Resistance in Bread Wheat (Triticum Aestivum L.) Accession Citr 2401

Genetics of Russian wheat aphid (Diuraphis noxia) resistance in bread wheat (Triticum aestivum L.) accession CItr 2401

THANDEKA NOKUTHULA SIKHAKHANE

Submitted in accordance with the requirements for the degree of

MASTER OF SCIENCE

in the subject

LIFE SCIENCES

at the

UNIVERSITY OF SOUTH AFRICA

SUPERVISOR : Prof T.J. Tsilo

CO-SUPERVISOR : Dr V.L. Tolmay

JANUARY 2017

DECLARATION

Name: ______

Student number: ______

Degree: ______

Exact wording of the title of the dissertation or thesis as appearing on the copies submitted for examination:

Genetics of Russian wheat aphid (Diuraphis noxia) resistance in bread wheat (Triticum aestivum L.) accession CItr 2401

I declare that the above dissertation/thesis is my own work and that all the sources that I have used or quoted have been indicated and acknowledged by means of complete references.

______

SIGNATURE DATE STUDENT NUMBER: 57652538

ACKNOWLEDGEMENTS

I would like to extend my sincere appreciation and gratitude to:

 My supervisors, Prof Toi Tsilo and Dr Vicki Tolmay for always being there for me whenever I required assistance, for teaching and grooming me,  The Agricultural Research Council and the National Research Foundation for funding,  Dr Astrid Jankielsohn, for all the RWA information she shared with me and her images and figures she allowed me to use,  Dr Scott Sydenham, for all the molecular study information, practical demonstrations and editing of my writings,  Ms Emma Mollo, Ms Bongiwe Nhlapho and Mr Timmy Baloyi, for their technical assistance,  Ms Juliette Kilian, for her assistance with acquiring literature,  Ms Sandiswa Figlan and Mr Learnmore Mwadzingeni for all their invaluable inputs and support,  Students at the ARC-Small Grain Institute, for all their assistance,  My mentor and dear friend Dr Oluwatosin Ijabadeniyi, his wise advice, continuous encouragement and for always believing in me,  My God-fearing parents, Siphiwe and Zanele Sikhakhane, for believing in me and for all their emotional support and prayers during this project,  Above all, My Lord and Saviour Jesus Christ for giving me the strength, knowledge and understanding, for always being present and for constantly reminding me that through him, all things are possible.

ABSTRACT

The Russian wheat aphid (RWA) (Diuraphis noxia Kurdjumov) is one of the important insect pests of wheat (Triticum aestivum L.), barley (Hordeum vulgare L.) and other grasses. To date, there are four RWA biotypes identified in South Africa. The virulent biotypes emerged, partly due to climate change and new genetic variations within populations of RWA; hence there is a need to improve host-plant resistance, as an effective control measure. Bread wheat (Triticum aestivum L.) accession Cereal Introduction (CItr) 2401 is known to be resistant to all RWA biotypes worldwide. The goal of this study was to use a backcrossed near-isogenic line (NIL) BC5F5 mapping population, developed from a cross between CItr 2401 and susceptible Kavkaz, to identify and validate single nucleotide polymorphism (SNP) markers linked to the resistance phenotype in CItr 2401. This was achieved by (i) conducting a preliminary study that evaluated the suitability of simple sequence repeat (SSR) markers previously reported in literature for discriminating stacked RWA resistance genes and, (ii) employing SNP markers for the first time in a RWA resistance study as a future alternative to the widely used SSR markers. None of the tested SSR markers showed potential use in marker-assisted selection (MAS). The mapping population was phenotypically evaluated for RWA resistance using the four South African biotypes, viz. RWASA1, RWASA2, RWASA3 and RWASA4. Analysis of variance (ANOVA) showed significant (P<0.001) differences of genotypes after confirming the normality of residuals and homogeneity of variance. The Illumina iSelect 9,000 wheat SNP platform was used to genotype the two crossing parents and a selection of 24 NIL genotypes from the mapping population. Eight SNP markers found to be linked to the phenotype were converted to breeder-friendly and high-throughput Kompetitive allele-specific polymerase chain reaction (KASP) markers. The designed KASP markers were validated on the two crossing parents, the 24 NIL sent for SNP genotyping, on the mapping population and on the preliminary study genotypes for their effectiveness. The KASP assays developed in this study will be useful for stacking the RWA resistance from CItr 2401 with other Dn genes effective against the RWA.

Keywords: gene stacking, genotyping, KASP assay, linkage mapping, resistance, Russian wheat aphid, sequencing, simple sequence repeats, single nucleotide polymorphism, wheat

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TABLE OF CONTENTS

DECLARATION ...... i

ACKNOWLEDGEMENTS ...... ii

ABSTRACT ...... iii

LIST OF FIGURES ...... vi

LIST OF TABLES ...... viii

LIST OF APPENDICES ...... viii

LIST OF ABBREVIATIONS AND ACRONYMS...... ix

CHAPTER 1 ...... 1

INTRODUCTION ...... 1 1. Introduction ...... 1 2. Motivation of the study ...... 4 3. Aim and Objectives of the study ...... 5 4. Dissertation Outline ...... 5

CHAPTER 2 ...... 7

LITERATURE REVIEW ...... 7 2.1 Russian wheat aphid ...... 7 2.1.1 Brief background ...... 7 2.1.2 RWA feeding and symptoms ...... 9 2.1.3 RWA distribution in different areas of the world ...... 9 2.1.4 Types of host-plant resistance mechanisms ...... 11 2.1.5 Resistance genes for RWA control ...... 15 2.2 Wheat ...... 16 2.2.1 Brief background ...... 16 2.2.2 Uses, health benefits and side effects of wheat consumption ...... 17 2.2.3 Climatic preferences of wheat ...... 18 2.2.4 Bread wheat (T. aestivum L.) production ...... 18 2.2.5 Genomics of T. aestivum L...... 21 2.3 Sequencing ...... 22 2.3.1 DNA sequencing ...... 22 2.3.2 Next-generation sequencing ...... 23 2.3.3 Genotyping-by-Sequencing (GBS) ...... 25

2.4 Molecular markers ...... 26 2.4.1 Brief background ...... 26 2.4.2 Microsatellite markers ...... 27 2.4.3 SNP markers ...... 27 2.4.4 Genetic Mapping ...... 29 2.4.5 Application of molecular markers using MAS in wheat research ...... 30 Conclusion of Chapter ...... 32

CHAPTER 3 ...... 33

METHODOLOGY ...... 33 3. Chapter outline ...... 33 3.1 Preliminary study ...... 33 3.1.1 Plant Material ...... 33 3.1.2 Phenotypic screening ...... 33 3.1.3 Molecular screening ...... 37 3.1.3.1 DNA isolation ...... 37 3.1.3.2 SSR marker analysis ...... 38 3.1.3.3 Gel electrophoresis ...... 38 3.2 Research study ...... 41 3.2.1 Plant material ...... 41 3.2.2 Aphid colonies used in the study ...... 42 3.2.3 Phenotypic screening ...... 42 3.2.4 Molecular screening ...... 42 3.2.4.1 DNA isolation ...... 42 3.2.4.2 SSR marker analysis ...... 43 3.2.4.3 Gel electrophoresis ...... 43 3.3 9K SNP array genotyping ...... 43 3.4 Data analysis ...... 44 3.5 Linkage map construction ...... 45 3.6 Designing of Kompetitive allele specific PCR (KASP) assays ...... 45 3.7 KASP assay validation ...... 46

CHAPTER 4 ...... 47

RESULTS AND DISCUSSION...... 47 4. Chapter outline ...... 47 4.1 Preliminary study ...... 47 4.1.1 Results and Discussion ...... 47

Preliminary study conclusion...... 52 4.2 Research study ...... 52 4.2.1 Results and Discussion ...... 52 Research study discussion and conclusion ...... 68

CHAPTER 5 ...... 71

CONCLUSION ...... 71 5. Chapter outline ...... 71 5.1 Final Conclusions ...... 71 5.2 Limitations and Recommendations ...... 72 5.3 Suggestions for Future work ...... 72 5.4 Contribution of the study ...... 72

REFERENCES ...... 74

APPENDICES ...... 97

LIST OF FIGURES

Figure 2.1 The Russian wheat aphid ...... 7

Figure 2.2 Alternate hosts for the RWA in S.A...... 8

Figure 2.3 Susceptible symptoms of RWA infested wheat ...... 10 Figure 2.4 Comparison of the percentages of each biotype in the total biotype complex from 2009 to 2015 in S.A...... 10 Figure 2.5 Distribution of Russian wheat aphid biotypes in the Summer Rainfall region (Free State) during 2013 (A) and 2015 (B) ...... 12 Figure 2.6 Distribution of Russian wheat aphid biotypes in the Winter Rainfall region (Western Cape) during 2013 (A) and 2015 (B) ...... 13

Figure 2.7 Wheat production figures per production area over seasons ...... 14

Figure 2.8 Area planted per production area over seasons ...... 14

Figure 2.9 Wheat heads and grains ...... 19

Figure 2.10 South African wheat imports and exports for the 2015/2016 season...... 20

Figure 2.11 South African wheat exports for the 2015/2016 season...... 20

Figure 3.1 98-cone seedling trays filled with Professional Potting Mix® displaying a randomised complete block design planting plan...... 35

Figure 3.2 Damage scale for phenotypic analysis ...... 36

Figure 3.3 Controls displaying different phenotypes ...... 37

Figure 3.4 Leaf material collection...... 39

Figure 3.5 Conducting gel electrophoresis at the ARC-SGI gel laboratory ...... 39

Figure 3.6 Schematic diagrams of how the BC3F5 and BC5F5 mapping populations were created using the backcrossing and selfing method...... 41

Figure 3.7 Mean phenotypic scores of genotypes taken for the 9K SNP genotyping ...... 44

Figure 3.8 The Qubit® 2.0 Fluorometer used at ARC-BTP during DNA quantification ... 44 Figure 3.9 Real-Time PCR used in KASP marker validation at the ARC-SGI PCR laboratory ...... 46

Figure 4.1 Histogram of acquired scores for the four biotypes ...... 53 Figure 4.2 Scatter-plot showing the means of entries at different biotypes……………….54 Figure 4.3 Residuals scores: (A) Normality plot (B) Homogeneity plot………………….54 Figure 4.4 Gel photographs displaying the results for each tested SSR marker...... …...55 Figure 4.5 A demonstration of how the input data appeared in Tassel 4.3.15…………….56 Figure 4.6 LD measurements (R2, above the diagonal line) and probability value (P, below the diagonal line) for 618 SNP markers…………………………………………………...57 Figure 4.7 Cladogram showing the relationship between the different genotypes to the parents CItr 2401 and Kavkaz……………………………………………………………..58 Figure 4.8 The 8 linkage groups acquired from linkage mapping………………………...60 Figure 4.9 The linkage group eight displaying a single QTL……………………………..61 Figure 4.10 Dual colour scatter plot showing different clusters for the eight KASP markers on the two parental lines…………………………………………………………………...66 Figure 4.11 Dual colour scatter plot showing different clusters for three KASP markers on the 24 genotyped NIL……………………………………………………………………...67 Figure 4.12 Sequence alignments between the 9K SNP assay and KASP genotyping for the 24 NIL………………………………………………………………………………...... 68

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LIST OF TABLES

Table 2.1 Currently known South African RWA biotypes and the genes that offer effective and ineffective resistance against them ...... 16

Table 2.2 Comparison of SSR and SNP markers ...... 29

Table 3.1 Genotypes used in the study, their pedigree and gene information ...... 34 Table 3.2 Differential checks used in the study, their genes and reaction to the four South African RWA biotypes ...... 36

Table 3.3 Four SSR markers located on wheat chromosome 7D used in the study ...... 40 Table 3.4 Differential checks used in the study, their genes and reactions to the four South African RWA biotypes ...... 42

Table 4.1 Genotype ranking based on the multiple t-distribution test for the 27 tested genotypes ...... 49 Table 4.2 Phenotypic scores and gel fragment sizes (bp) corresponding to each plant score………………………………………………………………………………………..51 Table 4.3 KASP assay markers used to validate the result ...... 62

LIST OF APPENDICES

Appendix 1 Ethics approval letter…………………………………………………………97

Appendix 2 Genotypes of the BC5F3 and BC5F5 mapping populations, RWA biotypes they were tested with and their phenotypic data ……………………………………………….98 Appendix 3 Photographs of plant symptoms, scores and gels for the 24 accessions and the 3 checks ………………………………………………...………………………………..101

Appendix 4 Summary statistics of the four RWA biotypes following ANOVA ……………………………………………………………….…………………………...107

Appendix 5 Histogram for scores………………………………………………………...108

Appendix 6 The 178 SNP markers used in linkage mapping with their relevant information…………………………………………………...………………………...... 110

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Appendix 7 List of publications and conference presentations………………………...... 116

LIST OF ABBREVIATIONS AND ACRONYMS

ANOVA Analysis of Variance

AFLP Amplified fragment length polymorphism

ARC-BTP Agricultural Research Council-Biotechnology Platform

ARC-SGI Agricultural Research Council-Small Grain Institute

BC Backcross bp Base pair(s)

CIMMYT International Maize and Wheat Improvement Center circa Approximately

CItr 2401 Cereal Introduction 2401 cM Centimorgan(s)

DH Double Haploid

Dn Diuraphis noxia

DNA Deoxyribonucleic Acid

EDTA Ethylene-diaminetetraacetate

FAO Food and Agriculture Organization

FAOSTAT Food and Agriculture Organization Statistics

F1, F2, F3, F5 Filial 1, Filial 2, Filial 3, Filial 5 generations

Gbp Giga-base pair

GBS Genotyping-by-Sequencing

GWAS Genome-Wide Association

IWGSC International Wheat Genome Sequencing Consortium

KASP Kompetitive Allele-Specific polymerase chain reaction

LD Linkage Disequilibrium

LOD Logarithm of Odds

LSD Least Significant Difference

MAS Marker Assisted Selection ml Millilitre(s) mm Millimetre(s)

NAM Nested Association Mapping ng Nano gram(s)

NGS Next-generation Sequencing

NIL Near-isogenic Line(s) nm Nanometre(s)

PAGE Polyacrylamide Gel Electrophoresis

PCR Polymerase Chain Reaction pH Power of Hydrogen

QTL Quantitative Trait Loci

R Rand(s)

RAPD Random Amplified Polymorphic DNA

RFLP Restriction Fragment Length Polymorphism(s)

RIL Recombinant Inbred Line(s)

RWA Russian wheat aphid(s)

S.A. South Africa

SAGL Southern African Grain Laboratory

SNP Single Nucleotide Polymorphism(s)

SSR Simple Sequence Repeat(s)

TASSEL Trait Analysis by Association, Evolution and Linkage

TBE Tris-borate-EDTA

TE Tris-Cl/EDTA

UNISA University of South Africa

U.S. United States

USDA United States Department of Agriculture

V Volt(s) viz. Namely v/v Volume per volume w/v Weight per volume

°C Degrees Celsius

μg Microgram(s)

μl Microlitre(s)

μM Micromolar(s)

CHAPTER 1

INTRODUCTION

1. Introduction

Wheat (Triticum spp.) is one of the most important staple food crops and widely grown worldwide (Food and Agriculture Organization-FAO, 2016). In South Africa (S.A.), wheat is second on the list of most grown cereals after maize (FAO Statistics-FAOSTAT, 2016). Its production levels in the country however, have been fluctuating resulting in high dependency on imports (FAOSTAT, 2016). The fluctuations have even resulted in a decrease of the area planted to this important cereal due to uncertainties experienced by farmers and the wheat industry as a whole (United States Department of Agriculture-USDA, 2016). Different biotic and abiotic factors such as pests and diseases as well as adverse weather conditions, make it difficult to achieve sustainable yields and quality of wheat. The current statistics of wheat production by the USDA (2016) in S.A. nevertheless, are hopeful of the possibility of increasing the production rate once again. The production has increased from 1.457 million tonnes in 2015 to 1,757 million tonnes in 2016 (USDA, 2016). However, this is still below the expected South African commercial production of approximately 1.766 million tonnes (Department of Agriculture, Forestry and Fisheries, Republic of South Africa, 2016). Therefore, the continuation of wheat research and improvement by researchers and breeders is important in offering new effective ways of safeguarding this important cereal from the devastating effects of pathogens and pests such as the Russian wheat aphid (RWA).

The RWA, Diuraphis noxia (Kurdjumov) (Homoptera: Aphididae) is an important global pest of wheat, barley and other small grains. It has been present in wheat and barley producing areas for over a century. The RWA was first discovered in Russia around 1912 (Grossheim, 1914). Later on, it was reported in most wheat and barley producing areas of the world including S.A. and Texas where it was first reported in 1978 and 1986, respectively (Walters, 1984; Webster et al., 1994). Most recently, the aphid was reported in parts of Australia (Agriculture Victoria, 2016; Department of Agriculture and Food-Western Australia, 2016) where it had not been a threat before. RWA feeding has caused significant yield and economic losses in wheat-producing areas that lack adequate management of its infestation. In S.A., RWA control has been achieved through the use of either insecticides or host plant resistance in addition to other less used control measures such as ecological and biological 1

control. Insecticides are chemical agents that can be effective in combating RWAs. However, due to the ability of the aphids to hide inside rolled leaves, contact insecticides often fail and systemic insecticides become a better option. Despite the effectiveness of the use of chemicals, their use is not promoted since they are associated with several disadvantages that include; loss of efficacy due to pesticide resistance build up among the aphid species, the high costs of chemical pesticides and their harmful nature to plants, humans and the environment. Therefore, breeding resistant cultivars remains the best and long-term option for effective aphid management.

In S.A. to date, as an alternative to chemical use, RWA control has been through conventional breeding methods, through breeding using phenotypic screening (Tolmay et al., 2012). This has seen the release of more than 27 wheat cultivars (with varying resistance/susceptibility to the four RWA biotypes) for cultivation over the years (Tolmay and van Deventer, 2005; Tolmay et al., 2007; Burger and Killian, 2016a,b). Recently, more efforts have been placed in using molecular breeding methods for host plant resistance through marker-assisted selection (MAS). MAS allows for the genotypic screening of resistance in instances when it is undesirable, expensive or inefficient to carryout phenotypic screenings (Collard and Mackill, 2008; Xu and Crouch, 2008). MAS also allows for the stacking of genes thus creating durable resistance to several different pests through direct selection for desirable traits (Melchinger, 1990; Liu et al., 2002). For effective use in MAS, molecular markers should be robust with tight linkage and/or closely flanking the gene of interest, inexpensive, have high levels of polymorphism, and allow for high-throughput detection (Collard and Mackill, 2008; Paux et al., 2010). Identification of RWA resistance using molecular markers has primarily been with the aid of simple sequence repeats (SSR). These include Xgwm111 (Liu et al., 2001, 2002), Xgwm44 (Liu et al., 2001, 2002), Xgwm437 (Liu et al., 2001), Xgwm642 (Liu et al., 2001) and Xgwm635 (Liu et al., 2001) among others. The continuing emergence of resistance- breaking biotypes (Jankielsohn, 2014; Puterka et al., 2014) necessitates the development of cultivars with multiple genes in order to attain durable resistance.

Du Toit (1987) was the first to report genetic resistance to the aphid in two hard white bread wheat accessions. This accomplishment led to researchers in the RWA affected areas to search and identify other effective sources of resistance. A number of these genes, termed Diuraphis noxia (Dn) genes, have been found and characterized. The Dn1 and Dn2 genes were the first to be reported in S.A. by Du Toit (1987, 1988, 1989). Currently, numerous Dn genes available have been characterized and genetically mapped on either the 1D or 7D wheat

chromosome. The currently known genes include; Dn1 to Dn9, Dnx, Dny, Dn2414, Dn626580, and Dn2401 (Du Toit, 1987, 1988, 1989; Nkongolo et al., 1991a,b; Marais and Du Toit, 1993; Schroeder-Teeter et al., 1993; Marais et al., 1994; Du Toit et al., 1995; Saidi and Quick, 1996; Ma et al., 1998; Liu et al., 2001, 2002, 2005; Miller et al., 2001; Peng et al., 2007; Valdez et al., 2012; Fazel-Najafabadi et al., 2015). Some of these genes can be found stacked together in certain wheat cultivars, making their resistance even better. Tolmay et al. (2016) recently explored the possibility of combining/stacking different resistance genes for different traits (RWA and rust resistance) into one cultivar in order to get an improved crop with multi-trait resistance. Wheat cultivars with such combinations are very appealing to wheat breeders and farmers who are interested in those specific traits.

The focus of this study is the resistance gene found in the Tajikistan wheat accession CItr 2401. Resistance conferred by CItr 2401 is said to consist of two resistance (R) genes, Dn2401, which has been the main research focus for RWA resistance studies and another gene which is reportedly allelic to Dn4 (Dong et al., 1997; Collins et al., 2005; Voothuluru et al., 2006). Dn2401 is currently reported to be resistant to all known RWA biotypes in the world (Weiland et al., 2008). However, this valuable gene cannot be effectively used in RWA pre- breeding and breeding programs as there might be a risk of resistance-breaking biotypes development against it. For good gene stewardship, Dn2401 has to be deployed in combination with other effective RWA resistance genes. This requires the use of diagnostic markers, which are currently not available for RWA resistance. Different studies conducted around the world have mapped Dn2401 and Dn4 on the 7DS and 1DS wheat chromosomes, respectively (Nkongolo et al., 1991b; Ma et al., 1998; Fazel-Najafabadi et al., 2015; Staňková et al., 2015) but there are still no diagnostic markers available for them yet. In S.A., CItr 2401 resistance has not yet been mapped, presenting a huge research and knowledge gap in local RWA resistance improvement.

This study aims: (i) to conduct a preliminary study which evaluates the discriminatory ability and accuracy of SSR markers previously reported in literature for identifying stacked RWA resistance genes on genotypes utilised in the Agricultural Research Council- Small Grain Institute (ARC-SGI, S.A.) pre-breeding programme and, (ii) to develop and validate Kompetitive Allele-Specific Polymerase chain reaction (KASP) markers from single nucleotide polymorphisms (SNP) that are linked to the resistance phenotype of a backcrossed mapping population. The developed markers would then be used in future studies aimed at

improving RWA resistance mechanisms in S.A. and globally thereby minimizing the costly phenotypic (conventional) evaluation for this important trait.

2. Motivation of the study

This study forms part of a bigger research project on RWA pre-breeding at the ARC- SGI. Currently, research on RWA resistance employs conventional pre-breeding and breeding methods. Efforts are being made however, to implement molecular methods such as marker- assisted selection (MAS) in RWA resistance research. The RWA was chosen for this study because it is a global pest of important cereal crops and continues to evolve and adapt in various environments despite all the research efforts being made. The aphid poses a huge threat to the wheat industry: the producers, millers, bakers, retailers and consumers. Damage and losses of susceptible wheat cultivars is caused by different biotypes that have been identified in the different wheat-producing areas of the world. In S.A., there are currently four known RWA biotypes that have caused great yield and economic losses. As much as the currently available control methods (insecticides and host plant resistance) have been successful in many instances, the biotypes continue to evolve and acquire resistance to the methods. It is therefore important for researchers, pre-breeders and breeders to continuously work together on finding and deploying new sources of RWA resistance.

This study is aimed at assisting the wheat industry by contributing to the current ongoing research in wheat genetics and genomics. Wheat is an important agricultural crop as it provides important dietary nutrients required by the human body for a healthy well-being. The crop is also one of the important agricultural feeds for livestock. Therefore, wheat production in the world should always be kept high, not only to keep-up with the growing human population, but also to ensure the security of the wheat import and export business. In S.A, improvement of wheat is important to improve food security and reduce overreliance on imports that might become problematic as the global demand for wheat rises. With the addition of this research to the other current research, wheat yields, quality and economic revenues of the world are anticipated to be improved.

3. Aim and Objectives of the study

Aim:

To contribute to Russian wheat aphid resistance improvement in wheat through the designing and validation of KASP markers using a BC5F5 NIL mapping population developed from a CItr 2401 and Kavkaz crossing.

Objectives:

1. To conduct a preliminary study that evaluates the discriminatory ability and accuracy of SSR markers previously reported in literature for identifying stacked RWA resistance genes.

2. To screen the BC5F3 and BC5F5 NILs mapping populations for resistance to four RWA South African biotypes.

3. To genotype a representation of the BC5F5 mapping population using SSR markers and the 9K Illumina Infinium SNP iSelect assay.

4. To analyse the phenotypic and genotypic data with GenStat® for Windows™ 15th Edition, Plink 1.9, Tassel 4.3.15 and QTL IciMapping Version 4.0 software’s.

5. To identify SNP markers linked to the resistance phenotype and to design KASP assays from them followed by their validation on various genotypes.

4. Dissertation Outline

The outline of this dissertation consists of five chapters. The content of each chapter is as follows:

Chapter 1 is an introductory chapter that gives the background to the research. The chapter also states the research motivation as well as the aim and objectives of the study.

Chapter 2 contains the literature relevant to the study. This chapter has sections on the Russian wheat aphid’s feeding, distribution and control strategies, among others, as well as information pertaining to wheat such as its health benefits and climatic preferences. The tools and technologies used throughout the study are also reviewed. Asterisks are used to separate the different sections in this chapter.

Chapter 3 gives the exact procedures and materials used in conducting the study; this includes phenotypic and genotypic analysis studies followed by data analysis. Since the chapter is also comprised of different research analyses, it will be clearly separated by asterisks as well.

Chapter 4 encompasses of all the phenotypic and genotypic results acquired from the conducted analyses. These results are also discussed in this chapter.

Chapter 5 is the last chapter of the dissertation and is composed of the general conclusion, limitations and recommendations, as well as suggestions for future studies.

All the references cited in this study can be found in the reference list presented after chapter 5.

Appendices are placed at the end of the dissertation.

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CHAPTER 2

LITERATURE REVIEW

2.1 Russian wheat aphid 2.1.1 Brief background

The Russian wheat aphid (RWA) is a small, green, about 2 mm long spindle-shaped insect pest with very short antennae and a double tail (Figure 2.1). It is a pest of wheat and barley (Hordeum vulgare L.) and occurs on other small grains such as volunteer wheat, oats, barley and rye (Figure 2.2). The aphid also occurs on wild grasses such as rescue grass, false barley and wild oats (Jankielsohn, 2014). These volunteer plants act as hosts for the aphids during the off-seasons of wheat (Walters et al., 1980) and provide them with nutrients that sustain them during those periods.

Figure 2.1 The Russian wheat aphid (Source: Dr Astrid Jankielsohn, ARC-SGI)

Figure 2.2 Alternate hosts for the RWA in S.A.: (A) Volunteer wheat (B) Oats (C) Barley (D) Rye (Source: Dr Astrid Jankielsohn)

The RWA populations in the United States (U.S.) consist of parthenogenic females that give birth on cultivated wheat, barley and other alternative hosts. Temperatures below -2°C hinder the aphid’s ability to continue producing parthenogenically. The RWA maturation depends highly on the temperature of their environment. All RWA prefer cooler temperatures between 18-22°C to favourably produce, temperatures above 25°C usually slows maturation and decreases the reproductive rate. The aphids use the cereals as a food source during the reproductive stage. In South Africa (S.A.), the RWA is also adapted to dry climatic conditions and cool temperatures of 25°C and below and does not grow well above 25°C (Aalbersberg et al., 1987). In addition, no eggs are involved in RWA reproduction in S.A., there is only parthenogenic reproduction as far as it is known (Dr Vicki Tolmay, personal communication1).

1 Dr Vicki Tolmay, Germplasm Development: Senior Researcher, ARC-Small Grain Institute, Private Bag X29 Bethlehem 9700. E-mail: [email protected] 8

2.1.2 RWA feeding and symptoms

Russian wheat aphid feeding can lead to death of susceptible plants if the infestation is not being effectively controlled (Deol et al., 2001). RWA are phytotoxic insects as they cause damage to plants by feeding and injecting toxins that break down plant chloroplasts (Voothuluru et al., 2006). The aphids damage plants directly by sucking the sap from vascular tissues of host plants. Damage symptoms caused by the aphid can be easily seen on susceptible hosts. There is a slight difference in visible symptoms portrayed by young and mature plants. Younger plants show susceptibility by remaining stunted and their leaves rolling tightly closed. Mature plants on the other hand, show susceptibility by developing longitudinal, white or pale yellow streaks, which can turn purple during cold conditions. They also form tightly rolled leaves and trapped heads. Susceptible plants also lose photosynthetic efficiency and increased sensitivity to environmental stresses (Jyoti et al., 2006; Voothuluru et al., 2006). Figure 2.3 shows some of the typical symptoms of mature plants that include; the development of white, yellow, or purple longitudinal streaks on the leaves and stems; rolled leaves and trapped heads. The resistant plants, on the other hand, only form small white or yellow spots and blotches on the leaves and the leaves do not roll as tight as with the susceptible plants.

2.1.3 RWA distribution in different areas of the world

Reports by Durr (1983), Hewitt et al. (1984) and Dolatii et al. (2005) state the RWA was initially endemic to southern Russia, central Asia, Iran, Afghanistan and the countries bordering the Mediterranean Sea. However, the aphid is now found in most major small grain producing areas of the world, with the recent report in South Australia and Victoria where it had not been detected before (Department of Agriculture and Food-Western Australia, 2016). In S.A, the four known RWA biotypes (viz. RWASA1, RWASA2, RWASA3 and RWASA4) are pests both in the summer and winter rainfall regions with the summer region being the most affected. Initially, the distribution was confined to the Bethlehem area in the Eastern Free State but by 1979, the RWA had spread to other wheat producing provinces in the country (Walters et al., 1980). Figure 2.4 shows the levels of occurrences of the different biotypes in S.A. for the past six years in the biotype complex. RWASA2 was initially the most prevalent biotype in the years 2009 and 2010 but its dominance decreased during 2012. On the other hand, RWASA3 had the lowest incidence during 2009 but its incidence increased in the year 2012.

Figure 2.3 Susceptible symptoms of RWA infested wheat: (A) Purple streaking (B) Head trapping (C & D) Chlorosis (E) Leaf rolling (Source: Dr A. Jankielsohn)

Figure 2.4 Comparison of the percentages of each biotype in the total biotype complex from 2009 to 2015 in S.A. (Source: Dr A. Jankielsohn)

The 2013 and 2015 report of aphid distribution in the major wheat-producing provinces (Free State and Western Cape) in S.A. is demonstrated in Figures 2.5 and 2.6. Comparison of the two seasons shows that aphid distribution in the country is variable. In 2013, all the biotypes were present in high numbers in the summer rainfall region (Free State), but in 2015, RWASA1 and RWASA4 were the most prevalent. In the winter rainfall region (Western Cape), RWASA1 was the prevalent biotype in 2013, with the other three biotypes appearing in lower numbers. In 2015 however, the Western Cape had only one biotype, RWASA1 (Dr

Astrid Jankielsohn, personal communication2). These findings demonstrate the importance for researchers to not base their conclusions or assumptions about the RWA on results from single seasons. This is due to the dynamic nature of the RWA biotype complex as influenced by various environmental factors that include; host plants, altitude and climate thus making the RWA complex prone to diversification (Jankielsohn, 2016). In S.A., the prevalence of the aphid is currently lower than the previous years, but this is partly due to climatic change and a decrease in the area planted to wheat (United States Department of Agriculture-USDA, 2016). The Southern African Grain Laboratory (SAGL)’s wheat production report for 2015/2016 further shows this decline on their graphs (Figures 2.7 and 2.8) for area planted to wheat from 2006/2007 to 2015/2016 (SAGL, 2016). Hence, there is a need for continued research on wheat and all other wheat stressors that inhibit healthy growth and development.

2.1.4 Types of host-plant resistance mechanisms

Host-plant resistance mechanisms play a crucial role in determining whether a plant can cope with and withstand infestation by damaging insect pests. Host-plant resistance was first described by Painter (1951) using three functional categories: antibiosis, antixenosis and tolerance. Antibiosis is described as the negative effect a plant has on the biology of an insect attempting to use that plant as a host (Smith, 2005). The insect normally results in having a reduced body size and mass and it does not fully develop into the mature stage. Antixenosis or non-preference occurs when the plant acts as an unfavourable host to the insect, which results in reduced colonisation by the insect, thus reducing yield losses (Pedigo, 1999; Smith, 2005). Tolerance is the ability of the plant to withstand and/or recover from damage caused by the insect. These resistance mechanisms are desirable when breeding resistant cultivars as they delay the need for chemicals that have negative effects on the environment and other beneficial insects (Pedigo, 1999; Smith, 2005).

2 Dr Astrid Jankielsohn, Crop Protection: Entomologist, ARC-Small Grain Institute, Private Bag X29 Bethlehem 9700. E-mail: [email protected] 11

Figure 2.5 Distribution of Russian wheat aphid biotypes in the Summer Rainfall region (Free State) during 2013 (A) and 2015 (B) (Map by Dr A. Jankielsohn)

Figure 2.6 Distribution of Russian wheat aphid biotypes in the Winter Rainfall region (Western Cape) during 2013 (A) and 2015 (B) (Map by Dr A. Jankielsohn)

Figure 2.7 Wheat production figures per production area over seasons (Source: SAGL, 2016)

Figure 2.8 Area planted per production area over seasons (Source: SAGL, 2016)

Between antibiosis and antixenosis, antixenosis is the better choice as it does not interfere with the insect’s development and functioning but rather creates an unfavourable environment for the aphid to colonise. Interfering with the aphids’ development and functioning makes it more possible for the aphid to develop resistance, which will therefore make it difficult to control. The three resistance mechanisms are however mostly found working in conjunction with each other, as seen recently by Adendorff et al. (2016), thereby ensuring effective control of the aphids. 14

2.1.5 Resistance genes for RWA control

Initially, the most successful form of control against RWA was the use of insecticides (aphicides) but because of environmental concerns and negative effects on beneficial organisms, such as, pollinators, decomposers and insect predators, alternative means of insect control were developed (Chagnon et al., 2015). The high costs of these chemicals, especially for smallholder farmers, is another factor contributing to a decreased interest in their promotion and use. Breeding for genetic resistance has been an effective alternative and is currently extensively used. A number of genes for resistance have been introduced into the wheat genetic pool to manage infestation by aphids.

The first genetic resistance to RWA in wheat was discovered and reported by Du Toit (1987, 1988, 1989) in two hard white bread wheat accessions. The first RWA-resistant cultivar, TugelaDn, which was postulated to contain the Dn1 resistance gene, was released in 1992 (Van Niekerk, 2001). Following that success, several Dn resistance genes were found and reported on different wheat relatives (Marais et al., 1994; Liu et al., 2001, 2002; Smith et al., 2004). In the U.S., the first RWA-resistant wheat cultivar, ‘Halt’, was released in Colorado in 1994 (Quick et al., 1996) and it showed high production yields. Thereafter, the RWA-resistant wheat cultivar, ‘Stanton’, was developed and released in Kansas by Martin et al. (2001). Several other resistant wheat cultivars that are being used in the battle against the different RWA biotypes have been developed mainly in S.A. and the Unites States.

In S.A., there are currently four known RWA biotypes that are virulent to different RWA resistance (Dn) genes (Table 2.1). RWASA1 is virulent to fewer resistance genes while RWASA4 is virulent to more. Certain reports in the U.S. however, have stated their RWA2 as being the most devastating biotype of the four, in both barley and wheat (Weiland et al., 2008; Jimoh et al., 2011; Puterka et al., 2013). In S.A. however, RWASA3 and RWASA4 have been the most virulent (in terms of the number of resistance genes they overcome; Table 2.1) and damaging out of the four biotypes. This can be partly attributed to their high population growth rates compared to the other two biotypes (Dr A. Jankielsohn, personal communication2). Nonetheless, effective resistance and moderate resistance genes against all the S.A. biotypes are available (Table 2.1) such as Dn6, Dn7, Dnx and Dn2401, which currently confer resistance to all four known biotypes.

Table 2.1 Currently known South African RWA biotypes and the genes that offer effective and ineffective resistance against them (Adapted from Jankielsohn, 2014, 2016) Effective Ineffective Biotypes 1st Occurrence References resistance genes resistance genes RWASA1 1978 Dn1, Dn2, Dn4, Dn3 Walters (1984); Dn5, Dn6, Dn7, Jankielsohn (2016) Dn8, Dn9, Dnx, Dny, Dn2401

RWASA2 2005 Dn4, Dn5, Dn6, Dn1, Dn2, Dn3, Tolmay et al. Dn7, Dnx, Dny, Dn8, Dn9 (2007); Dn2401 Jankielsohn (2016)

RWASA3 2009 Dn5, Dn6, Dn7, Dn1, Dn2, Dn3, Jankielsohn Dnx, Dn2401 Dn4, Dn8, Dn9, (2011); Dny Jankielsohn (2016)

RWASA4 2011 Dn6, Dn7, Dnx, Dn1, Dn2, Dn3, Jankielsohn Dn2401 Dn4, Dn5, Dn8, (2014); Dn9, Dny Jankielsohn (2016)

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2.2 Wheat 2.2.1 Brief background

Cereals are important since they form an integral part of the human diet. These crops supply humans with essential nutrients required for healthy growth and development. With global efforts being placed on hunger and malnutrition reduction, especially in developing countries, cereal improvement is fundamental. Futhermore, with projections of an increase in the human population of 9.7 billion by 2050 (United Nations Department of Economic and Social Affairs, 2015), the demand for edible cereals such as wheat (Triticum aestivum L.) and maize (Zea mays L.) is highly expected to increase.

Wheat refers to any of the several species of cereal grasses from the Triticum genus in the family Poaceae (Gramineae) and their edible grains (Briggle and Reitz, 1963). Wheat was domesticated in the Fertile Crescent (Zohary et al., 2000) more than 10 000 years ago when

man started moving towards agricultural practices. Since then, wheat has been serving as a staple food that provides important nutrients to many people around the world (FAO, 2016). There are different types or species of wheat available, each with different diverse uses. Common wheat (Triticum aestivum L.), usually called “bread wheat” is used in the flour/bread making industries, while Durum wheat (T. turgidum subsp. durum) is used for making pasta (such as spaghetti and macaroni) and couscous preparations. There is also club wheat (T. aestivum subsp. compactum) which is a softer type, used for cakes, crackers, cookies, pastries, and flours. The uses of wheat are not only limited to food-making as some wheat varieties are used by industry to produce commodities such as starch, paste, malt, dextrose, gluten and even animal feed (Gibson and Benson, 2002).

2.2.2 Uses, health benefits and side effects of wheat consumption

The components that make wheat suitable in food and non-food products as well as in industrial applications, is the gluten proteins and starch it is composed of. Some people however, are allergic to these proteins, making wheat unsuitable for them (Food Allergy Research and Education, 2016). Other people are gluten intolerant, otherwise known as celiac disease or celiac sprue. This disease affects the small intestine and is caused by an abnormal immune reaction to gluten. Medical doctors (specifically gastroenterologists) diagnose it as a digestive disease that can cause serious complications, including malnutrition and intestinal damage, if left untreated. Individuals with celiac disease have to avoid gluten that is found in wheat, rye, barley and sometimes oats (Food Allergy Research and Education, 2016). The uses of wheat depend on the type of wheat being cultivated. Hard wheat such as T. turgidum subsp. durum is mainly used for making pasta products such as spaghetti and macaroni. Another type of wheat is the white- and soft-wheat that consists of paler and starchy kernels as opposed to the hardy kernels of T. durum wheat. Bread wheat falls under this type and this flour is good for preparing bread, piecrusts, biscuits and breakfast cereals. Wheat is also widely used in the brewery industry for the production of beers and whiskey. The wheat grain residue, which is formed after milling practices, is used as a valuable source of animal feed (Day et al., 2006; Agricultural Utilization Research Institute, 2012).

Wheat has many health benefits, as it is rich in essential nutrients. Benefits include; controlling obesity (especially in women), improving body metabolism, preventing Type 2 diabetes, reducing chronic inflammation, preventing gallstones, assuring a healthy lifestyle (whole grain wheat), promoting women’s gastrointestinal health, protection against breast

cancer, preventing childhood asthma, protection against coronary diseases, improving cardiovascular system in postmenopausal women and preventing heart attack (Anderson et al., 1994, 2009). The American Journal of Clinical Nutrition conducted various research studies that showed wheat as a good choice for obese patients wanting to lose weight (Klesges et al., 1991; Davy et al., 2002). The American Journal of Gastroenterology (McRorie et al., 2000) conducted various surveys that proved that wheat helps women avoid gallstones. Another study conducted by the Journal of Allergy and Clinical Immunology, wheat was shown to contribute in reducing childhood asthma (Bunyavanich et al., 2014). These are just some of the studies that have been conducted in order to uncover the health benefits of wheat.

2.2.3 Climatic preferences of wheat

Wheat is grown under different climatic conditions depending on the preference of a particular cultivar. Wheat can be grown under a varied range of climates and soils and is generally grown as a rain-fed crop (FAOWATER, 2015). Modern wheat varieties are usually classified as either winter or spring wheat. Winter wheat requires a certain cold exposure (vernalisation) before flowering can occur and cool temperatures ranging from five to 25°C are suitable for this type. Spring wheat requires warmer temperatures that range from 22 to 34°C. In S.A., wheat is planted in different wheat-producing provinces with considerations of the climatic conditions of those areas. For example, in the Western Cape Province (winter rainfall area), wheat is planted between April and June, while in the Free State Province (summer rainfall area) it is planted between May and July (Burger and Kilian, 2016a,b).

2.2.4 Bread wheat (T. aestivum L.) production

Bread wheat (T. aestivum L.) is one of the most commonly utilized cereals available all over the world and has been in high demands owing to its abundant health benefits (Organic Facts, 2016). The cereal (Figure 2.9) is often favoured among other cereals largely because it contains proteins with unique chemical and physical properties (Stone and Savin, 1999). In 2012, FAO ranked wheat thirteenth in the commodities produced in S.A. with maize taking first place in the cereals list (FAOSTAT, 2016). With the predicted increase in human population in the coming years, a demand for edible cereal crops is highly expected. Due to the agricultural importance of wheat, crop researchers around the world have devoted their time to improve its production through targeting agronomic traits such as yield, quality, nutritional characteristics and its ability to withstand biotic and abiotic stresses.

Biotic factors greatly influencing wheat include; diseases caused by the three fungal wheat rusts (Puccinia spp.), Fusarium head blight (F. graminearum) and insect pests such as the RWA and the Bird cherry-oat aphid (Rhopalosiphum padi L.). Abiotic factors are those caused by adverse weather conditions such as heat stress, drought and physiological stress such as pre-harvest sprouting (PHS). Another factor that greatly affects the successful growth and development of wheat in the field are weeds. Some weeds have developed herbicide resistance that makes it difficult to manage them (Heap, 2014). All these production constraints contribute to a decline of farmer incomes and the economy as a whole. This has resulted in farmers moving from wheat to other less problematic crops.

Figure 2.9 Wheat heads and grains

Production regions of wheat in S.A. are; the Western Cape, Northern Cape, Free State, Eastern Cape, Kwazulu-Natal, Mpumalanga, Limpopo, Gauteng and North West provinces. The Western Cape have been the highest wheat producer of the crop for the last reporting period (2014/2015 season) with 899,000 tonnes. Gauteng was the lowest producer with 3 600 tonnes (GrainSA, 2016). The current product price of wheat in S.A. for both dryland and irrigated land is R4 169.00 per tonne (GrainSA, 2016). Figure 2.10 is a portrayal of how poorly the South African wheat industry is currently performing when it comes to exporting wheat. From October 2015 to September 2016, there has been a high reliance on wheat imports from other countries and less exports. According to GrainSA reports, South African wheat export is confined to the African continent, with Zimbabwe being the country supplied the most (Figure 2.11). 19

Figure 2.10 South African wheat imports and exports for the 2015/2016 season (Source: GrainSA, 2016)

Figure 2.11 South African wheat exports for the 2015/2016 season (Source: GrainSA, 2016)

2.2.5 Genomics of T. aestivum L.

T. aestivum L. is an allohexaploid (2n=6x=42) species consisting of three sub-genomes, A, B and D (Sears, 1952). The wheat hexaploid (AABBDD) genome came about from a spontaneous hybridization of the wild diploid grass Aegilops tauschii (2n=14; DD) with the cultivated tetraploid wheat Triticum turgidum (2n=4x=28; AABB) (Salamini et al., 2002; Dubcovsky and Dvorak, 2007; Marcussen et al., 2014). T. aestivum L. has a large genome size of ~17 Giga-base pairs (Gbp) (Brenchley et al., 2012) with high numbers (~80 %) of repetitive sequences (International Wheat Genome Sequencing Consortium (IWGSC), 2014). When compared to other related species, wheat is three times larger than barley (Hordeum vulgare L.) (~5.1 Gbp) (International Barley Genome Sequencing Consortium (IBGSC), 2012) and eight times larger than maize (Zea mays L.) (~2.3 Gbp) (Schnable et al., 2009). The large genome of wheat makes it a difficult crop to study, as it is also recalcitrant to most genetic and genomic methods. This has resulted in the limited knowledge of the functions of many genes present in the genome including those influencing important traits. However, having the draft genome sequence (Brenchley et al., 2012; IWGSC, 2014) has made it possible to access all the 96,000 genes and 132,000 single nucleotide polymorphism (SNP) which are found in the wheat genome. This has been a major breakthrough for wheat, considering that most of the important traits can now be studied and understood. Comparative genomics has also assisted in understanding the complex genome of wheat. Comparative genomics is based on the belief that cereals such as maize, rice (Oryza sativa L.), sorghum (Sorghum bicolor L.) and wheat diverged from a common ancestor about 50 to 70 million years ago (MYA) (Kellogg, 2001).

Comparative genomics has somewhat simplified the studying of complex genomes by introducing the possibility of using smaller genomes, such as ~ 0.4 Gbp of rice (Sorrells et al., 2003; International Rice Genome Sequencing Project (IRGSP), 2005) as reference to study them. This was made possible by the noteworthy research on grasses that provided various datasets demonstrating high degree of collinearity or synteny among genomes at chromosome (macro) and gene (micro) levels (Devos and Gale, 2000; Feuillet and Keller, 2002). Sikhakhane et al. (2016) recently reviewed comparative genomics integrated with next- generation sequencing and came to a conclusion that these two technologies or tools, hold the potential to allow for the full sequencing and annotation of conserved and non-conserved regions in cereal genomes. This means that more research on these tools/technologies is required in order to exploit their full potential.

Continued wheat research is essential as it allows for the development and deployment of improved wheat cultivars. Therefore, improving the currently available tools and scientific methods as well as applying novel tools and technologies to practical wheat improvement is highly important.

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2.3 Sequencing 2.3.1 DNA sequencing

Deoxyribonucleic Acid (DNA) Sequencing is described as the determination of the order of four chemical building blocks or bases that make up the DNA molecule (National Human Genome Research Institute (NHGRI), 2016). Through sequencing, scientists learn about the kind of genetic information that is carried in a particular DNA fragment. With the aid of functional analysis, DNA sequencing provides information on where genes or segments that carry regulatory instructions are situated. Most importantly, sequencing allows for the determination of any gene changes that might be responsible for causing specific diseases or disorders (NHGRI, 2016). In plants, sequencing allows for the understanding of plant functioning and their interaction with the environment. Important genes or sequences responsible for key plant traits can be determined from sequence data.

The first possibilities of sequencing were brought about by Sanger’s (Sanger and Coulsen, 1975) and Gilbert’s (Maxam and Gilbert, 1977) groups with their landmark publications in the late 1970’s. The development of the chain-termination method (Sanger et al., 1977) by Sanger and his colleagues laid a firm foundation for the sequencing research that later followed. One of the many successes of the Sanger sequencing method was witnessed during the human genome sequencing initiative led by the International Human Genome Sequencing Consortium (IHGSC) (2004) and Celera Genomics (Lander et al., 2001; Venter et al., 2001). Due to the success of the human genome sequencing project and the reductions in sequencing costs (Collins, 2010), a new generation of sequencing technologies was developed. This led to the partly succeeding of the Sanger method by the newly developed Next- Generation Sequencing (NGS) technologies.

2.3.2 Next-generation sequencing

Next-generation sequencing (NGS), also known as high-throughput sequencing, is the term used to describe a number of different modern sequencing technologies including: Illumina (Solexa) sequencing, Roche 454 sequencing, Ion torrent: Proton/PGM sequencing and SOLiD sequencing. These technologies allow scientists to sequence larger DNA and RNA much quicker and cheaper than the previously used Sanger sequencing, and as such, have revolutionised the study of genomics and molecular biology. First introduced to the market by Margulies et al. (2005), NGS technologies have had major impacts on genomic research. The first NGS technology to be introduced to the market in 2005 was the 454 technology by 454 as it introduced the possibility to bypass the cloning requirement. The following year, the Genome Analyzer, developed by Solexa GA was released followed by Sequencing-by-Oligo- Ligation-Detection (SOLiD) from Agencourt. In 2006, Applied Biosystems bought Agencourt, the following year; Roche bought 454, while Illumina bought Solexa (reviewed by Liu et al., 2012). These three technologies have allowed for better sequencing owing to their advantages of having long read length, high accuracy and various applications. The technologies also require less consumables, manpower and informatics infrastructure.

The field of genome sequencing is rapidly expanding and improved platforms are continuously being developed and released, like Heliscope by Helicos, Ion Torrent PGM by Life Technologies and a real-time sequencing platform by Pacific Biosciences. Currently, Illumina is the most used sequencer as it has made it possible to sequence large and complex genomes at lower costs (Luo et al., 2012). The different sequencing methods currently available can be grouped into three main categories: sequencing by synthesis (Ronaghi et al., 1998; Nyrén, 2007), sequencing by ligation (Landegren et al., 1988; Ashelford et al., 2011), and single molecule sequencing (Thompson and Steinmann, 2010; Orlando et al., 2011). NGS methods provide means that complement the traditional methods, such as conventional plant breeding for crop improvement. Science communities, particularly those interested in DNA sequencing data, have greatly profited from the introduction of these technologies.

The NGS technologies have greatly transformed genomic and genetic research by providing means to access unlimited gene pools through the capturing of desirable genes. NGS technologies are constantly evolving and improving and have been applied in different circumstances, which were not possible with the first sequencing methods. These include; whole-genome sequencing (Henry, 2005; Huang et al., 2010), transcriptome sequencing (Li et

al., 2011) and amplicon sequencing (Kharabian-Masouleh et al., 2011). NGS provides the opportunity to explore the diversity in plant genetics and their wild relatives on a larger scale than was possible with earlier sequencing technologies (reviewed by Ganal et al., 2009; Varshney et al., 2009) and allows even larger and more complex plant genomes to be studied (reviewed by Edwards and Batley, 2010).

In plants, the first breakthrough in genome sequencing was the sequencing of the model plant Arabidopsis thaliana (Arabidopsis Genome Initiative, 2000) which was later followed by rice (IRGSP, 2005). To date, several other plant genomes have been sequenced, either with the Sanger or NGS technologies, including sorghum (Paterson et al., 2009), maize (Schnable et al., 2009) and Brachypodium distachyon (International Brachypodium Initiative (IBI), 2010). Due to the complex nature (large size and polyploidy) of the T. aestivum L. genome, there have been substantial obstacles to whole genome analysis. Brenchley et al. (2012) reported on the sequencing of this complex cereal using the 454-pyrosequencing technology. In their study, they identified between 94,000 and 96,000 genes, and assigned two-thirds of these genes to the three part genomes (A, B and D). The IWGSC (2014) also played a great role in wheat genome studies by providing a draft of the wheat genome with 124,201 annotated gene loci. These gene loci were also distributed nearly evenly across the homeologous chromosomes and subgenomes of wheat. Both these studies have provided resources for accelerating gene discovery and improving the wheat crop. Khan and Budak (2015) have recently reviewed the sequencing of crop plant genomes with an emphasis on cereal crops and their wild relatives. In their review, they document different cereal species that have been sequenced thus far together with the details on the sequencing technologies employed. Their conclusion is in agreement with that of Pennisi (2014) who stated that sequencing would facilitate the creation of considerably tolerant and superior crop varieties. Different methods, tools and technologies are constantly being produced in order to make studying complex genomes even more feasible than current.

All the advances in sequencing methods, including next generation technologies, have led to the reduction of DNA sequencing costs thereby allowing for genotyping-by-sequencing methods that makes association studies and genomics-assisted breeding, to be possible for complex genome crops.

2.3.3 Genotyping-by-Sequencing (GBS)

GBS, a next-generation genotyping tool, introduced the possibility and opportunity to study a variety of species with large and complex genomes and those that lack reference sequences. This method has been widely exploited for its many advantages including the ability to discover and identify large numbers of SNP and the capacity to screen large sets of known markers that can be used in genetic analysis. Elshire et al. (2011) developed a highly multiplexed GBS system for the construction of short libraries to be used in the Illumina NGS platform. Their method was tested on maize and barley recombinant inbred lines (RILs) and their results offered plant breeders and conservation biologists an opportunity to conduct genomic selections and to study population structures on germplasm and species not previously studied without the need to first develop any molecular tools. Poland et al. (2012) was able to develop high-density genetic maps for Barley and Wheat using a GBS method. In their study, they succeeded in mapping over 34,000 SNP and 240,000 tags onto the Oregon Wolfe Barley reference map, and, 20,000 SNP and 367,000 tags on the Synthetic W97846 x Opata85 (SynOpDH) wheat reference map. They then further constructed a de novo genetic map using only SNP markers from the GBS data. Their results provide powerful means and possibilities of developing high-density markers in species without a sequenced genome.

Various studies have been conducted on wheat diseases using the GBS approach. Arruda et al. (2016) reported on the use of GBS for genome-wide association (GWAS) mapping of Fusarium Head Blight resistance. Ten significant SNP–trait associations were detected on different wheat chromosomes. Another recent study on Fusarium Head Blight (FHB) and leaf rust (Xiao et al., 2016) used GBS in the remapping of a Quantitative trait loci (QTL) for type II FHB resistance and leaf rust (Puccinia triticina Eriks) resistance. Their study identified tightly linked SNP markers for the FHB resistance QTL and the leaf rust resistance locus Lr19. A study by Bajgain et al. (2016) used GBS in nested association mapping (NAM) with the hope of understanding the genetic manner of controlling stem rust resistance in wheat. Their study was able to detect 59 minor and medium-effect QTL responsible for adult plant resistance. For wheat pest resistance, Li et al. (2015a) used GBS to accurately map a major gene conferring resistance to Hessian fly (Mayetiola destructor Say). At present, no study employing the GBS method has been reported for RWA resistance. Having such a study would be beneficial to the RWA research community to accurately map important genes using highly dense maps offered by GBS. Ultimately, the new breeding genotypes with different

combinations of favourable alleles would be cost-effectively and uniquely created with GBS derived markers.

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2.4 Molecular markers 2.4.1 Brief background

Molecular markers or genetic markers are described as fragments of DNA that reveal genetic differences between different genotypes or alleles of a gene (Collard et al., 2005). These differences are often termed ‘polymorphism’ and are usually what distinguish a particular length or sequence of DNA fragment of one individual from that of other individuals in a population. Differences in length or sequence could be caused by insertions, deletions, duplications or nucleotide change when DNA is replicated or exchanged. Specific position within a chromosome that all individual alleles of a molecular marker occupy is called ‘locus’ (plural ‘loci’). Molecular markers are classified into two groups: classical markers and DNA markers. Classical markers consist of morphological markers, cytological markers and biochemical markers. DNA markers have different types based on the different polymorphism- detecting techniques or methods used. There have been two widely used basic methods: Southern blotting, which is a nuclear acid hybridization technique (Southern, 1975), and the Polymerase Chain Reaction (PCR) (Mullis, 1990). PCR methods include markers such as restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), microsatellites or simple sequence repeat (SSR) and single nucleotide polymorphism (SNP).

Molecular markers have been available for a couple of decades with their extensive use being in the development of genetic and physical chromosome maps in various organisms. The first use of markers was by Botstein et al. (1980) who used RFLPs in human linkage mapping. Since then, significant progress has been achieved in developing and improving molecular techniques that help to easily identify markers of interest on a largescale. DNA markers have been widely used in different research fields such as in human genetics, animal genetics and breeding, plant genetics and breeding, and germplasm characterization and management. Some earlier studies suggested that biotechnological tools such as molecular markers would play a vital role in enhancing global food production by improving the efficiency of conventional

plant breeding programs (Ortiz, 1998; Kasha, 1999). Judging by the current wide use of molecular markers in food security research, their suggestions were appropriate.

For the purpose of this study, SSR and SNP markers will be discussed further.

2.4.2 Microsatellite markers

Microsatellite markers are PCR-based and are often called by different names; simple sequence repeats (SSR), short tandem repeats (STR), or sequence-tagged microsatellite sites (STMS) (Beckmann and Soller, 1990; Edwards et al., 1991; Hearne et al., 1992). These markers are small segments of DNA, usually 2- to 5-bp in length and are repeated a number of times within genomes of plants and animals. Microsatellite markers are easy to use, often at low cost, and they provide high degree of polymorphism (Table 2.2).

Microsatellite markers are characterized by their hyper-variability, reproducibility, co- dominant nature, locus-specificity, and random genome-wide distribution in most cases. The advantages of SSR markers include that they can be readily analysed by PCR and easily detected by polyacrylamide gel electrophoresis (PAGE). Microsatellite markers can be multiplexed, have high throughput genotyping and can be automated. Microsatellite assays require only very small DNA samples (~100 ng per individual) and low start-up costs for manual assay methods. However, microsatellite technique requires nucleotide information for primer design, labour-intensive marker development process and high start-up costs for automated detections. Since the 1990s, SSR markers have been extensively used in constructing genetic linkage maps, QTL mapping, marker-assisted selection and germplasm analysis in plants. In many species, plenty of breeder-friendly SSR markers have been developed and are available for breeders. For instance, there are over 35,000 SSR markers developed and mapped onto all 20 linkage groups in soybean, and this information is available for the public (Song et al., 2010).

2.4.3 SNP markers

SNP markers are the most common type of sequence variation in the genome (Rafalski, 2002) and usually provide the best map resolution. A SNP is a single nucleotide base difference between two individual DNA sequences. SNP can be categorized according to nucleotide substitutions either as transitions (C/T or G/A) or transversions (C/G, A/T, C/A or T/G). These markers are bi-allelic and their genotyping is automated. SNP provide the simplest

form of molecular markers as a single nucleotide base is the smallest unit of inheritance, and thus they can provide maximum markers. SNP use has been explored in studies of various organisms including human genetics (Nikiforov et al., 1994) and plants (Gupta et al., 2001). Typically, SNP frequencies are in a range of one SNP every 100- to 300-bp in plants (Edwards et al., 2007; Xu, 2010). SNP may be present within coding sequences of genes, non-coding regions of genes or in the intergenic regions between genes at different frequencies in different chromosome regions.

There are several SNP genotyping assays, such as allele-specific hybridization, primer extension, oligonucleotide ligation and invasive cleavage based on the molecular mechanisms employed (Sobrino et al., 2005). There are also different detection methods to analyse the products of each type of allelic discrimination reaction, such as gel electrophoresis, mass spectrophotometry, chromatography, fluorescence polarization and arrays or chips (Sobrino et al., 2005). Currently, SNP are also widely detected by next-generation sequencing. SNP are co-dominant markers, often linked to genes and present in the simplest form for polymorphism, and thus they have become very attractive and potential genetic markers in genetic study and breeding. Moreover, SNP can be very easily automated and quickly detected, with a high efficiency for detection of polymorphism. SNP are being increasingly used for various purposes, particularly as whole DNA sequences become available for more and more species such as soybean, rice, wheat and maize (Hyten et al., 2008; McNally et al., 2009; Ganal et al., 2011; Allen et al., 2013). In wheat-based studies, two popular SNP genotyping arrays are available, the iSelect 9,000 (9K) (Cavanagh et al., 2013) and 90,000 (90K) (Wang et al., 2014) SNP arrays. The 9K SNP array consists of 8,632 functional SNP while the 90K SNP array is currently composed of 81,587 markers of which 43,999 have been mapped and 41,704 unambiguously mapped (CerealsDB, 2016). Individual SNP from both these arrays can be converted into cost-effective, high-throughput Kompetitive allele-specific polymerase chain reaction (KASP) assays. These developed SNP chips and maps of a wide range of genetic variation offer resources for advancing wheat pre-breeding and breeding methods. They serve as valuable resources for diversity studies and genetic basis of trait variations in wheat.

SNP markers have some limitations that include high costs for start-up or marker development, high-quality DNA requirement and lastly, the need for high technical/equipment (next-generation sequencers). These limitations however, do not hinder the continued use of these valuable markers as their benefits outweigh their downfalls. Array- or chip-based single

nucleotide polymorphism (SNP) markers are widely used in genomic studies because of their abundance in a genome and lower cost per data point than older marker technologies.

Table 2.2 Comparison of SSR and SNP markers (Adapted from Collard et al. (2005); Semagn et al. (2006) and Xu (2010)) Feature SSR SNP Genomic abundance Moderate to high Very high Genomic coverage Whole genome Whole genome Expression/inheritance Co-dominant Co-dominant Level of polymorphism High High Type of polymorphism Changes in length of repeats Single base changes, indels Cloning and/or sequencing Yes Yes PCR-based Yes Yes Reproducibility/ reliability High High Genotyping throughput High High Ease of use Easy Easy

2.4.4 Genetic Mapping

Genetic (or linkage) mapping refers to the method of placing markers, genes or QTL in a specific linear order along chromosomes of any species (Collard et al., 2005). Genetic maps serve as guides for researchers to locate a specific gene and to estimate the distance and location of a gene using markers nearby. The first publication of a genetic map of the fruit fly chromosome by Morgan and his student (Morgan, 1911; Sturtevant, 1913) started a revolution in the molecular studies discipline. Since then, many genetic maps of different species have been constructed using more advanced methods than the first methodologies.

Linkage or genetic maps provide a framework for detecting marker-trait associations and for choosing markers to use in marker-assisted breeding. Therefore, a genetic linkage map, particularly high-density linkage map is very important for MAS. To use markers and select a desired trait present in a specific germplasm genotype, a proper population of segregation for the trait is required to construct a linkage map. Once a marker or a few markers are found to be associated with the trait in a given population, a dense molecular marker map in a standard reference population will help identify makers that are close to (or flank) the target gene. If a region is found associated with the desired traits of interest, fine mapping also can be done with additional markers to identify the marker(s) tightly linked to the gene controlling the trait. 29

A favourable genetic map should have an adequate number of evenly spaced polymorphic markers to accurately locate desired QTL/genes (Babu et al., 2004).

In genetic mapping, it is important to choose two genetically different parents in order to produce a polymorphic mapping population (Semagn et al., 2006). The mapping population is usually constructed using any one of the following populations. They can be from an F2, a backcross (BC), double haploid (DH), recombinant inbred lines (RIL), and near isogenic lines (NIL) populations (Burr et al., 1988; He et al., 2001; Doerge, 2002). The choice of the mapping population to be used lies with the researchers aim for the project. In plants, there are different molecular markers that are used during map constructions. The different markers have different benefits and limitations, thus, the researcher is responsible for the selection of an appropriate marker for their particular study.

Genetic mapping of wheat using different molecular markers has provided means to identify quantitative trait loci (QTL) that control all the complex traits (Appels et al., 2001). QTL are regions of the genome where genetic variation is associated with a particular quantitative trait such as, height, quality, yield and some forms of disease resistance (Collard et al., 2005). QTL are usually identified by different statistical evaluations of the association between molecular markers and acquired phenotypes (Tanksley, 1993; Liu, 1998). Many QTL for various important traits have been identified globally in wheat such as those responsible for; rust resistance (Rosewarne et al., 2013; Agenbag et al., 2014; Bajgain et al., 2016), F. graminearum resistance (Zhuang et al., 2013), resistance to multiple Leaf Spot diseases (Gurung et al., 2014), kernel-related characteristics (Tsilo et al., 2010; Li et al., 2015b), adaptation and morphology traits (Bennett et al., 2012; Li et al., 2016), nutrient content (Tiwari et al., 2016) and the quality of wheat (Tsilo et al., 2011a,b). These above-mentioned traits are a representation of the vast amount of work and research that has gone into wheat improvement studies.

2.4.5 Application of molecular markers using MAS in wheat research

Marker assisted selection refers to a breeding procedure in which DNA marker detection and selection are integrated into a traditional breeding program. MAS involves using the presence/absence of a marker as a substitute for or to assist in phenotypic selection, in a way which may make it more efficient, effective, reliable and cost-effective compared to the more conventional plant breeding methodology. The use of molecular markers in plant

breeding has opened a new area in agriculture called ‘molecular breeding’ (Rafalski and Tingey, 1993).

Molecular breeding in wheat has resulted in the development of numerous cultivars with different genes encoding important traits. These include markers linked to the resistance to fungal diseases such as the wheat rusts caused by Puccinia species (Ghazvini et al., 2012; Neelam et al., 2013; Periyannan et al., 2014), Fusarium head blight caused by F. graminearum (Zhang et al., 2014), and SSR markers linked to insect pest resistance such as the RWA (Joukhadar et al., 2013; Staňková et al., 2015). Genetic mapping using molecular markers in wheat dates back to as early as 1991 when Gill et al. (1991) used RFLP for mapping the D genome of a mapping population of T. tauschii (TA1691 x TA1704). Cadalen et al. (1997) conducted another study on a DH bread wheat population using RFLP and on a ‘Chinese Spring’ × ‘Courtot’ using SSR. The following year, Blanco et al. (1998) used RFLP and PCR to create a map of the T. turgidum and ‘Messapia × MG4343’ mapping populations, respectively. The most common of the early-developed maps is that by Röder et al. (1998) who created an SSR map using the “W7984 × Opata85” mapping population. The map by Röder et al. (1998) is used as a reference to this day.

Advances in molecular techniques have seen an improvement in DNA sequencing technologies. DNA sequencing is the process of determining the precise order of nucleotides within a DNA molecule. It includes any method or technology that is used to determine the order of the four bases-adenine, guanine, cytosine, and thymine-in a strand of DNA. A pilot 9K SNP Infinium assay was developed by a U.S./Australia collaborative project. It includes SNP discovered in transcriptomes generated from a set of 27 U.S. and Australian genotypes. The assay is being used for genotyping a diverse set of tetraploid and hexaploid wheat genotypes and cultivars. Cavanagh et al. (2013) reported a consensus map based on data from seven mapping populations. In their study, 7,160 SNP were mapped with 332 SNP localized onto two positions. A set of 3,469 and 3,425 SNP were mapped onto the A and B genomes, respectively, while the D genome only had 620 SNP (Cavanagh et al., 2013). This wheat consensus map has 8,632 mapped SNP markers and is currently being used in wheat genotyping studies.

Conclusion of Chapter

This chapter discussed wheat and the Russian wheat aphid together with the technologies and tools available for wheat improvement. The next chapter covers the materials and methods used in order to achieve the set objectives of the study.

*****

CHAPTER 3

METHODOLOGY

This study received ethical clearance, Appendix 1 [Ref no. 2015/CAES/118]

3. Chapter outline

This chapter describes the methodologies used in this study. The chapter is composed of two phases. The first one is a preliminary study that is divided into two sections, namely phenotypic screening of the test genotypes with RWASA2 and molecular screening using SSR marker analysis. The second phase is the research study that is divided into five sections, namely, the screening of the mapping populations with four South African Russian wheat aphid biotypes, SSR and SNP marker analysis, data collection and analysis, linkage map construction and designing and validation of KASP assays.

3.1 Preliminary study 3.1.1 Plant Material

The 24 wheat accessions (Table 3.1) used in this study are a selection of cultivars and genotypes from the Agricultural Research Council-Small Grain Institute (ARC-SGI) Russian wheat aphid (RWA) pre-breeding programme. They were selected to represent different RWA resistance genes postulated to be present in each of them based on their pedigrees (Table 3.1). The wheat genotypes Gariep, Yumar and PAN3144 were used as differential checks, their different RWA biotype responses are shown in Table 3.2.

3.1.2 Phenotypic screening

The wheat accessions were tested using a 21-day seedling assay described by Tolmay et al. (2012). The accessions were planted in two 98-cone (40 x 95 mm each) seedling trays filled with Professional Potting Mix® (www.cultera.co.za) (Figure 3.1). Individual genotypes were planted in a closed glasshouse cubicle with natural day/night conditions of 11/13 hours (light/dark) and temperatures of 22/12°C in a randomised complete block design with five replicates. Three seeds per replicate were planted in individual cones and watered with KynoPop™ (www.kynoch.co.za) seedling fertilizer. At seedling stage (+ 7 days), fresh leaf tissue was harvested from a single seedling per cone for DNA isolation purposes. The other plants that had germinated within each replicate were discarded, while the ones that supplied

leaf tissue were maintained and infested with circa five individuals of apterous mixed instars of RWASA2.

Table 3.1 Genotypes used in the study, their pedigree and gene information Genes expected to be Wheat accession Synonym and pedigree possibly present CItr 2401 Syn: PI 9798; Landrace from Tajikistan Dn2401 donor and resistant check A50 Kavkaz/CItr2401 // 5*Kavkaz_ F5 Advanced genotype: Dn2401 Hugenoot Betta//Flamink/Amigo Susceptible check Tugela Syn: PI 634771; Kavkaz/Jaral Susceptible check PI 137739 Syn: SA 1684; Landrace from Iran Dn1 donor BettaDn Syn: PI 634768; Betta/PI 137739 // 4*Betta Cultivar Dn1 TugelaDn Syn: PI 591932; Tugela/PI 137739 // 4*Tugela Cultivar Dn1 PI 262660 Syn: SA 2199; Landrace from the former Soviet Dn2 donor Union TugelaDn2 Syn: PI 634772; Tugela/PI 262660 // 4*Tugela Advanced genotype Dn2 BettaDn2 Syn: PI 634769; Betta/PI 262660 // 4*Betta Advanced genotype Dn2 PI 294994 Syn: SA 463; Landrace from Bulgaria Dn5,8,9 donor T05/02 Molen/PI 294994 // 4*Molen Advanced genotype Dn5,8,9 T06/13 Karee /4/ PI 294994/4*Gamtoos /3/ Yding"S"/Bon // Advanced genotype Dove"S" Dn5,8,9 PI 047545 Landrace from Iran Dn6 donor PI 243781 Landrace from Iran Dn6 donor PI 634775 Syn: KareeDn8; Karee/PI 294994 // 6*Karee Dn8 PI 634770 Syn: BettaDn9; Betta/PI 294994 // 4*Betta Dn9 PI 586954 Syn: KS94WGRC29; PI Dnx 220127/P5//TAM200/KS*7H66 PI 586955 Syn: KS94WGRC30 PI Dnx 220127/P5//TAM200/KS*7H66 T03/17 SST124*4/PI 262660 // 661L 1-33/TugelaDn Dn1 + Dn2 T06/16 Molopo*4/PI 137739 /4/ PI 294994/4*Gamtoos /3/ Dn1 + Dn5,8,9 Yding"S"/Bon // Dove"S" BW991405 PI 294994/*4Betta // Truimph/CI13523-Stewart46408 Dn2401 + Dn5,8,9 /4/ FKS*3 /3/ W66-136//Mayo/Warrior4255-49-5 /5/ CItr 2401/*4Kariega BW991308 PI 294994/4*Molen // CItr 2401/*4Kariega Dn2401 + Dn5,8,9 PI 626580 Landrace from Iran Dn626580 donor

RWASA2 was the biotype of choice for phenotypic screening and evaluation because it has been the most virulent or damaging in South Africa (Tolmay et al., 2007; Dr Vicki Tolmay, personal communication1). It also gives consistent and accurate results/responses with all the differential checks as shown in Table 3.2. The RWASA2 biotype used in this study was obtained from a colony maintained at ARC-SGI. A susceptible reaction is expected from accessions containing the genes Dn1, Dn2, Dn8 and Dn9 while a resistance reaction is expected from accessions containing Dn2401, Dn5, Dn6, Dnx and Dn626580 based on the virulence profile of RWASA2. The individual plants were scored 21-days post-infestation using a damage rating scale of 1-10 (Figures 3.2 and 3.3); where 1-3, 4-5, 6, 7 and 8-10 encoded highly resistant, resistant, moderately resistant, moderately susceptible and highly susceptible, respectively (Tolmay et al., 2012). Following scoring, phenotypic data were subjected to a two-way analysis of variance (ANOVA) on GenStat® for Windows™ 15th Edition (Payne et al., 2012) after confirming the normality of residuals and homogeneity of variance. Thereafter, the genotypes were ranked from most resistant to most susceptible using the multiple t-distribution statistical analysis according to Gupta and Panchapakesan (1979).

Figure 3.1 98-cone seedling trays filled with Professional Potting Mix® displaying a randomised complete-block-design planting plan

Table 3.2 Differential checks used in the study, their genes and reaction to the four South African RWA biotypes (Adapted from Tolmay et al., 2016)

Differential Gene RWASA1 RWASA2 RWASA3 RWASA4 checks Gariep Dn1 MR S S S Yumar Dn4 MR MR S S PAN 3144 Dn5 R R R S MR- Moderately Resistant, S-Susceptible, R- Resistant

Figure 3.2 Damage scale for phenotypic analysis: (1,2) highly resistant; (3,4) resistant; (5,6) moderately resistant; (7,8) moderately susceptible; (9,10) highly susceptible (Tolmay et al., 2012)

Figure 3.3 Controls displaying different phenotypes: (A) Resistant control CItr 2401, which gave a mean score of 3; (B) Susceptible control Hugenoot, which gave a mean score of 9

3.1.3 Molecular screening 3.1.3.1 DNA isolation

Total genomic DNA was isolated using a modified Diversity Arrays Technology (DArT) extraction protocol (http://www.diversityarrays.com/submission-sample). A volume of 1000 µl freshly prepared buffer solution (prepared as per DArT instructions) was added to 2 ml Eppendorf tubes with 1 to 2 cm cut pieces of collected leaf tissue material (Figure 3.4). Two round stainless steel balls (5 mm in diameter) were added to the Eppendorf tubes and the samples were homogenised in a Qiagen TissueLyser I (QIAGEN Sciences) for 1 minute at 30 revolutions per second. The tubes were then incubated in a 65°C water bath for one and half hours with gentle shaking of tubes every 20 minutes. The suspension was extracted with 1000 µl chloroform: isoamyl alcohol [24:1 (v/v)] and centrifuged at 12,000 g for 20 minutes. DNA was precipitated with the same volume of ice-cold isopropanol and centrifuged at 12,000 g for 30 minutes. The supernatant was discarded and the DNA pellet was washed with 2 ml 70 % (v/v) ethanol followed by centrifugation at 12,000 g for 5 minutes. The DNA pellet was air- dried for 2 hours and re-suspended in 150 µl TE buffer (10 mM TrisHCl, pH 8.0, 1 mM EDTA, pH 8.0) followed by treatment with DNase-free RNase to a final concentration of 100 μg/ml followed by incubation at 37°C for 3 hours. DNA quality and quantity were checked on

a NanoDrop 2000 spectrophotometer (ND-2000 V3.5, NanoDrop Technologies, Inc.) using the absorbance ratio 260/280 and by gel electrophoresis on 0.8 % (w/v) agarose gel. The DNA was adjusted to a 50 ng/µl final concentration and stored at 4°C until further use.

3.1.3.2 SSR marker analysis

Four SSR markers, viz. Xgwm111, Xgwm44, Xgwm635 and Xgwm437, previously reported to be linked RWA resistance on the 7D chromosome of wheat were tested on the different wheat accessions. Table 3.3 shows the selected markers, their annealing temperatures, the genes that are reportedly linked to them and their chromosome positions. These markers were not expected to pick up resistance conferred by Dn4 (Yumar) as this gene is reported on chromosome 1D (Ma et al., 1998).

PCR was set up using KAPATaq 2× Ready Mix DNA Polymerase (KAPA Biosystems (Pty) Ltd, Cape Town South Africa) in a final volume of 20 µl and placed in Mycyler Thermal cyclers (Bio-Rad Laboratories, Inc. U.K.). The PCR conditions were as follows: 95°C for 4 minutes (1 cycle), followed by 95°C for 30 seconds, annealing temperature 50-60°C (Table 3.3) for 30 seconds and extension at 72°C for 30 seconds (35 Cycles), followed last by a final extension at 72°C for 5 minutes (SSR marker information was acquired from GrainGenes (2016)).

3.1.3.3 Gel electrophoresis

Relevant gene-specific SSR-PCR products were separated on 3 % (w/v) high-resolution agarose gel (Certified Low Range Ultra Agarose, Bio-Rad) stained with GelStar™ Nucleic Acid Gel stain (Lonza, Lonza Rockland, Inc.). The separation was performed in an electrophoresis chamber (Figure 3.5) containing 1x Tris-borate-EDTA (TBE) buffer and was run at 75 V for 4 to 5 hours. For measuring the acquired band fragments, two DNA ladders were used, a 100-bp (O'RangeRuler™, Thermo Fisher Scientific Inc.) on the right side of the gel and either a 20-bp (SimplyLoad®, Lonza Rockland, Inc.) or 50-bp (Quick-Load®, New England BioLabs Inc.) on the left. Following UV light exposure, gel photographs were taken with a Bio-Rad gel documentation system (Bio-Rad Laboratories, Inc. U.K.). The band sizes of the different fragments were manually determined, scored and compared to those reported in literature.

Figure 3.4 Leaf material collection: (A) 2 weeks old seedlings; (B) Cleaning of scissors with 70 % ethanol; (C) Cutting of leaf material; (D) Collection of leaf material using Eppendorf tube; (E) Storage of collected leaf material in cooler box with ice

Figure 3.5 Conducting gel electrophoresis at the ARC-SGI gel laboratory

Table 3.3 Four SSR markers located on wheat chromosome 7D used in the study

SSR Band size for target gene Chromosome Annealing References markers temperature identification position Xgwm111 55°C 200-bp Dn2 ex PI 262660 7DS Liu et al. (2001)

200-bp Dn6 ex PI 243781 Liu et al. (2002)

200-bp Dn6 ex PI 262660 Liu et al. (2005)

210-bp Dn1 ex PI 137739 Liu et al. (2001)

210-bp Dn6 ex PI 047545 Liu et al. (2005)

220-bp Dn5 ex PI 294994 Liu et al. (2001)

225-bp Dnx ex PI 220127 Liu et al. (2001)

Fazel-Najafabadi et al. 274-bp Dn2401 ex CI2401 (2015)

Null in susceptible parent Liu et al. (2001, 2002)

Xgwm44 60°C 180-bp Dn6 ex PI 243791 7DS Liu et al. (2002)

180-bp Dn6 ex PI 243791 Liu et al. (2005)

200-bp Dn6 ex PI 047545 Liu et al. (2005)

190-bp in susceptible parent Liu et al. (2002)

Xgwm635 60°C 100-bp Dn8 ex PI 294994 7DS Liu et al. (2001)

Null in susceptible parent Liu et al. (2001)

Xgwm437 50°C 105-bp Dn5 ex PI 294994 7DL Heyns et al. (2006)

124-bp Dn626580 ex PI 626580 Valdez et al. (2012) 112- and 127-bp in susceptible Heyns et al. (2006);

parent Valdez et al. (2012)

***

3.2 Research study 3.2.1 Plant material

Two near-isogenic lines (NIL) mapping populations, BC5F3 and BC5F5, were obtained from the ARC-SGI (Bethlehem, South Africa) pre-breeding programme. These populations were developed from a cross between CItr 2401 (donor parent), a resistant winter wheat genotype originating from Tajikistan, and Kavkaz (recurrent parent), a RWA susceptible genotype originating from Russia and developed by the International Maize and Wheat

Improvement Center (CIMMYT). A total of 209 BC5F3 and 112 BC5F5 genotypes were rescued from the initially developed mapping populations for evaluation in this study (all the genotypes in the populations are on Appendix 2), together with three differential checks/controls, viz. Gariep, Yumar and PAN 3144. The wheat accessions CItr 2401, Hugenoot and Kavkaz were also used as controls to make 327 genotypes that were evaluated.

Figure 3.6 summarises the development of the BC5F3 and BC5F5 mapping populations and Table 3.4 details the reaction of the checks to the four South African RWA biotypes.

Figure 3.6 Schematic diagrams of how the BC5F3 and BC5F5 mapping populations were created using the backcrossing and selfing method

Table 3.4 Differential checks used in the study, their genes and reactions to the four South African RWA biotypes (Adapted from Tolmay et al., 2016)

Differential Gene RWASA1 RWASA2 RWASA3 RWASA4 checks

Gariep Dn1 MR S S S

Yumar Dn4 MR MR S S

PAN 3144 Dn5 R R R S

CItr 2401 Dn2401 R R R R

Hugenoot - S S S S

Kavkaz - S S S S

MR-Moderately resistant, S-Susceptible, R- Resistant

3.2.2 Aphid colonies used in the study

The clone colonies of the RWASA1, RWASA2, RWASA3 and RWASA4 biotypes that were maintained at ARC-SGI were used for screening the BC5F3 mapping population. Only

RWASA2 was used for screening the BC5F5 mapping population.

3.2.3 Phenotypic screening

Glasshouse evaluations were conducted at ARC-SGI. The BC3F5 population was evaluated following the procedure in section 3.1.2 with slight amendments. Three 98-cone seedling trays were used and instead of three seeds, five seeds of each genotype in the mapping population were planted. To avoid cross contamination during the evaluation of the different biotypes, screenings of each biotype was performed in separate, closed glasshouse cubicles. Individual plants were evaluated using the same method in section 3.1.2. The same procedure was followed for evaluating the BC5F5 population with only the RWASA2 biotype.

3.2.4 Molecular screening 3.2.4.1 DNA isolation

DNA isolation was conducted from the harvested leaves of the BC5F5 mapping population at the ARC-SGI DNA extraction lab following the same method as in section 3.1.3.1.

3.2.4.2 SSR marker analysis

SSR marker analysis was performed on the BC5F5 mapping population in order to verify the preliminary study results. In addition, it was conducted to check whether the reported markers Xgwm473 and Xbarc214, located 1.8 centiMorgan (cM) and 1.1 cM from Dn2401 respectively, (Fazel-Najafabadi et al., 2015), would work on our developed mapping population. From the 112 BC5F5 mapping population, ten genotypes: five that showed a resistance phenotype (A9, A53, A77, B20 and B103) and five that showed a susceptible phenotype (A17, A46, B58, B62 and B72) were chosen for this study. The two crossing parents, CItr 2401 and Kavkaz were used as controls. Six SSR markers that have been previously reported to be associated with RWA resistance on the chromosome 7D of the wheat genome were tested on the genotypes. The same SSR markers in Table 3.3 were used together with an additional two, Xgwm473 and Xbarc214 with annealing temperatures of 55°C and 52°C, respectively. PCR was set up as described in section 3.1.3.2.

3.2.4.3 Gel electrophoresis

The same procedure as in section 3.1.3.3 was followed. In this instance, only a 100-bp DNA ladder (O'RangeRuler™, ThermoFisher Scientific Inc.) was used for measuring the acquired band fragments.

3.3 9K SNP array genotyping

Genomic DNA (isolated in section 3.2.4.1) from 22 selected genotypes together with the two crossing parents, CItr 2401 and Kavkaz (Figure 3.7), was taken to the ARC- Biotechnology Platform (ARC-BTP, Pretoria, South Africa) for genotyping. The DNA samples were quantified on the Qubit® 2.0 Fluorometer (Figure 3.8) following the manufactures instructions (www.invitrogen.com/qubit) and a final DNA sample volume of 20 µl was used for genotyping. A three-day genotyping protocol was followed using the Illumina Infinium® HD Assay Ultra kit with no amendments. The wheat 9K SNP Infinium iSelect array comprising of 8,632 functional gene-associated SNP (described by Cavanagh et al., 2013) was used for genotyping. The Illumina’s GenomeStudio v2011.1 (Illumina Inc., Hayward, CA, U.S.) was used in exporting the generated SNP raw data into a Plink input file format.

9 9 9 9 9 9 9 9 9 9 9 9

5 5 5 5 5 5

4 4 4 4 4 4

A9 A1

B20 B24 B78 B19 B32 B37 B42 B66 B73 B79 B99

A53 A33 A62 A39 A46

B102 B132 B136 B137

Kavkaz CItr2401 Resistant Susceptible

Figure 3.7 Mean phenotypic scores of genotypes taken for the 9K SNP genotyping

Figure 3.8 The Qubit® 2.0 Fluorometer used at ARC-BTP during DNA quantification

3.4 Data analysis

Phenotypic data were subjected to a two-way analysis of variance (ANOVA) on GenStat® for Windows™ 15th Edition (Payne et al., 2012) after confirming the normality of residuals and homogeneity of variances. The two independent variables tested were the genotype (entry) and biotype. To analyse the generated 9K SNP genotyping data, Plink 1.9

(https://www.cog-genomics.org/plink2) and Tassel 4.3.15 software (http://www.maizegenetics.net/#!tassel/c17q9) were used.

To minimize the problems caused by missing genotypic data, the data was pruned using the Plink 1.9 software. There are two standard files for Plink, ‘map’ (contains information about the genetic markers) and ‘ped’ (contains information about the family, phenotype). Different commands were used to inspect for missing information, heterozygosity, minimum allele frequency (5 %) and Hardy Weinberger equilibrium (p < 0.0001). Thereafter, SNP with more than 10 % missing genotypes and samples with more than 10 % missing SNP were removed. From the initial 8,632 SNP markers, 618 SNP remained, which were then used in Tassel 4.3.15 for two analyses: linkage disequilibrium (LD) and the construction of the Cladogram.

3.5 Linkage map construction

The 618 SNP markers from Plink 1.9 analysis were further manually pruned to acquire the best segregating markers. The remaining 178 markers were used in linkage mapping. Allele calling for each SNP was performed using Illumina’s GenomeStudio v2011.1 and results were manually inspected for call accuracy. Linkage groups were constructed using the Grouping and Ordering commands on the QTL IciMapping Version 4.0 (http://www.isbreeding.net/) software with a minimum logarithm of odds (LOD) value of 3.0. The LOD threshold for declaring significant Quantitative trait loci (QTL) was calculated by 1000 permutations. The robustness of the marker order in a linkage group was checked using the Ripple command. The Map command was used to determine linkage distances between the markers which were calculated in cM based on the Kosambi mapping function (Kosambi, 1943). QTL effects were estimated as the proportion of phenotypic variance (R2) explained by the QTL. SNP marker chromosome positions were acquired from the consensus map by Cavanagh et al. (2013).

3.6 Designing of Kompetitive allele specific PCR (KASP) assays

The eight SNP markers that co-segregated for the resistant phenotype were used to develop allele-specific primers for a Kompetitive Allele-Specific polymerase chain reaction (KASP) assay. The marker sequences were sent to the LGC Biosearch Technologies distributors, Anatech (Anatech Instruments (Pty) Ltd, Olivedale Gauteng, South Africa; http://www.anatech.co.za) for SNP-specific KASP assays designing. The KASP assay consists of three KASP primers that are specific to the SNP of interest. The KASP Master Mix was

ordered separately and it contained the universal FRET cassettes (HEX and FAM), ROX™ passive reference dye, Taq polymerase, free nucleotides and MgCl2 in an optimised buffer solution. The KASP Master Mix is universal and can be used in conjunction with all KASP assays.

3.7 KASP assay validation

After the SNP-specific KASP assays and KASP Master mix were acquired, DNA of the two parents (CItr 2401 and Kavkaz) was genotyped on the real-time (RT) PCR (Agilent Technologies, Inc.) instrument (Figure 3.9) at the ARC-SGI PCR laboratory following the LGC method (LGC Genomics, https://www.lgcgroup.com/kasp/) with no amendments. The eight designed KASP assays were used in the genotyping of the two parents. Each KASP reaction had a total volume of 10 µl, 5 µl template DNA and 5 µl genotyping mix. Thereafter,

DNA of each of the 24 NILs from the 9K SNP genotyping as well as DNA of the whole BC5F5 mapping population were genotyped using only the KASP markers that could clearly

differentiate the two parents.

Figure 3.9 Real-Time PCR used in KASP marker validation at the ARC-SGI PCR laboratory

*****

CHAPTER 4

RESULTS AND DISCUSSION

4. Chapter outline

Detailed analysis of the data was performed for the two phases of this study. This chapter covers the results of each of the analyses. This chapter consists of three parts of which the first part relates to the preliminary study results. The second part relates to the five phases of the research study and the last part is the overall discussion of the results. The chapter begins with the results of the first phase of the study, followed by the other phases in their respective order.

4.1 Preliminary study

Objective 1: To evaluate the discriminatory ability and accuracy of previously reported SSR markers for identifying stacked RWA resistance genes.

4.1.1 Results and Discussion

Table 4.1 shows the ranking of the entries according to their mean damage ratings as well as the grouping of entries into “resistant”, “moderately resistant” and “susceptible” based on the multiple t-distribution test (Gupta and Panchapakesan, 1979). Based on literature, RWASA2 is virulent to Dn1, Dn2, dn3, Dn8 and Dn9 (Tolmay et al., 2007). Among the accessions that were tested, the ones that contained those genes or a combination of those genes displayed susceptible symptoms except for PI 137739 (Dn1 donor) and T06/16 (advanced genotype with Dn1 + Dn5,8,9) which appeared in the “resistant” group. Though unexpected, the resistant phenotype of PI 137739 can conceivably be explained by genetic diversity within the landrace PI 137739 that was not transferred to cultivars bred from this genotype. On the other hand, the resistance in individuals of the advanced genotype T06/16 could be due to the presence of Dn5.

Test entries that were expected to be resistant to biotype RWASA2 include CItr 2401, PAN 3144, PI 047545, PI 243781, PI 294994, PI 586954, PI 586955 and PI 626580. Existence of both moderately resistant and resistant individuals in four of these genotypes (PI 243781, PI 47

294994, PI 586955 and PI 626580) can be explained by their landrace status as well as their acquired phenotypic scores. These four genotypes had susceptible individual plants (highlighted in Appendix 3) which resulted in them being classified as moderately resistant. It is known that RWA resistance donor landraces, can be mixed for resistance (Xu et al., 2015) and this is probably the explanation for these four lines. None of these genotypes were ranked in the susceptible group. Using published markers, it should be possible to identify a particular resistance gene in a genotype based on the band sizes present in the resistant genotype while testing phenotypically for another resistance gene using the appropriate biotype based on the donor accession. For example: How does one identify a genotype containing both Dn1 and Dn5? A plant with both genes will test resistant to RWASA1, RWASA2 and RWASA3, as will a plant that contains only Dn5. The resistance reaction to RWASA2 and RWASA3 will indicated a different gene conferring resistance than Dn1, because Dn1 is susceptible to these biotypes. A marker is thus needed to confirm the presence of Dn1 while phenotypic screening can confirm Dn5.

Using genotypes postulated to contain Dn5, one would expect resistant genotypes to contain the markers Xgwm111220 and Xgwm437105. However, resistant individuals of the advanced genotype T06/16 contained Xgwm111200,150 and Xgwm437125. Fragments

Xgwm111220,170 and Xgwm437120 were obtained from both susceptible and resistant individuals of advanced genotype T06/13, and advanced genotype with resistance from PI 294994 (potentially conferring Dn5,8,9). The susceptible phenotype of T05/02 (advanced genotype with Dn5,8,9), BW 991308 and BW 991405 (advanced genotypes with Dn2401 + Dn5,8,9) suggested that none of these genotypes contain Dn5 or Dn2401. However, three susceptible individuals of the advanced genotype T05/02 contained Xgwm111220,170 and all contained

Xgwm437120. BW 991308 and BW 991405 contained Xgwm111190,160 and Xgwm437105 and

Xgwm111200 and Xgwm437160, respectively. Although the expected band Xgwm437105 was present, the genotypes did not express resistance to RWASA2 as they should have if Dn5 was present.

In the pre-breeding programme, individual plants have been selected based on their phenotypic reactions when tested using the seedling assay. To further test whether the phenotype data would correspond with the marker data, the accessions PI 262660 (Dn2 donor), TugelaDn2 and PI 634769 (both advanced genotypes with Dn2) were checked. Only marker Xgwm437 was similar for PI 262660 and PI 634769, with 100-bp for a damage score of 9. Unexpectedly, the marker Xgwm44 produced similar band sizes (200- and 190-bp) on both CItr

2401 (resistant) and Hugenoot (susceptible). The inconsistency of these results is in discord to Liu et al. (2001, 2002, 2005) who reported that these tested SSR markers can be used in the identification and/or stacking of resistance genes. The inconsistent marker results from this study demonstrate a huge challenge in RWA resistance molecular studies.

Table 4.1 Genotype ranking based on the multiple t-distribution test for the 27 tested genotypes

Phenotypic response Genotypes in their respective ranks Resistant PI 137739 CItr 2401 T06/16 PI 586954 PI 047545 Pan 3144 PI 626580 Moderately Resistant PI 586955 T06/13 PI 243781 PI 294994 Susceptible T03/17 T05/02 PI 262660 TugelaDn2 Yumar BW 991308 BW 991405 PI 634775 A50 TugelaDn BettaDn Tugela Gariep PI 634769 Hugenoot PI 634770

The results from this study were also compared to previous reports by Liu et al. (2001, 2002, 2005) and Heyns et al. (2006). Table 3.3 presents the marker names, their linked genes and the PCR amplified fragment sizes from literature, while Table 4.2 gives the results from this study for the same markers. Phenotypic scores were also included in order to see if the 49

phenotype and genotype correspond as they are theoretically supposed to. For most of the markers tested in this study, the phenotype results corresponded to the genotype results. For instance, accession PI 137739 replicates had resistant phenotypic scores of 3 and gave similar band sizes for markers Xgwm111240,170,160, Xgwm44200,190, Xgwm635100 and Xgwm437110. PI 294994 on the other hand, had different phenotypic scores, both resistant and susceptible but gave similar band sizes for marker Xgwm44210,200. Liu et al. (2005) reported a single band from marker Xgwm111210 for PI 047545 (Dn6), while in this study, three different band sizes; 220-, 160- and 130-bp were observed on the same accession using the same marker. This observation makes it difficult to tell which band size is representing which gene, especially in resistant accessions with unknown genes which might be of interest in pre-breeding programmes. Liu et al. (2002, 2005) observed similar band sizes of Xgwm44180 for PI 243781 and PI 262660 (both genotypes contain Dn6). In this study, multiple band sizes were acquired for both accessions, with the 180-bp band size also showing for the same marker. Notably, Liu et al. (2001) also reported the presence of marker Xgwm111210 for PI 137739 (Dn1). On the other hand, Heyns et al. (2006) reported Xgwm635105 for PI 294994 (Dn5). In this study, although multiple bands were acquired for the same marker, the 105-bp band was similar in only two single plants that displayed the resistance phenotype in that accession. In contrast to the findings from the present study, Liu et al. (2001) reported PI 294994 (for Dn8) as having Xgwm437105.

To further show that the phenotype and SSR markers were inconclusive, the accessions used in this study, that had either Dn5, Dn8 or Dn9 in combination or as single genes were compared. The accessions were; T06/16 (resistant), T06/13 (moderately resistant), PI 634775 (susceptible) and PI 634770 (susceptible) (Appendix 3). The four accessions had multiple bands except for T06/13 (Xgwm437) and PI 634775 (Xgwm635). Some band sizes were similar but they would not be helpful in MAS since they were observed on accessions which had different phenotypic reactions. Marker Xgwm44 gave band sizes of 200- and 190-bp for the four accessions but their phenotypic scores did not correspond. Also, the band size 220-bp appeared in more than one accession with the marker Xgwm111. From these results alone, the associated gene depicted could not be confirmed.

Analysis of variance showed the damage ratings of the different accessions to be significantly different (P < 0.001; df = 26; SS = 633,871; LSD = 1.495), suggesting that the individual plant entries responded differently to RWASA2, as was expected. Although the acquired molecular results proved to be inconsistent, results from the t-distribution test are important in pre-breeding programmes that still use conventional methods. These results would

be helpful in the selection for multiple resistance donor genotypes to be given to breeders interested in breeding for RWA resistance.

Table 4.2 Phenotypic scores and gel fragment sizes (bp) corresponding to each plant score

Genotype Damage rating Phenotypic Xgwm111 Xgwm44 Xgwm635 Xgwm437 (±SD) [∑Values] scores PI 047545 3.80 (0.477) [19] 4 220; 160 190 100; 95 100 4 220; 160 190 100 4 220; 160 190 100; 95 100 3 190; 160; 130 200 100 4 190; 160; 130 200; 190 100 PI 137739 3.00 (0.000) [15] 3 240; 170; 160 200; 190 100 110 3 240; 170; 160 200; 190 100 110 3 240; 170; 160 200; 190 100 110 3 240; 170; 160 200; 190 100 110 3 240; 170; 160 200; 190 100 110 PI 243781 6.20 (2.950) [31] 8 220; 160; 140 220; 200 105; 100 105 8 220; 160; 140 220; 200 105; 100 105 9 200; 160; 140 200; 180 105; 100 100 3 220; 140 200; 180 105; 100 110 3 220; 160; 140 200; 180 105; 100 100 PI 262660 8.00 (0.707) [40] 7 200; 140 200; 180 100 8 200; 140 200; 180 100 100 8 200; 140 200; 180 100 100 8 200; 140 200; 180 100 9 200; 140 200; 180 100 PI 294994 6.80 (2.588) [34] 4 220; 160; 140 210; 200 105; 100 100 9 210; 140 210; 200 110; 105 130 4 220; 160; 140 210; 200 105; 100 100 8 190; 140 210; 200 110; 105 125 9 190; 140 210; 200 110; 105 125 T06/16 3.2 (0.477) [16] 4 200; 150 200; 190 120; 100 125 3 200; 150 200; 190 120; 100 125 3 200; 150 200; 190 120; 100 125 3 200; 150 200; 190 120; 100 125 3 200; 150 200; 190 120; 100 125 T06/13 5.8 (3.033) [29] 8 220; 170 200; 190 120; 100 120 8 220; 170 200; 190 120; 100 120 8 220; 170 200; 190 120; 100 120 3 220; 170 200; 190 120; 100 120 2 220; 170 200; 190 120; 100 120 PI 634775 8.5 (1.000) [34] 9 200; 160;140 210; 200 105 100 7 220;190;160;140 200; 190 105 120; 100 9 200; 140 200; 190 105 115 9 200; 140 200; 190 105 115 200; 160;140 PI 634770 9.2 (0.447) [46] 9 220; 140 200; 190 110; 100 105 9 220; 140 200; 190 110; 100 105 9 220; 140 200; 190 110; 100 105 10 230; 160; 140 200; 190 110; 100 90 9 230; 160; 140 200; 190 110; 100 90

Preliminary study conclusion

To fast track or speed up the breeding process, plant breeders now use marker-assisted selection (MAS) to help identify and select for specific target genes. Having tight linkage of a marker to a gene of interest allows indirect selection for the trait of interest even without phenotyping. Since the conventional breeding process is costly, labor intensive, slow and is influenced by the environment, MAS comes in as a better and feasible alternative. However, employing MAS for RWA resistance remains a challenge unless diagnostic markers are developed. Diagnostic markers would be those markers that would have complete linkage with RWA resistance genes in wheat genetic background. If markers are non-diagnostic, they cannot be used to indicate the presence of a gene in an unknown set of germplasm. None of the tested SSR markers used in this study showed potential use in MAS and continuous use of the currently available SSR markers will result in little progress in molecular pre-breeding and breeding for RWA resistance. Therefore, there is need for robust diagnostic markers that will be able to give reproducible and reliable results on wheat backgrounds.

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4.2 Research study

Objective 2: To screen the BC5F3 and BC5F5 NILs mapping populations for resistance to four RWA South African biotypes.

4.2.1 Results and Discussion

Following RWA screening, statistical analysis of the scores for the BC5F3 mapping population was performed using GenStat® for Windows™ 15th Edition (Payne et al., 2012). The two-way ANOVA showed the mean damage rating of the four biotypes to differ significantly (P < 0.001) with the lowest mean obtained from RWASA4 and the highest from RWASA1, whereas means for RWASA2 and RWASA3 were intermediate to the other biotypes (RWASA1 = 8.446; RWASA2 = 8.352; RWASA3 = 7.604 and RWASA4 = 7.566). The summary statistics for the four biotypes is presented on Appendix 4.

Figure 4.1 shows a histogram of the scores for each of the four biotypes. This graph explains the above results which state RWASA1 as having the highest mean and RWASA4 as 52

having the lowest mean. From these results, it can be deduced that RWASA1 caused the most damage, or displayed more virulence to the BC5F3 mapping population compared to the other three biotypes. The reaction of the mapping population to RWASA4 was normally distributed. These results show that the four South African biotypes differ in virulence and that the resistance gene (Dn2401), possibly together with other resistance genes, is present in the mapping population because resistance phenotypes were observed. The histograms of acquired scores for individual entries are shown on Appendix 5a and 5b. They show the responses/scores of individual entries to the different biotypes.

Figure 4.1 Histogram of acquired scores for the four biotypes shown as the number of plants per damage rating score

Figure 4.2 shows a scatter plot of the means for the entries under different biotypes.

This graph demonstrates how the entries/genotypes in the BC5F3 mapping population reacted to the four RWA biotypes. This graph is in agreement with the above results as RWASA1 (red cross) is mostly at the top (susceptible levels) while biotypes RWASA2 (green circle) and RWASA3 (purple cross) are distributed at different scores. RWASA4 (blue star) is mostly at the bottom (towards resistance levels). The normality of residuals and homogeneity of variance were also tested and confirmed that the residuals were normally distributed and the variances were homogeneous and comparable. Figure 4.3A and 4.3B show the normality and homogeneity plots, respectively.

Figure 4.2 Scatter-plot showing the means of entries at different biotypes

Figure 4.3 Residuals scores: (A) Normality plot (B) Homogeneity plot

***

Figure 4.4 shows the results obtained from SSR marker analysis. This test was done in order to confirm the results obtained from the preliminary study. SSR marker analysis was performed on a selection of genotypes (10 genotypes) from the BC5F5 mapping population 54

together with the two parents. Only marker Xgwm437 was able to detect polymorphism between the parents (CItr 2401 and Kavkaz). However, this marker could not be tested any further on the whole population because the acquired bands for the genotypes tested corresponded with the susceptible parent. None of the other markers showed polymorphism among the tested genotypes as all the acquired band sizes were the same. These results were in agreement with the preliminary study results presented in section 4.1.1. Therefore, they were not used to further test the BC5F5 mapping population.

Figure 4.4 Gel photographs displaying the results for each tested SSR marker: (L=100-bp DNA ladder; 1= Kavkaz, 2=CItr 2401, 3=A9, 4=A53, 5=A77, 6=B20, 7=B103, 8=A17, 9=A46, 10=B58, 11=B62, and 12=B72)

***

Objective 3 To genotype a representation of the BC5F5 mapping population using SSR markers and the 9K Illumina Infinium SNP iSelect assay.

The 9K SNP Array genotyping data generated were manually called using Illumina’s GenomeStudio 2011.1 (Illumina Inc., Hayward, CA, U.S.). The data was “cleaned” using the Plink 1.9 software. The ‘map’ and ‘ped’ files acquired from the genotyping report were used for analysis. Plink used different commands to inspect for missing information, heterozygosity, the minimum allele frequency (5 %) and Hardy-Weinberg equilibrium (p < 0.0001). Following that, SNPs with more than 10 % missing genotypes and samples with more than 10 % missing SNPs were removed. This resulted in 618 high-quality SNP markers that were retained for further analysis. These marker data were analysed using Tassel 4.3.15 (Figure 4.5) to construct the LD plot (Figure 4.6) and the cladogram for relationship of genotypes (Figure 4.7).

Figure 4.5 A demonstration of how the input data appeared in Tassel 4.3.15. All alleles highlighted in blue are “minor alleles”, yellow are “major alleles” and white are null alleles

Figure 4.6 demonstrates the linkage disequilibrium (LD) plot. The LD measurements (R2, above the diagonal genotype) and probability value (P, below the diagonal genotype) are for 618 SNP markers. The figure represents all pair-wise comparisons of polymorphic sites. The genetic map locations of all the tested SNP markers can be found on the 9K consensus map (Cavanagh et al., 2013).

The red boxes demonstrate SNP that had a P-value of <0.0001. These were the highly significant markers. The green boxes also had significant SNP with a P-value of <0.001. The blue boxes were the least significant SNP with a P-value <0.01. All the white boxes were the non-significant SNP. Due to the size of the mapping population (24 genotypes) sent for genotyping, chances of recombination are close to non-existence, thus it was expected that the LD would be very low since it is highly dependent on population size and the number of markers. The LD was analysed in this study just to demonstrate its significance in association studies.

Figure 4.6 LD measurements (R2, above the diagonal genotype) and probability value (P, below the diagonal genotype) for 618 SNP markers

Figure 4.7 demonstrates the relationship of the individual tested genotypes with the two parents CItr 2401 and Kavkaz. Since the mapping population was created from a backcrossing, with the susceptible genotype Kavkaz as the recurring parent, it is not odd to see that most genotypes have a closer relationship with Kavkaz. Genotypes that acquired the resistance from CItr 2401, such as B132, B137, B24 and A33 had a closer relationship with it.

Figure 4.7 Cladogram showing the relationship between the different genotypes to the parents CItr 2401 and Kavkaz

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Objective 4 & 5: To analyse the genotypic data with QTL IciMapping Version 4.0 software and to identify SNP markers linked to the resistance phenotype and to design KASP assays from them.

From the 178 SNP markers (Appendix 6) used in linkage mapping, eight linkage groups were constructed (Figure 4.8). Linkage analysis revealed that the RWA resistance locus was tightly linked to a group of ten SNP markers. These SNP markers (wsnp_Ex_c12480_19889644; wsnp_Ex_c3906_7086294; wsnp_Ra_c4135_7565040; wsnp_CAP12_c1960_972031; wsnp_Ex_c13164_20793506_x; wsnp_Ex_rep_c67697_66363222; wsnp_Ex_c64327_63176640; wsnp_Ex_c7252_12453079; wsnp_Ex_c8364_14095508; wsnp_Ku_c1629_3206989) were located within a 9.11 cM on the linkage map (Figure 4.9). Figure 4.9 shows a close up of linkage group 8, the only group that had relevant information. The black dot on the right hand side of the map is where the SNP that co-segregated fully with the resistance phenotype lies.

To select the best SNP markers for KASP assays, the SNP markers tightly linked or flanking the RWA resistance locus were manually inspected. The map on Figure 4.9 shows four groups of SNP markers with unique segregation patterns. Two of these groups closest to the marker wsnp_Ku_c1629_3206989 that had 100 % co-segregation with the resistance phenotype, were selected for KASP marker designing. Eight SNP markers that co-segregated with the resistance phenotype were chosen for KASP marker development (Figure 4.9; Table 4.3). The sequences of those SNP markers were obtained from the T3 Wheat website (triticeaetoolbox.org). The SNP marker sequences were sent to the South African LGC Biosearch Technologies distributors, Anatech (Anatech Instruments (Pty) Ltd, Olivedale Gauteng, South Africa; http://www.anatech.co.za/) for KASP assay designing. The KASP markers were used to validate the acquired results from the 9K SNP genotyping. The KASP assays were validated on the 24 genotypes and the BC5F5 mapping population as well as on other genotypes from the preliminary study that contain the Dn2401 resistance gene. The KASP assays developed in this study will be useful for stacking the RWA resistance from CItr 2401 with other available resistance genes.

Figure 4.8 The 8 linkage groups acquired from linkage mapping. Marker names are indicated on the right side of the map and Distances (in cM) between markers are listed on the left side

Figure 4.9 The linkage group eight displaying a single QTL. Marker names are indicated on the right side of the map and Distances (in cM) between markers are listed on the left side

Table 4.3 KASP assay markers used to validate the result Marker Name Primer Sequences Name

ACGGTGTGTGGTCGGTCACAAGTCCTACCAAAATTCTGCTCCCCGTGCGTGCAGCTACTACCAGTTCCATAACAGAACGGGGAAGACAAAAATAAAGAAT[A/G] wsnp_Ra_c4135_7565040 IWA7921 GGAACCAGTGGAACTACGTAACCTATTTTTGTATGAGCATGAAGAAAAACTGTTCCGTGTCCAGCCCGTGTCCTCCAGCTTCGCCAAGCTCCTTTGGAGA

AAGAAACCGGTTCAACAAGCGGGCGGCAATATAGATGCCTTAAGTCTTCAATATAGAGTAGAACCGGCTGGCGGTTCGGGCATTTATCGGAGGCGGCTTC[A/G] wsnp_CAP12_c1960_972031 IWA931 GCTTAGAAGCCGTGGATCTTGATGAGCTCCTTCTTCGCAATYCCAGCCTGGACTAGGAAAGTAGCAACATTCTTACGCTGATCACCCTGAAGCTGAATGA

TAACTTATATAACCTATAAATTATCTTATAAGACAATATTTTAAATACAGATTCATATGCAACACAAATATAAGGATAATCCAGAGCTTTCAACTAATGG[A/C] wsnp_Ex_c13164_20793506_x IWA1734 ACATCCTACAACCGAAAAAGACTCYTACCTTCCTATTTTCTTTATTTCATTTATTAGTTACTTGGCCATCTCTTTGATATAGGTTTTTGCCCCTGCCCTT

GCAAGTGGTTCAGCGGTGTCTGTGCATGGAAGAACCCCATTACCACTGTGCTAGTTCACATCCTCTTTATAATGCTGGTGTGCTTTCCAGAGCTCATACT[C/T] wsnp_Ex_rep_c67697_66363222 IWA5442 CCCACAGTGTTCCTGTACATGTTCCTGATAGGGATCTGGAACTACCGTTACCGGCCTCGCTACCCTCCACACATGAACACCAAGATCTCTCATGCAGAGG

AGTAGCTGTTAACACCTTGTGTCATGTCAGAAATGTTTAAGGTTGTATTGTAAATGCTTTTGCCATTTTCCAGAATATGGCCCGTATCTTGCAGACCGGA[A/G] wsnp_Ex_c64327_63176640 IWA4506 GCCTCATGAACAGCACTTCGGCCCTTAACTTTAACAGTGACCGTGCTACCTTTTGATGACTCCTCCAAGGTAGTCGCTACTTTGTATAAATCAAAT

TCATGCCTGATGCAAAGCGCATGCTGCGTTCACCGTGCCTAAGTGGGCTACCAGATGCAACAGGTGAAGAGGGCAGAACAGGACTCGTCAAAGGGCTACC[G/T] wsnp_Ex_c7252_12453079 IWA4644 CCATAACCAAGGCCAAATCCAAGATTTCCAYAGCAGCCATAGTTCTTCTGTGACTGAAGCAGTGGGCTAAGGCAACCCTTCTGRGGACCAAGTAAATCCA

CGCCAAATTACGCTGCTACTGGTGTGTTTACTGCTCCATAATTGAGTCAGTTTTGTGTGCATTATCTGAACAATGTCAACGCTGCCTTCCTTCTGACATA[C/T] wsnp_Ex_c8364_14095508 IWA4797 TATTTTGGCTGTTGATCGATGTTGCGGCTGCTGCGGCTGGTGGTGCTCTGGGCACGCAAGGGGAGCGCGGCACACAAACTCCGCCTGCTCAAGAC

AGGCTAAAAACTTGTGAGATGGTACATACTTCAGGTTGTTGCCATCATCCGAGAGCCACCTATCTGGGTTATAGTCGAGGCAGTCTTTACCCCACAAGCC[A/C] wsnp_Ku_c1629_3206989 IWA6587 TCCATTCTACCCATGGAGTGAAGAGAAATAAAGATGGTGTCGCCGGCTTGCACCTGGTGGCCACTTGGCATGATATCATCGCCGAACACCGTCTTGCGCT

Objective 5: Validation of KASP assays on various genotypes.

From the eight KASP markers tested on the parental genotypes, marker wsnp_Ex_rep_c67697_66363222 (IWA 5442) was able to clearly distinguish between the two genotypes as the resistant allele (A) was clustered separate from the susceptible allele (B) (Figure 4.10). CItr 2401 was fluoresced by the FAM dye while Kavkaz was fluoresced by the HEX dye. Two other markers, wsnp_Ex_c7252_12453079 (IWA 4644) and wsnp_Ra_c4135_7565040 (IWA 7921) also showed potential to distinguish between the two parental genotypes while the other five markers failed to do so. The three markers that showed potential were tested on the 24 NIL from the 9K SNP genotyping alongside an additional six genotypes (three resistant and three susceptible). Figure 4.11 shows marker IWA 5442 as being reproducible because it could still give the same distinguishable results as seen by the separate clusters for resistant and susceptible genotypes. The other two markers only displayed heterozygous results and could therefore not be further used. Sequence alignment (Figure 4.12) was then conducted on the sequences of the 24 NIL acquired from the SNP genotyping and those acquired from the KASP assay with the marker IWA 5442. To confirm that the KASP marker was really working, the sequences acquired from the KASP genotyping had to be exactly similar to those from the SNP genotyping. The alignment showed a similar pattern for all the resistant and susceptible genotypes with only the resistant genotype, B78, displaying heterozygosity following the KASP assay. These results showed that the genotypic data acquired with the marker IWA 5442 KASP assay were consistent with the genotypic data from the 9K SNP assay.

To examine if the marker could further work on a larger set of genotypes, it was used in genotyping 118 genotypes from the BC5F5 mapping population. Following that analysis, the markers’ reliability and reproducibility was tested on genotypes with different genetic backgrounds, other than the ones it was produced from. This was done by genotyping the 23 wheat accessions used in the preliminary study. With both these test groups, the resistant control CItr 2401 remained in the A-allele cluster and the susceptible control, Hugenoot, in the B-allele cluster. In the preliminary study group, a total of five out of ten genotypes with a resistance phenotype were fluoresced by the FAM dye, while a total of seven out of 13 genotypes with a susceptible phenotype were fluoresced by the HEX dye. The remaining 11 genotypes were misplaced in incorrect clusters (meaning resistant genotypes were found in the susceptible HEX cluster and susceptible genotypes in the resistance FAM cluster). A possible reason for this is that since these genotypes come from different pedigrees, which were created 63

by different people, it is not known exactly what was inherited. In this study, the only information known are the genes reported to be in the accessions and not what was lost or inherited along the way. An example of this is seen with the Dn1 donor PI 137739 which gave a resistance phenotype when Dn1 is supposed to be ineffective against RWASA2. This accession also appeared in the A-allele cluster where CItr 2401 is found, further verifying its resistance phenotype. On the other hand, TugelaDn and BettaDn, genotypes with Dn1, gave a susceptible phenotype and were found in the B-allele and A-allele clusters, respectively. This means that BettaDn, to some extent, inherited something similar to PI 137739, whereas TugelaDn did not. Another example is seen with the Dn5,8,9 donor PI 294994 which had a resistance phenotype (assumed to be from Dn5) but was found in the B-allele cluster. The same pattern was seen with T06/13, a genotype reported to have Dn5,8,9. On the other hand, BW 991308, which has Dn5,8,9 + Dn2401 gave a susceptible phenotype but was found in the B- allele cluster as well. Having resistant (A-allele in this case) and susceptible (B-allele) genotypes in different clusters, helps in determining the usefulness and efficiency of the KASP marker/s. If a marker is able to clearly distinguish between resistant and susceptible individuals, that marker is diagnostic. Results from this study merely show that for RWA resistance, it is clear that the relationship between the phenotype and genotype is not yet understood. The same gene can be reported in different genetic backgrounds, but it is nowhere proven whether it is the exact gene or not. Because of this gap in knowledge, the genes behave differently genotypically and phenotypically as witnessed in this study.

The mapping population results showed five genotypes with a resistance phenotype in the A-allele cluster, a total of 46 genotypes with a susceptible phenotype in the B-allele cluster, 21 heterozygous genotypes and 46 misplaced genotypes. With the mapping population, the KASP assay was tested with the expectation that not all the genotypes would give the “supposed” results. This was due to the nature of the phenotyping method where there are many negative factors that could have resulted in the misreading of the results or mixing of seeds. Human error is inevitable and could have resulted in false positive or false negative results. In addition, the DNA used was bulked, meaning that every single replicate of an individual genotype was placed in a single test tube. There is a possibility of mixing those genotypes that had incorrect phenotypes. The resistant genotypes that were fluoresced by the susceptible dye, HEX, could be due to the backcrossing method during the development of the mapping population that only relied on conventional phenotyping results. Nevertheless, the marker was successful in distinguishing between the resistant and susceptible parental

genotypes, as well as other genotypes in different genetic backgrounds. Therefore, there is potential to refine a reliable marker for RWA breeding.

Figure 4.10 Dual colour scatter plot showing different clusters for the eight KASP markers on the two parental genotypes: Red diamonds = Resistant parent replicates; Blue circles = Susceptible parent replicates; Green squares = Heterozygous; Yellow triangles = Negative controls

Figure 4.11 Dual colour scatter plot showing different clusters for three KASP markers on the 24 genotyped NIL: Red diamonds = Mostly Resistant genotypes; Blue circles = Mostly Susceptible genotypes; Green squares = Heterozygous; Yellow triangles = Negative controls

Figure 4.12 Sequence alignments between the 9K SNP assay and KASP genotyping for the 22 NIL and parents: (A) Resistant genotypes with the resistance A-allele and (B) Susceptible genotypes with the susceptible B-allele

Research study discussion and conclusion

Since the first RWA resistance-breaking biotype in the U.S. in 2003 (Haley et al., 2004) and in South Africa (S.A.) in 2005 (Tolmay et al., 2007), a lot of studies have been conducted in order to understand the RWA-resistance mechanism. Different studies have been conducted mainly in the U.S. and S.A. with efforts to find feasible and successful strategies to control the pest. These studies include mapping of resistance genes on the 1D and 7D chromosomes of wheat. Ma et al. (1998), Liu et al. (2001, 2002, 2005) and Heyns et al. (2006) conducted RWA research using SSR markers to map different resistance genes on the two wheat chromosomes. A study by Fazel-Najafabadi et al. (2015) also succeeded in mapping the important RWA resistance gene, Dn2401, on the 7DS chromosome using SNPs and silicoDArTs markers. In their study, they used an F2 derived F3 (F2:3) segregating population as opposed to the backcross five F5 used in this study. Possibly high levels of heterozygosity still present in their population may be limiting. With the present study, there is little heterozygosity and heterogenity, which provides an advantage to specifically target resistance due to the Dn2401 gene. Fazel-Najafabadi et al. (2015) however, mapped the gene of interest using two flanking markers, proximal Xbarc214 at 1.1 cM and distal Xgwm473 at 1.8 cM. Their study employed SNP and silicoDArTs marker sequencing compared to this study that only used SNP genotyping. They also screened more SSR markers in their study compared this study, but that would have been redundant to do in the present case since they had already narrowed down the markers close to the gene. In addition, it had already been shown in the preliminary study that those SSR markers were not diagnostic. The results of this study do not invalidate SSR markers as completely inefficient, however, markers closer to the gene of interest may need to be identified. There can be reliance on SSR markers when they are

diagnostic for the genes, such as for disease resistance (Hayden et al., 2004; Tsilo et al., 2008); however, they have never worked for RWA and have been proven difficult and time consuming. Various studies by aforementioned Liu et al. (2001, 2002, 2005), among others, identified SSR markers.

Following the work by Fazel-Najafabadi et al. (2015), Staňková et al. (2015) also mapped the Dn2401 RWA resistance gene on chromosome 7DS using a different strategy called chromosomal genomics. In their study, they employed the comparative genomics strategy by using a synteny-based tool which combined sequence data information of rice, Brachypodium, sorghum and barley. They succeeded in narrowing down the gene region even further to 0.83 cM. With all these knowledge, our study considered the gene of interest, Dn2401, to be on the 7DS chromosome of wheat but the precise position of the gene would need fine-mapping and ultimate cloning of the gene. There is still the possibility of the gene being on 1D as Dong et al. (1997) showed a Dn4-allelic gene to be also present in the wheat accession CItr 2401. Our study merely introduced and designed another type of markers that might aid in successful mapping of the RWA resistance gene. These markers, KASP markers, have been widely and successfully used in disease resistance studies such as wheat stem rust (Babiker et al., 2015, 2016, Gao et al., 2015) and wheat mosaic virus (Liu et al., 2014; Tan et al., 2016). They have also been employed in wheat for pre-harvest sprouting tolerance (Cabral et al., 2014). For insect pest studies, Tan et al. (2015) identified a SNP marker for the selection of a Hessian fly-response gene in wheat while Emebiri et al. (2016) recently identified and validated SNP markers for Sunn pest resistance. This study will be the first reported study that tested the possibility of using SNP/KASP markers for RWA resistance in pre-breeding and breeding.

In this study, a co-dominant KASP marker IWA 5442 was developed, which will be useful for RWA resistance germplasm development, marker-assisted selection, and resistance gene stacking. The results from this study show the developed assay to be diagnostic, accurate, inexpensive and less time-consuming than the widely reported SSR gel-based markers for RWA resistance, and therefore can be used for high-throughput genotyping of many individuals. The markers designed in this study will be useful for surveying the RWA resistance germplasm collection at the ARC-SGI to determine the relative distribution of Dn2401 among landraces of diverse geographic origin. The tightly linked markers for the Dn2401 gene in CItr 2401 will provide a useful tool to stack this gene with other genes effective against RWA and with other effective resistances located on other chromosomes.

KASP assays used in this study may facilitate rapid introgression of this resistance into wheat breeding genotypes, but only in combination with other effective genes to preserve the effectiveness of this resistance source.

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CHAPTER 5

CONCLUSION

5. Chapter outline

This is the final chapter of the dissertation and it presents the final conclusions, as well as limitations and recommendations. The conclusions presented are linked to the results/findings and offer a solution to the research problem. The chapter ends off with suggestions for future research and the contribution of this study to the research community.

5.1 Final Conclusions

The results from this study stress the importance of continuous research aimed at protecting wheat and other cereals against the ever-changing RWA biotype complex. The lack of robust and reliable diagnostic markers for RWA-resistance as demonstrated in the preliminary study poses a huge gap in research. This study succeeded in demonstrating the need for such markers as it could be seen that the currently available markers were not reliable and diagnostic. It is often recommended to use multiple resistance sources in combination rather than over-relying on cultivars with single resistance genes. The strategy of pyramiding multiple resistance genes helps to avoid the development of resistance-breaking biotypes and ensures the protection of the best genes. Dn2401 is an important resistance gene and is resistant against all known biotypes worldwide. Therefore, it is imperative to only deploy it in the field against RWA once it has been stacked/combined with other resistance genes. The only way it can be combined with other Dn genes is through the use of diagnostic markers. With phenotyping alone, it is not effective to combine genes that are effective against all biotypes. This study explored the possibility of using SNP/KASP markers as markers of choice in RWA research. These types of markers have been widely explored in other wheat research disciplines such as quality studies, yield improvement, pre-harvest sprouting and disease resistance such as stem rust. In insect pest resistance studies, KASP markers have not been as widely explored but there has been some research reported for Hessian fly, Sunn pest and the wheat curl mite resistance. From this study, we can conclude that the genetics of RWA resistance are still at the infant stage and are highly misunderstood. This calls out for more research to be conducted before new resistance-breaking biotypes arise again.

5.2 Limitations and Recommendations

There has not been any reported study on the RWA that employs KASP markers for mapping RWA resistance. None of the SSR markers reported before are diagnostic for any of the Dn genes. From the results of this study, we recommend the ingression of more SNP and KASP markers in all current RWA resistance research. KASP markers have many advantages to them and since SSR markers have proven unreliable, KASP markers would therefore be a better alternative. Another recommendation is for the formation of more research consortiums in South Africa and the world as a whole. We have seen the amount of success that results from huge teams working together. The continuous success in the sequencing of the wheat genome is one such example. The RWA research community in particular, needs to get together more often and share ideas on how to achieve durable control of this important pest.

5.3 Suggestions for Future work

Based on the results from this study, marker IWA 5422 displayed a co-dominant nature, as it was able to clearly show polymorphism between most resistant and susceptible genotypes. This marker will have to be thoroughly and carefully validated on a wide variety of RWA pre-breeding genotypes in order to see their efficiency across different genetic backgrounds. If results still show this co-dominant nature, the marker will then be a true diagnostic marker, a first for RWA resistance. Moving forward, a larger mapping population needs to be developed, with the focus still being on the CItr 2401 resistance gene. A larger population with thousands of genotypes is being developed for fine mapping. Narrowing down the distance of markers close to the gene will make it more possible to efficiently use the gene in pre-breeding and breeding programmes.

Previous studies that have been referenced in this study have identified the resistance in CItr 2401 on the 7D chromosome of wheat. However, the resistance is yet to be fine-mapped and most importantly, diagnostic markers are yet to be developed. The ARC-Small Grain Institute in Bethlehem has all the necessary wheat germplasm necessary and all the South African RWA biotypes, making the study more feasible.

5.4 Contribution of the study

A drafting of the preliminary study results is currently underway for publication purposes. This paper is now at the final stages of drafting and will be sent in for publication

soon. Following validation of the KASP markers on different germplasm at the ARC-SGI, those results will also be made available to the public by means of a publication. Both these papers will shed some light in all those areas that are currently affected by the RWA but lack viable control strategies. This research study’s purpose it to show where complications regarding the control of RWA lie. Genetics of the RWA resistance are still highly misunderstood. This study has contributed to a better understanding of this field but the results from this study call for more consorted work to be done.

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APPENDICES

Appendix 1 Ethics approval letter

Appendix 2 Genotypes of the BC5F3 and BC5F5 mapping populations, RWA biotypes used and their phenotypic data

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Appendix 3 Photographs of plant symptoms, scores and gels for the 24 accessions and the 3 checks Name Damage rating Photograph Damage Photograph gel marker Photograph gel marker Photograph gel marker Photograph gel marker (±SD) [∑Values] RWASA2 Xgwm111 (bp) Xgwm44 (bp) Xgwm635 (bp) Xgwm437 (bp) Genotype 8.60 (0.548) [43] A50

120 120 120 120 120 200 200 200 200 200 200 200 200 200 200 100 100 100 100 100 190 190 190 190 190 100 100 100 100 100 8 9 9 9 8 160 160 160 160 160

BettaDn 9.00 (0.000) [45]

200 200 200 200 200 200 200 200 200 200 115 120 120 120 115 190 190 190 190 190 100 100 100 100 100 140 140 140 140 140 9 9 9 9 9 140 140 140 140 140

BW 991308 8.40 (0.894) [42]

200 200 200 200 200 190 190 190 190 190 120 120 120 120 120 105 105 105 105 105 190 190 190 190 190 160 160 160 160 160 100 100 100 100 100 9 9 8 9 7 130 130 130 130 130

BW 991405 8.40 (0.548) [42]

120 120 120 100 120 200 200 200 200 200 190 200 200 190 210 130 130 130 130 130 160 160 160 160 160 180 190 190 180 200 100 100 100 100

8 9 8 9 8

CItr 2401 3.20 (0.447) [16]

200 200 200 200 200 200 200 200 200 200 170 170 170 170 170 120 120 120 120 120 115 115 115 115 115 190 190 190 190 190 100 100 100 100 100 160 160 160 160 160

3 3 3 4 3

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Name Damage rating Photograph Damage Photograph gel marker Photograph gel marker Photograph gel marker Photograph gel marker (±SD) [∑Values] RWASA2 Xgwm111 (bp) Xgwm44 (bp) Xgwm635 (bp) Xgwm437 (bp) Gariep 9.00 (0.000) [45]

200 200 200 200 200 120 120 120 120 120 9 9 9 9 9 100 100 100 100 100

200 200 200 200 200 190 190 190 190 190 140 140 140 140 140 140 140 140 140 140

Hugenoot 9.00 (0.000) [45]

240 240 240 240 240 200 200 200 200 200 160 160 160 160 160 190 190 190 190 190

9 9 9 9 9

PAN3144 4.00 (0.000) [20]

170 170 170 170 170 180 180 180 180 180 110 110 125 125 125 125 125

130 130 130 130 130 140 140 140 140 4 4 4 4 4

PI 047545 3.80 (0.477) [19]

220 220 220 190 190 190 190 190 200 200 160 160 160 160 160 100 100 100 100 100 100 100 190 95 95 130 130 4 4 4 3 4

PI 137739 3.00 (0.000) [15]

240 240 240 240 200 200 200 200 200 170 170 170 170 100 100 100 100 100 110 110 110 110 110

190 190 190 190 190 3 3 3 3 3 160 160 160 160

102

220 200 220 200 220 220 220 105 105 105 105 105 160 160 160 160 200 200 200 200 200 8 8 9 3 3 100 100 100 100 100 105 105 100 110 100

140 140 140 140 190 190 190

PI 262660 8.00 (0.707) [40]

200 200 200 200 200 200 200 200 200 200 100 100 100 100 100 100 100 7 8 8 8 9 140 140 140 140 140 180 180 180 180 180

PI 294994 6.80 (2.588) [34]

220 220 210 190 190 210 210 210 210 210 210 220 210 220 220 100 210 100 200 200 4 9 4 8 9 160 160 200 200 200 200 200 200 210 200 210 210

140 140 140 140 140

PI 586954 3.40 (0.548) [17]

220 220 220 220 220 200 200 120 120 120 120 120 100 120 120 120 200 210 210 200 200 4 3 4 3 3 100 100 100 100 100 160 160 160 160 160 190 190 190 140 140 140 140

103

220 220 220 220 220 210 210 210 210 200 120 120 120 120 120 120 120 120 125 160 160 160 160 160 4 8 4 3 7 200 200 200 200 190 100 100 100 100

140 140 140 140 140

PI 626580 5.00 (1.225) [25]

200 200 200 200 200 200 210 210 210 210 120 125 125 120 120 120 120 120 120 120 160 160 160 160 160 4 5 4 7 5 190 200 200 200 200 100 105 105 100 100

140 140 140 140 140

PI 634769 9.00 (0.000) [36]

200 200 200 200 200 210 210 210 210 210 100 100 100 105 100 100 100 100 100

9 9 9 9 140 140 140 140 140 190 190 190 190 190

PI 634770 9.2 (0.447) [46]

230 230 220 220 220 200 200 200 200 200 110 110 110 110 110 9 9 9 10 9 105 105 105 90 90

160 160 190 190 190 190 190 100 100 100 100 100

140 140 140 140 140

104

220 220 100 120 100 115 115 9 7 9 9 200 190 200 200 210 200 200 200 200 105 105 105 105

160 160 160 200 190 190 190 190 100

140 140 140 140 140

T03/17 7.6 (0.894) [34]

120 120 120 120 120 210 210 210 210 210 200 200 200 200 100 100 100 100 100 120 120 120 120

7 9 7 7 8 160 160 160 160 160 190 190 190 190 190

T05/02 7.8 (0.447) [39]

210 210 210 120 120 120 120 120 190 190 190 190 190 120 120 120 120 120

200 200 200 7 8 8 8 8 180 180 180 180 100 100 100 100 100

170 170 170 170 170

T06/13 5.8 (3.033) [29]

120 120 120 120 120 220 220 220 220 220 200 200 200 200 200 120 120 120 120 120 170 170 170 170 170 190 190 190 190 190 100 100 100 100 100

8 8 8 3 2

105

120 120 120 120 120 200 200 200 200 200 200 200 200 200 200 125 125 125 125 125 4 3 3 3 3 150 150 150 150 150 190 190 190 190 190 100 100 100 100 100

Tugela 9.0 (0.000) [18]

190 190 190 190 190 200 200 200 200 200 9 9 160 160 160 160 160 190 190 190 190 190 140 140 140 140 140 150 150 150

TugelaDn 8.8 (0.447) [44]

200 200 200 120 120 120 120 120 240 240 240 240 240 190 190 190 190 190 100 100 100 100 100 160 160 160 160 160 9 9 9 9 8 120

TugelaDn2 8.2 (0.447) [41]

210 210 210 210 210 200 200 200 200 200 200 200 200 200 200 110 110 105 105 105 120 100 100 120 120

9 8 8 8 8 160 160 160 160 160

140 140 140 140 140

Yumar 8.2 (0.837) [41]

210 210 210 210 210 200 200 200 200 120 120 120 120 120 120 120 120 120 120

200 200 200 200 200 7 9 9 8 8 170 170 170 170 100 100 100 100 100 140 140 140 140 140 160 160 160 160

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Appendix 4 Summary statistics of the four biotypes following ANOVA

RWASA1 RWASA2 RWASA3 RWASA4

Number of 912 928 970 958 observations

Number of 138 122 80 92 missing values

Mean 8.446 8.352 7.604 7.566

Median 9 9 8 7

Minimum 4 2 3 4

Maximum 9 10 10 10

Standard 0.943 1.191 1.469 1.359 deviation

Standard error of 0.0312 0.0391 0.0472 0.04 mean Standard Error of 0.0723 0.124 0.107 0.0514 Variance

Coefficient of 11.16 14.26 19.32 17.96 variation

Sum of values 7703 7751 7376 7248

Sum of squares 809.4 1316 2092 1767 Uncorrected sum 65871 66055 58180 56604 of squares Skewness -1.875 -1.980 -0.912 -0.101

Standard Error of 0.0810 0.0803 0.0785 0.0790 Skewness Kurtosis 4.044 5.142 0.384 -1.257

Standard Error of 0.162 0.160 0.157 0.158 Kurtosis

107

Appendix 5a Histogram for scores (A group) showing how each individual entry reacted to the biotypes

108

Appendix 5b Histogram for scores (B group) showing how each individual entry reacted to the biotypes

109

Appendix 6 The 178 SNP markers used in linkage mapping with their relevant information

Marke Chromo Posit Size(2 Size Size(0 Size( Chi- Pr>C *Het r ID Marker Name some ion /12) (1) /10) -1) Square hiSq Band wsnp_Ex_c5323_940 0.00 0.001 1 8829 1 00 19 0 4 1 9.7826 8 NA wsnp_Ex_c3565_652 5.02 12.565 0.000 2 1098 1 00 3 0 20 1 2 4 NA wsnp_Ex_c14400_22 5.02 14.727 0.000 3 381548 1 00 2 0 20 2 3 1 NA wsnp_Ex_c30368_39 5.02 16.666 0.000 4 293223 1 00 2 0 22 0 7 0 NA wsnp_Ex_c21773_30 5.02 16.666 0.000 5 934348 1 00 2 0 22 0 7 0 NA wsnp_Ex_c14172_22 5.02 19.173 0.000 6 104887 1 00 1 0 22 1 9 0 NA wsnp_Ex_c15188_23 5.02 20.166 0.000 7 387754 1 00 1 0 23 0 7 0 NA wsnp_CAP8_rep_c94 5.02 18.181 0.000 8 77_4129165 1 00 1 0 21 2 8 0 NA wsnp_BE489901D_T 5.02 19.173 0.000 9 a_2_1 1 00 1 0 22 1 9 0 NA wsnp_Ku_c19251_28 5.02 20.166 0.000 10 705893_x 1 00 1 0 23 0 7 0 NA wsnp_Ku_c21787_31 5.02 20.166 0.000 11 570491_x 1 00 1 0 23 0 7 0 NA wsnp_BE403378B_T 5.02 20.166 0.000 12 a_2_1 1 00 1 0 23 0 7 0 NA wsnp_Ex_c2314_433 5.02 20.166 0.000 13 3242 1 00 1 0 23 0 7 0 NA wsnp_Ex_c1997_375 5.02 19.173 0.000 14 7415 1 00 1 0 22 1 9 0 NA wsnp_Ex_c12220_19 5.02 20.166 0.000 15 528388 1 00 1 0 23 0 7 0 NA wsnp_Ex_c57601_59 5.02 20.166 0.000 16 245380 1 00 1 0 23 0 7 0 NA wsnp_Ex_c20041_29 5.02 20.166 0.000 17 076295 1 00 1 0 23 0 7 0 NA wsnp_Ex_c23638_32 5.02 18.181 0.000 18 875196 1 00 1 0 21 2 8 0 NA wsnp_Ex_c9149_152 5.02 19.173 0.000 19 20489 1 00 1 0 22 1 9 0 NA wsnp_Ex_rep_c66615 5.02 19.173 0.000 20 _64916512 1 00 1 0 22 1 9 0 NA wsnp_Ex_c2178_408 5.02 20.166 0.000 21 6161_x 1 00 1 0 23 0 7 0 NA wsnp_Ex_c30552_39 5.02 20.166 0.000 22 457767 1 00 1 0 23 0 7 0 NA wsnp_Ex_c2043_382 5.02 20.166 0.000 23 9362 1 00 1 0 23 0 7 0 NA wsnp_Ku_c33917_43 5.02 18.181 0.000 24 336035 1 00 1 0 21 2 8 0 NA wsnp_Ex_c3572_653 5.02 19.173 0.000 25 1810 1 00 1 0 22 1 9 0 NA wsnp_Ku_c851_1762 5.02 20.166 0.000 26 904 1 00 1 0 23 0 7 0 NA wsnp_Ex_c13564_21 5.02 19.173 0.000 27 327699_x 1 00 1 0 22 1 9 0 NA wsnp_Ex_c56097_58 5.02 16.666 0.000 28 352130 1 00 2 0 22 0 7 0 NA

110

wsnp_Ku_c21412_31 5.02 16.666 0.000 29 166369 1 00 2 0 22 0 7 0 NA wsnp_BE605194B_T 5.02 16.666 0.000 30 a_2_1 1 00 2 0 22 0 7 0 NA wsnp_Ex_c43096_49 5.02 15.695 0.000 31 510164 1 00 2 0 21 1 7 1 NA wsnp_Ex_c1997_375 5.02 13.500 0.000 32 7508 1 00 3 0 21 0 0 2 NA wsnp_Ex_c9534_157 5.02 12.565 0.000 33 93556 1 00 3 0 20 1 2 4 NA wsnp_Ex_c35886_43 5.02 12.565 0.000 34 950574 1 00 3 0 20 1 2 4 NA wsnp_Ex_c23598_32 5.02 14.727 0.000 35 827681 1 00 2 0 20 2 3 1 NA wsnp_Ex_c1997_375 5.02 12.565 0.000 36 5945 1 00 3 0 20 1 2 4 NA wsnp_Ex_c4605_823 5.02 0.001 37 9915 1 00 4 0 19 1 9.7826 8 NA wsnp_Ex_c18433_27 7.52 14.727 0.000 38 269748 1 00 2 0 20 2 3 1 NA wsnp_Ex_rep_c70358 7.52 19.173 0.000 39 _69302556 1 00 1 0 22 1 9 0 NA wsnp_BE497361A_T 7.52 20.166 0.000 40 a_1_1 1 00 1 0 23 0 7 0 NA wsnp_Ex_c9149_152 7.52 20.166 0.000 41 20489_x 1 00 1 0 23 0 7 0 NA wsnp_BF293181A_T 7.52 20.166 0.000 42 a_2_4 1 00 1 0 23 0 7 0 NA wsnp_Ex_c56097_58 7.52 20.166 0.000 43 352130_x 1 00 1 0 23 0 7 0 NA wsnp_Ex_c44049_50 7.52 19.173 0.000 44 205457 1 00 1 0 22 1 9 0 NA wsnp_Ex_c4310_777 7.52 19.173 0.000 45 0452_x 1 00 1 0 22 1 9 0 NA wsnp_BE403214B_T 7.52 16.666 0.000 46 a_2_1 1 00 2 0 22 0 7 0 NA wsnp_Ex_c15475_23 7.52 14.727 0.000 47 756906 1 00 2 0 20 2 3 1 NA wsnp_Ex_c14866_22 7.52 16.666 0.000 48 995097 1 00 2 0 22 0 7 0 NA wsnp_Ex_c12963_20 7.52 16.666 0.000 49 529964 1 00 2 0 22 0 7 0 NA wsnp_BE497169B_T 7.52 16.666 0.000 50 a_2_1 1 00 2 0 22 0 7 0 NA wsnp_Ex_c56097_58 7.52 16.666 0.000 51 351893 1 00 2 0 22 0 7 0 NA wsnp_Ex_c11929_19 7.52 16.666 0.000 52 133203 1 00 2 0 22 0 7 0 NA wsnp_Ex_c33461_41 7.52 19.173 0.000 53 945399 1 00 1 0 22 1 9 0 NA wsnp_Ex_c13164_20 7.52 20.166 0.000 54 793506 1 00 1 0 23 0 7 0 NA wsnp_Ra_rep_c7023 12.0 13.500 0.000 55 3_67968353 1 800 3 0 21 0 0 2 NA wsnp_Ku_c851_1762 12.0 13.500 0.000 56 904_x 1 800 3 0 21 0 0 2 NA wsnp_RFL_Contig47 16.8 0.006 57 53_5709032 1 500 5 0 18 1 7.3478 7 NA wsnp_Ra_c55026_58 16.8 0.010 58 116021 1 500 5 0 17 2 6.5455 5 NA

111

wsnp_Ex_c5898_103 16.8 0.006 59 47629 1 500 5 0 18 1 7.3478 7 NA wsnp_Ex_rep_c66846 16.8 0.006 60 _65240088 1 500 5 0 18 1 7.3478 7 NA wsnp_Ex_c14733_22 16.8 0.006 61 819350 1 500 5 0 18 1 7.3478 7 NA wsnp_Ex_c8963_149 19.1 0.041 62 48293 1 300 7 0 17 0 4.1667 2 NA wsnp_Ex_c29867_38 26.6 0.021 63 850724 1 800 6 0 17 1 5.2609 8 NA wsnp_BE444359B_T 34.2 0.004 64 d_2_3 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c2814_520 34.2 0.004 65 3467 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c4310_777 34.2 0.004 66 0452 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c8131_137 34.2 0.004 67 54852 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c3565_652 34.2 0.004 68 0901 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c1374_263 34.2 0.006 69 0830 1 400 5 0 18 1 7.3478 7 NA wsnp_Ex_c50235_54 34.2 0.004 70 588957 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_rep_c68201 34.2 0.004 71 _66980814 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c1145_219 34.2 0.004 72 8433 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c22963_32 34.2 0.004 73 183009 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c10500_17 34.2 0.004 74 163855 1 400 5 0 19 0 8.1667 3 NA wsnp_Ra_c2078_403 34.2 0.004 75 7878 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_rep_c66919 34.2 0.004 76 _65342127 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c2178_408 34.2 0.004 77 6161 1 400 5 0 19 0 8.1667 3 NA wsnp_JD_c6544_769 34.2 0.004 78 7235 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c13564_21 34.2 0.004 79 327699 1 400 5 0 19 0 8.1667 3 NA wsnp_JD_c222_3523 34.2 0.004 80 20 1 400 5 0 19 0 8.1667 3 NA wsnp_Ex_c15458_23 34.2 0.006 81 737002 1 400 5 0 18 1 7.3478 7 NA wsnp_Ku_c27177_37 34.2 0.004 82 127542 1 400 5 0 19 0 8.1667 3 NA wsnp_Ku_c5560_985 34.2 0.004 83 1459 1 400 5 0 19 0 8.1667 3 NA wsnp_Ku_c4900_877 34.2 0.006 84 8960 1 400 5 0 18 1 7.3478 7 NA wsnp_Ex_c3906_708 0.00 0.102 85 6162 2 00 16 0 8 0 2.6667 5 NA wsnp_JD_c24506_20 0.00 0.102 86 670773 2 00 16 0 8 0 2.6667 5 NA wsnp_Ex_rep_c66331 0.00 0.060 87 _64502363 3 00 7 0 16 1 3.5217 6 NA wsnp_Ex_c5543_976 2.27 0.220 88 3520 3 00 9 0 15 0 1.5000 7 NA

112

wsnp_Ex_c34260_42 2.27 0.297 89 602746 3 00 9 0 14 1 1.0870 1 NA wsnp_BF474862A_T 2.27 0.297 90 a_2_1 3 00 9 0 14 1 1.0870 1 NA wsnp_Ex_c2314_433 2.27 0.414 91 3242_x 3 00 10 0 14 0 0.6667 2 NA wsnp_CAP7_rep_c55 0.00 0.297 92 24_2482342 4 00 14 0 9 1 1.0870 1 NA wsnp_CAP7_c3635_1 4.78 1.000 93 688824 4 00 12 0 12 0 0.0000 0 NA wsnp_CAP7_c599_31 11.9 0.220 94 2057 4 700 9 0 15 0 1.5000 7 NA wsnp_Ra_c4184_763 19.5 0.531 95 7695 4 300 10 0 13 1 0.3913 6 NA wsnp_Ra_rep_c6982 21.8 0.834 96 0_67401482 4 000 11 0 12 1 0.0435 8 NA wsnp_Ra_c32175_41 24.0 0.834 97 221223 4 700 12 0 11 1 0.0435 8 NA wsnp_Ra_c26947_36 24.0 0.834 98 495207 4 700 12 0 11 1 0.0435 8 NA wsnp_Ex_c2273_425 24.0 0.834 99 9708 4 700 12 0 11 1 0.0435 8 NA wsnp_Ex_c3119_576 24.0 0.834 100 3762 4 700 12 0 11 1 0.0435 8 NA wsnp_Ku_c16295_25 24.0 0.834 101 149034 4 700 12 0 11 1 0.0435 8 NA wsnp_Ex_c3096_570 24.0 0.669 102 8642 4 700 12 0 10 2 0.1818 8 NA wsnp_Ra_rep_c6938 24.0 0.834 103 4_66802201 4 700 12 0 11 1 0.0435 8 NA wsnp_CAP11_c269_2 24.0 0.834 104 33382 4 700 12 0 11 1 0.0435 8 NA wsnp_Ra_rep_c1082 26.4 1.000 105 84_91604017 4 600 11 0 11 2 0.0000 0 NA wsnp_Ex_rep_c70809 28.8 0.683 106 _69689636 4 400 13 0 11 0 0.1667 1 NA wsnp_Ex_c6378_110 28.8 0.834 107 87794 4 400 12 0 11 1 0.0435 8 NA wsnp_Ra_c60161_61 31.1 0.683 108 164295 4 100 13 0 11 0 0.1667 1 NA wsnp_Ex_rep_c69123 31.1 0.834 109 _68034403 4 100 12 0 11 1 0.0435 8 NA wsnp_Ra_c53181_56 31.1 0.683 110 932563 4 100 13 0 11 0 0.1667 1 NA wsnp_Ra_c19637_28 38.3 0.414 111 840370 4 100 14 0 10 0 0.6667 2 NA wsnp_BM140362A_T 38.3 0.414 112 a_2_2 4 100 14 0 10 0 0.6667 2 NA wsnp_CAP7_c599_31 38.3 0.297 113 2057_x 4 100 14 0 9 1 1.0870 1 NA wsnp_Ex_c22881_32 0.00 0.834 114 098459 5 00 11 0 12 1 0.0435 8 NA wsnp_Ku_c47386_53 0.00 0.683 115 862969 5 00 11 0 13 0 0.1667 1 NA wsnp_JD_c5861_701 0.00 0.683 116 8974 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c24936_34 0.00 0.683 117 192036 5 00 11 0 13 0 0.1667 1 NA wsnp_JD_c40990_29 0.00 0.531 118 127031 5 00 10 0 13 1 0.3913 6 NA

113

wsnp_Ex_rep_c81556 0.00 0.683 119 _76277906 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c4148_749 0.00 0.683 120 4665 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c4148_749 0.00 0.531 121 5656 5 00 10 0 13 1 0.3913 6 NA wsnp_Ex_rep_c71376 0.00 0.683 122 _70138381 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_rep_c10174 0.00 0.683 123 6_87053634 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_rep_c10141 0.00 0.683 124 4_86780996 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c9729_160 0.00 0.683 125 71358 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c2609_485 0.00 0.683 126 2360 5 00 11 0 13 0 0.1667 1 NA wsnp_Ku_c19251_28 0.00 0.683 127 705893 5 00 11 0 13 0 0.1667 1 NA wsnp_Ku_c21787_31 0.00 0.683 128 570491 5 00 11 0 13 0 0.1667 1 NA wsnp_BF482891A_T 0.00 0.683 129 a_2_2 5 00 11 0 13 0 0.1667 1 NA wsnp_Ra_rep_c7086 0.00 0.683 130 4_68811253 5 00 11 0 13 0 0.1667 1 NA wsnp_Ra_rep_c7493 0.00 0.683 131 6_72685894 5 00 11 0 13 0 0.1667 1 NA wsnp_BF293181A_T 0.00 0.683 132 a_2_4_x 5 00 11 0 13 0 0.1667 1 NA wsnp_Ex_c6381_110 2.18 0.414 133 93111 5 00 14 0 10 0 0.6667 2 NA wsnp_Ra_c51684_55 2.18 0.414 134 934684 5 00 10 0 14 0 0.6667 2 NA wsnp_RFL_Contig17 2.18 0.414 135 36_858448 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c7266_125 2.18 0.414 136 51309 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c665_1371 2.18 0.414 137 121 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c854_1768 2.18 0.414 138 346 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c3844_705 2.18 0.414 139 3350 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c37925_46 2.18 0.531 140 678703 5 00 10 0 13 1 0.3913 6 NA wsnp_Ku_c2614_497 2.18 0.414 141 0880 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c3572_653 2.18 0.414 142 3892 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c3831_696 2.18 0.531 143 5890 5 00 10 0 13 1 0.3913 6 NA wsnp_Ra_c3045_575 2.18 0.414 144 4073 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c14082_22 2.18 0.414 145 272647 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_rep_c69044 2.18 0.414 146 _67947270 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_rep_c10152 2.18 0.414 147 6_86881496 5 00 10 0 14 0 0.6667 2 NA wsnp_Ku_c16295_25 2.18 0.414 148 148628 5 00 10 0 14 0 0.6667 2 NA

114

wsnp_Ku_c16895_25 2.18 0.414 149 861847 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c34821_43 2.18 0.297 150 076533 5 00 9 0 14 1 1.0870 1 NA wsnp_Ex_c293_5670 2.18 0.414 151 35 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c26776_36 2.18 0.414 152 003586 5 00 10 0 14 0 0.6667 2 NA wsnp_Ra_c11594_18 2.18 0.414 153 777085 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c25628_34 2.18 0.414 154 888439 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c25043_34 2.18 0.414 155 305764 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c13955_21 2.18 0.297 156 833712 5 00 9 0 14 1 1.0870 1 NA wsnp_Ex_c12875_20 2.18 0.414 157 407926 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c12399_19 2.18 0.297 158 776420 5 00 9 0 14 1 1.0870 1 NA wsnp_Ex_c12220_19 2.18 0.414 159 528388_x 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c11573_18 2.18 0.414 160 650189 5 00 10 0 14 0 0.6667 2 NA wsnp_Ex_c10783_17 2.18 0.297 161 554146 5 00 9 0 14 1 1.0870 1 NA wsnp_RFL_Contig35 2.18 0.220 162 01_3652740 5 00 9 0 15 0 1.5000 7 NA wsnp_Ex_c18665_27 2.18 0.220 163 541726 5 00 9 0 15 0 1.5000 7 NA wsnp_Ex_c15399_23 0.00 0.531 164 662312 6 00 10 0 13 1 0.3913 6 NA wsnp_Ex_c32467_41 5.02 0.531 165 117359 6 00 10 0 13 1 0.3913 6 NA wsnp_CAP12_c680_3 0.00 0.834 166 63345 7 00 11 0 12 1 0.0435 8 NA wsnp_Ra_c4481_811 0.00 0.834 167 9609 7 00 11 0 12 1 0.0435 8 NA wsnp_Ex_c20041_29 0.00 1.000 168 076295_x 7 00 11 0 11 2 0.0000 0 NA wsnp_Ex_c12480_19 0.00 0.414 169 889644 8 00 14 0 10 0 0.6667 2 NA wsnp_Ex_c3906_708 2.27 0.297 170 6294 8 00 14 0 9 1 1.0870 1 NA wsnp_Ra_c4135_756 4.55 0.414 171 5040 8 00 14 0 10 0 0.6667 2 NA wsnp_CAP12_c1960_ 4.55 0.669 172 972031 8 00 12 0 10 2 0.1818 8 NA wsnp_Ex_c13164_20 4.55 0.531 173 793506_x 8 00 13 0 10 1 0.3913 6 NA wsnp_Ex_rep_c67697 4.55 0.414 174 _66363222 8 00 14 0 10 0 0.6667 2 NA wsnp_Ex_c64327_63 4.55 0.414 175 176640 8 00 14 0 10 0 0.6667 2 NA wsnp_Ex_c7252_124 4.55 0.414 176 53079 8 00 14 0 10 0 0.6667 2 NA wsnp_Ex_c8364_140 4.55 0.414 177 95508 8 00 14 0 10 0 0.6667 2 NA wsnp_Ku_c1629_320 9.11 1.000 178 6989 8 00 12 0 12 0 0.0000 0 NA *NA- Not Applicable 115

Appendix 7 List of publications and conference presentations

Publication: 1. Book chapter Sikhakhane, T.N., Figlan, S., Mwadzingeni, L., Ortiz, R. and Tsilo, T.J. (2016). Chapter 2: Integration of next-generation sequencing technologies with comparative genomics in cereals. In: Abdurakhmonov, I.Y. (Ed). Plant Genomics, InTech, Croatia. Publisher: InTech, ISBN 978-953-51-2456-6. pp.29-44.

Conference presentations: 1. South African Plant Breeders’ Association Conference (March 8-10, 2016)

Poster: Phenotypic screening of a wheat BC5F3 population infested with the four South African Russian wheat aphid biotypes Authors: T.N. Sikhakhane, V.L. Tolmay and T.J. Tsilo 2. 22nd Biennial International Plant Resistance to Insects Symposium (March 5-8, 2016) Oral: Analysing molecular markers linked to Russian wheat aphid resistance on different lines of wheat (Received 2nd prize for Best MSc presentation) Authors: T.N. Sikhakhane, V.L. Tolmay, S. Sydenham and T.J. Tsilo 3. Combined Congress (Jan 18-21, 2016) Poster: Integration of next-generation sequencing technologies with comparative genomics in cereals Authors: T.N. Sikhakhane, S. Figlan, L. Mwadzingeni, R. Ortiz and T.J. Tsilo 4. Combined Congress (Jan 19-22, 2015) Poster: Improvement of integrated plant host resistance to pests in agriculturally important crops Authors: TN Sikhakhane, CC Dweba, A Jankielsohn and TJ Tsilo 5. Combined Congress (Jan 19-22, 2015) Poster: Plant fungal disease resistance genes: improving our understanding Authors: CC Dweba, TN Sikhakhane, T Baloyi, T Hlongoane and TJ Tsilo

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