromosome-level assembly (Fierst, 2015; Jiao and Schneeberger, 2017). Tools such as Chromonomers (Small et al., 2016) are designed for increasing genome contiguity based on RAD-Seq derived genetic maps, which may be suitable for our data. The annotation of the Streptocarpus genome should be improved for searching the candidate genes. The current annotation results were partially based on aligning the genes of distantly related model species (i.e. N. tabacum ) to the Streptocarpus genome, and the pipeline had a stringent cut-off threshold for the alignment, which must show > 90% identity and coverage when aligning (Numa and Itoh, 2014). This suggests that if a Nicotiana gene provided in the pipeline is sharing low sequence homology to the Streptocarpus gene, it may not be aligned and hence the gene in our genome will not be annotated. This can be resolved by mapping the Streptocarpus RNA-Seq data to the genome assembly, which will help predicting correct intron-exon structures and potentially identify more functioning genes (Bolger et al., 2017b; Dominguez Del Angel et al., 2018). Tools such as BRAKER were developed for this purpose and have been widely applied to plant genome annotation (Hoff et al., 2016). On the other hand, molecules such as small RNAs are important regulator for plant growth, and are involved in developmental processes such as meristem regulation, leaf

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Chapter 7: Discussion and conclusions development, flower development, and floral pigmentation patterning (D’Ario et al., 2017; Bradley et al., 2017). Yet, the prediction of small RNAs in genome assemblies is not as straightforward as predicting protein coding genes due to their small size and poorly conserved sequence homology (20 to 24 nucleotides; D’Ario et al., 2017). Current bioinformatics tools are limited to small RNA predictions in bacterial genomes (Lindgreen et al., 2014; Li and Kwan, 2014; Dominguez Del Angel et al., 2018). The recently released Rfam 13.0, a genome-centric resource, greatly expanded the collection of small RNA sequences (Kalvari et al., 2018), and incorporation of the RNA alignment tools such as ‘Infernal’ may be applicable for small RNA prediction in the near future (Nawrocki and Eddy, 2013; Barquist et al., 2016). In addition to the rosulate / unifoliate growth forms, several major effect loci were found for the floral traits examined in this study that could be interesting targets for further genetic fine mapping. The photo records of all plant materials, particularly the floral photos, may potentially be used for different phenotyping approaches such as geometric morphometrics for more precise quantification of shape and pattern variations (e.g. Hsu et al., 2015; Sun et al., 2017; Hsu et al., 2018). Establishment of transgenic systems for the Streptocarpus materials would be another important method to establish for functional verification of candidate genes. Agrobacterium -mediated transformation and particle bombardment systems were developed for Saintpaulia (now Streptocarpus ) materials (Mercuri et al., 2000; Kushikawa et al., 2001; Ghorbanzade and Ahmadabadi, 2015) and were used in glucanase -chitinase and AtIPT5 (ISOPENTENYLTRANSFERASE ) transgenic studies (Ram and Mohandas, 2003; Ye et al., 2014). Genome editing methods, such as the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9, has yet to be applied to Gesneriaceae species, but could potentially be used to construct knockout mutant or alter transcription levels to study candidate gene function (Bortesi and Fischer, 2015). Incorporation of the new technologies mentioned above would enable the determination of the molecular mechanisms underlying the diverse morphologies present in Streptocarpus species, and ultimately improve our understanding on how this diversity has evolved.

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Appendices Appendices Appendix 2.1 4% CTAB extraction method Chemicals: 4% CTAB solution (100 mM Tris HCl pH 8.0, 1.4 M NaCl, 20 mM EDTA, 4% CTAB) β-mercaptoethanol (Sigma-Aldrich, Merck, Darmstadt, Germany) Chloroform:isoamyl alcohol (24:1) Isopropanol (Sigma-Aldrich) Wash buffer (10 mM ammonium acetate, 76% ethanol) TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich)

Procedures: Day 1 1. Preheat CTAB buffer (1 ml 4% CTAB; 2 µl β-ME; 2% PVPP) at 65°C 2. Add 1 ml of preheated buffer to the sample and gently shake 3. Incubate the sample in a 65°C heat-block for at least 60 minutes. Occasionally mix by inverting 4. Remove the tube from the heat-block and keep at room temperature for ~5 minutes 5. Add 500 µl chloroform-IAA (24:1) and shake vigorously. Open the lid of the Eppendorf tube to release gas, then put in an orbital shaker to shake at minimum speed for 30 minutes 6. Gently mix by shaking, then centrifuge at 11,000 rpm for 10 minutes 7. Carefully transfer the aqueous phase (about 800 - 850 µl) to a clean Eppendorf tube 8. Repeat step 5 - 7 (transfer about 650 – 700 µl of the aqueous phase this time) 9. Add an equal amount (700 µl) of ice-cold isopropanol and rock gently 10. Store the sample in the -20°C freezer overnight Day 2 11. Centrifuge at 8,000 rpm for 10 minutes 12. Remove supernatant and add 500 µl of wash buffer. Shake gently and check that the pellet is floating. Leave at room temperature for at least 30 minutes 13. Centrifuge at 8,000 rpm for 10 minutes 14. Remove the supernatant and dry the pellet in a SpeedVac machine for 10 -15 minutes 15. Dissolve the pellet in 50 µl TE buffer 16. Incubate the sample at 50°C for 10 minutes, then leave at room temperature for 1 hour to fully dissolve the DNA 17. Store the stock DNA at -20°C for long-term storage

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Appendices Appendix 2.2 ChargeSwitch gDNA Plant Kit protocol Extend time Note: the protocol included below refers to the ChargeSwitch Kit protocol Chemicals: From the kit – Precipitation buffer (N5) Lysis buffer (L18) 10% SDS 10% Detergent (D1) Magnetic beads solution Wash buffer (W12) Elution buffer (E6)

Procedures: 1. Chill the precipitation buffer (N5) on ice 2. Grind the leaf tissue (4 leaf discs for one sample) 3. Add 1 ml of Lysis buffer (L18) to the sample and incubate at room temperature for 1 hour 4. Vortex the ground tissue until the sample is completely resuspended 5. Add 100 µl 10% SDS to the 1 ml plant lysate and leave for 30 minutes at room temperature 6. Add 400 µl of Precipitation buffer (N5). Mix by inversion and leave for 30 minutes on ice 7. Centrifuge at maximum speed for 5 minutes 8. Transfer the clear lysate to a clean tube 9. Thoroughly vortex the magnetic beads tube and fully resuspend the beads 10. Add 100 µl 10% Detergent (D1) to the lysate 11. Add 40 µl beads to the lysate, and mix gently by pipetting up and down five times 12. Incubate for 30 minutes 13. Place the tubes on the MagnaRack until a tight pellet is formed. Carefully remove the supernatant without removing the tubes from the rack 14. Remove the tubes from the rack 15. Add 1 ml Wash buffer (W12) and gently pipette five times to mix 16. Place the tubes on the MagnaRack until a tight pellet is formed. Remove the supernatant. 17. Repeat steps 15-16 18. Make sure no supernatant remains, and remove the tubes from the rack 19. Add 150 µl Elution buffer (E6), and pipette at least 30 times to mix, until no bead clumps are visible 20. Incubate at room temperature for 30 minutes 21. Place the tube on the rack until a tight pellet is formed. Transfer the clear supernatant to a clean Eppendorf tube 22. Store the stock DNA at -20°C for long-term storage

309

Appendices Appendix 2.3 DNAzol method Chemicals: Plant DNAzol TM reagent (Invitrogen, Thermo Fisher Scientific) 100% ethanol 75% ethanol TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich)

Procedures: 1. Grind the tissue in liquid nitrogen 2. Add 0.3 ml of DNAzol to the tube and mix vigorously 3. Shake for 30 minutes 4. Add 0.3 ml of chloroform, mix vigorously and shake for 5 minutes 5. Centrifuge at 10,000 rpm for 10 minutes 6. Transfer the aqueous phase to a clean tube 7. Add 225 µl of 100% EtOH, mix by inverting 6-8 times and incubate for 5 minutes 8. Centrifuge at 7,000 rpm for 4 minutes 9. Add 0.3 µl of DNAzol-EtOH wash solution, mix by vortex * Wash solution: 1 volume of DNAzol with 0.75 volumes of 100% EtOH To prepare 0.6 ml of wash solution, mix 343 µl DNAzol with 257 µl EtOH 10. Incubate the sample for 5 minutes 11. Centrifuge at 7,000 rpm for 4 minutes 12. Remove the EtOH and air dry 13. Dissolve the DNA pellet in 70 µl TE buffer 14. Store the stock DNA at -20°C for long-term storage

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Appendices Appendix 2.4 DNeasy Plant Mini Kit protocol Note: the protocol included below refers to the DNeasy Kit Extend time protocol Chemicals: From the DNeasy Kit – P3 buffer AW1 buffer AW2 buffer AE buffer Procedures: 1. Grind the tissue in liquid nitrogen 2. Add 400 µl AP1 to the tube and vortex 3. Incubate at 65°C for 30 minutes. Mix occasionally by inverting 2-3 times 4. Add 130 µl P3 buffer. Mix and incubate on ice for 5 minutes 5. Centrifuge at 13,000 rpm (~20,000 xg) for 5 minutes 6. Transfer the lysate to a QIAshredder Mini Spin column in a 2 ml collection tube 7. Centrifuge at 13,000 rpm for 2 minutes 8. Transfer the flow-through to a clean tube 9. Add 1.5 volumes AW1 and mix by pipetting 10. Transfer 650 µl of the mixture into a DNeasy Mini Spin column placed in a 2 ml collection tube 11. Centrifuge at ≥ 8,000 rpm (≥6000 xg) for 1 minute 12. Discard flow-through 13. Repeat steps 9 - 12 with the rest of the sample 14. Place the DNeasy Mini Spin column into new 2 ml collection tube 15. Add 500 µl AW2 16. Centrifuge at ≥ 8,000 rpm (≥6000 xg) for 1 minute 17. Discard the flow-through 18. Add 500 µl AW2 19. Centrifuge at 13,000 rpm (20000 xg) for 2 minutes 20. Discard the flow-through 21. Transfer the column to a clean Eppendorf tube 22. Add 100 µl AE to the membrane and incubate for 5 minutes 23. Centrifuge at ≥ 8000 rpm (≥6000 xg) for 1 minute to collect the DNA 24. Keep the DNA at -20°C for long-term storage

311

Appendices Appendix 2.5 CTAB extraction + RNase A treatment + phenol-chloroform purification (used for preparation of RAD sequencing samples) Chemicals: 4% CTAB solution (100 mM Tris HCl pH 8.0, 1.4 M NaCl, 20 mM EDTA, 4% CTAB) β-mercaptoethanol (Sigma-Aldrich, Merck, Darmstadt, Germany) Chloroform:isoamyl alcohol (24:1) Isopropanol (Sigma-Aldrich) Wash buffer (10 mM ammonium acetate, 76% ethanol) TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich) RNase A (1/5 from the original stock, 12091021, Invitrogen, Thermo Fisher Scientific) Phenol:chloroform:isoamyl alcohol 25:24:1 (pH 8.0) Chloroform:isoamyl alcohol 24:1 3 M sodium acetate (Sigma-Aldrich, Merck) 100% ethanol

Note: perform four CTAB reactions (totally 16 leaf discs) for each individual to be extracted, which will be combined before the RNase A treatment.

Procedures: Day 1 1. Preheat CTAB buffer (1 ml 4% CTAB; 2 µl β-ME; 2% PVPP) at 65°C 2. Add 1 ml of preheated buffer to the sample and gently shake 3. Incubate the sample in a 65°C heat block for at least 60 minutes. Occasionally mix by inverting 4. Remove the tube from heat block and keep at room temperature for ~5 minutes 5. Add 500 µl chloroform:isoamyl alcohol 24:1 and shake vigorously. Open the lid of the Eppendorf tube to release gas, then put on an orbital shaker to shake at minimum speed for 30 minutes 6. Gently mix by shaking, then centrifuge at 11,000 rpm for 10 minutes 7. Carefully transfer the aqueous phase (about 800 - 850 µl) to a clean Eppendorf tube 8. Repeat step 5 - 7 (transfer about 650 – 700 µl of the aqueous phase this time) 9. Add an equal amount (700 µl) of ice-cold isopropanol and rock gently 10. Store the sample in -20°C freezer overnight Day 2 11. Centrifuge at 8,000 rpm for 10 minutes 12. Remove supernatant and add 500 µl of wash buffer. Shake gently and check that the pellet is floating. Leave at room temperature for at least 30 minutes 13. Centrifuge at 8,000 rpm for 10 minutes 14. Remove the supernatant and dry the pellet in a SpeedVac machine for 10-15 minutes 15. Dissolve the pellet in 100 µl of TE buffer 16. Incubate the sample at 50°C for 10 minutes, then keep at room temperature for 1 hour to fully dissolve the pellet 17. Briefly spin down the sample, than carefully mix up all 4 tubes of extractions from the same individual (so the total amount is 400 µl). Make sure to transfer all liquid and possible pellet

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Appendices 18. Add in 2 µL RNase and mix by inversion. Keep at room temperature for 5-10 minutes (<10 minutes) 19. Add 400 µl phenol:chloroform:isoamyl alcohol (pH 8.0) and mix by shaking vigorously 20. Centrifuge at 11,000rpm for 10 minutes 21. Transfer the aqueous phase to a clean Eppendorf tube (~400 µl) 22. Add 400 µl chloroform:isoamyl alcohol 24:1 and mix by shaking vigorously 23. Centrifuge at 11,000 rpm for 10 minutes 24. Transfer the aqueous phase to a clean Eppendorf tube (~400 µl) 25. Add in 1/10 times volume of 3 M sodium acetate (about 40 µl), followed by 2-2.5 times volume ethanol (about 1,000 µl) 26. Keep the sample in -20°C freezer overnight Day 3 27. Centrifuge at 8,000 rpm for 10 minutes 28. Remove the supernatant 29. Add 1 ml of 70% EtOH for washing. Keep for 30 minutes 30. Centrifuge at 11,000 rpm for 5 minutes 31. Remove the supernatant 32. Dry the pellet using the SpeedVac machine for 20-40 minutes. Dry the pellet completely 33. Add 20 µl of TE buffer 34. Incubate the sample at 55°C for 30 minutes and leave for a while at 4°C to fully dissolve the DNA. If the pellet did not dissolve, add an additional 10 µl of TE buffer 35. Take 1 µl of DNA and mix with 9 µl of sterilised water for evaluation (1/10x dilution) Check a. NanoVue (use 3 µl diluted sample) b. Gel electrophoresis (use 5 µl diluted sample) c. Qubit assay (use 2 µl diluted sample) 36. Store the stock DNA at -20°C for long-term storage

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Appendices Appendix 2.6 ChargeSwitch Kit Extend time + RNase A treatment + phenol-chloroform purification (used for preparation of whole genome shotgun sequencing samples) Chemicals: From the kit – Precipitation buffer (N5) Lysis buffer (L18) 10% SDS 10% Detergent (D1) Magnetic beads solution Wash buffer (W12) Elution buffer (E6) RNase A (1/5 from the original stock, 12091021, Invitrogen, Thermo Fisher Scientific) Phenol:chloroform:isoamyl alcohol 25:24:1 (pH 8.0) Chloroform:isoamyl alcohol 24:1 3 M sodium acetate (Sigma-Aldrich, Merck) 100% ethanol TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0, Sigma-Aldrich)

Note: process at least 2 tubes of reactions at a time, which will be mixed before the RNase A treatment.

Procedures: Day 1 1. Chill the Precipitation buffer (N5) on ice 2. Grind the leaf tissue (4 leaf discs for one sample) 3. Add 1 ml Lysis buffer (L18) to the sample and incubate at room temperature for 1 hour 4. Vortex the ground tissue until the sample is completely resuspended 5. Add 100 µl 10% SDS to the 1 ml plant lysate and leave for 30 minutes at room temperature 6. Add 400 µl Precipitation buffer (N5). Mix by inversion and leave for 30 minutes on ice 7. Centrifuge at maximum speed for 5 minutes 8. Transfer the clear lysate to a clean tube 9. Thoroughly vortex the magnetic beads solution until the beads are completely resuspended 10. Add 100 µl 10% Detergent (D1) to the lysate 11. Add 40 µl of magnetic beads to the lysate, and mix gently by pipetting up and down for five times 12. Incubate for 30 minutes 13. Place the tubes on the MagnaRack until a tight pellet is formed. Carefully remove the supernatant without removing the tubes from the magnetic rack 14. Remove the tubes from the rack 15. Add 1 ml Wash buffer (W12) and gently pipette for 5 times 16. Place tubes on the MagnaRack until a tight pellet is formed. Remove supernatant.

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Appendices 17. Repeat steps 15-16 18. Make sure no supernatant remains and remove the tubes from the rack 19. Add 150 µl Elution buffer (E6), and pipette for at least 30 times until no bead clumps are visible 20. Incubate at room temperature for 30 minutes 21. Place tube on rack until a tight pellet is formed. Transfer the clear supernatant to a clean Eppendorf tube 22. Combine both tubes of eluate, making a total volume of 300 µl 23. Warm up the DNA solution to 65°C for 30 minutes, with occasional inversion 24. Add 2 µl RNase A, mix by inversion and incubate for 5-10 minutes 25. Add 300 µl of phenol:chloroform:isoamyl alcohol pH 8.0 (PCI) solution to the sample 26. Shake on an orbital shaker for 30 minutes 27. Centrifuge at 12,000 g (ca.11,000 rpm) for 10 minutes 28. Transfer the supernatant to a clean Eppendorf tube 29. Add 300 µl PCI pH 8.0 to the sample 30. Shake on an orbital shaker for 30 minutes 31. Centrifuge at 12,000 g (ca.11,000 rpm) for 10 minutes 32. Transfer the supernatant to a clean Eppendorf tube 33. Add equal amount of chloroform:isoamyl alcohol 24:1 to the sample 34. Shake on an orbital shaker for 30 minutes 35. Centrifuge at 12,000 g (ca.11,000rpm) for 10 minutes 36. Transfer the supernatant to a clean Eppendorf tube 37. Add 1/10 volumes of 3 M sodium acetate 38. Add 2.5 volumes of 100% ethanol 39. Keep the sample at -20°C overnight Day 2 40. Centrifuge at 12,000 g (ca. 11,000 rpm) for 10 minutes 41. Discard the supernatant 42. Add 1000 µl of 70% ethanol for washing. Leave for 30 minutes with occasional gently tapping 43. Centrifuge at 12,000 g (ca. 11,000 rpm) for 10 minutes 44. Discard the supernatant 45. Dry the pellet completely using the SpeedVac machine 46. Dissolve the pellet in 20 µl TE buffer. Keep at 65°C for 1 hour to fully dissolve the pellet 47. Take 1 µl of DNA and mix with 9 µl of sterilised water for quality check (1/10x dilution) Check a. NanoVue (use 3 µl of diluted sample) b. Gel electrophoresis (use 5µl of diluted sample) c. Qubit assay (use 2 µl of diluted sample) 48. Store the stock DNA at -20°C for long-term storage

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Appendices Appendix 2.7 TRIzol reagent RNA extraction + acidic phenol purification + PureLink RNA Mini Kit clean up Chemicals: TRIzol reagent (Invitrogen, Thermo Fisher Scientific) Chloroform (BDH, VWR International) Isopropanol DEPC water Phenol:chloroform 5:1 pH 4.3 – 4.7 (Sigma-Aldrich, Merck) 75% ethanol β-mercaptoethanol (Sigma-Aldrich, Merck, Darmstadt, Germany) From the PureLink RNA Mini Kit – Lysis buffer Wash buffer I Wash buffer II RNase-free water

Note: Extract multiple tubes for each tissue type. The tubes are combined before the acidic phenol purification.

Procedures: Day 1 1. Place 1 ml of TRIzol in a 2 ml Eppendorf tube 2. Grind the tissue to fine powder using mortar and pestle with liquid nitrogen 3. Transfer the powder into the tube containing the TRIzol. The total amount of the mixture should be around 1.2 – 1.8 ml (avoid too little or too much tissue) 4. Gently shake on orbital shaker and incubate for 55 minutes to 1 hour 5. Centrifuge at 11,000 rpm and 4°C for 10 minutes 6. Transfer the supernatant to a clean 2 ml tube 7. Add 0.2 ml chloroform to the sample and mix by shaking vigorously for 15 seconds. Leave the tube for 2-3 minutes 8. Centrifuge at 11,000 rpm and 4°C for 15 minutes 9. Transfer the aqueous phase to a clean 1.5 ml Eppendorf tube 10. Add 0.5 ml of ice-cold isopropanol 11. Keep the sample at -20°C for more than 1 hour 12. Centrifuge at 11,000 rpm and 4°C for 10 minutes 13. Add in DEPC water to dissolve the pellet. The amount added is according to the note below: For 4 extraction tubes add 75 µl to each tube. Combine all 4 tubes to obtain a total of 300 µl. For 3 extraction tubes add 100 µl to each tube. Combine all 3 tubes to obtain a total of 300 µl. For 2 extraction tubes add 150 µl to each tube. Combine both tubes to obtain a total of 300 µl. 14. Add 300 µl phenol:chloroform 5:1 to the sample and mix by shaking vigorously 15. Centrifuge at 11,000 rpm for 10 minutes

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Appendices 16. Transfer the supernatant to a clean 1.5 ml Eppendorf tube 17. Repeat steps 14 – 16 18. Add 300 µl of ice-cold isopropanol 19. Keep the sample at -20°C overnight Day 2 20. Centrifuge at 11,000 rpm for 10 minutes 21. Discard the supernatant 22. Add 500 µl of 75% ethanol 23. Centrifuge at 9,000 rpm for 5 minutes 24. Discard the supernatant and remove any remaining liquid with a pipette 25. Dilute the pellet in 50 µl DEPC water Day 2 - PureLink # Prepare the lysis buffer freshly before the experiment: for each reaction add 4 µl of β- mercaptoethanol with 0.4 ml of Lysis buffer 26. Add 0.4 ml of freshly prepared Lysis buffer to the RNA sample 27. Vortex and incubate for 3 minutes 28. Add 0.2 ml ethanol (0.5 volumes) 29. Transfer up to 700 µl of the sample to the spin cartridge attached to the collection tube 30. Centrifuge at 11,000 rpm for 1 minute (>15 sec) 31. Discard the flow-through 32. Add 600 µl Wash buffer I 33. Centrifuge at 11,000 rpm for 1 minute (>15 sec) 34. Replace the collection tube with a clean one 35. Add 400µl Wash buffer II 36. Centrifuge at 11,000 rpm for 1 minute (>15sec) 37. Discard the flow-through 38. Repeat steps 35 - 37 39. Centrifuge at 11,000 rpm for 2 minutes 40. Add 15 - 30 µl RNase free water and incubate for 5 - 10 minutes 41. Centrifuge at 11,000 rpm for 2 minutes to collect the RNA 42. Keep the RNA at -80°C for long-term storage

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Appendices Appendix 2.8 Gel electrophoresis result of S. grandis DNA extracted using DNeasy kit protocol

Appendix 2.9 Leaf disc weight measurement Six leaf discs were freshly collected from S. grandis and S. rexii. The weights were measured and compared. The average weight per leaf disc for S. grandis is 0.0277 g (27.7 mg), and for S. rexii is 0.0433 g (43.3 mg). The difference between the average leaf disc weight is statistically different (Unpaired t test, d.f. = 10, P-value = 0.0006).

Table. Average fresh weight of leaf disc Species N Average (g) Median Standard deviation S. grandis 6 0.0277 0.0283 0.0042 S. rexii 6 0.0433 0.0428 0.0065

Figure. Box plot of the leaf disc weight of S. grandis and S. rexii (N = 6)

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Appendices Appendix 3.1 Materials used for whole genome sequencing

Streptocarpus rexii Used for Accession Qualifier Lineage and collection information genome seq

20150819 A Yes Descendent of 19990270 by selfing

19990270 E - Descendent of 19870333 by selfing

Collector: Jong, K. (Collector no. K JNG1226) Collection date: 29th October 1986 19870333 N/A - Collection location: Grahamstown, ‘Faraway’ Estate, South Eastern Cape Province, South Africa

Streptocarpus grandis Accession Qualifier Used for Lineage and collection information genome seq

20150821 A Yes Descendent of 20130764 by selfing

20130764 A - Descendent of 20120713 by selfing

20120713 A - Descendent of 19771210 by selfing

Collector: Hilliard, O. and Burtt, B. L. (Collector no. HBT5923) Collection date: March 1977 19771210 A - Collection location: Ngome forest, Zululand, Natal Province, South Africa

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Appendices Appendix 3.2 Flowchart of the genome assembly, filtering and analysis procedures

K-mer frequency KAT for k-mer frequency check

Preliminary genome assemblies

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Appendices Appendix 3.3 List of contaminant species identified in the S. rexii genome assembly Phylum Actinobacteria Acidipropionibacterium acidipropionici , Actinoalloteichus hoggarensis , Actinoplanes missouriensis , Actinoplanes sp., Actinosynnema mirum , Actinosynnema pretiosum , Aeromicrobium erythreum , Agromyces aureus , Agromyces flavus , Amycolatopsis mediterranei , Amycolatopsis methanolica , Amycolatopsis orientalis , Auraticoccus monumenti , Beutenbergia cavernae , Brachybacterium sp., Cellulomonas gilvus , Clavibacter capsici , Clavibacter insidiosus , Clavibacter michiganensis , Clavibacter sepedonicus , Cnuibacter physcomitrellae , Corynebacterium doosanense , Corynebacterium frankenforstense , Cryobacterium arcticum , Cryobacterium sp., Cupriavidus necator , Curtobacterium pusillum , Curtobacterium sp., Frankia sp., Friedmanniella luteola , Friedmanniella sagamiharensis , Frondihabitans sp., Geodermatophilus obscurus , Gordonia bronchialis , Gordonia sp., Intrasporangium calvum , Jatrophihabitans sp., Jiangella alkaliphila , Jiangella sp., Kocuria palustris , Kribbella flavida , Leifsonia sp., Leifsonia xyli , Microbacterium aurum , Microbacterium pygmaeum , Microbacterium sp., Microcella alkaliphila , Micrococcus sp., Micromonospora echinaurantiaca , Micromonospora echinospora , Micromonospora inositola , Microterricola viridarii , Mycobacterium abscessus , Mycobacterium aurum , Mycobacterium avium , Mycobacterium bovis , Mycobacterium canettii, Mycobacterium chelonae , Mycobacterium chimaera , Mycobacterium chubuense , Mycobacterium colombiense , Mycobacterium dioxanotrophicus , Mycobacterium fortuitum , Mycobacterium gilvum , Mycobacterium goodii , Mycobacterium haemophilum , Mycobacterium immunogenum , My cobacterium indicus , Mycobacterium intracellulare , Mycobacterium kansasii , Mycobacterium leprae , Mycobacterium lepraemurium , Mycobacterium liflandii , Mycobacterium litorale , Mycobacterium marinum , Mycobacterium marseillense , Mycobacterium paraintracellulare , Mycobacterium phlei , Mycobacterium rhodesiae , Mycobacterium rutilum , Mycobacterium shigaense , Mycobacterium simiae , Mycobacterium sinense , Mycobacterium smegmatis , Mycobacterium sp., Mycobacterium stephanolepidis , Mycobacterium terrae , Mycobacterium thermoresistibile , Mycobacterium tuberculosis , Mycobacterium ulcerans , Mycobacterium vaccae , Mycobacterium vanbaalenii , Mycobacterium yongonense , Nakamurella multipartita , Nocardia asteroides , Nocardia brasiliensis , Nocardia cyriacigeorgica , Nocardia farcinica , Nocardia nova , Nocardia seriolae , Nocardia soli , Nocardiopsis dassonvillei , Nonomuraea gerenzanensis , Nonomuraea sp., Plantibacter flavus , Propionibacterium freudenreichii , Pseudomonas sp., Pseudonocardia dioxanivorans , Pseudonocardia sp., Rathayibacter tritici , Rhodococcus hoagii , Rhodococcus opacus , Rhodococcus rhodochrous , Rhodococcus ruber , Rhodococcus sp., Saccharothrix espanaensis, Sinomonas atrocyanea , Streptomyces albulus , Streptomyces albus , Streptomyces cattleya , Streptomyces chartreusis , Streptomyces coelicolor , Streptomyces griseochromogenes , Streptomyces hygroscopicus , Streptomyces parvulus , Streptomyces rapamycinicus , Streptomyces sp., Streptomyces venezuelae , Streptosporangium roseum , Tessaracoccus flavus , Thermobispora bispora , uncultured Mycobacterium Phylum Apicomplexa Toxoplasma gondii Phylum Arthropoda Culicoides sonorensis , Dendroctonus ponderosae , Nasonia vitripennis , Odontocepheus oblongus , Platynothrus peltifer , Zeugodacus cucurbitae

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Appendices Phylum Ascomycota Aspergillus fumigatus , Aspergillus nidulans , Aspergillus oryzae , Dandida albicans , Dandida dubliniensis , Dandida parapsilosis , Dapnobotryella renispora , Dhaetothyriales sp. , Dladophialophora bantiana , Exophiala mesophila , Kluyveromyces lactis , Naumovozyma castellii , Neurospora crassa , Parapenidiella pseudotasmaniensis , Phialophora verrucosa , Pseudocercospora mori , Saccharomycopsis fibuligera , Suhomyces tanzawaensis , Talaromyces marneffei , Talaromyces stipitatus , Tropicoporus linteus , Zasmidium cellare , Zymoseptoria tritici Phylum Bacteroidetes Uncultured Porphyromonas Phylum Basidiomycota Cryptococcus gattii , Cryptococcus neoformans , Exobasidium pachysporum , Fibroporia vaillantii , Geminibasidium donsium , Kalmanozyma brasiliensis , Kwoniella bestiolae , Kwoniella mangrovensis , Kwoniella pini , Malassezia sympodialis , Melanopsichium pennsylvanicum , Moesziomyces bullatus , Pseudozyma sp., Saitozyma ninhbinhensis , Sporisorium scitamineum , Tilletia laevis , Tremella fuciformis, Ustilago bromivora, Ustilago esculenta, Ustilago maydis , Wallemia mellicola Phylum Chlorophyta Edaphochlorella mirabilis , Stichococcus bacillaris Phylum Chordata Cyprinus carpio, Oryzias latipes Phylum Mucoromycota Lichtheimia ramose , Mucoromycota sp., Rhizopus microsporus Phylum Nematoda Aphelenchoides fragariae , Heterakis gallinarum Phylum Proteobacteria Achromobacter spanius, Achromobacter xylosoxidans, Acidovorax citrulli, Alicycliphilus denitrificans, Aminobacter aminovorans, Amycolatopsis mediterranei, Archangium gephyra, Aureimonas sp. , Azorhizobium caulinodans, Azospirillum brasilense, Azospirillum lipoferum, Azospirillum thiophilum, Azotobacter chroococcum, Beijerinckia indica, Blastochloris viridis, Bordetella bronchialis, Bordetella bronchiseptica, Bordetella hinzii, Bordetella pertussis, Bosea sp. , Bosea vaviloviae, Bradyrhizobium canariense, Bradyrhizobium diazoefficiens, Bradyrhizobium erythrophlei, Bradyrhizobium japonicum, Bradyrhizobium oligotrophicum, Bradyrhizobium sp. , Brucella vulpis, Burkholderia ambifaria, Burkholderia cenocepacia, Burkholderia cepacia, Burkholderia gladioli, Burkholderia glumae, Burkholderia lata, Burkholderia mallei, Burkholderia multivorans, Burkholderia oklahomensis, Burkholderia pyrrocinia, Burkholderia sp. , Burkholderia stabilis, Burkholderia territorii, Burkholderia thailandensis, Burkholderia ubonensis, Burkholderia vietnamiensis, Castellaniella defragrans, Caulobacter henricii, Caulobacter mirabilis, Caulobacter sp. , Caulobacter vibrioides, Chelatococcus daeguensis, Chelatococcus sp. , Chromobacterium vaccinii, Chromobacterium violaceum, Cupriavidus oxalaticus, Desulfovibrio vulgaris, Devosia sp. , Dokdonella koreensis, Dyella japonica, Dyella jiangningensis, Dyella sp. , Dyella thiooxydans, Ensifer adhaerens, Escherichia coli , Frateuria aurantia, Gluconacetobacter diazotrophicus, Granulibacter bethesdensis, Luteibacter rhizovicinus, Luteitalea pratensis, Lysobacter antibioticus, Lysobacter capsici, Lysobacter enzymogenes, Lysobacter gummosus, Magnetospirillum sp. ,

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Appendices Mannheimia haemolytica, Martelella mediterranea, Massilia sp. , Mesorhizobium amorphae, Mesorhizobium australicum, Mesorhizobium ciceri, Mesorhizobium loti, Methylibium petroleiphilum, Methylobacterium aquaticum, Methylobacterium extorquens, Methylobacterium nodulans, Methylobacterium oryzae, Methylobacterium phyllosphaerae, Methylobacterium populi, Methylobacterium radiotolerans, Methylobacterium sp. , Methylobacterium zatmanii, Methylocella silvestris, Methylocystis bryophila, Methylocystis sp. , Methylophilus sp. , Methylosinus trichosporium, Micromonospora narathiwatensis, Microvirga ossetica , Nitrobacter winogradskyi , Oligotropha carboxidovorans, Pandoraea pnomenusa, Pandoraea thiooxydans, Pantholops hodgsonii, Paraburkholderia aromaticivorans, Paraburkholderia caribensis, Paraburkholderia fungorum, Paraburkholderia hospita, Paraburkholderia phymatum, Paraburkholderia phytofirmans, Paraburkholderia sprentiae, Paraburkholderia xenovorans, Polaromonas sp. , Polymorphum gilvum, Pseudomonas aeruginosa, Pseudomonas citronellolis, Pseudomonas putida, Pseudomonas stutzeri, Pseudomonas veronii, Pseudoxanthomonas spadix, Pseudoxanthomonas suwonensis, Ralstonia mannitolilytica, Ralstonia solanacearum, Rheinheimera sp. , Rhizobium gallicum, Rhizobium leguminosarum, Rhizobium phaseoli, Rhizobium sp. , Rhodanobacter denitrificans, Rhodobacter sp. , Rhodomicrobium vannielii, Rhodoplanes sp. , Rhodopseudomonas palustris, Rhodospirillum rubrum, Roseomonas gilardii, Rubrivivax gelatinosus, Ruegeria pomeroyi, Shinella sp. , Sinorhizobium americanum, Sinorhizobium fredii, Sinorhizobium meliloti, Sphingomonas melonis, Sphingomonas panacis, Sphingomonas wittichii, Sphingopyxis macrogoltabida, Starkeya novella, Stenotrophomonas acidaminiphila, Stenotrophomonas maltophilia, Stenotrophomonas rhizophila, Stenotrophomonas sp. , Steroidobacter denitrificans , Verminephrobacter eiseniae, Xanthomonas albilineans, Xanthomonas campestris, Xanthomonas citri, Xanthomonas fuscans, Xanthomonas oryzae, Xanthomonas sacchari, Xanthomonas translucens, Pseudomonas mesoacidophila , uncultured Pseudomonas , uncultured Shewanella, uncultured bacterium Undefined eukaryota Peronospora tabacina , Pythium ultimum , uncultured alveolate , uncultured eukaryote Undefined bacteria Uncultured bacterium Viruses undefined Dahlia mosaic, Escherichia virus, Mycobacterium phage

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Appendices Appendix 3.4 List of contaminant species identified in the S. grandis genome assembly Phylum Chordata Cyanistes caeruleus Phylum Proteobacteria Alcaligenes faecalis , Aquaspirillum sp., Beggiatoa leptomitoformis , Bordetella holmesii , Bordetella sp., Bradyrhizobium erythrophlei , Candidatus Methylopumilus , Chelatococcus daeguensis , Collimonas arenae , Collimonas pratensis , Dechloromonas aromatic , Escherichia coli , Gallionella capsiferriformans , Herbaspirillum seropedicae , Herminiimonas arsenitoxidans , Janthinobacterium agaricidamnosum , Laribacter hongkongensis , Methylobacillus flagellates , Methylobacterium nodulans , Methylomonas clara , Methylomonas sp., Methylophaga nitratireducenticrescens , Methylophilus methylotrophus , Methylophilus sp., Methylotenera mobilis , Methylotenera versatilis , Methylovorus sp., Moraxella osloensis , Neisseria meningitides , Nitrobacter winogradskyi , Nitrosomonas sp., Oligotropha carboxidovorans , Oxalobacter formigenes , Pandoraea sputorum , Paracoccus yeei , Pectobacterium polaris , Pseudoalteromonas piscicida , Pseudomonas aeruginosa , Pseudomonas cichorii , Pseudomonas fluorescens , Ricinus communis , Salmonella enterica , Serratia marcescens , Shewanella baltica , Vibrio fluvialis , Xenorhabdus poinarii , Zhongshania aliphaticivorans Undefined Eukaryota Environmental Viridiplantae, uncultured eukaryote Undefined Eukaryota Environmental Viridiplantae, uncultured eukaryote Undefined viruses Escherichia virus, Streptomyces phage

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Appendices Appendix 3.5 Annotation of Streptocarpus teitensis and Haberlea rhodopensis chloroplasts Annotation of both chloroplasts was carried out using GeSeq as described in section 3.2.3

Streptocarpus teitensis rRNA Location on the genome (bp, start location .. stop location) rrn16 100,136..101,626 rrn23 104,068..106,880 rrn4.5 106,979..107,081 rrn5 107,306..107,426 rrn5 129,884..130,004 (complementary strand) rrn4.5 130,229..130,331 (compleme ntary strand) rrn23 130,430..133,242 (complementary strand) rrn16 135,684..137,174 (complementary strand)

Haberlea rhodopensis rRNA Location on the genome (bp, start location .. stop location) rrn16 100,525..102,015 rrn23 104,444..107,253 rrn4.5 10 7,352..107,454 rrn5 107,679..107,799 rrn5 129,755..129,875 (complementary strand) rrn4.5 130,100..130,202 (complementary strand) rrn23 130,301..133,110 (complementary strand) rrn16 135,539..137,029 (complementary strand) Appendix 3.6 Annotation of Dorcoceras hygrometricum and Erythrantes guttata mitochondria

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Appendices Annotation of both mitochondrias was carried out using GeSeq as described in section 3.2.3

Figure. D. hygrometricum mitochondrion (NC_016741), 152 protein coding genes annotated

Figure. E. guttata mitochondrion (JN098455), 149 protein coding genes annotated

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Appendix 4.1 Statistical summary of orthogroup analysis result in the S. rexii and S. grandis transcriptome assemblies S. rexii S. rexii S. grandis S. grandis de novo assembly ref-guided assembly de novo assembly ref-guided assembly No. contigs 60500 53322 51267 46429 No. contigs assigned to orthogroups 51375 47580 45908 42555 No. unassigned contigs 9125 5742 5359 3874 Contigs assigned to orthogroups (%) 84.9 89.2 89.5 91.7 Unass igned contigs (%) 15.1 10.8 10.5 8.3 No. species -specific orthogroups 7 3 1 5 No. contigs in species -specific orthogroups 30 19 2 19 Contigs in species -specific orthogroups (%) 0 0 0 0

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Appendix 5.1 (a) List of materials used for RAD-Seq, and the amount of data obtained before and after preprocessing Raw data After preprocessing Accession DNA ID Taxon Qualifier Total Bases Read Count Total Bases Read Count No. YYD17 S grandis 20150821 C 312,506,427 6,127,577 119,192,300 2,383,846 YYD33 S grandis 20151810 S 220,846,932 4,330,332 83,450,250 1,669,005 YYD16 S rexii 20150819 A 118,086,624 2,315,424 32,638,150 652,763 YYD19 S rexii 19990270 I 99,823,116 1,957,316 34,431,200 688,624 YYD1001 (S.grandis × rexii ) × grandis 20150825 A 3,912,822 76,722 1,033,600 20,672 YYD1002 (S.grandis × rexii ) × grandis 20150825 B 1,600,329 31,379 269,900 5,398 YYD1003 (S.grandis × rexii ) × grandis 20150825 C 4,157,367 81,517 1,314,250 26,285 YYD1004 (S.grandis × rexii ) × grandis 20150825 D 19,539,681 383,131 9,128,850 182,577 YYD1005 (S.grandis × rexii ) × grandis 20150825 E 138,372,741 2,713,191 62,472,750 1,249,455 YYD1006 (S.grandis × rexii ) × grandis 20150825 F 83,970,888 1,646,488 26,162,750 523,255 YYD1007 (S.grandis × rexii ) × grandis 20150825 G 85,332,282 1,673,182 44,084,000 881,680 YYD1008 (S.grandis × rexii ) × grandis 20150825 H 19,406,061 380,511 7,641,900 152,838 YYD1010 (S.grandis × rexii ) × grandis 20150825 J 76,875,411 1,507,361 35,307,250 706,145 YYD1011 (S.grandis × rexii ) × grandis 20150825 K 55,234,122 1,083,022 20,714,650 414,293 YYD1012 (S.grandis × rexii ) × grandis 20150825 L 37,202,205 729,455 8,910,000 178,200 YYD1013 (S.grandis × rexii ) × grandis 20150825 M 318,338,940 6,241,940 120,943,450 2,418,869 YYD1014 (S.grandis × rexii ) × grandis 20150825 N 5,912,073 115,923 2,396,700 47,934 YYD1015 (S.grandis × rexii ) × grandis 20150825 O 61,176,846 1,199,546 22,919,700 458,394 YYD1016 (S.grandis × rexii ) × grandis 20150825 P 33,795,048 662,648 14,715,550 294,311

328

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1017 (S.grandis × rexii ) × grandis 20150825 Q 240,459,135 4,714,885 108,180,850 2,163,617 YYD1018 (S.grandis × rexii ) × grandis 20150825 R 90,328,038 1,771,138 33,559,750 671,195 YYD1019 (S.grandis × rexii ) × grandis 20150825 S 9,721,518 190,618 4,903,950 98,079 YYD1020 (S.grandis × rexii ) × grandis 20150825 T 142,060,500 2,785,500 38,033,650 760,673 YYD1021 (S.grandis × rexii ) × grandis 20150825 U 168,920,415 3,312,165 49,218,400 984,368 YYD1022 (S.grandis × rexii ) × grandis 20150825 V 149,462,181 2,930,631 67,513,600 1,350,272 YYD1023 (S.grandis × rexii ) × grandis 20150825 W 40,151,076 787,276 19,287,350 385,747 YYD1024 (S.grandis × rexii ) × grandis 20150825 X 191,774,739 3,760,289 49,841,350 996,827 YYD1025 (S.grandis × rexii ) × grandis 20150825 Y 19,918,764 390,564 10,803,650 216,073 YYD1026 (S.grandis × rexii ) × grandis 20150825 Z 10,462,089 205,139 5,742,350 114,847 YYD1027 (S.grandis × rexii ) × grandis 20150825 AA 137,677,560 2,699,560 57,162,100 1,143,242 YYD1028 (S.grandis × rexii ) × grandis 20150825 AB 13,729,302 269,202 5,391,550 107,831 YYD1029 (S.grandis × rexii ) × grandis 20150825 AC 20,805,858 407,958 8,960,200 179,204 YYD1030 (S.grandis × rexii ) × grandis 20150825 AD 30,731,019 602,569 14,759,950 295,199 YYD1031 (S.grandis × rexii ) × grandis 20150825 AE 127,202,568 2,494,168 51,506,600 1,030,132 YYD1032 (S.grandis × rexii ) × grandis 20150825 AF 75,740,508 1,485,108 19,862,800 397,256 YYD1033 (S.grandis × rexii ) × grandis 20150825 AG 8,989,413 176,263 3,890,300 77,806 YYD1034 (S.grandis × rexii ) × grandis 20150825 AH 35,656,803 699,153 18,756,300 375,126 YYD1035 (S.grandis × rexii ) × grandis 20150825 AI 18,826,140 369,140 9,287,600 185,752 YYD1036 (S.grandis × rexii ) × grandis 20150825 AJ 114,254,841 2,240,291 16,474,650 329,493

329

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1037 (S.grandis × rexii ) × grandis 20150825 AK 28,448,820 557,820 10,757,200 215,144 YYD1038 (S.grandis × rexii ) × grandis 20150825 AL 135,336,456 2,653,656 67,186,750 1,343,735 YYD1040 (S.grandis × rexii ) × grandis 20150825 AN 39,906,021 782,471 17,137,650 342,753 YYD1041 (S.grandis × rexii ) × grandis 20150825 AO 53,147,814 1,042,114 23,253,850 465,077 YYD1042 (S.grandis × rexii ) × grandis 20150825 AP 165,504,027 3,245,177 67,327,250 1,346,545 YYD1043 (S.grandis × rexii ) × grandis 20150825 AQ 109,334,412 2,143,812 56,704,600 1,134,092 YYD1044 (S.grandis × rexii ) × grandis 20150825 AR 9,475,086 185,786 5,207,150 104,143 YYD1045 (S.grandis × rexii ) × grandis 20150825 AS 40,705,854 798,154 17,668,350 353,367 YYD1046 (S.grandis × rexii ) × grandis 20150825 AT 40,964,322 803,222 23,162,150 463,243 YYD1047 (S.grandis × rexii ) × grandis 20150825 AU 23,431,491 459,441 12,327,550 246,551 YYD1048 (S.grandis × rexii ) × grandis 20150825 AV 42,251,409 828,459 19,790,300 395,806 YYD1049 (S.grandis × rexii ) × grandis 20150825 AW 55,059,957 1,079,607 20,850,300 417,006 YYD1050 (S.grandis × rexii ) × grandis 20150825 AX 270,472,227 5,303,377 90,661,000 1,813,220 YYD1051 (S.grandis × rexii ) × grandis 20150825 AY 9,536,949 186,999 4,026,200 80,524 YYD1052 (S.grandis × rexii ) × grandis 20150825 AZ 73,666,950 1,444,450 25,675,150 513,503 YYD1053 (S.grandis × rexii ) × grandis 20150825 BA 127,931,103 2,508,453 54,381,750 1,087,635 YYD1056 (S.grandis × rexii ) × grandis 20150825 BD 135,333,294 2,653,594 38,058,000 761,160 YYD1058 (S.grandis × rexii ) × grandis 20150825 BF 211,712,118 4,151,218 75,272,600 1,505,452 YYD1059 (S.grandis × rexii ) × grandis 20150825 BG 81,889,680 1,605,680 25,917,900 518,358 YYD1060 (S.grandis × rexii ) × grandis 20150825 BH 249,284,430 4,887,930 115,172,950 2,303,459

330

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1061 (S.grandis × rexii ) × grandis 20150825 BI 167,866,551 3,291,501 65,181,050 1,303,621 YYD1062 (S.grandis × rexii ) × grandis 20150825 BJ 52,228,437 1,024,087 30,935,950 618,719 YYD1063 (S.grandis × rexii ) × grandis 20150825 BK 54,243,600 1,063,600 23,959,600 479,192 YYD1064 (S.grandis × rexii ) × grandis 20150825 BL 66,148,326 1,297,026 28,886,550 577,731 YYD1065 (S.grandis × rexii ) × grandis 20150825 BM 15,757,266 308,966 7,881,250 157,625 YYD1066 (S.grandis × rexii ) × grandis 20150825 BN 61,391,658 1,203,758 18,461,550 369,231 YYD1067 (S.grandis × rexii ) × grandis 20150825 BO 100,624,377 1,973,027 33,326,950 666,539 YYD1069 (S.grandis × rexii ) × grandis 20150825 BQ 21,978,297 430,947 9,996,050 199,921 YYD1070 (S.grandis × rexii ) × grandis 20150825 BR 181,928,526 3,567,226 62,792,500 1,255,850 YYD1071 (S.grandis × rexii ) × grandis 20150825 BS 84,470,076 1,656,276 29,470,600 589,412 YYD1072 (S.grandis × rexii ) × grandis 20150825 BT 27,896,847 546,997 10,134,700 202,694 YYD1073 (S.grandis × rexii ) × grandis 20150825 BU 84,691,161 1,660,611 28,400,600 568,012 YYD1074 (S.grandis × rexii ) × grandis 20150825 BV 213,076,725 4,177,975 80,051,950 1,601,039 YYD1075 (S.grandis × rexii ) × grandis 20150825 BW 37,865,205 742,455 20,669,350 413,387 YYD1076 (S.grandis × rexii ) × grandis 20150825 BX 28,876,404 566,204 10,656,550 213,131 YYD1077 (S.grandis × rexii ) × grandis 20150825 BY 42,124,572 825,972 17,329,250 346,585 YYD1078 (S.grandis × rexii ) × grandis 20150825 BZ 185,661,930 3,640,430 81,810,700 1,636,214 YYD1079 (S.grandis × rexii ) × grandis 20150825 CA 486,147,963 9,532,313 181,971,550 3,639,431 YYD1080 (S.grandis × rexii) × grandis 20150825 CB 111,675,924 2,189,724 40,156,800 803,136 YYD1081 (S.grandis × rexii ) × grandis 20150825 CC 73,183,980 1,434,980 26,684,700 533,694

331

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1082 (S.grandis × rexii ) × grandis 20150825 CD 116,755,524 2,289,324 36,518,850 730,377 YYD1083 (S.grandis × rexii ) × grandis 20150825 CE 221,016,660 4,333,660 88,413,000 1,768,260 YYD1084 (S.grandis × rexii ) × grandis 20150825 CF 32,581,758 638,858 13,027,650 260,553 YYD1085 (S.grandis × rexii ) × grandis 20150825 CG 158,930,943 3,116,293 65,525,600 1,310,512 YYD1086 (S.grandis × rexii ) × grandis 20150825 CH 34,696,422 680,322 13,126,200 262,524 YYD1087 (S.grandis × rexii ) × grandis 20150825 CI 199,554,891 3,912,841 86,746,850 1,734,937 YYD1088 (S.grandis × rexii ) × grandis 20150825 CJ 80,734,683 1,583,033 32,349,400 646,988 YYD1089 (S.grandis × rexii ) × grandis 20150825 CK 101,587,512 1,991,912 32,540,500 650,810 YYD1090 (S.grandis × rexii ) × grandis 20150825 CL 292,302,828 5,731,428 123,615,800 2,472,316 YYD1091 (S.grandis × rexii ) × grandis 20150825 CM 184,437,930 3,616,430 83,473,250 1,669,465 YYD1092 (S.grandis × rexii ) × grandis 20150825 CN 12,223,272 239,672 5,017,500 100,350 YYD1093 (S.grandis × rexii ) × grandis 20150825 CO 3,538,839 69,389 172,850 3,457 YYD1094 (S.grandis × rexii ) × grandis 20150825 CP 45,060,336 883,536 17,543,050 350,861 YYD1095 (S.grandis × rexii ) × grandis 20150825 CQ 56,440,731 1,106,681 27,067,950 541,359 YYD1096 (S.grandis × rexii ) × grandis 20150825 CR 29,826,126 584,826 15,323,450 306,469 YYD1098 (S.grandis × rexii ) × grandis 20150825 CT 94,231,221 1,847,671 40,868,900 817,378 YYD1099 (S.grandis × rexii ) × grandis 20150825 CU 40,737,780 798,780 19,087,800 381,756 YYD1100 (S.grandis × rexii ) × grandis 20150825 CV 25,544,268 500,868 11,886,500 237,730 YYD1101 (S.grandis × rexii ) × grandis 20150825 CW 14,418,159 282,709 7,240,250 144,805 YYD1102 (S.grandis × rexii ) × grandis 20150825 CX 274,179,417 5,376,067 76,873,150 1,537,463

332

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1103 (S.grandis × rexii ) × grandis 20150825 CY 110,732,526 2,171,226 35,739,850 714,797 YYD1104 (S.grandis × rexii ) × grandis 20150825 CZ 5,607,399 109,949 833,900 16,678 YYD1105 (S.grandis × rexii ) × grandis 20150825 DA 139,092,708 2,727,308 48,056,800 961,136 YYD1106 (S.grandis × rexii ) × grandis 20150825 DB 111,340,752 2,183,152 42,815,700 856,314 YYD1107 (S.grandis × rexii ) × grandis 20150825 DC 9,277,665 181,915 4,854,150 97,083 YYD1108 (S.grandis × rexii ) × grandis 20150825 DD 38,192,880 748,880 19,190,900 383,818 YYD1109 (S.grandis × rexii ) × grandis 20150825 DE 81,108,615 1,590,365 28,108,900 562,178 YYD1110 (S.grandis × rexii ) × grandis 20150825 DF 194,473,149 3,813,199 59,054,650 1,181,093 YYD1111 (S.grandis × rexii ) × grandis 20150825 DG 111,168,780 2,179,780 46,401,350 928,027 YYD1112 (S.grandis × rexii ) × grandis 20150825 DH 292,349,748 5,732,348 81,701,600 1,634,032 YYD1113 (S.grandis × rexii ) × grandis 20150825 DI 47,569,332 932,732 18,786,850 375,737 YYD1114 (S.grandis × rexii ) × grandis 20150825 DJ 74,048,940 1,451,940 34,488,700 689,774 YYD1115 (S.grandis × rexii ) × grandis 20150825 DK 1,871,496 36,696 842,600 16,852 YYD1116 (S.grandis × rexii ) × grandis 20150825 DL 546,006 10,706 209,500 4,190 YYD1117 (S.grandis × rexii ) × grandis 20150825 DM 51,162,486 1,003,186 16,978,600 339,572 YYD1118 (S.grandis × rexii ) × grandis 20150825 DN 3,167,151 62,101 1,383,800 27,676 YYD1119 (S.grandis × rexii ) × grandis 20150825 DO 7,143,468 140,068 3,035,400 60,708 YYD1120 (S.grandis × rexii ) × grandis 20150825 DP 2,494,716 48,916 1,129,200 22,584 YYD1121 (S.grandis × rexii ) × grandis 20150825 DQ 5,671,200 111,200 2,550,400 51,008 YYD1122 (S.grandis × rexii ) × grandis 20150825 DR 5,976,435 117,185 2,959,850 59,197

333

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1123 (S.grandis × rexii ) × grandis 20150825 DS 41,305,665 809,915 16,450,550 329,011 YYD1124 (S.grandis × rexii ) × grandis 20150825 DT 51,364,446 1,007,146 23,406,650 468,133 YYD1125 (S.grandis × rexii ) × grandis 20150825 DU 14,736,705 288,955 6,235,450 124,709 YYD1127 (S.grandis × rexii ) × grandis 20150825 DW 7,942,995 155,745 3,294,100 65,882 YYD1128 (S.grandis × rexii ) × grandis 20150825 DX 27,799,029 545,079 11,845,800 236,916 YYD1129 (S.grandis × rexii ) × grandis 20150825 DY 11,828,940 231,940 4,093,600 81,872 YYD1130 (S.grandis × rexii ) × grandis 20150825 DZ 14,185,242 278,142 5,636,650 112,733 YYD1131 (S.grandis × rexii ) × grandis 20150825 EA 8,442,285 165,535 3,839,500 76,790 YYD1132 (S.grandis × rexii ) × grandis 20150825 EB 192,718,494 3,778,794 37,343,000 746,860 YYD1133 (S.grandis × rexii ) × grandis 20150825 EC 17,845,104 349,904 8,718,950 174,379 YYD1134 (S.grandis × rexii ) × grandis 20150825 ED 10,517,373 206,223 4,583,100 91,662 YYD1135 (S.grandis × rexii ) × grandis 20150825 EE 91,672,602 1,797,502 33,457,400 669,148 YYD1136 (S.grandis × rexii ) × grandis 20150825 EF 23,931,087 469,237 7,356,000 147,120 YYD1137 (S.grandis × rexii ) × grandis 20150825 EG 36,713,370 719,870 13,406,950 268,139 YYD1138 (S.grandis × rexii ) × grandis 20150825 EH 186,869,712 3,664,112 67,481,200 1,349,624 YYD1139 (S.grandis × rexii ) × grandis 20150825 EI 5,312,772 104,172 2,594,650 51,893 YYD1140 (S.grandis × rexii ) × grandis 20150825 EJ 7,342,470 143,970 2,633,450 52,669 YYD1141 (S.grandis × rexii ) × grandis 20150825 EK 10,973,160 215,160 5,006,100 100,122 YYD1142 (S.grandis × rexii ) × grandis 20150825 EL 1,628,022 31,922 818,050 16,361 YYD1143 (S.grandis × rexii ) × grandis 20150825 EM 2,773,176 54,376 612,800 12,256

334

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1144 (S.grandis × rexii ) × grandis 20150825 EN 30,578,937 599,587 14,421,750 288,435 YYD1145 (S.grandis × rexii ) × grandis 20150825 EO 483,344,595 9,477,345 169,305,950 3,386,119 YYD1146 (S.grandis × rexii ) × grandis 20150825 EP 6,497,196 127,396 3,062,250 61,245 YYD1147 (S.grandis × rexii ) × grandis 20150825 EQ 10,763,040 211,040 4,973,800 99,476 YYD1148 (S.grandis × rexii ) × grandis 20150825 ER 7,228,536 141,736 2,408,550 48,171 YYD1149 (S.grandis × rexii ) × grandis 20150825 ES 66,071,826 1,295,526 35,476,800 709,536 YYD1150 (S.grandis × rexii ) × grandis 20150825 ET 205,599,258 4,031,358 80,476,700 1,609,534 YYD1151 (S.grandis × rexii) × grandis 20150825 EU 184,422,477 3,616,127 48,961,600 979,232 YYD1152 (S.grandis × rexii ) × grandis 20150825 EV 15,942,447 312,597 6,248,900 124,978 YYD1153 (S.grandis × rexii ) × grandis 20150825 EW 142,576,824 2,795,624 37,805,800 756,116 YYD1154 (S.grandis × rexii ) × grandis 20150825 EX 18,075,930 354,430 6,651,750 133,035 YYD1155 (S.grandis × rexii ) × grandis 20150825 EY 205,922,190 4,037,690 64,165,850 1,283,317 YYD1156 (S.grandis × rexii ) × grandis 20150825 EZ 27,866,706 546,406 9,888,700 197,774 YYD1157 (S.grandis × rexii ) × grandis 20150825 FA 12,184,716 238,916 5,604,100 112,082 YYD1158 (S.grandis × rexii ) × grandis 20150825 FB 10,628,706 208,406 4,284,250 85,685 YYD1159 (S.grandis × rexii ) × grandis 20150825 FC 13,950,591 273,541 4,302,450 86,049 YYD1160 (S.grandis × rexii ) × grandis 20150825 FD 23,614,581 463,031 8,833,600 176,672 YYD1161 (S.grandis × rexii ) × grandis 20150825 FE 17,254,320 338,320 6,006,700 120,134 YYD1162 (S.grandis × rexii ) × grandis 20150825 FF 32,017,494 627,794 11,714,850 234,297 YYD1163 (S.grandis × rexii ) × grandis 20150825 FG 94,249,581 1,848,031 33,364,200 667,284

335

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1164 (S.grandis × rexii ) × grandis 20150825 FH 310,167,312 6,081,712 126,140,300 2,522,806 YYD1165 (S.grandis × rexii ) × grandis 20150825 FI 27,757,311 544,261 12,353,500 247,070 YYD1166 (S.grandis × rexii ) × grandis 20150825 FJ 8,012,049 157,099 4,042,250 80,845 YYD1167 (S.grandis × rexii ) × grandis 20150825 FK 21,590,085 423,335 8,998,750 179,975 YYD1168 (S.grandis × rexii ) × grandis 20150825 FL 30,350,304 595,104 11,295,350 225,907 YYD1169 (S.grandis × rexii ) × grandis 20150825 FM 14,135,517 277,167 3,396,800 67,936 YYD1170 (S.grandis × rexii ) × grandis 20150825 FN 109,648,725 2,149,975 29,023,550 580,471 YYD1171 (S.grandis × rexii ) × grandis 20150825 FO 16,519,665 323,915 7,137,150 142,743 YYD1172 (S.grandis × rexii ) × grandis 20150825 FP 5,228,367 102,517 2,955,650 59,113 YYD1173 (S.grandis × rexii ) × grandis 20150825 FQ 6,785,652 133,052 3,061,950 61,239 YYD1174 (S.grandis × rexii ) × grandis 20150825 FR 36,968,574 724,874 15,000,950 300,019 YYD1175 (S.grandis × rexii ) × grandis 20150825 FS 14,612,010 286,510 6,374,100 127,482 YYD1176 (S.grandis × rexii ) × grandis 20150825 FT 9,994,725 195,975 3,794,000 75,880 YYD1177 (S.grandis × rexii ) × grandis 20150825 FU 119,514,930 2,343,430 29,273,050 585,461 YYD1178 (S.grandis × rexii ) × grandis 20150825 FV 112,144,359 2,198,909 54,624,850 1,092,497 YYD1180 (S.grandis × rexii ) × grandis 20150825 FX 15,026,844 294,644 6,694,250 133,885 YYD1181 (S.grandis × rexii ) × grandis 20150825 FY 119,809,302 2,349,202 36,633,350 732,667 YYD1183 (S.grandis × rexii ) × grandis 20150825 GA 21,536,076 422,276 9,650,000 193,000 YYD1184 (S.grandis × rexii ) × grandis 20150825 GB 31,931,049 626,099 11,911,200 238,224 YYD1185 (S.grandis × rexii ) × grandis 20150825 GC 223,307,274 4,378,574 63,450,200 1,269,004

336

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1188 (S.grandis × rexii ) × grandis 20150825 GF 23,446,944 459,744 10,995,200 219,904 YYD1189 (S.grandis × rexii ) × grandis 20150825 GG 242,056,455 4,746,205 58,370,400 1,167,408 YYD1190 (S.grandis × rexii ) × grandis 20150825 GH 142,536,330 2,794,830 60,443,200 1,208,864 YYD1191 (S.grandis × rexii ) × grandis 20150825 GI 47,192,187 925,337 12,266,400 245,328 YYD1192 (S.grandis × rexii ) × grandis 20150825 GJ 35,802,459 702,009 16,243,750 324,875 YYD1193 (S.grandis × rexii ) × grandis 20150825 GK 92,502,729 1,813,779 38,733,750 774,675 YYD1194 (S.grandis × rexii ) × grandis 20150825 GL 5,611,479 110,029 1,698,850 33,977 YYD1195 (S.grandis × rexii ) × grandis 20150825 GM 143,617,071 2,816,021 42,027,650 840,553 YYD1196 (S.grandis × rexii ) × grandis 20150825 GN 24,900,189 488,239 9,832,600 196,652 YYD1197 (S.grandis × rexii ) × grandis 20150825 GO 118,473,102 2,323,002 37,770,650 755,413 YYD1198 (S.grandis × rexii ) × grandis 20150825 GP 47,556,990 932,490 19,524,000 390,480 YYD1199 (S.grandis × rexii ) × grandis 20150825 GQ 431,722,191 8,465,141 171,791,400 3,435,828 YYD1200 (S.grandis × rexii ) × grandis 20150825 GR 45,186,204 886,004 14,554,150 291,083 YYD1201 (S.grandis × rexii ) × grandis 20150825 GS 126,339,291 2,477,241 42,255,400 845,108 YYD1203 (S.grandis × rexii ) × grandis 20150825 GU 11,557,059 226,609 4,639,700 92,794 YYD1204 (S.grandis × rexii ) × grandis 20150825 GV 153,200,430 3,003,930 47,538,000 950,760 YYD1205 (S.grandis × rexii ) × grandis 20150825 GW 25,366,890 497,390 10,616,500 212,330 YYD1206 (S.grandis × rexii ) × grandis 20150825 GX 67,899,666 1,331,366 21,341,750 426,835 YYD1207 (S.grandis × rexii ) × grandis 20150825 GY 41,944,287 822,437 14,508,450 290,169 YYD1208 (S.grandis × rexii ) × grandis 20150825 GZ 37,442,568 734,168 13,849,250 276,985

337

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1209 (S.grandis × rexii ) × grandis 20150825 HA 31,766,472 622,872 10,109,300 202,186 YYD1210 (S.grandis × rexii ) × grandis 20150825 HB 152,959,710 2,999,210 60,450,900 1,209,018 YYD1211 (S.grandis × rexii ) × grandis 20150825 HC 68,802,162 1,349,062 20,893,300 417,866 YYD1212 (S.grandis × rexii ) × grandis 20150825 HD 42,265,638 828,738 18,431,400 368,628 YYD1213 (S.grandis × rexii ) × grandis 20150825 HE 22,158,735 434,485 9,267,050 185,341 YYD1214 (S.grandis × rexii ) × grandis 20150825 HF 100,445,520 1,969,520 34,413,600 688,272 YYD1215 (S.grandis × rexii ) × grandis 20150825 HG 402,140,253 7,885,103 153,480,850 3,069,617 YYD1216 (S.grandis × rexii ) × grandis 20150825 HH 28,111,353 551,203 10,446,400 208,928 YYD1217 (S.grandis × rexii ) × grandis 20150825 HI 75,722,505 1,484,755 27,336,000 546,720 YYD1218 (S.grandis × rexii ) × grandis 20150825 HJ 65,964,675 1,293,425 18,461,350 369,227 YYD1219 (S.grandis × rexii ) × grandis 20150825 HK 117,961,878 2,312,978 50,395,950 1,007,919 YYD1220 (S.grandis × rexii ) × grandis 20150825 HL 7,851,399 153,949 2,636,900 52,738 YYD1221 (S.grandis × rexii ) × grandis 20150825 HM 57,692,526 1,131,226 19,172,050 383,441 YYD1224 (S.grandis × rexii ) × grandis 20150825 HP 29,078,823 570,173 8,580,600 171,612 YYD1226 (S.grandis × rexii ) × grandis 20150825 HR 59,718,195 1,170,945 27,803,000 556,060 YYD1227 (S.grandis × rexii ) × grandis 20150825 HS 120,454,962 2,361,862 37,522,050 750,441 YYD1228 (S.grandis × rexii ) × grandis 20150825 HT 207,001,911 4,058,861 73,190,000 1,463,800 YYD1229 (S.grandis × rexii ) × grandis 20150825 HU 161,189,274 3,160,574 82,588,000 1,651,760 YYD1230 (S.grandis × rexii ) × grandis 20150825 HV 748,942,191 14,685,141 363,067,650 7,261,353 YYD1231 (S.grandis × rexii ) × grandis 20150825 HW 78,133,377 1,532,027 32,001,900 640,038

338

Accession Total Bases Read Count DNA ID Taxon Qualifier Total Bases (raw) Read Count (raw) No. (Preprocessed) (Preprocessed) YYD1232 (S.grandis × rexii ) × grandis 20150825 HX 70,569,006 1,383,706 27,848,150 556,963 YYD1233 (S.grandis × rexii ) × grandis 20150825 HY 97,131,999 1,904,549 37,817,300 756,346 YYD1234 (S.grandis × rexii ) × grandis 20150825 HZ 84,620,679 1,659,229 31,035,200 620,704 YYD1235 (S.grandis × rexii ) × grandis 20150825 IA 16,090,602 315,502 6,225,200 124,504 YYD1236 (S.grandis × rexii ) × grandis 20150825 IB 51,583,899 1,011,449 19,056,750 381,135 YYD1237 (S.grandis × rexii ) × grandis 20150825 IC 12,727,254 249,554 5,034,850 100,697 YYD1238 (S.grandis × rexii ) × grandis 20150825 ID 24,125,142 473,042 8,488,200 169,764 YYD1239 (S.grandis × rexii ) × grandis 20150825 IE 24,016,104 470,904 7,228,950 144,579 YYD1240 (S.grandis × rexii ) × grandis 20150825 IF 37,106,835 727,585 17,560,650 351,213 YYD1241 (S.grandis × rexii ) × grandis 20150825 IG 170,383,605 3,340,855 47,431,450 948,629 YYD1242 (S.grandis × rexii ) × grandis 20150825 IH 43,762,641 858,091 20,118,000 402,360 YYD1244 (S.grandis × rexii ) × grandis 20150825 IJ 48,816,027 957,177 21,037,600 420,752 YYD1245 (S.grandis × rexii ) × grandis 20150825 IK 44,690,433 876,283 16,859,650 337,193 YYD1246 (S.grandis × rexii ) × grandis 20150825 IL 91,771,644 1,799,444 25,458,450 509,169 YYD1250 (S.grandis × rexii ) × grandis 20150825 IP 22,193,160 435,160 10,188,700 203,774 YYD1251 (S.grandis × rexii ) × grandis 20150825 IQ 20,825,238 408,338 8,963,700 179,274 YYD1252 (S.grandis × rexii ) × grandis 20150825 IR 30,886,263 605,613 9,612,900 192,258 YYD1253 (S.grandis × rexii ) × grandis 20150825 IS 83,329,053 1,633,903 38,189,100 763,782

339

(b) List of materials used for Stacks analyses and genetic map calculation. The DNA ID listed here are corresponded to the material details summarised in Appendix 5.1. v: used for analyses. -: not used for analyses. Used for MapA calculation Used for MapB calculation Ref-based Ref-based Ref-based Ref-based Parameter De novo De novo Parameter De novo approach approach approach approach DNA ID optimisation approach approach optimisation approach with BWA with Stampy with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) (200 indi.) (200 indi.) YYD17 v v v v v v v v v YYD33 ------YYD16 v v v v v v v v v YYD19 v v v v v v v v v YYD1001 ------YYD1002 ------YYD1003 ------YYD1004 - - v v v - v v v YYD1005 v v v v v v v v v YYD1006 - v v v v - v v v YYD1007 v v v v v v v v v YYD1008 - - v v v - v v v YYD1010 - v v v v - v v v YYD1011 - v v v v - v v v YYD1012 - - v v v - v v v YYD1013 v v v v v v v v v YYD1014 ------YYD1015 - v v v v - v v v

340

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1016 - v v v v - v v v YYD1017 v v v v v v v v v YYD1018 - v v v v - v v v YYD1019 - - v v v - v v v YYD1020 - v v v v - v v v YYD1021 v v v v v v v v v YYD1022 v v v v v v v v v YYD1023 - v v v v - v v v YYD1024 v v v v v v v v v YYD1025 - - v v v - v v v YYD1026 - - v v v - v v v YYD1027 v v v v v v v v v YYD1028 - - v v v - v v v YYD1029 - - v v v - v v v YYD1030 - v v v v - v v v YYD1031 v v v v v v v v v YYD1032 - v v v v - v v v YYD1033 ------YYD1034 - v v v v - v v v

341

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1035 - - v v v - v v v YYD1036 - v v v v - v v v YYD1037 - - v v v - v v v YYD1038 v v v v v v v v v YYD1040 - v v v v - v v v YYD1041 - v v v v - v v v YYD1042 v v v v v v v v v YYD1043 v v v v v v v v v YYD1044 - - v v v - v v v YYD1045 - v v v v - v v v YYD1046 - v v v v - v v v YYD1047 - v v v v - v v v YYD1048 - v v v v - v v v YYD1049 - v v v v - v v v YYD1050 v v v v v v v v v YYD1051 ------YYD1052 - v v v v - v v v YYD1053 v v v v v v v v v YYD1056 - v v v v - v v v

342

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1058 v v v v v v v v v YYD1059 - v v v v - v v v YYD1060 v v v v v v v v v YYD1061 v v v v v v v v v YYD1062 - v v v v - v v v YYD1063 - v v v v - v v v YYD1064 - v v v v - v v v YYD1065 - - v v v - v v v YYD1066 - v v v v - v v v YYD1067 - v v v v - v v v YYD1069 - - v v v - v v v YYD1070 v v v v v v v v v YYD1071 - v v v v - v v v YYD1072 - - v v v - v v v YYD1073 - v v v v - v v v YYD1074 v v v v v v v v v YYD1075 - v v v v - v v v YYD1076 - - v v v - v v v YYD1077 - v v v v - v v v

343

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1078 v v v v v v v v v YYD1079 v v v v v v v v v YYD1080 - v v v v - v v v YYD1081 - v v v v - v v v YYD1082 - v v v v - v v v YYD1083 v v v v v v v v v YYD1084 - v v v v - v v v YYD1085 v v v v v v v v v YYD1086 - v v v v - v v v YYD1087 v v v v v v v v v YYD1088 - v v v v - v v v YYD1089 - v v v v - v v v YYD1090 v v v v v v v v v YYD1091 v v v v v v v v v YYD1092 - - v v v - v v v YYD1093 ------YYD1094 - v v v v - v v v YYD1095 - v v v v - v v v YYD1096 - v v v v - v v v

344

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1098 - v v v v - v v v YYD1099 - v v v v - v v v YYD1100 - v v v v - v v v YYD1101 - - v v v - v v v YYD1102 v v v v v v v v v YYD1103 - v v v v - v v v YYD1104 ------YYD1105 v v v v v v v v v YYD1106 - v v v v - v v v YYD1107 - - v v v - v v v YYD1108 - v v v v - v v v YYD1109 - v v v v - v v v YYD1110 v v v v v v v v v YYD1111 v v v v v v v v v YYD1112 v v v v v v v v v YYD1113 - v v v v - v v v YYD1114 - v v v v - v v v YYD1115 ------YYD1116 ------

345

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1117 - v v v v - v v v YYD1118 ------YYD1119 ------YYD1120 ------YYD1121 ------YYD1122 ------YYD1123 - v v v v - v v v YYD1124 - v v v v - v v v YYD1125 - - v v v - v v v YYD1127 ------YYD1128 - v v v v - v v v YYD1129 ------YYD1130 - - v v v - v v v YYD1131 ------YYD1132 - v v v v - v v v YYD1133 - - v v v - v v v YYD1134 - - v v v - v v v YYD1135 - v v v v - v v v YYD1136 - - v v v - v v v

346

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1137 - v v v v - v v v YYD1138 v v v v v v v v v YYD1139 ------YYD1140 ------YYD1141 - - v v v - v v v YYD1142 ------YYD1143 ------YYD1144 - v v v v - v v v YYD1145 v v v v v v v v v YYD1146 ------YYD1147 - - v v v - v v v YYD1148 ------YYD1149 - v v v v - v v v YYD1150 v v v v v v v v v YYD1151 v v v v v v v v v YYD1152 - - v v v - v v v YYD1153 - v v v v - v v v YYD1154 - - v v v - v v v YYD1155 v v v v v v v v v

347

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1156 - - v v v - v v v YYD1157 - - v v v - v v v YYD1158 ------YYD1159 ------YYD1160 - - v v v - v v v YYD1161 - - v v v - v v v YYD1162 - v v v v - v v v YYD1163 - v v v v - v v v YYD1164 v v v v v v v v v YYD1165 - v v v v - v v v YYD1166 ------YYD1167 - - v v v - v v v YYD1168 - v v v v - v v v YYD1169 ------YYD1170 - v v v v - v v v YYD1171 - - v v v - v v v YYD1172 ------YYD1173 ------YYD1174 - v v v v - v v v

348

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1175 - - v v v - v v v YYD1176 ------YYD1177 - v v v v - v v v YYD1178 v v v v v v v v v YYD1180 - - v v v - v v v YYD1181 - v v v v - v v v YYD1183 - - v v v - v v v YYD1184 - v v v v - v v v YYD1185 v v v v v v v v v YYD1188 - v v v v - v v v YYD1189 v v v v v v v v v YYD1190 v v v v v v v v v YYD1191 - v v v v - v v v YYD1192 - v v v v - v v v YYD1193 - v v v v - v v v YYD1194 ------YYD1195 - v v v v - v v v YYD1196 - - v v v - v v v YYD1197 - v v v v - v v v

349

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1198 - v v v v - v v v YYD1199 v v v v v v v v v YYD1200 - v v v v - v v v YYD1201 - v v v v - v v v YYD1203 - - v v v - v v v YYD1204 v v v v v v v v v YYD1205 - - v v v - v v v YYD1206 - v v v v - v v v YYD1207 - v v v v - v v v YYD1208 - v v v v - v v v YYD1209 - - v v v - v v v YYD1210 v v v v v v v v v YYD1211 - v v v v - v v v YYD1212 - v v v v - v v v YYD1213 - - v v v - v v v YYD1214 - v v v v - v v v YYD1215 v v v v v v v v v YYD1216 - - v v v - v v v YYD1217 - v v v v - v v v

350

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1218 - v v v v - v v v YYD1219 v v v v v v v v v YYD1220 ------YYD1221 - v v v v - v v v YYD1224 - - v v v - v v v YYD1226 - v v v v - v v v YYD1227 - v v v v - v v v YYD1228 v v v v v v v v v YYD1229 v v v v v v v v v YYD1230 v v v v v v v v v YYD1231 - v v v v - v v v YYD1232 - v v v v - v v v YYD1233 - v v v v - v v v YYD1234 - v v v v - v v v YYD1235 - - v v v - v v v YYD1236 - v v v v - v v v YYD1237 - - v v v - v v v YYD1238 - - v v v - v v v YYD1239 - - v v v - v v v

351

MapA MapA MapA MapA MapA MapB MapB MapB MapB Parameter De novo De novo Ref-based Ref-based Parameter De novo Ref-based Ref-based DNA ID optimisation approach approach with BWA with Stampy optimisation approach with BWA with Stampy (50 indi.) (150 indi.) (200 indi.) (200 indi.) (200 indi.) (50 indi.) (200 indi.) (200 indi.) (200 indi.) YYD1240 - v v v v - v v v YYD1241 v v v v v v v v v YYD1242 - v v v v - v v v YYD1244 - v v v v - v v v YYD1245 - v v v v - v v v YYD1246 - v v v v - v v v YYD1250 - - v v v - v v v YYD1251 - - v v v - v v v YYD1252 - - v v v - v v v YYD1253 - v v v v - v v v

352

Appendices Appendix 5.2 180 BWA-Stampy marker pairs that show identical segregation patterns in the calculation of combined-approach MapA BWA Stampy Scaffold Position Strand Marker Marker BW3767 ST5796 C14183102 326 - BW3941 ST6113 C14624983 1879 + BW4339 ST6 747 scaffold10117 17371 - BW4391 ST6839 scaffold10190 23436 - BW4668 ST7344 scaffold10702 4089 + BW4699 ST7401 scaffold10783 93269 - BW4758 ST7523 scaffold10913 7401 + BW4836 ST7681 scaffold11081 27767 - BW5010 ST7986 scaffold11393 43412 + BW5038 ST 8049 scaffold11462 7171 + BW5107 ST8190 scaffold11598 25969 - BW5474 ST8892 scaffold12313 13102 + BW5568 ST9062 scaffold1250 43526 + BW5804 ST9457 scaffold12952 60093 - BW5820 ST9477 scaffold12961 18454 + BW5828 ST9495 scaffold12983 15506 - BW5841 ST9512 scaffold12996 17314 - BW6046 ST9891 scaffold1342 5483 + BW6061 ST9913 scaffold13433 88946 + BW6195 ST10143 scaffold1365 39627 - BW6297 ST10330 scaffold13904 32869 - BW6347 ST10435 scaffold14042 39962 + BW6370 ST10476 scaffold14085 58879 + BW64 82 ST10663 scaffold14272 8369 + BW6489 ST10676 scaffold14286 42131 + BW6641 ST10943 scaffold14541 26750 - BW6652 ST10961 scaffold14550 78473 - BW6957 ST11548 scaffold15269 38671 - BW6971 ST11572 scaffold15277 129542 + BW6990 ST11595 scaffold15305 255 54 - BW6993 ST11598 scaffold15316 13317 + BW7131 ST11859 scaffold15612 14728 + BW7134 ST11862 scaffold15612 29442 + BW7272 ST12100 scaffold15829 5375 + BW7384 ST12285 scaffold16071 44967 + BW7404 ST12323 scaffold16119 1402 + BW7418 ST12347 scaffold1 6151 40170 - BW7420 ST12349 scaffold16151 48404 + BW7448 ST12406 scaffold16210 55227 -

353

Appendices

BWA Stampy Scaffold Position Strand Marker Marker BW7488 ST12485 scaffold16309 21425 - BW7502 ST12512 scaffold16314 4805 - BW7676 ST12868 scaffold16777 98893 + BW 7679 ST12872 scaffold16787 21837 + BW7842 ST13201 scaffold17298 50104 + BW7893 ST13297 scaffold17411 6876 - BW7925 ST13356 scaffold17492 10316 - BW8013 ST13516 scaffold17701 20205 - BW8058 ST13606 scaffold17843 32328 - BW8286 ST14001 scaffold18287 87 83 - BW8394 ST14215 scaffold18611 19423 + BW8477 ST14371 scaffold18808 27271 - BW8517 ST14440 scaffold18918 56713 + BW8632 ST14655 scaffold1922 10652 - BW8681 ST14758 scaffold19363 15553 + BW8864 ST15105 scaffold19804 4115 - BW9061 ST15462 scaffold2 0280 33531 + BW9148 ST15605 scaffold20494 70756 + BW9232 ST15747 scaffold20707 9923 + BW9295 ST15854 scaffold20823 40154 - BW9383 ST15993 scaffold2098 33140 - BW9419 ST16057 scaffold21114 12335 - BW9428 ST16071 scaffold21133 22160 - BW9449 ST16105 scaffold21183 41365 + BW9535 ST16245 scaffold21365 1117 + BW9717 ST16590 scaffold21885 28980 + BW9870 ST16858 scaffold22293 49537 + BW9893 ST16901 scaffold22380 30140 - BW9901 ST16915 scaffold22405 6224 - BW10092 ST17255 scaffold22922 14237 - BW10250 ST17535 scaffold2331 53274 + BW10351 ST17711 scaffold23630 45200 - BW10352 ST17712 scaffold23630 70792 + BW10524 ST18034 scaffold24222 9012 - BW10601 ST18183 scaffold24488 9298 + BW10623 ST18217 scaffold24581 8741 + BW10630 ST18225 scaffold24601 319 00 - BW10749 ST18423 scaffold24914 30692 - BW10764 ST18445 scaffold24965 11829 - BW11020 ST18891 scaffold25607 44619 + BW11070 ST18980 scaffold25715 52298 -

354

Appendices

BWA Stampy Scaffold Position Strand Marker Marker BW11088 ST19006 scaffold25779 12291 - BW11 130 ST19071 scaffold25848 49278 - BW11216 ST19224 scaffold26126 4859 + BW11420 ST19598 scaffold26726 68350 - BW11582 ST19877 scaffold27202 30650 + BW11591 ST19895 scaffold27259 19178 - BW11594 ST19900 scaffold2726 12602 + BW11595 ST19901 scaffold2726 12883 - BW11684 ST20056 scaffold2751 58773 - BW11697 ST20079 scaffold27596 48497 - BW11717 ST20111 scaffold27621 53347 + BW11826 ST20332 scaffold27999 4796 + BW11831 ST20346 scaffold28029 7158 + BW11895 ST20452 scaffold28187 28994 + BW12036 ST20709 scaffold28705 119999 - BW12159 ST20958 scaffold29082 105292 + BW12176 ST20984 scaffold29097 64019 - BW12214 ST21048 scaffold2920 113681 + BW12266 ST21157 scaffold29374 35664 - BW12283 ST21202 scaffold2950 26710 + BW12302 ST21229 scaffold2959 39139 + BW12303 ST21230 scaffold2959 45440 + BW12361 ST21349 scaffold29891 16647 + BW12362 ST21350 scaffold29891 30118 + BW12399 ST21435 scaffold30120 12104 + BW12584 ST21790 scaffold30851 29472 - BW12648 ST21925 scaffold31095 86337 + BW12663 ST21952 scaff old31158 58652 + BW12696 ST22023 scaffold31294 5516 + BW12699 ST22027 scaffold31295 54800 - BW12983 ST22545 scaffold32543 15254 - BW13099 ST22763 scaffold33166 14592 - BW13130 ST22814 scaffold33385 9709 + BW13134 ST22818 scaffold33395 21050 + BW1316 3 ST22871 scaffold33618 12934 - BW13230 ST23010 scaffold3404 25166 + BW13264 ST23085 scaffold34277 16067 - BW13313 ST23160 scaffold34477 35533 - BW13346 ST23220 scaffold34647 4634 - BW13402 ST23328 scaffold35016 29130 + BW13574 ST23658 scaffold3606 77508 -

355

Appendices

BWA Stampy Scaffold Position Strand Marker Marker BW13599 ST23696 scaffold36183 25933 + BW13693 ST23851 scaffold36602 10449 - BW13749 ST23960 scaffold36995 57 - BW13829 ST24107 scaffold3742 41855 + BW13904 ST24264 scaffold3798 15826 + BW13908 ST24268 scaffold3798 57252 - BW14273 ST24933 scaffold4036 94508 - BW14350 ST25076 scaffold40816 36054 + BW14432 ST25200 scaffold4113 83743 + BW14521 ST25363 scaffold41884 13874 + BW14696 ST25695 scaffold43209 6656 + BW14702 ST25706 scaffold4321 5263 6 + BW14712 ST25723 scaffold43263 7987 + BW14901 ST26079 scaffold44762 18009 + BW14910 ST26095 scaffold44835 43066 + BW14913 ST26099 scaffold44839 23353 + BW15066 ST26391 scaffold46013 420 + BW15107 ST26456 scaffold46316 3995 + BW15286 ST26805 scaff old48317 8103 - BW15354 ST26936 scaffold4898 28693 - BW15427 ST27075 scaffold49619 7068 + BW15444 ST27112 scaffold4980 33434 + BW15453 ST27123 scaffold49854 27112 - BW15709 ST27585 scaffold5245 129912 - BW15710 ST27588 scaffold5245 175677 + BW16056 ST28142 scaffold5580 30104 - BW16213 ST28437 scaffold5810 47216 - BW16403 ST28818 scaffold6153 3814 - BW16404 ST28820 scaffold6153 4480 - BW16422 ST28869 scaffold6220 21373 - BW16603 ST29187 scaffold6421 14104 + BW16718 ST29401 scaffold66222 6091 - BW16934 ST29804 scaffold6932 34971 + BW16944 ST29825 scaffold69534 39521 - BW16956 ST29850 scaffold69601 2675 - BW17071 ST30055 scaffold714 8615 + BW17076 ST30065 scaffold7167 117507 - BW17203 ST30322 scaffold7386 13802 + BW17205 ST30326 scaffold7386 48478 - BW17206 ST30328 scaffold7389 156178 + BW17232 ST30368 scaffold741 32123 -

356

Appendices

BWA Stampy Scaffold Position Strand Marker Marker BW17250 ST30404 scaffold7435 75673 + BW17355 ST30593 scaffold7589 166494 - BW17458 ST30791 scaffold7734 40721 + BW17 572 ST31003 scaffold7904 9448 - BW17642 ST31137 scaffold802 36538 - BW17718 ST31262 scaffold811 15736 - BW17964 ST31676 scaffold8486 60014 + BW18217 ST32118 scaffold8764 39770 - BW18246 ST32184 scaffold8797 119176 + BW18295 ST32273 scaffold8874 51834 + BW18357 ST32380 scaffold8944 21134 - BW18551 ST32763 scaffold92180 6098 - BW18666 ST32971 scaffold9373 24348 - BW18783 ST33161 scaffold9482 34049 + BW19000 ST33527 scaffold97118 4255 + BW19004 ST33533 scaffold97142 8683 + BW19008 ST33539 scaffold 97180 2036 - BW19041 ST33604 scaffold97354 9107 -

357

Appendices Appendix 5.3 De novo approach MapB calculated based on 50 individuals with three different parameter- settings. (a) Parameter: m=3 M=1 N=1 n=1. (b) Parameter: m=3 M=1 N=1 n=8. (c) Parameter: m=3 M=1 N=1 n=16. The marker positions are shown on the left (cM), and the marker names are shown on the right of each linkage.

(a) LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15

LG1(b) LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15

LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15 (c)

358

Appendix 5.4 Optimisation of the reference-based approach using the BWA aligner for MapB calculation based on 50 BC individuals. (a) Default BWA parameters. (b) Parameter: n=12 k=3. The marker positions are shown on the left (cM), and the marker names are shown on the right of each linkage.

(a) (b)

LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15 LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG16LG15

137920.0 137902.1 104005.2 10758.3 977510.3 14.5 4351 18253 11968 22.2 0.0 3489 4970 14545 28.8 0.0 8.0 349034.1 1394933.1 877616.7 481738.3 0.0 2680 989540.2 1113723.1 15398 1731642.5 6825 718958.8 0.0 18744.6 1264446.7 628629.0 1220765.8 7433 8851 11267 94240.0 8.9 1873 1356650.9 0.0 1052 1625 31.7 8792 0.0 9322 99370.0 32840.0 718770.0 5810.0 63490.0 15174 15177 121270.0 58140.0 14.7 11466 7831 4111 5020.0 61910.0 0.0 4010 63580.0 4370.0 7312 91154.2 0.0 5685 72950.0 871714.7 0.0 17642 0.0 18020 0.0 10280 0.0 13869 1998157.4 0.0 18107 0.0 11236 0.0 1680 0.0 2890 0.0 4200 10502.0 586740.6 6502 51114.3 9648 8174 4951 1958513.2 7184 718374.3 4350 175218.0 100292.5 4416 8379 88362.0 2716 8761 68886.8 814 35352.9 17.4 4412 8232 2.4 4744 5152 5355 61.5 10.9 6005 43.4 5103 9.1 15.2 76.3 19980 4347 1445110.1 9555.2 44174.2 250415.9 14.7 10045 11.1 4008 222910.7 3388 8108 99354.5 864916.9 4146 6836 63.6 332813.0 1013549.2 626616.8 19.4 21.6 76.4 1251422.4 5571 1752312.2 2792 813 14.8 7633 8.6 5838 8652 961621.1 4147 8889 65.9 1560 16.7 8764 16.8 5521 16.7 4007 400615.4 14.9 19.1 7507 7217 4504 1031 125021.5 479619.2 25.8 79.4 1788335.5 8279 18.5 1700914.4 1244121.8 24.4 10637 55.7 10258 5108 3381 763419.7 985519.7 60423.1 18381 18009 70.7 104824.0 2909 283318.7 2320.9 17.4 2992 25.7 1247 54717.7 32.3 82.6 1525742.0 699576.8 248027.0 1029316.5 155026.3 28.7 10638 57.8 5102 80229.1 23.9 9854 1826728.1 29.6 6753 668538.7 989287.3 1173628.4 1001 3875 5037 9764 2624 380918.3 24.8 7871 19.8 5954 1371843.1 30.6 7451 965582.7 25.3 30.8 5696 20.1 33.9 7048 800828.1 780335.2 1826837.0 36.1 8646 89245.2 982496.2 17500 156441.0 890335.2 777561.9 22.9 647429.8 9919 1431 3930 1371944.1 1384590.4 1363939.1 31.9 32.4 8195 22.8 2534 11202 9761 19.4 7067 703 48.0 911 28.9 922246.0 40.3 17822 1532256.5 2358101.7 82 1107647.7 37.3 8904 66.1 10260 38.6 32.3 8006 9034 5992 1812846.1 7953 1456498.0 13630 1363446.4 41.4 161224.8 235836.1 7739 11343 7740 561921.6 73337.5 11186 7642 764034.0 555162.8 868250.3 46.4 836 785256.6 9023104.1 1515146.0 1293749.8 715944.7 34243.7 777770.3 25.0 7012 398440.6 37.8 9857 48.2 13809 6017 14343100.1 1363849.5 1134249.9 7736 5015 764438.6 52.3 8680 11059 444546.6 424962.9 5443112.8 1663154.0 29.0 5153 235745.0 26.0 42.7 6989 6990 777070.4 52.4 13800 511353.2 2325102.9 38052.6 55.1 10523 1868952.7 224849.5 647175.2 46.5 8562 54.9 1815 69.3 15326 4672 123.8 2629 60.2 13321 328331.1 27.0 6382 30.5 7864 82.2 11275 1432160.4 56.5 18129 409759.7 107.4 2318 56.8 2651 1342361.2 368959.2 33852.3 1155882.6 4334 10095 416451.4 596944.8 787952.9 72.5 15713 124.1 9414 19600 963963.5 60.4 16104 12765 18152.9 232 606529.1 562035.8 54.3 58 479265.5 1431671.7 58.6 3219 1375366.3 112.8 15380 60.9 11187 2083 64.4 9092 33.1 4857 240686.4 80.0 3388 130.3 14544 15631 71.5 1308 1709670.9 58.3 8691 58.8 88.1 433557.8 31.1 6064 5584 676659.3 57.1 3694 956 13595 1359771.9 1105172.6 2048663.8 72.9 114.8 8131 11744 1957 65.1 14706 1913671.1 72.3 15588 299935.2 47.0 88.4 11274 2403 84.2 1577 132.4 5358 73.0 9322 15465 73.6 7142 12379 1247278.8 65.2 5425 103.9 2896 494163.6 78679.0 85073.7 861669.8 119.0 16414 15960 71.5 12989 975 37.2 1905 9540 33.1 11031 510054.0 65.9 6619 68.6 10744 312990.6 1532988.3 10097136.5 79.7 18740 21275.4 86.4 12341 1234275.6 84.0 682867.3 542267.3 5533116.6 1053869.0 1086967.8 597476.0 520785.8 15424123.1 498282.7 574695.5 4763 5152 1103335.2 305962.7 585676.5 4601 8730 5221 5222 1713090.4 7494141.3 83.1 15400 18739 18738 1369876.1 1054977.7 92.7 9362 39.3 200 95.2 92.0 1791184.0 40092.3 11315129.6 1929599.7 12466 840 13793 69.3 124.0 884980.2 3921 1537 17965 1982 1567391.4 15195143.9 1057589.6 422 208682.1 92.3 14424 1340686.4 813641.3 44.1 1394 534873.9 84.7 77.6 6309 97.4 3903 444695.0 975098.8 7800136.0 95.0 1555 14888103.9 15759 3359 13538 574884.2 1113273.5 424126.1 4914 79.3 2781 1726599.8 1954 11648 3897 12808146.3 963396.1 1634586.2 99.1 43.4 5150 10553 86.7 52.9 2924 571182.6 82.8 7559 89.1 6936 7155102.0 8807103.8 92.4 9746101.5 4942138.1 18976 19760106.0 11978101.7 8806 78.5 5742 2895 2893 11289 4424 17263104.8 5827 2741146.4 9755 6690102.8 871491.4 103.7 14643 3775 95.6 7712 12090.9 386057.1 2997 762793.3 86.4 106.5 11061 2825 109.5 13007 15385 6474140.2 2692 11006 5745114.9 10218101.2 953847.5 128.1 86.8 106.5 7629 815889.5 18579 3595111.2 1141194.5 8667147.2 14251 782 10576109.2 1559799.5 4968115.4 111.0 18889 384799.9 86.5 2453 11279 95.1 8773 618090.6 108.7 13670 121.4 7015140.9 11011 1928121.5 110.9 14448103.3 52.3 5151 292063.5 822288.9 482994.9 7154111.0 113.3 3150 98.6 20078 153.4 5888 5206 19992 20001 110.8 18566 4969127.7 117.1 102.0 10686 6487128.9 435197.1 621797.9 8648111.4 120.1 1896 142.2 15944 11821 538125.8 10338119.0 8684105.3 4852 101.5 1123 568770.2 97.6 8223 585395.3 9034 9285 119.7 17261 102.8 6083 158.4 9857 123.4 5203 122.1 7832 11928135.6 126.3 109.1 2014 64.8 11284 7290102.6 117.9 98.0 1429 9614117.6 127.1 15437 143.7 445 18971132.8 130.0 593 12440108.0 106.3 1769 130.1 1124102.4 905 126.2 17271 107.0 15585 168.8 5884 11469124.7 127.6 7473 16237136.6 126.9 2893 485569.6 77.8 5489 8740122.1 6222103.7 131.4 15529 144.2 19945 4033 18974135.5 134.1 12474 5977113.8 8505110.4 5566122.6 5129134.3 202 109.3 12160131.0 109.0 10220 13117180.0 19998125.5 130.4 17819 11838 1659137.6 131.3 12278 908070.6 103.5 366688.3 110.3 6939 126.3 4133 8712 135.2 17262 16309133.5 16311147.1 13275141.2 3563 3134 19340 127.7 14447 384 8213125.0 136.4 2825 3555113.5 113.3 6663 768122.9 11021186.4 5069127.5 7831132.5 9768139.7 136.2 13556138.8 114.6 5520110.0 91.7 128 115.6 6758 12635141.3 12668133.7 17091 19317152.6 12561151.5 9245 9900 6489 139.0 1610 8212131.4 1054771.7 121.5 5686 6400128.3 10778119.8 10775142.6 10267138.8 3908190.7 9089129.5 4272138.9 141.7 6380 10329146.4 7747119.2 5128138.4 112.1 1406 119.6 5949 8242138.9 5646153.2 3351156.5 12220138.2 73.7 9078 5038100.3 10225147.2 11254134.1 6720192.7 9787131.6 4273147.8 143.8 10917 8587147.1 9370 125.1 4580 5567 5568137.8 140.5 5562 126.5 4310 122.8 10782 17848151.9 13748 6896144.1 18142155.1 4799158.5 13705143.3 150.8 394875.7 118.5 189 114.0 4793 151.3 6441 9234 1495156.1 5155 12809194.7 133.6 14667 152.5 19345 13562149.1 139.8 3395 5555 6674 3621 846128.5 136.7 18086159.8 18765149.7 12973160.6 162.7 11540 13107151.3 8807131.2 143.2 7674121.7 10044 7100132.9 4055160.0 18371199.3 135.7 9779 14071 10036161.3 12282158.7 79.9 3949 11269 3944 124.7 154.1 5370 6731 2208140.4 10188164.3 5940152.5 17379163.1 169.9 19609 135.8 8755 6565141.8 7934 5556 7937148.9 7670 7673124.8 130.6 139.2 6307 3440137.1 11018206.2 144.4 2218 12238 11997 9719166.5 7798166.5 82.5 1711 10049131.3 6124159.7 146.6 4312 5131169.3 4928 158.9 165.1 182.9 3491 168.7 10420 6912140.0 143.9 1464 153.1 7936 7399134.7 140.0 1534 145.7 4423 178.8 12060 17743174.1 213.1 154.7 14064 15528 5320 19755169.2 171.5 8068 7676132.1 137.9 10043 2395 6517141.3 17324 164.1 167.2 15635 4084191.8 9157145.9 514990.8 167.6 144.4 2772 2747150.2 5130179.2 9955188.0 15697178.3 215.3 9780162.1 8022 4490 905183.9 19756 11429 9916171.7 144.4 5290 1193161.8 143.6 1328 147.8 4421 5948 14084 15643 18612 170.6 173.6 2144196.0 93.8 4898 2321140.0 7098170.0 2252158.3 11087149.0 9625190.0 8425178.4 222.4 14069166.2 12237 10565 3560196.3 4932175.9 144.5 8861 150.1 6881 168.2 8303 152.2 857 149.9 6310 1277225.1 178.3 184.7 10614204.8 4850104.0 142.0 198 172.5 2324 2771155.0 2484169.5 1794156.5 13166190.8 4486192.1 3669 18153182.5 12703170.4 11488185.4 8559187.6 2654202.9 923180.1 188.8 10050 154.3 8061 5729 8060 236172.4 160.7 5739 5089 17773230.2 5055209.0 8719148.8 7413150.9 158.9 2663 15893194.1 10268183.4 455176.9 11655193.6 20286191.5 9718206.2 4774187.6 197.9 11602 115.4 10554 5197163.3 178.8 3089 9233173.7 159.0 3785197.5 19444232.8 17564213.2 155.4 5983 9641160.7 176.5 6084 6082 4844 165.4 8518 18336195.1 18488184.5 8601 10735 10732181.0 2722199.6 213.7 2477 2854196.9 206.3 15829 14577 3431124.4 5198 160.5 1262 10896 2060 7115180.9 175.7 9653 8488 10960 3665 10259 17772237.0 4239 13238217.3 202.2 160.8 3743 167.2 9992 180.7 4843 165.5 169.7 5093 15262 12596 10896196.1 186.6 11706183.1 17346205.3 13833203.7 225.1 9186 202.0 212.3 15155 128.7 387 5190170.2 2062182.9 8869182.1 6439172.1 12085212.1 198.3 10897 18152188.6 4073243.4 8600 229.3 10144 11378208.5 3568219.4 4625218.8 5465166.5 6705178.4 6604183.3 7411175.5 808175.4 171.8 6011 185.2 16737216.7 18909208.0 5187 7822 7317 4461 5987185.0 9907 203.0 5527 10263 8994190.7 20015251.2 10729 231.3 9185 2888210.5 223.6 13272223.1 5418181.5 133.0 1766 5195172.2 188.2 192.7 16742225.6 18459208.9 6315 220.1 7926 10670175.5 188.3 207.6 7006 2449197.1 256.2 6001 8259 11843 9176233.4 225.6 17062225.2 234135.0 178.7 2555 187.0 5988 3202183.0 189.5 8567 203.1 18709236.0 210.0 15016 16164 19920 10688 11751 10671179.0 6706184.6 9624193.4 2739189.2 212.2 2461 8249201.3 258.3 13431 3307 9180235.5 228.9 15392 11343229.4 139.2 252 6540 9238 2545189.7 1315 4632 3851 209.4 245.3 10851 214.1 9168 227.6 3373 18513 5426232.9 991 186.7 3442 197.6 6678 2584187.4 190.9 6192 516 190.5 10085 7269 233.1 56 2200214.5 831207.8 260.3 14892 11349 3798 847217.2 4639241.9 9137232.6 187.3 3120 193.4 190.6 192.2 250.3 6515 214.2 3932 6712 14254 5881229.7 5425239.6 5299 192 2150 3726141.2 189.5 10242 204.1 197.5 10640 9232 7331 5438 859219.0 18635216.7 263.4 19773 7021 11111220.6 18904252.7 5597235.7 2847200.5 188.3 201.7 751 201.4 2654 252.4 18711 216.2 9870 12105 10687237.1 20328 16928 11973243.8 145.5 3255 11125195.9 200.8 1043 222.3 9449 3919 233 9023192.6 13286221.2 10237231.0 1966 8819266.5 18348224.9 4696255.1 4775244.4 239.9 7018 209.5 7825 8062189.7 8623206.0 7577206.3 6412200.1 1125207.2 248.1 4440 3831256.5 13835218.3 10689 13000247.2 9282250.2 2307 4635233.5 10322 5608 6832 2021225.3 7508240.9 8774270.3 16230 11318226.9 18105257.1 248.8 9383 192.8 8064 152.9 2394208.0 202.5 10243 206.0 194.7 3754258.6 4644220.3 13441252.4 150 6577253.5 3776252.3 3595221.1 208.4 11158 218.4 6052 7352 12354229.5 246.4 8863 15009274.5 4723235.7 258.1 7818 8596163.4 237.6 4488 11292 4244 9859198.8 5268265.0 354 6521222.4 13439259.9 4562 8177254.3 227.6 710 41203.5 10210210.1 213.6 10996 5628 11159210.4 227.8 6050 10438233.7 253.0 13130 11984276.3 10283239.4 259.2 13878 8597167.6 9772242.1 217.5 4468 201.9 7332 462 15553 13868 4571226.6 18289266.3 260.7 9474 10687 214.2 1462 223.9 4645 240.1 15932 11685261.9 17211276.8 246.3 10818 265.2 8144 268.9 3749 51268.6 229.6 9013 1179214.6 8052234.7 269.1 15552 11497 11652 16228228.6 17833 268.9 4362 10890224.7 8593174.2 10244223.8 229.7 5240 243.2 5895 10957 9692205.1 3715274.9 244.2 10350 11876270.7 15925276.9 246.4 12516 12513 15363 2293 265.5 9939 8166 9247 273.9 3345231.7 7843235.3 1520223.4 224.8 226.5 10372 234.8 3315 14883 11651 235.0 6379281.4 276.6 12788 180.6 5386 2162236.8 8487244.3 209.0 10271 247.5 16188 8543275.0 281.0 250.5 4755 10158 10682 19116 9940268.2 5059282.9 233.7 11120 6886241.3 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11795 719.3 3308 1101721.2 7069670.3 889.4 2714 859.1 4797 676.9 988 18531 9106 18534893.6 868.3 15578 729.9 471 3696897.9 2342733.2 6807699.4 888.2 3988 4347 8671 10077734.1 8592902.1 897.9 5638 739.6 9156713.3 9720741.6 739.7 8817 3695908.6 5898 13626921.8 2573 8816745.0 719.7 12488912.6 743.7 3193 10402 1319724.0 13625930.5 8879744.7 9504 728.4 8882 938.2 1889921.3 756.6 4954 940.2 8841 11815 11270926.8 2135 3272745.7 3102737.7 747.8 6457 941.2 9610 14303933.8 742.0 3909 11816 7388937.1 768.2 943 942.3 14299940.3 8843751.9 8840946.4 749.3 11028 14979949.0 756.1 3419 10960949.4 18170947.4 9398759.3 16502951.0 5223767.3 11820953.5 8956782.6 8839 4905955.2 777.7 17490959.1 778.8 2967 8476959.4 7924 15214963.3 20278966.4 787.8 6105796.9 15219 13449965.9 595794.8 967.5 8741 2531789.9 15217 972.3 802.1 2340 972.5 20564 798.9 2532 6413 974.4 6102804.1 5875811.6 980.5 4404984.7 8737 16799 976.5 10586805.4 9061 982.6 5126985.3 810.6 9477817.5 2574991.5 813.1 4182 14660991.7 475812.7 822.1 9582 1002.5 5791 4799818.0 2101993.8 5010823.4 1005.0 6665 1008.1 6539 12259998.1 823.0 10233 1007.0 11362 824.2 469 20991008.3 7922827.4 829.6 5339 1013.6 4194 6917826.3 62121016.9 5426 1020.1 8059 13892 12948 837.8 10204 20212 840.4 1022.2 1030.1 14829 1026.4 448 4083 148301036.8 848.3 2603858.5 1030.6 16317 475 11579 855.2 3270 8887 1032.6 3937 876.4 1032.7 383 4498882.0 1036.8 730 884.9 1927 1041.8 6099 655 3254 2840 1049.8 13905 15601 889.1 7091 4433 8888 1051.8 19825 4601 2644881.3 1056.0 16809 11423 1060.2 9115 4123893.3 1066.6 14391 4648 9114 18589 9578898.2 895.3 1068.6 462897.3 1070.6 16811 3538 903.7 1670 1072.7 4691 10695915.1 1080.6 3231 2899910.1 1085.4 7075 11231921.8 8348914.3 8160928.4 1085.6 19972 2898 513918.5 1092.0 18580 2960934.9 743920.6 1097.4 8610 5295 8614 944.5 922.7 7321 1102.6 1102.8 16039 5784947.6 3184925.0 950.0 5775 1107.8 16037 938.5 690 1110.2 7078 5782954.2 1115.3 9112 956.3 6430 1117.8 8675 960.4 6230 961.1 9651 1128.7 5836 2670960.5 1130.7 9118 962.6 6431 1134.9 3047 5928974.9 1139.2 18590 2671972.2 1145.6 443 5976983.7 1149.8 5365 1153.9 6941 8474993.4 10072 10074994.8 1154.0 5228 5083 1156.0 15457 100711001.4 1156.9 13060 1158.1 19967 7369 98241012.8 1160.1 3530 27221019.0 1017.2 6264 1161.2 15449 1023.2 4740 1162.1 13183 20300 12111 4890 93391027.4 1163.2 1164.2 11570 1033.9 11591 1166.5 10529 49221036.0 1210.3 20429 44451043.4 1237.3 2857 83941047.4 1241.5 2248 15989 15997 15996 83931053.9 1241.6 2250 1251.4 18650 34871066.7 1264.0 12992 1280.5 11687 1075.0 1126 1283.0 9737 28581083.9 1291.1 12666 1089.5 1128 1298.6 419 1312.2 20381 69271091.6 1331.9 8796 1095.8 8296 1342.1 854 72141100.0 1344.2 1828 1106.4 4924 1350.9 17790

1371.4 15534 80861125.7 2911134.5 1388.0 2050 1140.3 8727 78901143.0 1400.1 15548 1150.5 2378 1405.7 10766 1407.7 4248 1160.0 123 1412.0 10772 1563 60191162.6 1423.0 15814 1164.6 9228 18481166.7 1437.7 863 1168.8 9927 1440.3 16004 37481170.8 1173.7 1083 1451.0 17784 17782 91301177.0 1457.5 17781 111511183.5 1197.4 2354 1473.8 9398 63621206.4 1214.3 2173 94221219.1 1493.8 4717 68161221.5 68711224.2 1511.2 15688 105301230.0 1520.0 7910 1241.6 11530 1520.5 10001 1528.9 6816 1253.2 4052 1533.1 918 5210 1537.3 5663 105291264.6 1548.5 1278

1567.9 10559 53551281.3 1584.3 17014 7481 100101291.7 1597.2 1600.1 14748 1293.7 4496 1605.1 4126 7595 7880 4039 12388 1294.4 1612.9 1321 5354 1619.3 17298 1298.7 3080 1621.4 135 13924 1625.6 3154 488 107271307.5 1631.7 8706 1319.0 2535 1632.0 842 88571321.5 1634.0 8346 59621332.5 1636.1 7394 1639.4 20231 1341.5 2024 1642.0 15691 104231345.8 1642.3 19645 1350.0 9129 1643.0 8122 109811352.7 1644.3 9289 5603 1646.4 5214 1646.6 16112 1650.1 12544 1654.7 19549 1661.1 3582 5091 1192 5789 67 1665.2 1384.5 1667.4 11072 1693.6 8019 359

Appendices Appendix 5.5 Reference-based approach using the Stampy aligner for MapB calculation based on 50 BC individuals. The marker positions are shown on the left (cM), and the marker names are shown on the right of each linkage.

LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15 LG16

0.0 6859 0.0 14114 0.0 19598 0.0 17401 0.0 17833 0.0 12612 0.0 9845 0.0 18073 0.0 7426 7.0 11608 7.0 14113 19.9 6097 0.0 17152 9.8 4526 9.8 19459 14.1 9346 18.1 5745 19.1 17494 5.4 4668 0.0 10925 11.4 6856 13.0 1884 27.5 3990 1053012.7 16.6 17400 18.0 327 23.7 10211 25.1 14253 31.3 5572 7.6 12406 12921 12924 15.8 5506 15.9 13959 36.6 16914 4.8 16.4 6213 26.0 8863 25.3 12049 0.0 14634 32.0 18031 0.0 8994 29.5 4873 4874 0.0 13804 35.7 4027 13932 9.9 10785 0.0 15213 0.0 14413 18930 20.7 7700 2.3 4156 7997 20.2 6854 6855 22.7 11661 36.7 7482 32.9 8352 31.4 9330 38.7 6482 11908 10323 36.6 7337 12.0 14632 19.6 10230 5087 1882 14562 129269.2 20.8 6745 8354 1599 11139 2190 14602 11911 11811 8254 18094 22.3 33.3 42.8 35.0 34.1 48.5 96118.0 31.6 310728.1 37.8 14.2 23.0 18552 8545 5920 4392 16.0 12909 21.5 14 4523 15195 15200 4967 8583 9848 4028 14891 26.0 41.2 45.4 45.0 53.1 38.7 265 38.3 17.0 27.8 18553 20.3 3808 22.4 10386 29.6 13293 53.5 4860 47.6 14948 49.8 12932 12933 36.2 15202 2036 57.4 17378 36.0 10322 43.3 14585 39.3 17737 22.9 16674 53.5 10857 9478 8393 16071 27.0 10981 8695 22.9 11981 702 17828 6851 6584 9849 11509 7661 6547 18598 36.0 62.3 49.7 56.9 59.6 40.3 52.7 24.1 73.9 14880 31.4 15673 15671 27.3 2907 44.4 3930 42.2 6509 17143 1959264.4 140156.4 8386 1773 256440.6 1020861.7 447844.7 70.3 9716 39.9 17971 2422 5522 7937 82.5 1936 33.9 9963 69.4 47.2 9477 66.6 8397 60.7 14567 4948 17105 43.6 2035 1070657.1 66.1 17372 50.0 16731 82.1 14771 17336 4366 2200 1224296.3 38.2 4227 25.0 56.5 9411 73.4 12801 65.0 7100 82.8 9629 55.9 17831 75.3 12697 60.9 5502 86.5 11639 42.0 2897 6585 15509 135 13789 38.3 15100 2213 66.8 3408 213562.3 810880.2 3779 14916 4866101.7 1238366.1 1740982.0 1678870.1 91.5 6577 693246.5 9192 107.6 17683 11196 1748838.7 64.6 8676 84.6 4629 65.1 1735 5565 110.2 4865 83.3 1847 77.4 1392 88.7 1850 1221 78.9 879 76.8 10686 10688 94.1 14772 49.4 6007 25.7 12361 19012 2934 14244 10891 11106 42.1 8928 12541 13685 9520 11348 7471 3664 66.7 89.0 120.0 90.0 86.9 11182 95.4 81.2 102.2 7643 53.7 27.2 107.7 5997 73.5 5191 91.1 14243 67.2 19164 43.0 16800 128.3 3515 92.2 14071 110.0 1852 85.5 10769 110.5 7863 59.0 3608 29.3 13594 16590 5731 117.9 1881 2359 10737 5804 168057.0 15376 14789 10931 97.1 10689 13088 419 12362 85.0 108.5 71.6 136.1 98.9 101.9 12277 122.0 89.9 115.8 64.1 32.7 128.4 12398 10198 9040 13642 14834 64.2 11878 11596 17001 18993 16786 16465 19294 16330 5378 85.3 115.3 75.9 140.5 103.3 128.6 94.2 10592120.2 138.8 10196 78.1 11576 68.5 35.8 92.0 13855 122.0 13639 78.0 9773 142.6 2462 110.2 8444 135.4 18989 96.9 16737 132.2 15149 985 18487 146.4 5629 89.0 12011 122.7 8562 122.0 16335 96.5 13856 126.4 10740 87.1 8856 145.2 9703 124.8 16964 142.2 2434 107.1 9850 143.9 7641 75.5 7341 349 3776 13588146.5 5093 457 13306 97.9 9513 4191 774 557 4435 17970 37.9 19044 103.2 133.2 93.8 156.8 129.7 150.1 134.8 3898 110.3 146.3 3283 82.7 151.0 5998 18555 98.7 15300 107.6 9650 139.9 8959 98.6 13390 173.3 61 132.0 1624 140.3 84 160.8 8245 122.4 11909 148.4 9422 635 85.8 16210 40.0 15514 153.1 4914 102.7 13563 17235 116.8 5640 142.1 5031 103.4 8190 186.9 16228 144.0 10277 165.3 3535 153.2 131.8 17049 148.5 3027 96.1 11360 46.0 832 3660 806 9446122.4 10848 9759 17205108.1 104.9 14217 13605193.6 3988147.5 3414169.7 167.8 4094 8680138.6 152.8 12499 17972107.0 1829348.9 155.3 144.2 4017 127.9 3638 13620 121.0 8189 105.8 13679 9686 13607 149.6 4843 10607161.7 176.6 3413 15018176.1 146.5 2230 157.2 6568 109.1 3584 53.2 12368 193.7 157.4 3093 10197 12493 10847 4322 107.0 13559 17325 13610 15033 12957 178.1 7604 7964 14297 9036 4648 132.3 146.3 130.6 156.5 190.4 157.3 161.5 111.7 57.6 160.0 13788 10495 10497 5044 115.7 17014 5843 8193 15208 12956 178.9 16344 13252 17738 13004 136.7 150.6 141.2 195.8 165.9 199.8 164.0 170.0 16141 126.3 59.7 165.3 9249 124.6 17203 183.1 1386 181.2 4299 7801 140.3 6997 150.7 4540 155.4 4323 197.1 8965 168.1 784 207.9 9114 166.2 10585 172.8 14295 148.0 3345 62.3 19435 170.5 6260 19371 3810 9761 17807 130.0 9740 13609 16949 19451 183.3 14998 3503 4080 7500 12840 19379 145.3 158.2 165.0 198.9 180.0 204.8 4706 210.1 170.6 13780177.1 155.2 72.7 173.1 1051 5636 9752 11313 148.8 7012 19507 16955 8747 17705 189.4 11359 14493 14469 8204 154.9 161.9 172.0 202.2 185.7 4708217.3 212.2 174.9 181.5 4845 167.9 76.8 181.8 11775 167.2 1117 12169 164.1 16945 168.7 13752 176.5 12507 209.8 9162 191.2 16950 16954 229.0 1501 214.4 8507 8340 196.1 177.1 1291 13059 187.0 3866 172.6 8884 7787 79.8 10084 188.1 8052 10491 19005 11137 184.1 5423 11325 16951 11249 203.8 9301 8686 9519 7476 170.8 173.1 183.4 213.4 193.4 235.7 6051 9957 217.4 179.2 17961192.5 175.4 91.5 205.3 12243 13870 18045 17977 7875 204.8 12637 11327 9029 10434 210.5 9090 8679 13203 17101 178.0 192.8 220.1 203.1 9601242.4 222.4 181.3 196.9 5689 181.4 98.5 225.4 4225 177.5 217.9 1815 4111 9659 11808 188.4 17527 194.9 3387 9817 224.5 15745 213.1 9028 258.3 17468 225.3 8505 219.2 185.7 17249 199.0 18816 190.6 4363 101.7 5047 852244.8 10438 13049 3450 17278 17654 222.5 11760 18834 4429 11247 235.0 13875 17247 8816 4232 179.8 193.5 197.0 228.8 233.9 270.6 12793 229.7 187.8 201.1 1214 197.3 109.3 264.6 6075 229.6 6429 247.3 9618 19489187.1 2653199.9 2198202.3 9171231.0 18717248.1 275.5 845 9210235.1 13141189.9 203.8 17965 8811199.5 4304113.7 276.5 9323 234.0 15129 13939258.0 190.3 450 10153 9050 207.4 3391 236.4 9165 249.4 11927 281.5 5477 241.6 16685 194.3 13144 212.2 14873 203.8 18090 115.8 14268 4793284.5 19106 12832 6908 9822 240.6 9975 15744 5340 14935 14440 268.4 4485 5739 4410 15353 192.7 200.1 209.8 242.1 284.3 19123 11215 246.6 197.2 212.6 8950 206.0 115.9 294.0 18696 12903 16839 8637 247.3 18325 15255 7730 5533 14446 11857 13940 19542 17940 4413 4981 193.3 211.3 244.2 252.3 286.4 6148 251.8 275.1 201.7 216.7 210.4 120.1 306.1 3286 1750 7726 17279 9829 5362247.6 549 19336 14445 18783 3078 8277 10093 17381 17910 195.8 201.0 212.3 250.9 288.6 1551 256.3 208.7 221.3 212.5 120.2 308.3 12607 6228 16849 8053 251.4 13520 14815 14932 3927 277.2 11862 764 13202 3844 199.2 202.2 214.8 257.6 256.7 299.1 4507 267.3 216.6 228.2 10091 213.6 124.5 312.8 16754 16648 13330 411 16923 251.6 15374 8653 6296 6092 18421 13136 7696 9231 18823 18908 204.4 220.1 266.8 262.0 312.3 2562 274.2 279.4 218.7 235.1 214.7 126.6 317.2 4786 10025 2725 864 14938 13570258.3 1774 2778 7152 2313 14989 10581 6393 11141 15067 199.3 222.1 225.4 268.9 270.2 314.8 3978 279.6 220.9 242.2 220.5 132.1 320.0 10134 18626 10027 18037 672 261.9 12548 10135 14048 16025 281.5 11410 16157 9906 7769 3313 237.0 237.5 278.2 272.4 9496 10911 286.3 225.4 249.0 14645 17941 224.3 136.1 323.1 15606 199.4 360 243.7 8128 249.5 742 264.5 14501 280.4 5651 274.6 18797 316.9 19325 7666 289.3 1997 13254 16383 253.2 17942 233.2 2158 137.5 2440 18882 288.5 326.0 1828 19073 13287 17810 7351 12547 553 3862 351 12333 299.9 16124 18791 530 18822 13172 203.7 248.1 259.0 264.8 289.8 278.9 319.1 9954 229.7 1684261.0 239.8 140.7 328.6 12877 17777 5217 9792 2959 14959 7071 19321 304.2 17272 2427 11925 17221 7447 4367 209.3 252.5 271.8 296.6 283.4 2586325.8 290.6 270.6 13092 244.3 144.0 337.3 12875 267.0 12643 306.4 15022 215.3 13207 254.7 3168 280.8 8522 303.5 17881 285.6 7970 334.9 1976 294.9 19323 5151 232.4 14457 280.6 11638 253.4 18824 146.2 255 6205 344.0 5426 219.3 1311 4189 5770 285.8 7177 269.8 1986 308.0 17880 287.7 759 8371 297.1 503 310.6 756 234.0 14540 288.6 14699 260.7 17222 150.4 9968 256.8 342.5 10598 346.2 4789 16375221.5 13337 12929295.2 277.4 14920 14960314.0 294.4 384 2868 15520299.2 12878 13766 9644236.1 13035 17223284.8 8804 6415 346.2 4508 310.7 295.7 350.5 14878 283.9 9940 3502 150.5 225.8 7630 263.6 4053 308.7 9173 317.6 3838 298.8 13259 352.9 4656 301.3 414 236.4 14731 298.6 7234 300.1 8819 13173 3822350.6 2636 15855 5562 295.4 17836 18838 124 378 11036 312.8 9771 7387 10789 15191 8818 9454 11518 230.2 270.4 322.3 329.0 300.2 358.8 7424 303.4 239.0 303.1 302.3 152.6 354.9 3063 313.9 10114 317.3 8361 10935240.9 5230274.8 10893327.9 15756340.2 4937300.9 361.7 6053 638305.6 16792242.2 309.2 7232 9698305.2 15144154.8 360.4 11945 4315 6924 3635 342.5 6224 2526 15301 5822 5755321.6 15424 3842 2226 17631 250.0 279.2 330.6 356.1 305.2 363.8 9361 310.0 243.0 312.0 311.3 155.4 366.0 4141 4529 10103 177 16371 14561 376.2 17571 7354 7546 4328 326.0 1999 10787 2012 12961 12964 6701 260.9 283.6 373.7 317.0 366.9 1264 316.7 245.7 318.8 4844 318.0 156.1 370.4 11004 333.2 396.5 13651 330.4 18778 263.3 3530 288.0 17130 14557 375.8 16073 323.5 9578 370.3 1462 318.6 6637 249.0 14311 5794 336.6 7121 321.2 12965 156.9 15443 6145 384.4 3218 13649412.0 270.0 14976 290.2 14580 345.0 813 378.3 18352 330.6 10547 372.4 11368 321.0 4955 4813 332.5 10064 249.7 9705 357.8 3816 324.5 281 159.0 668 14670 1985 4504 406.1 273.4 11941 291.2 11788 351.8 18745 4686 414.4 384.4 2252 330.7 9702 374.6 11266 9265 323.1 14715 337.0 18334 251.1 15692 7864370.2 328.9 2377 161.1 15162 15605 416.5 13451 1579 278.5 10315 3726 292.3 11493 2216 353.9 4886 394.4 11067 335.0 3295 379.8 13942 324.3 15080 337.4 252.2 13253 391.5 17195 331.0 2382 7738 11189 8537 165.5 283.7 9083 293.1 19199 356.1 3896 416.7 403.7 3436 8921 1100 388.1 10406 12557 14850 343.8 5168 253.2 17172 422.5 6056 333.8 9312 1742 2097 5722 337.1 286.4 11797 293.7 3456 360.4 3895 422.4 417.9 9387 4827 11231390.8 325.2 19067 11519 350.6 5169 260.1 3019 435.2 3479 342.5 3273 171.2 2090 444.7 8062 425.3 772 19422 289.0 1527 10058 13318 372.2 17548 420.4 13837 348.8 11793 403.7 18524 14705 356.6 266.8 11274 437.3 3570 355.5 828 176.5 14624 294.4 17069 291.5 10862 6399 388.5 2990 433.0 427.1 13835 356.4 17094 412.7 17004 337.1 11505 364.9 7799 271.8 11158 441.7 4994 374.1 9313 180.9 2092 443.7 13452 6244 8357 295.9 17194 296.5 1821 404.8 7228 433.8 9388 9389 369.8 16247 420.5 17430 354.0 578 371.1 276.9 10246 445.9 362 11113 393.5 7463 185.2 13669 5750 450.5 11012 472.4 18145 300.2 3955 300.9 17723 442.8 4491 435.9 5764 384.1 10080 434.3 9266 19393 7045 11834374.0 284.8 8258 14793 13197 400.2 7148 7462 189.6 12033 15937 359.1 479.3 18150 3528 2369 305.3 11584 457.3 438.3 11088 398.6 1173 455.3 3165 13234 380.2 5726 10248 10250 446.0 14792 11111 402.8 16988 196.4 7591 304.6 18364 8730 466.5 11013 13472 5396 12448 395.0 13765 289.2 8259 4014 16540 307.5 449.1 407.8 474.0 5775 359.7 10967 411.5 198.9 18315 475.8 15938 1429 493.9 306.7 5239 311.8 17693 456.1 5719 412.1 4933 491.7 422 360.4 4619 406.9 293.6 356 451.4 11117 420.7 4013 203.9 4552 748484.9 312.7 4346 326.7 15333 458.9 4198 412.2 842 502.8 19125 361.2 11028 419.2 8996 297.9 13973 461.0 10962 432.6 17734 208.8 3216 9215 315.6 10104 10108 341.3 4010 485.0 460.3 7845 414.7 16523 6045510.1 363.9 10043 437.3 7676 300.0 9383 474.5 17906 444.6 14841 215.4 3215 4195 14387517.7 326.5 14839 350.5 10717 492.9 461.2 15724 429.5 16162 2342 811 375.1 5534 454.9 17355 302.2 8731 488.1 15960 453.9 14003 221.0 17389 512.2 16693 10812 14161 359.9 16450 500.8 10536 4194 465.2 18866 16273 377.3 1200 464.2 1798 304.3 4927 19084 16561497.6 457.3 173 226.4 12904 536.9 331.6 12995 17713 1648 369.5 3710 503.0 473.1 3717 5383514.3 378.8 6364 470.9 17065 306.4 695 511.3 15958 460.5 14723 234.5 19076 549.3 505.1 2528 10759 556.4 16719 333.8 14884 371.6 15796 486.6 11605 518.0 713 379.4 3667 288 475.3 319.2 9339 535.7 13198 467.3 13026 238.8 4101 8465 16717 335.8 13418 382.6 18863 508.0 495.8 6863 524.7 2344 381.6 18949 478.5 8591 325.1 9331 560.5 19007 471.8 1873 10651 241.0 4097 19075 561.0 517.7 8322 381 4589 563.2 13768 16239 17775335.9 15323392.5 1019 13927 529.8 10020 2740383.7 301331.9 570.0 4473 1875473.1 5305243.4 15175 497.9 481.9 565.3 9859 338.0 1170 396.0 14865 524.3 6458 531.9 1165 10019 388.1 17754 8764 338.6 4930 576.8 10137 474.7 331 248.4 7893 16991 345 340.2 19256 4242 407.2 17722 527.5 500.5 9555 536.3 16054 392.5 8761 486.3 17063 19096 12861 588.6 13807 481.4 8378 255.2 6682 569.7 8319 345.3 10039 344.5 5872 3341 9504419.3 531.8 4829509.2 16510 2689 5582394.6 490.6 8596 12865 4929 595.3 13813 15547484.7 14643258.4 574.0 538.4 18755 346.7 8481 428.8 18545 534.0 9217 8324 515.9 1942 16055 16064 399.0 347 493.2 17062 346.4 10950 604.1 13816 496.4 12966 260.7 9258 581.8 518 13749 348.8 2290 431.6 15322 536.1 4861 16816 540.5 17603 402.3 348 504.6 2649 347.4 19091 606.8 11164 508.4 2229 268.4 13178 593.5 545.5 6445 518.1 17448 599.5 4718 348.9 14838 441.5 10054 10698 6137 542.7 10305 405.5 2099 515.2 349.5 4804 611.2 13805 281.8 4104 876554.7 611.4 12630 353.1 6709 456.6 19497 520.2 18633 544.8 5319 407.6 5102 525.7 8774 13404 9374 617.9 17907 288.5 4105 11226 353.9 18149 353.2 18904 487.5 17861 559.1 522.7 8418 549.2 7584 409.7 16037 3255 532.6 17443 13407 620.0 6199 292.9 9367 620.6 564.5 14829 7675 624.2 2417 357.5 12820 507.2 3616 526.6 5492 551.4 13361 411.9 17753 540.4 361.6 13403 625.5 10347 295.0 978 4575 11647 626.6 10476 364.2 6810 570.2 530.8 810 558.0 13357 416.2 8759 557.7 392 372.4 2002 19604633.6 297.8 9946 17307 18141 7284 10576 579.6 533.2 18005 569.7 9864 1671 420.6 16035 572.2 9514 376.8 12552 638.1 9876 303.8 13135 13130 627.1 368.5 598.6 19373 3937 629.2 17027 19100 7711 535.5 1197 575.1 11041 427.4 13713 606.4 381.2 12086 649.7 316 4145 636.5 10485 380.4 16943 621.1 540.0 4281 586.0 10638 429.5 8758 624.0 8587 385.6 13406 8110668.2 644.3 12636 1795 648.8 11251 4893394.0 720544.6 592.8 10631 8351434.7 628.6 9375390.0 680.1 16700 654.3 776 667.3 14063 404.2 4878 554.4 1340 606.2 93 440.1 4406 1793635.5 399.1 10253 684.5 8895 9221 406.3 5873 663.5 570.4 3308 610.7 4380 442.7 8757 646.2 17066 416.4 8473 688.9 11616 667.7 3380 1792 5915408.5 580.2 3307 3306 613.8 16981 2444 6761447.1 658.1 5679428.4 698.8 8888 667.9 1580 409.6 17362 582.4 2860 616.0 16979 449.3 15239 7490678.1 440.3 14727 701.9 11623 14758 410.6 2748 672.2 586.8 16497 618.2 7962 453.7 17747 690.1 10407 457.9 14605 709.3 5667 679.0 17294 10615 411.7 11424 593.6 16492 626.2 10680 454.6 15241 709.4 711.8 3944 1000693.4 412.7 16452 593.7 17746 638.5 14368 455.8 8274 714.3 16683 16544 414.0 15125 707.5 598.0 16502 648.3 14695 462.1 8270 732.2 10033 722.6 1697 715.4 18678 414.8 14265 605.0 16494 653.6 12941 471.9 6762 6757 727.1 12128 10390 416.9 14153 720.1 612.1 10281 660.4 577 474.2 2928 758.7 1025 734.7 12129 5257 419.6 5195 724.6 621.5 9994 482673.8 478.7 19069 774.0 14314 729.2 6689 428.2 17726 636.6 16633 686.9 14697 493.1 2929 778.9 8669 18640 435.0 8343 737.5 719.9 7929 7607 10032 15383 783.2 437.2 4892 743.1 733.8 13011 13012 7606 752.9 15817 438.0 12188 743.0 7928 786.5 7608 14828 6421 2466 770.7 766.8 4768 789.7 10618 439.3 791.3 3724 16546 12187 771.2 1807 794.1 10141 5360 440.2 14421 812.2 10327 4769 800.8 7609 441.4 4652 821.5 11483 773.3 2640 812.6 8422 830.7 4110 445.8 18627 780.0 1590 1639 15921 10312842.5 817.0 447.9 6191 784.4 19212 6314 11498 450.1 17532 847.2 793.5 19218 19208 13314821.4 1762 454.4 3812 861.8 8553803.7 835.2 16587 872.6 18578 460.0 19101 820.0 14079 840.3 13503 13509 18748 465.4 7877 881.7 834.4 10158 847.1 16584 884.6 8838 469.8 15309 834.5 10171 861.0 12285 1766 10420 474.2 7946 886.7 838.8 16360 17927 486.9 16859 898.6 840.9 10175 923.6 10425 843.0 16846 16358 937.0 3948 845.1 10168 16902 939.1 858.2 8735 948.3 12510 867.5 5198 15368950.4 873.4 1906 3690 954.7 877.8 5205 961.5 8876 887.1 8567 10426 976.5 918.6 17546 1764 987.9 12376932.8 1002.6 12512 943.4 5486 14176 1016.9 950.1 18889 1019.3 14183 12511 952.3 14159 1022.6 2115 956.8 18465 16909 1030.6 961.2 440 1037.5 16905 965.7 1712 1046.7 16903 973.5 5485 8582 1061.2 985.9 16856 1073.0 32 1001.7 18054 1077.3 4733 4730 1017.1 9400 8581 16438 1079.5 1030.5 14837 4457 16432 1037.3 9237 62581082.8 1044.1 14390 13072 13073 43441050.8 1086.0 13069 13371 1053.0 10808 19196 1055.1 5331 10223 1090.3 87851060.3 1092.5 10222 1071.3 19231 18648 1094.6 1088.9 9011 1097.9 18654 1100.7 499 1101.1 6601 1102.8 2839 314 4977 1105.6 1107.7 16295 1115.0 6704 1123.9 11050 47401126.9 1131.3 15043 1135.2 9610 6181 9482 6602 1141.0 1140.6 8882 1149.8 4973 1143.1 3279 1558 1157.6 1147.5 11868 1149.6 4625 1158.0 13041 1163.1 13781 1169.2 10807 1173.1 17884 2587 1221.2 1189.6 12483 1243.5 11744 1207.6 9961 1245.7 10066 1232.1 5437 1247.9 11745 1261.7 12322 12323 1266.6 15858 1261.3 1477 1274.1 14336 1277.8 15295 1279.9 10549 1286.6 1394 1293.4 9679 1300.1 12142 1310.4 1376 1315.5 11839 1320.6 1201 1322.7 6824 11785 1325.2 12755 1342.3 11419 1351.6 18069 1359.2 9891 1369.6 2984 1380.2 11676 1393.8 11679 1403.5 2590 1406.1 11675 1414.0 12887 1421.9 9885 1422.8 8240 1424.7 12902 1434.7 11680

1511.9 8847

360

Appendices Appendix 6.1 Flowchart of QTL mapping analysis. QTL – quantitative trait loci. BTL – binary trait loci. SIM – standard interval mapping. CIM – composite interval mapping.

361

Appendices Appendix 6.2 Table. Statistical comparisons of the floral traits between S. grandis BC (N = 12) and S. grandis F1 (N = 10) lineages. Showing the average values of measurements ± standard deviation.

Trait S. grandis BC S. grandis F1 P-value Corolla length (cm) 3.81 ± 0.31 4.49 ± 0.28 < 0.01 Undilated tube length (cm) 1.59 ± 0.11 1.53 ± 0.07 0.82 Dilated tube length (cm) 1.58 ± 0.16 2.22 ± 0.10 < 0.01 Undilated tube height (cm) 0.53 ± 0.09 0.50 ± 0.19 0.32 Dilated tube height (cm) 0.69 ± 0.06 0.72 ± 0.43 0.35 Undilated tube width (cm) 0.55 ± 0.10 0.54 ± 0.09 0.79 Dilated tube width (cm) 0.71 ± 0.15 0.76 ± 0.07 0.31 Corolla face height (cm) 1.88 ± 0.45 2.07 ± 0.20 0.42 Tube opening height (Outer) (cm) 1.03 ± 0.25 1.13 ± 0.21 0.42 Tube opening height (Inner) (cm) 0.86 ± 0.19 1.00 ± 0.00 0.18 Corolla face width (cm) 2.06 ± 0.47 2.56 ± 0.39 < 0.01 Tube opening width (Outer) (cm) 1.25 ± 0.24 1.31 ± 0.17 0.72 Tube opening width (Inner) (cm) 0.84 ± 0.15 0.92 ± 0.23 0.54 Pistil length (cm) 2.36 ± 0.16 2.65 ± 0.21 < 0.01 Ovary length (cm) 1.52 ± 0.24 1.92 ± 0.09 < 0.01 Style length (cm) 0.84 ± 0.17 0.73 ± 0.10 0.25 Calyx length (cm) 0.43 ± 0.12 0.45 ± 0.11 0.38 Stamen length (cm) 2.13 ± 0.16 2.10 ± 0.12 0.69 Filament length (attached) (cm) 1.62 ± 0.13 1.53 ± 0.08 0.14 Filament length (detached) (cm) 0.51 ± 0.06 0.56 ± 0.14 0.11 Ventral tube length (cm) 3.16 ± 0.24 3.75 ± 0.18 < 0.01 Ventral lobe length (cm) 0.66 ± 0.12 0.73 ± 0.21 0.16 Dorsal tube length (cm) 2.60 ± 0.19 2.88 ± 0.07 <0.01 Dorsal lobe length (cm) 0.61 ± 0.08 0.64 ± 0.29 0.43 Time to flowering (DAS) 377.5 ± 69.90 265 ± 0.32 < 0.01 Note. Wilcoxon rank sum test was used to compare the difference. DAS: days after sowing.

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Appendices Appendix 6.3 Table. Statistical comparisons of the floral traits between S. rexii (N = 17) and S. grandis (data for S. grandis are those combined from the S. grandis F1 and S. grandis BC lineages; N = 22). Showing the average values of measurements ± standard deviation. Trait S. rexii S. grandis P-value Corolla length (cm) 6.79 ± 0.60 4.13 ± 0.46 < 0.01 Undilated tube length (cm) 2.65 ± 0.42 1.58 ± 0.10 < 0.01 Dilated tube length (cm) 2.74 ± 0.46 1.86 ± 0.38 < 0.01 Undilated tube height (cm) 0.49 ± 0.08 0.52 ± 0.09 0.18 Dilated tube height (cm) 1.01 ± 0.11 0.71 ± 0.06 < 0.01 Undilated tube width (cm) 0.44 ± 0.05 0.55 ± 0.09 < 0.01 Dilated tube width (cm) 1.03 ± 0.10 0.74 ± 0.13 < 0.01 Corolla face height (cm) 4.29 ± 0.86 1.97 ± 0.44 < 0.01 Tube opening height (Outer) (cm) 1.94 ± 0.49 1.08 ± 0.24 < 0.01 Tube opening height (Inner) (cm) 1.51 ± 0.34 0.93 ± 0.21 < 0.01 Corolla face width (cm) 5.06 ± 0.91 2.29 ± 0.52 < 0.01 Tube opening width (Outer) (cm) 2.73 ± 0.67 1.29 ± 0.22 < 0.01 Tube opening width (Inner) (cm) 1.88 ± 0.41 0.88 ± 0.16 < 0.01 Pistil length (cm) 3.91 ± 0.10 2.49 ± 0.23 < 0.01 Ovary length (cm) 2.60 ± 0.12 1.71 ± 0.31 < 0.01 Style length (cm) 1.31 ± 0.14 0.79 ± 0.15 < 0.01 Calyx length (cm) 0.56 ± 0.07 0.45 ± 0.11 < 0.01 Stamen length (cm) 3.44 ± 0.10 2.13 ± 0.12 < 0.01 Filament length (attached) (cm) 2.55 ± 0.08 1.59 ± 0.11 < 0.01 Filament length (detached) (cm) 0.88 ± 0.07 0.53 ± 0.08 < 0.01 Ventral tube length (cm) 5.39 ± 0.51 3.44 ± 0.40 < 0.01 Ventral lobe length (cm) 1.39 ± 0.17 0.69 ± 0.10 < 0.01 Dorsal tube length (cm) 4.62 ± 0.33 2.74 ± 0.24 < 0.01 Dorsal lobe length (cm) 1.13 ± 0.22 0.62 ± 0.08 < 0.01 Time to flowering (DAS) 237 ± 0.00 329 ± 77.08 < 0.01 Note. Wilcoxon rank sum test was used to compare the difference. DAS: days after sown.

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Appendices Appendix 6.4 Table. Three way comparisons between S. rexii (N = 17), S. grandis F1 (N = 10) and the F1 (N = 17) using Dunn’s post hoc test. P-values > 0.05 (indicate no statistical significant differences) are highlighted in grey

Trait 1. Corolla length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0003 - S. rexii 0.0 281 < 0.0001

Trait 2. Undilated tube length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0006 - S. rexii 0.0 073 < 0.0001

Trait 3. Dilated tube length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0012 - S. rexii 0.2 293 0.0082

Trait 4. Undilated tube height (S. grandis × S. rexii ) F1 S. grandis S. grandis 0. 2366 - S. rexii 0. 1047 0.3582

Trait 5. Dilated tube height (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0003 - S. rexii 0.0462 < 0.01

Trait 6. Undilted tube width (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0 847 - S. rexii 0.0 145 0.0006

Trait 7. Dilated tube width (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0121 - S. rexii 0.0020 < 0.0 00 1

Trait 8. Corolla face height (S. gra ndis × S. rexii ) F1 S. grandis S. grandis 0.0 00 1 - S. rexii 0. 0909 < 0.0 00 1

Trait 9. Tube opening height (outer) (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0003 - S. rexii 0.1 310 < 0.0 00 1

364

Appendices Trait 10. Tube opening height (inner) (S. grandis × S. rexii ) F1 S. g randis S. grandis 0.0005 - S. rexii 0. 2673 0.0 00 1

Trait 11. Corolla face width (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0003 - S. rexii 0.0264 < 0.0 00 1

Trait 12. Tube opening width (outer) (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0 019 - S. rexi i 0. 001 5 < 0.0 00 1

Trait 13. Tube opening width (inner) (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0029 - S. rexii 0.0015 < 0.0 00 1

Trait 14. Pistil length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0007 - S. rexii 0.0054 < 0.0 00 1

Trait 15. Ovary length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0005 - S. rexii 0.0094 < 0.0 00 1

Trait 16. Style length (S. grandis × S. rexii ) F1 S. grandis S. grandis < 0.0 00 1 - S. rexii 0.3 346 < 0.0 00 1

Trait 17. Calyx length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0. 0894 - S. rexii 0.0 153 0.0007

Trait 18. Stamen length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0042 - S. rexii 0.0 00 1 < 0.0 00 1

Trait 19. Filament length (attached) (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0030 - S. rexii 0.0002 < 0.0 00 1

Trait 20. Filament length (free) (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0020 - S. rexii 0.0038 < 0.0 00 1

365

Appendices Trait 21. Ventral tube length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0 00 1 - S. rexii 0.0766 < 0.0 00 1

Trait 22. Ventral lobe length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0002 - S. rexii 0. 0560 < 0.0 00 1

Trait 23. Dorsal tube length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0021 - S. rexii 0.0004 < 0.0001

Trait 24. Dorsal lobe length (S. grandis × S. rexii ) F1 S. grandis S. grandis 0.0 00 1 - S. rexi i 0.0766 < 0.0001

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Appendix 6.5 Table. Traits scoring results of the 200 Streptocarpus backcross individuals. The listed trait numbers corresponds to that in the Table 6.4. Qual.: qualifiers of the BC plants. Unit for trait 1 to 24: cm. Unit for trait 25: days after cotyledons unfold. Trait 29.1: rosulate / unifoliate scoring Method 1. Trait 29.2: rosulate / unifoliate scoring Method 2. Trait 29.3: rosulate / unifoliate scoring Method 3. Trait 29.4: rosulate / unifoliate scoring Method 4. For binary traits 26 to 28, 30 and 31, ‘1’ represent present and ‘0’ represent absent. For traits 29.1 to 29.4, ‘1’ represents rosulate and ‘0’ represents unifoliate. -: unknown. AVG: average value (cm). STD: Standard deviation (cm).

Trait numbers Qual. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29.1 29.2 29.3 29.4 30 31 D 5.691 1.622 4.069 0.603 1.030 0.560 0.972 3.375 1.279 1.051 3.462 1.676 1.102 3.087 1.992 1.095 0.704 2.828 2.250 0.578 4.724 0.967 3.618 0.881 280 1 1 1 1 1 1 1 0 0 E 5.446 1.742 3.704 0.632 0.932 0.606 0.875 3.300 1.563 1.226 3.611 1.703 1.106 3.170 2.110 1.061 0.609 3.251 2.500 0.751 4.321 1.126 3.869 0.847 231 1 1 1 0 0 0 0 1 0 F 4.809 1.492 3.317 0.578 0.926 0.704 1.038 2.769 1.358 1.080 3.154 1.637 1.248 2.837 1.817 1.020 0.643 2.627 2.185 0.442 3.798 1.011 3.288 0.912 196 1 1 1 0 1 - 1 1 0 G ------1 1 1 1 - 0 H 4.966 1.304 3.662 0.596 0.901 0.616 0.930 2.730 1.294 1.040 3.100 1.574 0.997 2.940 1.872 1.069 0.544 2.480 1.872 0.608 3.947 1.019 3.100 0.773 259 1 1 0 0 0 - 0 1 0 J 4.483 1.509 2.974 0.525 0.880 0.543 0.992 2.599 1.099 0.810 2.807 1.445 0.997 3.221 2.088 1.133 0.544 2.926 2.262 0.664 3.508 0.975 3.216 0.712 238 1 0 1 0 1 1 1 1 0 K 4.556 1.546 3.010 0.581 0.867 0.551 0.952 1.991 1.032 0.827 2.441 1.255 0.884 2.923 1.823 1.100 0.712 2.463 1.889 0.574 3.662 0.894 2.992 0.664 455 1 1 1 0 0 0 1 0 0 L 4.278 1.478 2.801 0.427 0.681 0.421 0.670 2.187 1.089 0.914 2.226 1.202 0.899 2.936 1.936 1.000 0.508 2.780 2.069 0.711 3.501 0.778 2.878 0.697 210 1 1 0 0 0 0 0 0 0 M 5.011 1.557 3.454 0.520 0.845 0.553 0.807 2.362 1.088 0.853 2.435 1.355 0.843 3.070 1.960 1.110 0.621 2.820 2.182 0.638 4.074 0.937 3.235 0.935 210 0 0 1 0 1 1 1 1 0 O 5.048 1.456 3.592 0.653 0.890 0.581 1.071 2.695 1.465 1.244 3.279 1.808 1.168 2.867 1.830 1.037 0.626 2.412 1.752 0.660 4.071 0.977 3.023 0.682 287 1 1 0 0 0 - 0 1 0 P 3.763 1.110 2.653 0.444 0.787 0.448 0.744 2.038 0.993 0.828 2.477 1.282 0.883 2.993 1.744 1.249 0.405 2.169 1.598 0.571 2.956 0.807 2.405 0.495 245 0 0 1 0 0 0 0 0 0 Q 5.268 1.485 3.783 0.557 0.864 0.521 1.081 2.614 1.433 1.086 3.387 1.669 1.182 3.250 2.129 1.121 0.668 2.900 2.243 0.657 4.248 1.020 3.265 0.869 210 1 0 1 0 1 1 1 1 0 R 4.763 1.420 3.343 0.479 0.850 0.556 0.986 2.417 1.275 1.055 2.867 1.621 1.180 3.103 2.056 1.047 0.499 2.859 2.101 0.758 3.757 1.006 3.128 0.822 196 1 1 1 0 0 0 0 0 0 T 5.102 1.956 3.146 0.670 0.931 0.598 0.808 2.791 1.396 1.098 3.284 1.643 1.032 3.065 1.914 1.151 0.676 2.680 2.014 0.666 3.980 1.122 3.032 0.898 224 1 1 1 0 1 - 1 1 0 U 4.348 1.329 3.020 0.472 0.663 0.634 1.017 2.633 1.410 1.468 2.411 1.435 1.126 2.955 1.869 1.086 0.708 2.492 1.766 0.726 3.446 0.902 2.805 0.886 217 1 1 1 0 0 0 0 0 0 V 5.042 1.622 3.420 0.477 0.837 0.603 0.490 2.319 1.255 0.965 3.126 1.396 0.930 3.275 2.203 1.072 0.735 2.884 2.287 0.597 4.066 0.976 3.207 0.794 238 1 1 0 0 0 0 0 0 0 W 4.614 1.433 3.181 0.446 0.718 0.509 0.776 2.242 1.057 0.781 2.525 1.347 0.989 3.027 1.898 1.129 0.530 2.575 2.102 0.473 3.763 0.850 2.816 0.931 455 0 0 1 1 1 1 1 1 0 X 4.981 1.236 3.745 0.443 0.849 0.685 0.863 2.714 1.372 1.190 3.095 1.572 1.083 2.878 1.864 1.014 0.730 2.842 2.248 0.594 3.995 0.986 3.192 0.803 210 1 0 0 0 0 - 0 1 0 Y 3.565 1.255 2.310 0.488 0.776 0.577 0.780 1.606 - - 2.750 - - 2.711 1.869 0.842 0.490 - - - 2.946 0.619 2.391 0.703 371 - 0 - 0 0 1 1 1 0 Z 4.641 1.329 3.311 0.467 0.815 0.501 0.880 2.705 1.326 1.108 3.243 1.636 1.179 3.213 2.007 1.206 0.465 2.752 1.961 0.791 3.622 1.018 3.013 1.028 364 1 1 0 1 1 1 1 1 0 AA 5.785 1.949 3.837 0.662 1.170 0.886 1.319 4.660 2.320 1.979 5.205 2.968 2.104 3.868 2.781 1.087 0.867 3.294 2.600 0.694 3.969 1.817 4.069 1.143 210 1 1 0 1 1 1 1 0 0 AB 3.859 1.570 2.289 0.448 0.713 0.493 0.789 2.532 1.378 1.220 2.999 1.642 1.240 3.121 1.994 1.127 0.379 2.546 1.949 0.597 2.811 1.048 2.535 0.718 252 1 1 0 0 0 0 0 0 0 AC 5.306 1.768 3.538 0.697 0.964 0.641 0.975 3.842 1.682 1.311 3.833 1.778 1.155 3.318 2.131 1.187 0.523 2.967 2.298 0.669 3.947 1.359 3.398 0.803 252 1 0 0 1 1 1 1 1 0 AD 5.633 1.717 3.916 0.590 0.912 0.615 0.960 2.858 1.328 0.893 3.305 1.458 0.939 3.173 1.920 1.253 0.604 3.085 2.168 0.917 4.480 1.153 3.396 1.019 259 1 1 0 1 1 1 1 0 0 AE 4.631 1.371 3.260 0.471 0.851 0.510 0.894 2.720 1.216 0.955 2.951 1.538 1.148 3.499 2.318 1.181 0.536 2.802 2.146 0.656 3.693 0.938 2.971 0.808 224 1 0 0 0 0 0 0 0 0 AF 5.052 1.573 3.479 0.599 0.929 0.682 1.098 3.316 1.455 1.112 3.728 1.830 1.205 3.438 2.111 1.327 0.498 3.043 2.141 0.902 3.842 1.210 3.226 1.157 210 1 0 1 1 1 1 1 0 0 AG 5.119 1.482 3.638 0.591 0.870 0.616 0.933 2.522 1.097 0.882 3.017 1.393 1.003 2.979 1.960 1.019 0.722 2.757 2.010 0.747 4.105 1.014 3.111 0.966 364 1 0 1 0 1 1 1 1 0 AH ------1 1 1 1 0 0 AI 4.094 1.326 2.768 0.378 0.653 0.371 0.705 2.093 1.063 0.932 2.359 1.224 0.957 2.941 1.686 1.255 0.489 2.436 1.946 0.490 3.438 0.656 2.905 0.526 455 1 0 0 1 1 1 1 0 0 AJ 5.832 1.685 4.147 0.649 0.965 0.585 1.018 2.686 1.466 1.270 3.354 1.820 1.261 3.457 2.146 1.311 0.693 2.990 2.064 0.926 4.589 1.243 3.358 1.086 196 1 1 0 1 1 1 1 1 0 AK 5.159 1.882 3.277 0.607 0.898 0.624 1.004 2.594 1.138 0.892 3.189 1.538 1.056 3.307 2.200 1.107 0.760 2.652 1.969 0.683 4.109 1.050 3.083 0.846 238 0 0 1 1 1 1 1 0 0 AL 4.818 1.434 3.384 0.558 0.913 0.471 0.794 1.532 0.828 0.720 1.949 1.169 0.794 2.931 1.816 1.115 0.469 2.326 1.686 0.639 4.174 0.644 3.213 0.632 399 1 0 1 1 1 1 1 1 0 AN 4.276 1.359 2.917 0.445 0.739 0.419 0.780 2.328 1.119 0.915 2.639 1.341 0.910 2.975 1.871 1.104 0.376 2.558 1.776 0.782 3.443 0.833 2.868 0.738 364 1 1 1 1 1 1 1 0 0 AO 3.977 1.308 2.669 0.402 0.612 0.474 0.788 2.116 1.060 0.893 2.545 1.354 0.925 3.315 2.123 1.192 0.516 2.873 2.143 0.730 3.036 0.942 2.761 0.792 238 1 1 0 1 1 1 1 0 0 AP 4.303 1.178 3.125 0.525 0.794 0.654 1.006 2.470 1.356 1.096 2.961 1.533 1.095 2.815 1.758 1.057 0.542 2.305 1.776 0.529 3.410 0.893 2.601 0.818 224 1 0 1 0 0 - 1 - - AQ 4.434 1.273 3.161 0.555 0.842 0.639 1.015 2.447 1.116 0.977 2.947 1.609 1.609 2.799 1.854 0.945 0.527 2.485 1.817 0.668 3.532 0.902 2.930 0.804 252 1 0 1 1 1 1 1 0 0 AS ------1 1 1 1 0 0 AT 4.151 1.198 2.953 0.377 0.672 0.455 0.807 2.358 1.117 0.933 2.771 1.379 0.988 2.848 1.854 0.994 0.502 2.509 1.791 0.718 3.213 0.938 2.644 0.676 455 1 1 0 1 1 1 1 1 0 AU 4.458 1.375 3.083 0.404 0.847 0.508 0.989 2.247 1.125 0.912 2.710 1.506 1.030 3.178 1.853 1.325 0.488 2.528 2.181 0.347 3.510 0.948 2.949 0.866 238 1 1 1 1 1 1 1 0 1

367

Trait numbers Qual. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29.1 29.2 29.3 29.4 30 31 AV 3.976 1.432 2.544 0.506 0.669 0.558 0.910 2.458 1.053 0.835 2.649 1.336 0.865 2.981 2.123 0.858 0.375 2.813 2.075 0.738 3.158 0.818 2.513 0.872 315 0 0 1 1 1 1 1 0 0 AW 5.042 1.467 3.575 0.592 0.814 0.587 1.159 2.012 1.235 1.014 2.899 1.511 1.129 2.995 2.066 0.929 0.644 2.632 2.035 0.597 4.047 0.995 3.131 0.805 238 1 1 0 1 1 1 1 1 0 AX 4.547 1.314 3.233 0.522 0.738 0.588 0.980 2.923 1.443 1.131 3.341 1.705 1.150 3.394 2.192 1.202 0.468 2.923 2.085 0.838 3.433 1.115 2.861 0.941 189 1 1 0 1 0 1 0 0 0 AZ 5.494 1.755 3.739 0.598 0.972 0.470 1.055 3.655 1.625 1.344 3.851 1.927 1.300 3.334 2.215 1.119 0.747 3.017 2.166 0.851 4.190 1.304 3.434 1.388 245 1 1 0 1 1 1 1 1 0 BA 4.852 1.539 3.313 0.660 0.820 0.758 1.136 2.955 1.394 1.180 3.279 1.836 1.197 3.207 2.166 1.042 0.425 2.673 1.906 0.767 3.832 1.020 2.990 0.834 231 1 0 0 1 1 1 1 0 0 BD 4.487 1.378 3.109 0.485 0.689 0.587 0.991 2.555 1.278 1.024 2.870 1.608 1.174 3.171 2.062 1.109 0.470 2.875 2.203 0.672 3.624 0.863 3.008 0.769 259 1 1 0 1 0 1 0 0 0 BF 5.016 1.555 3.461 0.589 0.931 0.627 0.969 2.585 1.430 1.123 3.036 1.492 0.956 3.193 1.930 1.263 0.520 3.060 2.270 0.790 4.074 0.942 3.125 0.832 238 1 1 1 0 0 0 0 0 0 BG 4.578 1.328 3.250 0.496 0.826 0.573 0.904 2.873 1.618 1.387 2.796 1.646 1.138 2.943 1.751 1.192 0.551 2.694 2.033 0.661 3.671 0.907 3.036 0.832 210 1 1 0 1 1 1 1 1 1 BH 4.687 1.275 3.413 0.528 0.848 0.605 0.928 2.590 1.356 1.121 2.895 1.526 1.020 2.859 1.862 0.997 0.440 2.642 1.858 0.784 3.700 0.988 3.007 0.803 252 1 1 0 1 1 1 1 0 0 BI 5.258 1.645 3.613 0.613 0.920 0.595 1.043 2.987 1.443 1.182 3.252 1.585 1.022 3.277 2.309 0.968 0.429 2.629 1.974 0.655 4.070 1.188 3.210 1.045 266 1 1 0 1 1 1 1 - 0 BJ 4.130 1.291 2.840 0.402 0.671 0.449 0.781 2.291 0.971 0.833 2.504 1.132 0.770 - - - 0.450 - - - 3.156 0.975 2.769 0.947 385 1 0 1 1 1 1 1 1 0 BK 4.871 1.491 3.380 0.507 0.741 0.434 0.838 1.959 1.055 0.861 2.590 1.438 0.966 2.964 2.032 0.932 0.407 2.741 1.991 0.750 4.091 0.780 3.394 0.845 406 1 1 0 1 1 1 1 1 0 BL 3.699 1.066 2.633 0.428 0.680 0.534 0.823 1.924 0.945 0.809 2.239 1.149 0.769 2.806 1.991 0.815 0.519 2.133 1.631 0.502 3.044 0.656 2.347 0.619 364 1 1 0 1 1 1 1 1 0 BM 5.205 1.486 3.719 0.557 0.803 0.557 0.967 2.263 0.972 0.812 2.532 1.187 0.823 2.940 1.948 0.992 0.522 2.697 1.874 0.823 4.329 0.877 3.512 0.772 301 0 0 1 1 1 1 1 - 0 BN 5.007 1.487 3.520 0.522 0.793 0.607 1.031 3.219 1.581 1.301 3.677 1.664 1.063 3.014 2.029 0.985 0.589 2.649 1.890 0.759 3.935 1.072 3.173 0.878 252 1 1 0 0 0 0 0 0 0 BO 4.477 1.298 3.179 0.508 0.791 0.555 0.861 2.682 1.475 1.249 3.106 1.804 1.219 3.204 1.921 1.283 0.623 2.844 2.085 0.759 3.652 0.825 2.984 0.705 203 1 1 0 0 0 0 0 0 0 BQ ------1 1 1 1 0 0 BR 4.944 1.293 3.651 0.535 0.896 0.676 1.087 3.277 1.337 1.088 3.363 1.577 0.988 2.950 1.917 1.033 0.421 2.716 1.971 0.745 3.776 1.168 2.971 0.826 364 1 1 0 0 1 - 1 1 0 BS 4.877 1.464 3.413 0.522 0.826 0.501 0.968 2.738 1.207 0.997 2.949 1.538 1.032 3.231 2.228 1.003 0.611 2.587 2.077 0.510 3.882 0.995 3.205 0.955 238 1 0 1 0 0 - 0 - 0 BT 4.702 1.349 3.353 0.440 0.721 0.481 0.827 2.522 1.060 0.959 2.768 1.333 0.827 2.674 1.720 0.954 0.369 2.488 1.750 0.738 3.788 0.914 2.934 0.708 364 1 1 0 1 1 1 1 1 0 BU 5.690 1.868 3.822 0.683 0.951 0.649 1.113 3.964 1.919 1.545 4.358 2.260 1.577 3.516 2.517 0.999 0.603 3.097 2.141 0.956 4.231 1.459 3.487 1.23 259 1 1 0 1 1 1 1 1 0 BV 5.034 1.782 3.252 0.620 0.891 0.499 0.873 2.608 1.076 0.913 2.630 1.478 0.924 2.994 1.945 1.049 0.531 2.567 1.993 0.574 4.108 0.926 3.059 0.839 287 1 0 1 0 1 - 1 1 0 BW 4.139 1.163 2.976 0.390 0.674 0.536 0.846 2.640 1.149 0.993 2.770 1.334 0.919 2.987 1.800 1.187 0.621 2.373 1.709 0.664 3.168 0.971 2.570 0.68 315 1 1 0 0 1 1 1 1 0 BX 4.547 1.212 3.336 0.480 0.757 0.523 0.874 2.624 1.365 1.128 2.856 1.453 1.028 2.887 1.770 1.117 0.414 2.479 1.770 0.709 3.572 0.975 2.669 0.85 259 1 1 0 0 1 - 1 - 0 BY ------1 1 1 1 0 0 BZ 4.330 1.420 2.910 0.452 0.671 0.611 0.988 2.079 0.982 0.909 2.551 1.203 0.965 2.879 1.863 1.016 0.409 2.699 1.895 0.804 3.402 0.928 2.970 0.837 280 1 1 0 0 1 - 1 1 0 CA 3.859 1.173 2.686 0.512 0.653 0.496 0.758 2.091 0.947 0.750 2.253 1.254 0.868 2.668 1.724 0.944 0.426 2.456 1.695 0.761 3.211 0.648 2.540 0.724 266 1 1 0 0 0 0 0 0 0 CB 5.307 1.467 3.840 0.593 0.824 0.547 0.875 2.065 0.991 0.794 2.579 1.215 0.832 3.190 1.883 1.307 0.622 2.854 2.213 0.641 4.402 0.905 3.543 0.808 210 1 0 0 0 1 - 1 1 0 CC 5.180 1.481 3.699 0.517 0.762 0.629 0.998 2.651 1.279 1.047 2.804 1.458 1.010 3.475 2.130 1.345 0.595 2.803 2.054 0.749 4.012 1.168 3.166 1.06 210 1 1 0 0 1 - 1 1 0 CD 5.331 1.508 3.823 0.550 0.842 0.556 0.963 2.470 1.244 1.035 3.284 1.450 0.893 3.228 1.883 1.345 0.885 3.045 2.007 1.038 4.061 1.270 3.154 0.955 224 1 1 0 1 1 1 1 0 0 CE ------1 1 1 1 0 0 CF 4.702 1.295 3.406 0.582 0.933 0.669 1.062 3.086 1.380 1.175 3.472 1.706 1.130 2.998 2.065 0.933 0.501 2.956 2.224 0.732 3.594 1.108 3.250 1.248 364 1 1 0 1 1 1 1 0 0 CG 4.010 1.368 2.642 0.412 0.821 0.531 0.914 3.065 1.545 1.215 3.105 1.574 1.033 2.999 2.050 0.949 0.485 2.816 2.082 0.734 2.865 1.145 3.032 0.915 259 1 1 0 0 1 - 1 - 0 CH 5.706 1.614 4.092 0.589 0.817 0.689 1.075 2.787 1.318 0.975 3.129 1.565 0.966 3.571 2.435 1.136 0.631 3.112 2.144 0.968 4.529 1.177 3.456 1.024 210 1 1 1 0 0 - 1 1 0 CI 4.483 1.466 3.017 0.438 0.708 0.506 0.849 2.697 1.210 1.780 3.147 1.398 1.066 3.487 2.120 1.367 0.560 3.153 2.307 0.846 3.428 1.055 3.051 0.806 203 1 0 1 0 0 0 0 0 0 CJ 4.529 1.155 3.374 0.523 0.817 0.593 0.977 2.288 1.163 0.993 2.632 1.418 1.027 3.197 1.928 1.269 0.557 2.586 1.867 0.719 3.523 1.006 2.775 0.902 231 1 1 0 0 1 1 1 1 0 CK 5.225 1.435 3.790 0.718 0.933 0.654 1.053 2.897 1.479 1.231 3.629 1.804 1.333 2.784 1.932 0.852 0.641 2.506 1.739 0.767 4.269 0.956 3.230 0.933 280 1 1 0 0 1 1 1 1 0 CL 4.661 1.506 3.155 0.557 0.802 0.518 0.838 2.549 1.107 0.792 2.820 1.417 0.968 3.014 1.936 1.078 0.518 2.669 1.959 0.711 3.628 1.033 3.174 0.818 455 1 1 0 1 1 1 1 - 0 CM 3.922 1.304 2.618 0.498 0.693 0.551 0.834 2.823 1.209 1.072 3.036 1.549 1.151 - - - 0.371 - - - 2.832 1.090 2.587 0.785 420 0 0 1 1 1 1 1 1 0 CN 4.580 1.441 3.139 0.407 0.659 0.450 0.822 2.206 1.024 0.820 2.650 1.411 0.952 3.130 2.020 1.111 0.389 2.627 1.951 0.676 3.682 0.897 2.825 0.789 385 1 0 0 1 1 1 1 - 0 CP 4.273 1.268 3.004 0.539 0.762 0.528 0.856 2.495 1.088 0.926 2.779 1.274 0.867 2.883 2.153 0.730 0.404 2.581 1.946 0.635 3.430 0.843 2.884 1.021 315 1 1 0 1 1 1 1 - 0 CQ 4.158 1.349 2.809 0.452 0.740 0.432 0.743 2.057 0.887 0.683 2.372 1.225 0.845 2.684 1.728 0.955 0.481 2.371 1.705 0.666 3.333 0.825 2.676 0.751 455 0 0 0 1 1 1 1 1 0 CR 4.668 1.394 3.274 0.381 0.686 0.423 0.682 1.580 0.800 0.682 2.271 1.164 0.830 2.982 1.555 1.427 0.413 2.754 2.052 0.702 3.822 0.845 3.329 0.77 455 1 0 0 1 1 1 1 0 0 CT 4.867 1.203 3.665 0.469 0.712 0.599 0.991 2.398 1.014 0.904 2.797 1.385 0.948 2.782 1.962 0.820 0.615 2.685 1.951 0.734 3.782 1.086 2.974 0.981 364 1 1 0 1 1 1 1 - 0

368

Trait numbers Qual. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29.1 29.2 29.3 29.4 30 31 CU 4.467 1.315 3.153 0.489 0.723 0.501 0.810 2.529 1.029 0.899 2.917 1.381 0.962 2.798 1.726 1.072 0.475 2.407 1.697 0.710 3.503 0.964 2.943 0.651 364 1 1 0 0 0 - 1 1 0 CV 4.397 1.681 2.716 0.550 0.811 0.474 0.845 2.718 1.367 1.192 3.254 1.781 1.276 3.025 1.873 1.152 0.454 2.829 2.053 0.776 3.607 0.790 3.228 0.908 266 1 0 1 1 1 1 1 1 0 CW 4.362 1.220 3.142 0.558 0.861 0.610 0.915 2.292 1.256 1.027 2.741 1.468 0.998 2.748 1.883 0.865 0.564 2.485 1.769 0.716 3.434 0.928 2.869 0.839 287 1 1 0 1 1 1 1 0 0 CX 4.686 1.492 3.194 0.437 0.697 0.594 0.947 2.809 1.285 1.106 2.928 1.548 1.160 3.299 1.967 1.332 0.464 2.892 2.140 0.752 3.691 0.995 3.000 0.95 203 1 1 0 1 1 1 1 1 0 CY 4.413 1.411 3.002 0.521 0.731 0.579 0.938 3.473 1.622 1.365 3.881 1.919 1.366 2.907 1.969 0.938 0.604 2.457 1.649 0.808 3.378 1.035 2.926 0.802 210 1 1 0 0 0 0 0 0 0 DA 5.406 1.579 3.827 0.548 0.797 0.494 0.897 2.266 1.097 0.920 2.690 1.254 0.944 2.762 1.825 0.937 0.594 2.463 1.775 0.688 4.441 0.965 3.123 1.061 224 1 1 1 0 0 - 1 1 0 DB 5.200 1.452 3.748 0.468 0.785 0.576 0.964 2.740 1.202 0.947 3.091 1.502 1.074 3.020 2.021 0.999 0.767 2.779 2.051 0.728 4.164 1.036 3.189 1.041 238 1 0 0 0 0 - 1 - 0 DD 5.802 1.915 3.887 0.603 0.987 0.643 1.091 2.775 1.252 1.035 3.080 1.396 0.931 3.140 2.241 0.899 0.923 2.904 2.120 0.784 4.681 1.121 3.554 1.052 210 1 1 0 0 1 1 1 1 0 DE 5.829 1.451 4.378 0.613 0.817 0.646 1.053 2.800 1.167 0.745 3.833 1.767 1.244 3.262 2.212 1.050 0.568 2.860 2.035 0.825 4.652 1.177 3.500 1.192 245 1 1 0 1 1 1 1 1 0 DF 5.211 1.601 3.610 0.476 0.776 0.596 1.134 3.599 1.518 1.222 3.715 1.812 1.316 3.440 2.283 1.157 0.722 3.094 2.179 0.915 3.976 1.235 3.190 1.318 224 1 1 0 1 1 1 1 - 0 DG 4.720 1.454 3.266 0.502 0.828 0.541 0.941 2.245 1.058 0.902 2.825 1.390 1.033 2.882 1.960 0.922 0.456 2.620 1.856 0.764 3.760 0.960 3.224 0.996 266 1 0 0 1 1 1 1 0 0 DH 5.034 1.521 3.513 0.507 0.812 0.585 0.976 3.476 1.535 1.251 3.554 1.762 1.180 3.448 2.226 1.222 0.653 2.855 2.153 0.702 3.993 1.041 3.288 0.86 203 0 0 1 1 0 1 0 0 0 DI 5.262 1.885 3.377 0.514 0.819 0.638 0.934 2.167 1.141 0.956 2.618 1.329 1.002 3.199 2.280 0.919 0.764 2.657 1.982 0.675 4.266 0.996 3.271 1.004 210 1 0 1 0 1 - 1 - 0 DJ ------1 1 1 1 0 0 DM 5.069 1.506 3.563 0.504 0.851 0.544 0.790 2.523 1.128 0.888 2.820 1.264 0.943 2.932 1.964 0.968 0.800 2.633 2.013 0.620 4.024 1.045 3.308 0.986 224 1 0 0 1 1 1 1 1 0 DO ------1 1 1 1 0 0 DS 4.434 1.344 3.090 0.483 0.753 0.611 0.861 2.288 1.076 0.883 2.423 1.284 0.888 2.780 1.831 0.949 0.506 2.722 2.068 0.654 3.615 0.819 2.846 0.835 364 1 1 0 1 1 1 1 1 0 DT 4.208 1.264 2.944 0.439 0.651 0.516 0.847 2.618 1.104 0.803 2.812 1.449 0.978 3.270 2.175 1.095 0.603 2.923 2.227 0.696 3.167 1.041 2.764 0.805 182 0 0 1 0 0 - 1 - 0 DU 5.184 1.495 3.689 0.611 0.769 0.585 0.943 2.567 1.386 1.038 3.213 1.707 1.221 3.261 2.275 0.986 0.813 2.864 2.081 0.783 4.124 1.060 3.004 1.038 259 1 1 0 1 1 1 1 0 0 DX 4.584 1.272 3.311 0.482 0.710 0.489 0.803 2.973 0.849 0.981 3.041 1.359 0.979 2.809 1.977 0.832 0.410 2.560 1.897 0.663 3.526 1.058 3.048 0.989 315 1 1 0 1 1 1 1 1 0 DZ ------455 1 0 1 0 1 - 1 1 0 EA 4.199 1.183 3.015 0.428 0.785 0.439 0.798 2.691 1.238 1.003 2.892 1.434 0.920 2.653 1.678 0.975 0.335 2.217 1.481 0.736 3.233 0.965 2.508 0.967 315 1 1 0 0 0 - 1 1 0 EB 4.283 1.361 2.922 0.429 0.630 0.418 0.725 2.313 1.090 0.824 2.666 1.411 0.863 2.964 1.753 1.211 0.412 2.761 1.813 0.948 3.524 0.759 2.890 0.819 196 0 0 1 1 1 1 1 1 0 EC 5.168 1.570 3.598 0.526 0.785 0.531 0.970 2.792 1.201 0.960 3.283 1.560 1.201 3.068 2.043 1.025 0.495 2.705 2.043 0.662 4.220 0.948 3.336 0.913 266 1 0 0 1 1 1 1 1 0 EE 5.809 1.662 4.147 0.544 0.865 0.615 0.971 3.226 1.368 0.996 3.683 1.665 1.245 3.562 2.202 1.360 0.550 3.085 2.318 0.767 4.593 1.216 3.364 1.173 231 1 1 0 0 1 1 1 1 0 EF 4.864 1.570 3.294 0.543 0.882 0.665 0.977 2.776 1.162 1.011 2.926 1.586 1.134 3.298 2.111 1.187 0.528 3.030 2.411 0.619 3.978 0.886 3.497 0.85 210 1 0 0 1 1 1 1 - 1 EG 3.905 1.571 2.334 0.424 0.722 0.532 0.836 3.090 1.265 1.035 3.019 1.438 1.043 3.064 2.215 0.849 0.614 3.010 2.151 0.859 2.845 1.060 2.852 1.032 266 0 0 1 1 0 1 1 0 0 EH 4.332 1.371 2.961 0.499 0.769 0.568 0.852 2.655 1.081 0.858 3.008 1.505 1.023 3.032 2.013 1.019 0.783 2.563 1.990 0.573 3.371 0.961 2.781 1.008 210 1 0 1 0 1 - - 1 0 EK 3.630 1.223 2.407 0.389 0.672 0.444 0.737 2.412 1.077 0.939 2.663 1.406 0.942 2.683 1.738 0.946 0.418 2.096 1.614 0.482 3.015 0.615 2.481 0.607 476 1 1 0 1 1 1 1 0 0 EN 4.948 1.602 3.346 0.588 0.828 0.557 0.925 2.964 1.132 0.933 3.244 1.613 1.119 3.471 2.357 1.114 0.674 3.283 2.499 0.784 3.820 1.128 3.371 0.984 210 1 0 1 1 1 1 1 1 0 EO 5.204 1.801 3.403 0.589 0.944 0.621 0.957 3.632 1.424 1.117 3.842 1.589 1.159 3.035 2.090 0.945 0.892 2.862 2.147 0.715 4.016 1.188 3.724 1.126 224 1 0 0 0 1 - 1 1 0 EQ 3.268 0.824 2.444 0.634 1.045 0.413 0.739 1.426 0.629 0.575 1.553 0.767 0.572 - - - 0.534 - - - 2.715 0.553 2.780 0.919 364 1 1 0 1 1 1 1 0 0 ES 4.754 1.448 3.306 0.603 0.891 0.667 0.971 2.664 1.138 0.891 3.252 1.549 1.076 2.771 1.844 0.927 0.625 2.577 1.855 0.722 3.760 0.994 2.888 0.887 287 1 0 0 0 1 - 1 1 0 ET 4.755 1.773 2.982 0.520 0.798 0.606 0.844 2.258 1.143 0.830 2.822 1.578 1.054 3.166 2.198 0.968 0.698 2.789 2.112 0.677 3.895 0.860 3.111 1.088 224 1 1 1 0 0 0 0 0 0 EU 4.644 1.447 3.197 0.592 0.747 0.617 0.932 2.721 1.164 0.875 2.964 1.481 1.038 2.616 1.840 0.776 0.406 2.426 1.816 0.610 3.751 0.893 2.976 0.997 210 1 1 0 0 0 0 0 0 0 EV 5.381 1.729 3.652 0.597 1.007 0.632 0.951 2.641 1.202 0.995 2.873 1.419 1.012 3.178 2.248 0.930 0.763 3.134 2.165 0.969 4.337 1.044 4.158 1.153 238 1 1 0 0 1 1 0 1 0 EW 4.616 1.359 3.257 0.543 0.820 0.473 0.807 2.365 1.164 0.966 2.775 1.408 0.944 2.945 1.928 1.017 0.378 2.599 1.962 0.637 3.722 0.894 2.987 0.899 455 1 1 0 1 1 1 1 0 0 EX 5.315 1.393 3.922 0.646 0.913 0.656 1.007 2.188 1.167 0.950 2.922 1.515 1.144 2.742 1.930 0.812 0.611 2.478 1.777 0.701 4.204 1.111 3.231 1.124 259 1 1 1 0 0 - 0 1 0 EY 5.725 1.675 4.050 0.580 0.913 0.653 1.006 3.064 1.362 1.032 3.481 1.581 1.164 3.254 2.319 0.935 0.896 3.071 2.412 0.659 4.612 1.113 3.475 1.346 224 0 0 1 0 0 - 1 1 0 EZ 4.051 1.362 2.689 0.453 0.690 0.577 0.897 2.687 0.996 0.809 2.863 1.453 1.090 2.951 1.995 0.956 0.538 2.519 1.824 0.695 3.255 0.796 2.964 0.762 287 0 0 1 0 0 - 1 1 0 FA 3.928 1.290 2.638 0.452 0.751 0.482 0.806 2.395 1.047 0.857 2.552 1.370 0.904 2.794 1.801 0.993 0.430 2.373 1.749 0.624 3.148 0.781 2.701 0.869 364 1 0 0 1 1 1 1 1 0 FB 4.366 1.172 3.194 0.598 0.823 0.532 0.890 2.772 1.142 0.908 3.120 1.428 0.879 2.631 1.635 0.996 0.392 2.370 1.677 0.693 3.397 0.968 2.597 0.975 364 0 0 1 1 1 1 1 1 0 FC 5.232 1.956 3.276 0.496 0.864 0.569 0.963 2.888 1.243 0.982 3.404 1.695 1.172 3.147 2.197 0.950 0.587 3.021 2.338 0.683 4.167 1.065 3.826 1.018 238 1 0 0 0 1 1 1 1 0 FD 4.093 1.094 2.999 0.540 0.707 0.506 0.888 2.155 1.090 0.869 2.543 1.532 0.907 2.989 1.908 1.081 0.459 2.530 1.772 0.758 3.190 0.903 2.535 0.665 266 1 1 0 1 1 1 1 0 1

369

Trait numbers Qual. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29.1 29.2 29.3 29.4 30 31 FE 3.946 1.310 2.636 0.497 0.775 0.551 0.801 1.748 0.918 0.782 2.080 1.247 0.871 2.656 1.509 1.147 0.350 - - - 3.126 0.820 2.504 0.742 399 1 1 1 1 1 1 1 0 0 FF 5.690 1.612 4.078 0.630 0.923 0.673 1.052 3.000 1.241 0.973 3.722 1.785 1.145 2.896 2.098 0.798 0.670 2.705 1.804 0.901 4.512 1.178 3.331 1.253 266 0 0 1 1 1 1 1 1 0 FG 4.927 1.511 3.416 0.567 0.887 0.584 0.837 2.859 1.275 0.986 3.005 1.556 1.037 3.074 2.115 0.959 0.495 2.506 1.777 0.729 4.052 0.875 3.164 1.044 238 1 1 0 1 1 1 1 1 1 FH 5.676 1.592 4.084 0.632 0.987 0.551 0.809 2.736 1.221 0.927 3.150 1.560 0.942 3.088 2.202 0.886 0.488 2.620 1.935 0.685 4.734 0.942 3.467 1.055 203 1 0 0 0 0 - 0 1 0 FI 5.198 1.594 3.604 0.522 0.832 0.659 0.908 2.564 1.179 0.947 3.030 1.542 1.043 2.921 1.989 0.932 0.613 2.758 2.034 0.724 4.066 1.132 3.292 1.035 287 1 1 0 1 1 1 1 0 0 FK 4.096 1.289 2.807 0.466 0.599 0.520 0.857 2.407 1.169 0.922 2.896 1.348 0.847 2.956 2.074 0.882 0.534 2.701 1.984 0.717 3.057 1.039 2.820 0.82 280 1 1 0 0 1 1 1 1 0 FL 4.284 1.218 3.066 0.490 0.741 0.518 0.830 2.481 1.092 0.849 2.545 1.467 1.048 2.927 2.045 0.882 0.499 2.583 1.803 0.780 3.528 0.756 2.741 0.746 245 1 1 0 0 1 - 1 - 0 FN 4.762 1.588 3.174 0.572 0.860 0.614 0.841 3.120 1.438 1.147 3.186 1.719 1.196 - - - 0.643 - - - 3.755 1.007 3.192 0.945 210 1 1 1 1 1 1 1 1 0 FO 4.300 1.280 3.021 0.497 0.745 0.507 0.800 2.153 1.005 0.838 2.481 1.320 0.865 2.675 1.732 0.944 0.471 2.478 1.850 0.628 3.539 0.761 2.894 0.802 287 0 0 1 0 1 1 1 1 0 FR ------315 - - - 1 1 1 1 0 0 FS 4.473 1.342 3.131 0.519 0.767 0.609 0.759 2.484 1.009 0.747 2.862 1.411 0.823 2.878 - - 0.558 - - - 3.344 1.129 2.551 0.957 315 1 1 0 1 1 1 1 0 0 FT 4.485 1.282 3.203 0.487 0.755 0.518 0.767 2.287 0.898 0.678 2.957 1.217 0.761 2.689 1.732 0.957 0.464 2.565 1.864 0.701 3.461 1.024 2.663 1 238 1 0 0 0 0 0 1 0 0 FU 5.626 1.791 3.835 0.597 0.913 0.697 1.025 3.548 1.383 0.988 3.906 1.829 1.161 3.462 2.300 1.162 0.601 3.183 2.427 0.756 4.527 1.099 3.670 1.128 280 1 0 1 0 0 - 0 0 0 FV 3.729 1.117 2.611 0.425 0.691 0.434 0.692 1.812 0.963 0.833 2.274 1.190 0.776 2.345 1.428 0.917 0.326 2.448 1.796 0.652 2.996 0.733 2.552 0.713 364 0 0 1 1 1 1 1 1 0 FX 5.416 1.609 3.807 0.560 0.858 0.617 0.995 2.905 1.318 1.115 3.444 1.856 1.219 3.071 2.013 1.058 0.483 2.780 2.146 0.634 4.456 0.960 3.451 1.144 364 1 1 0 1 1 1 1 1 0 FY 5.729 1.694 4.035 0.662 0.906 0.602 1.032 3.367 1.226 0.922 3.680 1.550 1.055 3.471 2.475 0.996 0.598 2.885 2.169 0.716 4.468 1.261 3.420 1.228 259 1 1 0 0 0 0 0 1 0 GA 4.470 1.238 3.233 0.487 0.793 0.514 0.856 2.888 1.213 3.115 1.503 1.050 3.128 1.963 1.165 0.480 2.619 1.811 0.808 3.337 1.133 2.914 0.871 364 1 0 1 1 1 1 1 1 0 GB 4.181 1.209 2.972 0.523 0.695 0.549 0.906 2.845 1.193 1.027 2.909 1.481 0.972 2.607 1.696 0.911 0.407 2.358 1.694 0.664 3.148 1.033 2.720 1.165 280 1 1 0 0 1 - 1 1 0 GC 5.550 1.689 3.861 0.560 0.915 0.648 1.032 3.523 1.452 1.130 3.767 1.704 1.163 3.469 2.231 1.238 0.960 3.041 2.199 0.842 4.365 1.185 3.518 1.242 231 1 1 0 0 1 1 1 1 0 GF ------1 1 1 1 0 0 GG 5.242 1.410 3.832 0.550 0.802 0.502 0.851 3.136 1.288 0.996 3.174 1.473 0.894 2.984 2.084 0.900 0.468 2.426 1.846 0.580 4.077 1.165 3.197 1.085 189 0 0 1 0 0 0 0 0 0 GH 4.464 1.289 3.175 0.475 0.771 0.488 0.806 2.851 1.193 0.927 2.946 1.548 0.943 2.621 1.662 0.959 0.456 2.148 1.518 0.630 3.554 0.909 2.921 0.78 224 1 0 0 0 1 - 1 1 0 GI 5.214 1.690 3.524 0.539 0.821 0.512 0.890 2.780 1.098 0.835 3.098 1.430 0.919 2.986 2.036 0.950 0.705 2.792 2.079 0.713 4.183 1.031 3.393 0.909 238 1 1 0 0 0 0 0 0 0 GJ 4.389 1.467 2.922 0.529 0.764 0.535 0.803 2.558 1.066 0.874 2.794 1.427 0.886 3.071 1.993 1.077 0.489 2.399 1.918 0.481 3.495 0.894 2.811 0.955 371 0 0 1 1 0 1 1 1 0 GK 5.112 1.541 3.571 0.694 0.848 0.565 0.893 2.962 1.210 0.930 3.222 1.660 1.060 3.208 2.084 1.124 0.463 2.808 2.095 0.713 3.950 1.163 3.106 0.939 420 1 1 0 1 1 1 1 - 0 GM 4.285 1.413 2.872 0.446 0.709 0.514 0.892 3.156 1.282 1.027 3.193 1.487 1.039 3.241 2.048 1.193 0.636 3.108 2.388 0.720 3.222 1.063 3.018 0.961 224 1 1 0 0 0 - 1 1 0 GN 5.258 1.553 3.705 0.565 0.859 0.603 0.948 2.984 1.185 1.036 3.260 1.813 1.143 3.366 2.258 1.108 0.575 2.825 2.125 0.700 4.286 0.972 3.364 0.866 238 1 1 0 1 1 1 1 0 0 GO 3.414 1.136 2.278 0.459 0.601 0.520 0.776 2.439 1.071 0.928 2.702 1.415 0.974 2.520 1.648 0.872 0.450 2.181 1.706 0.475 2.596 0.818 2.310 0.66 301 1 0 1 0 1 - 1 1 0 GP 5.404 1.572 3.832 0.589 0.865 0.481 0.825 2.628 1.223 0.989 2.898 1.473 0.984 3.256 2.352 0.904 0.529 2.653 1.922 0.731 4.508 0.896 3.434 0.959 364 1 1 1 0 0 - 1 1 0 GQ 4.384 1.400 2.985 0.455 0.747 0.430 0.872 2.181 0.887 0.777 2.890 1.466 1.145 2.722 1.709 1.013 0.529 2.253 1.725 0.528 4.354 0.866 2.811 0.894 455 1 0 0 1 1 1 1 0 0 GR 4.807 1.430 3.377 0.502 0.823 0.528 0.830 2.829 1.108 0.859 3.109 1.423 0.894 2.951 1.997 0.954 0.493 2.936 2.178 0.758 3.750 1.057 3.434 0.975 280 1 1 0 1 1 1 1 1 0 GS 4.694 1.398 3.296 0.489 0.789 0.558 0.919 2.932 1.118 0.895 3.179 1.505 0.918 3.234 2.296 0.938 0.449 3.043 2.241 0.802 3.695 0.999 3.267 1.217 245 1 0 1 1 1 1 1 1 0 GU 3.826 1.295 2.531 0.411 0.661 0.419 0.689 1.919 0.889 0.691 2.072 1.113 0.695 2.956 1.991 0.965 0.440 2.474 1.835 0.639 3.057 0.769 2.777 0.8 364 1 1 0 1 1 1 1 1 0 GV 5.109 1.565 3.544 0.561 0.798 0.635 1.004 3.244 1.205 0.923 3.678 1.790 1.159 2.833 1.930 0.903 0.665 2.533 1.941 0.592 4.020 1.089 3.346 1.036 210 1 0 0 0 0 - 0 1 0 GW 4.242 1.325 2.917 0.376 0.655 0.437 0.798 2.461 1.026 0.851 2.746 1.311 0.854 2.782 1.825 0.957 0.402 2.568 1.861 0.707 3.266 0.976 2.708 0.857 266 1 0 0 1 1 1 1 0 0 GX 4.337 1.373 2.964 0.553 0.856 0.528 0.924 2.702 1.142 0.948 2.879 1.410 0.856 2.875 1.894 0.981 0.416 2.725 2.066 0.659 3.420 0.917 2.914 0.901 238 1 0 0 1 1 1 1 0 1 GY 4.600 1.438 3.163 0.520 0.853 0.594 1.083 2.933 - - 3.139 1.536 - 3.287 2.145 1.142 0.412 2.840 2.103 0.737 3.540 1.060 3.158 1.064 266 1 1 0 0 0 0 0 1 0 GZ 5.170 1.629 3.541 0.503 0.947 0.443 0.825 2.218 1.064 0.914 2.441 1.199 0.763 3.044 2.016 1.028 0.483 2.739 2.064 0.675 4.032 0.775 3.534 1.129 392 1 1 1 1 1 1 1 1 0 HA 4.660 1.542 3.118 0.516 0.719 0.566 1.030 3.058 1.220 0.930 3.304 1.647 0.995 2.946 1.901 1.045 0.539 2.385 2.041 0.344 3.602 1.058 3.357 0.846 252 0 0 1 0 0 0 0 0 0 HB 4.947 1.393 3.554 0.624 0.927 0.696 1.098 3.044 1.239 1.006 3.284 1.593 1.004 2.946 1.978 0.968 0.496 2.720 1.887 0.833 3.910 1.037 3.087 1.091 280 1 0 1 1 1 1 1 1 0 HC 4.875 1.490 3.385 0.761 0.769 0.542 0.930 3.218 1.196 0.954 3.372 1.483 0.944 3.130 2.032 1.098 0.432 3.127 2.397 0.730 3.762 1.113 3.333 0.858 245 0 0 1 1 1 1 1 1 0 HD 5.523 1.654 3.869 0.491 0.897 0.714 1.073 3.541 1.264 0.979 3.666 1.822 1.196 3.102 2.124 0.978 0.800 2.823 1.982 0.841 4.284 1.239 3.408 1.14 238 0 0 0 1 1 1 1 0 0 HE 4.101 1.183 2.918 0.495 0.689 0.596 0.873 2.917 1.091 0.899 2.946 1.396 0.933 3.098 1.944 1.154 0.422 2.616 1.900 0.716 2.971 1.130 2.671 0.865 266 1 1 0 1 1 1 1 0 0 HF 5.428 1.583 3.845 0.477 0.881 0.637 1.032 3.252 1.171 0.926 3.860 1.725 1.076 3.347 2.293 1.054 0.716 2.972 2.326 0.646 4.220 1.208 3.452 1.175 203 0 0 1 0 1 - 1 1 0

370

Trait numbers Qual. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29.1 29.2 29.3 29.4 30 31 HG 4.229 1.340 2.889 0.422 0.766 0.436 0.749 2.252 0.997 0.840 2.378 1.285 0.821 3.078 2.132 0.946 0.350 2.799 2.123 0.676 3.411 0.818 2.886 0.764 217 1 1 1 0 0 - 0 1 0 HH 4.285 1.265 3.020 0.478 0.792 0.446 0.856 2.386 1.114 0.910 2.647 1.387 0.866 2.869 1.863 1.006 0.398 2.298 1.787 0.511 3.386 0.899 2.881 0.779 217 1 1 0 0 0 0 0 0 0 HI 5.121 1.707 3.414 0.646 0.808 0.735 1.233 3.135 1.357 1.060 3.395 1.833 1.162 3.063 2.120 0.943 0.637 2.615 1.669 0.946 3.961 1.160 3.022 1.035 168 1 1 1 0 1 - 1 1 0 HJ 5.182 1.587 3.595 0.450 0.790 0.471 0.835 2.337 1.117 0.917 2.858 1.309 0.806 3.369 2.384 0.985 0.622 3.165 2.378 0.787 4.190 0.992 3.244 1.063 154 1 0 1 0 0 - 0 0 0 HK 4.599 1.424 3.175 0.462 0.749 0.532 0.865 2.685 1.112 0.907 3.089 1.357 0.901 2.959 1.871 1.088 0.511 2.671 1.900 0.771 3.491 1.108 2.849 1.112 182 1 0 1 0 1 - 1 1 0 HM 5.468 1.540 3.928 0.579 0.865 0.530 0.830 2.792 1.255 0.928 3.455 1.784 1.117 3.261 2.161 1.100 0.483 2.843 2.188 0.655 4.233 1.235 3.442 1.113 294 1 1 0 0 1 1 1 1 0 HP 5.097 1.708 3.389 0.514 0.758 0.562 0.901 3.062 1.342 1.104 3.611 1.668 0.967 2.732 1.961 0.771 0.467 3.010 2.259 0.751 4.153 0.944 3.560 0.969 189 1 1 1 0 0 - 0 1 0 HR 4.979 1.436 3.544 0.542 0.877 0.553 0.907 2.536 1.179 0.873 2.908 1.524 0.954 2.941 1.853 1.088 0.339 2.727 1.959 0.768 3.998 0.981 3.112 1.045 343 1 1 1 1 1 1 1 1 0 HS 4.659 1.549 3.110 0.448 0.773 0.665 0.899 2.896 1.222 1.025 3.084 1.551 0.894 3.077 2.238 0.839 0.439 2.859 2.098 0.761 3.784 0.875 3.438 0.968 210 1 0 0 0 0 0 0 0 0 HT 4.271 1.540 2.731 0.560 0.824 0.576 0.888 2.735 1.116 0.895 2.771 1.329 0.849 2.964 1.948 1.016 0.607 2.880 2.185 0.695 3.265 1.006 2.975 0.882 168 0 0 1 0 0 - 0 1 0 HU 4.579 1.493 3.086 0.477 0.722 0.465 0.777 2.624 1.089 0.889 2.803 1.402 0.844 2.333 1.524 0.809 0.381 2.074 1.592 0.482 3.893 0.686 2.953 0.876 364 1 0 1 1 1 1 1 0 0 HV 4.839 1.604 3.235 0.514 0.819 0.562 0.857 2.556 1.099 0.859 2.992 1.443 0.788 2.932 1.998 0.934 0.457 2.915 2.212 0.703 3.822 1.017 3.283 1.002 385 1 0 1 1 1 1 1 1 0 HW 5.587 1.759 3.828 0.769 0.959 0.735 1.088 2.582 1.131 0.900 3.049 1.520 0.862 2.977 2.156 0.821 0.548 2.842 2.094 0.748 4.310 1.277 3.491 1.229 294 1 0 1 1 1 1 1 1 0 HX 4.454 1.433 3.021 0.541 0.729 0.543 0.783 1.841 0.970 0.756 2.321 1.154 0.754 3.008 1.871 1.136 0.420 2.538 1.888 0.650 3.541 0.913 2.983 0.78 294 1 1 1 1 1 1 1 1 0 HY 4.820 1.577 3.243 0.471 0.827 0.480 0.765 2.167 1.001 0.815 2.745 1.364 0.837 2.859 1.960 0.899 0.508 2.692 2.041 0.652 3.839 0.981 3.254 0.793 385 1 0 0 1 1 1 1 0 0 HZ 5.230 1.792 3.438 0.662 0.958 0.696 1.014 2.910 1.294 0.946 3.339 1.660 1.053 3.150 2.036 1.114 0.757 2.633 1.979 0.654 4.111 1.119 3.080 1.017 168 1 0 0 0 0 - 1 1 0 IA 4.166 1.454 2.712 0.438 0.737 0.488 0.852 2.693 1.132 0.940 2.762 1.430 0.954 2.905 2.150 0.755 0.396 3.004 2.231 0.773 3.289 0.876 3.086 0.873 175 1 0 0 1 1 1 1 0 0 IB 4.530 1.552 2.978 0.451 0.722 0.471 0.794 2.694 1.192 0.937 3.050 1.474 0.963 2.996 1.969 1.027 0.366 2.647 1.937 0.710 3.565 0.965 2.828 0.998 210 1 0 0 0 0 - 0 0 0 ID 5.049 1.706 3.343 0.640 0.924 0.696 1.020 3.717 1.438 1.114 4.061 1.884 1.317 3.226 2.098 1.128 0.753 2.872 2.027 0.845 3.839 1.210 3.396 1.101 168 1 0 0 0 0 - 0 0 0 IF ------1 1 1 1 0 0 IG 4.686 1.456 3.230 0.530 0.784 0.559 0.810 1.969 0.815 0.706 2.369 1.168 0.759 2.952 2.070 0.883 0.437 3.869 0.817 3.132 0.933 294 1 0 1 1 1 1 1 1 0 IH 4.870 1.496 3.374 0.549 0.883 0.513 0.869 1.972 0.916 0.776 2.405 1.238 0.772 2.931 1.855 1.076 0.464 2.623 1.990 0.633 3.832 1.038 3.058 0.948 385 1 1 0 1 1 1 1 1 0 IJ ------1 1 1 1 0 0 IK 6.432 1.856 4.576 0.728 1.100 0.718 1.089 3.433 1.163 0.896 3.279 1.843 1.297 3.392 2.288 1.104 0.861 3.313 2.426 0.887 5.117 1.315 3.944 1.367 154 1 0 1 0 1 - 0 1 0 IL 4.749 1.567 3.182 0.493 0.768 0.493 0.895 2.531 1.070 0.838 3.032 1.427 0.870 2.779 1.982 0.797 0.560 2.582 1.981 0.601 3.693 1.056 3.122 0.723 147 1 0 0 0 0 - 0 1 0 IP 3.699 1.132 2.567 0.416 0.664 0.513 0.751 1.913 0.929 0.769 2.232 1.149 0.718 2.319 1.594 0.725 0.452 - - - 3.008 0.691 2.497 0.573 252 1 1 1 0 1 - 1 1 0 IQ ------1 1 1 - 0 0 IR 4.379 1.297 3.082 0.512 0.773 0.461 0.771 2.468 1.098 0.839 2.757 1.381 0.826 2.698 1.717 0.981 0.447 2.492 1.782 0.710 3.490 0.889 2.834 0.787 168 1 1 1 0 1 - 0 1 0 IS 4.558 1.337 3.221 0.562 0.760 0.565 0.883 2.968 1.282 1.046 3.091 1.591 0.954 2.877 1.795 1.082 0.498 2.529 1.871 0.658 3.587 0.971 2.913 0.754 140 1 1 0 0 1 - 1 - 0 Statistics of the quantitativ e traits AVG 4.750 1.461 3.289 0.527 0.809 0.560 0.905 2.657 1.200 0.977 2.999 1.504 1.016 3.030 1.997 1.034 0.546 2.708 2.004 0.704 3.762 0.991 3.079 0.918 274.48 STD 0.561 0.195 0.432 0.079 0.098 0.081 0.117 0.479 0.204 0.182 0.467 0.228 0.177 0.250 0.205 0.139 0.132 0.254 0.211 0.111 0.468 0.165 0.332 0.166 77.36

371

Appendices Appendix 6.6 Table. Summary of the Shapiro-Wilk normality test results of the quantitative traits measured for the BC population (N = 200) Char. Trait P-value Type of distribution No. 1 Corolla length (cm) 0.64 Normal distribution 2 Undilated tube length (cm) 0.16 Normal distribution 3 Dilated tube length (cm) 0.89 Normal distribution 4 Undilated tube height (cm) 0.02 Nonparametric distribution 5 Dilated tube height (cm) 0.05 No rmal distribution 6 Undilated tube width (cm) 0.04 Nonparametric distribution 7 Dilated tube width (cm) 0.01 Nonparametric distribution 8 Corolla face height (cm) 0.11 Normal distribution 9 Tube opening height (outer) (cm) 0.01 Nonparametric distributi on 10 Tube opening height (inner) (cm) < 0.01 Nonparametric distribution 11 Corolla face width (cm) 0.25 Normal distribution 12 Tube opening width (outer) (cm) 0.03 Nonparametric distribution 13 Tube opening width (inner) (cm) < 0.01 Nonparametric dist ribution 14 Pistil length (cm) 0.26 Normal distribution 15 Ovary length (cm) 0.39 Normal distribution 16 Style length (cm) 0.01 Nonparametric distribution 17 Calyx length (cm) < 0.01 Nonparametric distribution 18 Stamen length (cm) 0.78 Normal distrib ution 19 Filament length (attached part) (cm) 0.71 Normal distribution 20 Filament length (detached part) (cm) < 0.01 Nonparametric distribution 21 Ventral tube length (cm) 0.80 Normal distribution 22 Ventral lobe length (cm) 0.92 Normal distribution 23 Dorsal tube length (cm) 0.28 Normal distribution 24 Dorsal lobe length (cm) 0.06 Normal distribution 25 Time to flowering a (DAS) < 0.01 Nonparametric distribution 31 Time to 1 st leaf initiation b (DAS) < 0.01 Nonparametric distribution a Days to flowering (DAS, days after sowing) b Days to first phyllomorph initiation (DAS, days after sowing)

372

Appendices Appendix 6.7

(a) (b) (c)

Corolla length Undialated tube length Dialated tube length ) ) ) s s l l s l a a a u u u d d i i d i v v i i v i d d d n n i i n i . .

. o o o n n ( ( n

(

y y y c c c n n n e e e u u u q q q e e r r e r F F

0 10 20 30 40 0 10 20 30 40 50 0 10 20 30 40 50 60

3 4 5 6 7 0.5 1.0 1.5 2.0 2.5 3.0 1.0 1.5 2.0 2.5 3.0 3.5 (cm) (cm) (cm) (d) (e) (f)

Undialated tube height Dialated tube height Undialated tube width ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 10 20 30 40 50 0 10 20 30 40 0 10 20 30 40 50

0.4 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 0.4 0.5 0.6 0.7 0.8 0.9 (cm) (cm) (cm) (g) (h) (i)

Dialated tube width Corolla face height TubeOuter opening tube opening height height (outer) ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 20 40 60 80 0 20 40 60 80 100 0 20 40 60 80

0.6 0.8 1.0 1.2 1.4 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 0.5 1.0 1.5 2.0 (cm) (cm) (cm) (j) (k) (l)

TubeInner opening tube height opening (inner) height Corolla face width TubeOuter opening tube widthopening (outer) width ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 20 40 60 80 0 10 20 30 40 50 60 0 20 40 60 80

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 1 2 3 4 5 0.5 1.0 1.5 2.0 2.5 3.0 (cm) (cm) (cm)

373

Appendices

(m) (n) (o) TubeInner opening tube opening width (inner) width Pistil length Ovary length ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 10 20 30 40 50 0 20 40 60 0 20 40 60 80

0.5 1.0 1.5 2.0 2.0 2.5 3.0 3.5 4.0 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 (cm) (cm) (cm) (p) (q) (r) Style length Calyx length Stamen length ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 10 20 30 40 50 60 70 0 20 40 60 0 5 10 15 20 25 30

0.6 0.8 1.0 1.2 1.4 1.6 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2.0 2.5 3.0 3.5 (cm) (cm) (cm) (s) (t) (u)

FilamentFilament lengthlength (attached)(fused) FilamentFilament length length (detached) (free) Ventral tube length ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 10 20 30 0 20 40 60 80 0 20 40 60 80

1.4 1.6 1.8 2.0 2.2 2.4 2.6 0.2 0.4 0.6 0.8 1.0 1.2 2.5 3.0 3.5 4.0 4.5 5.0 5.5 (cm) (cm) (cm) (v) (w) (x) Ventral lobe length Dorsal tube length Dorsal lobe length ) ) ) s s s l l l a a a u u u d d d i i i v v v i i i d d d n n n i i i

. . . o o o n n n ( ( (

y y y c c c n n n e e e u u u q q q e e e r r r F F F

0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 60

0.4 0.6 0.8 1.0 1.2 1.4 1.6 2.0 2.5 3.0 3.5 4.0 4.5 5.0 0.4 0.6 0.8 1.0 1.2 1.4 (cm) (cm) (cm)

374

Appendices

(y) (z) (aa)

(ab) (ac) (ad)

Appendix Figure 6.7 Phenotypic distribution of the traits measured in the BC population ( N = 200). (a) Corolla length. (b) Undilated tube length. (c) Dilated tube length. (d) Undilated tube height. (e) Dilated tube height. (f) Undilated tube width. (g) Dilated tube width. (h) Corolla face height. (i) Tuber opening height, outer. (j) Tube opening height, inner. (k) Corolla face width. (l) Tube opening width, outer. (m) Tube opening width, inner. (n) Pistil length. (o) Ovary length. (p) Style length. (q) Calyx length. (r) Stamen length. (s) Filament length, attached part. (t) Filament length, free part. (u) Ventral tube length. (v) Ventral lobe length. (w) Dorsal tube length. (x) Dorsal lobe length. (y) Flowering time. (z) Pigmentation on lateral lobe. (aa) Pigmentation on ventral lobe. (ab) Yellow spot on ventral corolla tube. (ac) Accessory phyllomorph. (ad) Two macrocotyledons. Blue vertical lines: average trait value of S. rexii . Red vertical lines: average trait value of S. grandis . Purple vertical lines: average trait value of F1 hybrid.

375

Appendices Appendix 6.8 Effect plots of the BTL detected in the mapping rosulate / unifoliate trait. (a) Loci detected in MapA. (b) Loci detected in MapB-1. (c) Loci detected in MapB-3.

376

Appendices

377

Appendices (c)

Method1 Method2 Method3 Method4

LG1 n/a n/a n/a

Effect plot for DN2208 Effect plot for DN2208

1.00

0.95 0.9 0.90

0.8 0.85

LG2 n/a Method2 n/a Method4 0.80 0.7 0.75

0.70 0.6 0.65

b h b h

DN2208 DN2208

Effect plot for BW3112

0.95

0.90

0.85

0.80 LG4 n/a n/a n/a Method4 0.75

0.70

0.65

b h

BW3112

Effect plot for BW1599 Effect plot for ST6585 Effect plot for ST6585 Effect plot for ST6585

0.95 1.0 0.9 0.7 0.90

0.9 0.85 0.6 0.8 0.80

LG14 Method1 0.5 Method2 0.7 Method3 0.8 Method4 0.75

0.70 0.4 0.6 0.7 0.65

b h b h b h b h BW1599 ST6585 ST6585 ST6585

378

Appendices Appendix 6.9 Scanning electron microscopy (SEM) of the parental plants. Used to produce the images in Figure 1.6 and 1.7 Seedlings of S. rexii , S. grandis BC and F1 hybrids were collected at 5 DAU (days after cotyledon unfolding), 20 DAU, 30 DAU, 40 DAU, 50 DAU, 60 DAU, 65 DAU, 70 DAU, 80 DAU, and 90 DAU. In addition, for S. grandis BC the stages 140 DAU and 150 DAU were also collected. The 5 DAU material represented the isocotylous stage; the 20 DAU and 30 DAU material represented the onset of anisocotylous development; the 60 DAU and 65 DAU materials represented the phyllomorph initiation stage in S. rexii and the F1 hybrid plant. Finally, the 140 DAU and 150 DAU samples of S. grandis BC represented the initiation of the inflorescence meristem. The seedling materials of S. grandis F1 were not available at the time of this study. The collected samples were fixed in FAA (50% ethanol, 5% acetic acid, 3.7% formaldehyde). The samples submerged in FAA and in infiltrated in a vacuum chamber overnight, and later transferred to 70% ethanol for long term storage. The samples stored in 70% ethanol were first dehydrated through the liquid substitution process in an ethanol and acetone series (Table 6.2). The samples were then transferred to a K850 critical point drier (Quorum, Lewes, United Kingdom) followed by liquid CO 2 exchange, to replace the acetone with liquid CO 2. The fluid exchange was repeated 10 times, each lasting for 1 minute. The heating system of the K850 machine was then turned on and the temperature inside the CPD raised until the critical point of CO 2 was reached, i.e. at 31 C and 1,071 psi. The chamber was then depressurised at a rate of ~1000 cm 3 per minute, which took about 20 minutes for complete depressurisation.

Table Ethanol and acetone dehydration series of samples prior to critical point drying Solution Incubation time 70% Ethanol Long term storage 95% Ethanol 1 hr 100% Ethanol 1 hr 100% Ethanol 1 hr 100 % Acetone (in molecular sieve) 5 min 100% Acetone (in molecular sieve) 5 min

The critical point dried samples were transferred to SEM stubs covered with carbon conductive tape. The stubs were then sputter coated with platinum particles using the K575X sputter coater (Quorum, Lewes, UK). The following settings for the sputter coater were used: sputter current 25 mA, sputter time 2 min, pump hold disabled. Finally, the sputter coated samples were observed using a Carl Zeiss SUPRA-55VP SEM machine (Carl Zeiss AG, Oberkochen, Germany). Photos were taken at 5 kV and a working distance of approximately 10 – 12 mm via the SmartSEM software (Carl Zeiss AG) integrated in the SEM machine.

379