Genotypic and Phenotypic Analysis of a Nepali Spring Wheat (Triticum Aestivum L.) Population

Genotypic and Phenotypic Analysis of a Nepali Spring Wheat (Triticum aestivum L.) Population

by Kamal Khadka

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Doctor of Philosophy in Plant Agriculture

Guelph, Ontario, Canada © Kamal Khadka, May, 2020

ABSTRACT

GENOTYPIC AND PHENOTYPIC ANALYSIS OF A NEPALI SPRING WHEAT (TRITICUM AESTIVUM L.) POPULATION

Kamal Khadka Advisor(s): Dr. Alireza Navabi University of Guelph, 2020 Dr. Manish N. Raizada

Nepal has been completely dependent on introduced wheat (Triticum aestivum L.) germplasm for variety development despite having >500 landraces in the national genebank. No

Nepali wheat genetic resources were involved in the development of any of the 43 varieties released in Nepal for commercial cultivation. Nepal’s capacity to genotype and phenotype its wheat germplasm, in order to utilize it for breeding, is in its infancy due to a lack of resources.

To assist breeding efforts for Nepal, here, I hypothesized that: (1) Nepali spring wheat germplasm is genetically and phenotypically diverse; (2) that the important physio- morphological traits have a genetic basis; and (3) that promising accessions for future targeted breeding can be identified using such genotyping and phenotyping. I assembled the Nepali

Wheat Diversity Panel (NWDP) consisting of 318 spring wheat accessions including landraces,

CIMMYT lines and released varieties. The NWDP was phenotyped in four different field experiments (2 each in Nepal and Canada) and also under controlled conditions. Analysis of 95K high density GBS markers showed greater genetic diversity in the Nepali landrace group compared to modern germplasm. Unexpectedly, the population structure analysis revealed four, rather than 3 subpopulations as was originally expected based on breeding history, with significant admixture within each subpopulation. A genome-wide association study (GWAS)

revealed 15 significant marker-trait associations (MTAs) for 6 agro-morphological traits.

Targeted genotyping was conducted to assess the accessions for allelic variation at dwarfing loci

(Rht) and a photoperiod insensitivity locus (Ppd), both targets of modern selection. Promising accessions for future breeding were identified that possessed dwarfing alleles but conversely also seedling vigour related traits with potential to promote early season drought tolerance. The

NWDP also showed significant variation for NDVI, SPAD values and shoot waxiness. I suggest that the Nepali landraces should be further characterized to identify the “authentic” landraces while the genotypic information available should be further utilized in genomic selection. The data suggest that shoot waxiness may be confounding spectral reflectance measurements especially when a germplasm population is extremely diverse. In conclusion, it is hoped that this thesis will better inform and accelerate wheat breeding for Nepal.

Keywords: Nepali Wheat Diversity Panel, population structure, GWAS, physio-morphological traits, dwarfing alleles, NDVI, waxiness

DEDICATION

This thesis is dedicated to Dr. Alireza Navabi, a one-of-a-kind professor, great mentor, dedicated breeder and friend. Thank you for the trust that you had in me.

ACKNOWLEDGEMENTS

First of all, I would like to extend my heartfelt gratitude to my primary advisor, Dr. Alireza Navabi, who passed away in early 2019 fighting bravely with pancreatic cancer; for his thorough and continuous support until the last few days of his life. Dr. Navabi was an excellent mentor, a friend, and a great human being. I feel proud that I had the opportunity to work with such a great soul. I would like to express my sincere thanks to Dr. Manish N. Raizada for his support throughout these five years not just as a co-advisor but more like a guardian. I am extremely grateful to Dr. Raizada’s endless support and guidance from the day I met him which has in fact, influenced my career. In addition, I would also like to candidly thank Professors Hugh Earl and Zeny Feng for serving on my Ph.D. advisory committee and advising me on my project to improve the quality of research and writing. In particular, Dr. Earl was very supportive despite his extremely busy schedule. Also, I thank Dr. Andrew J. Burt for helping me out on my research writing after the passing away of Dr Navabi. Further, Dr. Davoud Torkamaneh and Dr. Mina Kaviani deserve genuine acknowledgement for their support in handling genotypic data and manuscript edits. Special thanks go to Harwinder Singh Sidhu and Dr. Mitra Serazajari for their constant support and encouragement throughout these five years. I feel lucky that I had the opportunity to work with a bunch of great colleagues in the Wheat Breeding Lab who helped me enormously to take care of my research work. I would like to thank the lab technicians, Nick Wilker, Melinda Drummond, and Kat Bosnic, for their great support in managing seed and field trials in Canada. Also, helping hands from Ryan Costello, Zhang Zhanghao, Kaitlyn Ranft and Elijah Dalton were highly valued. I cannot stop thanking my other lab members Emily Gordon, Andy Chen, Dr. Soren Seifi and Dr. Ismael Schegoscheski, for their support and beautiful friendship. I also thank the members of Raizada lab especially Dr. Malinda Thilakarathna, Dr. Tejendra Chapagain, Dr. Travis Goron and Finlay Small for their support and friendship.

Furthermore, the growth facility coordinators in the Crop Science Building, Dietmar Scholz and Sue Couling, deserve appreciation from me. I would also like to recognize the primary funding from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by the International Development Research Centre (IDRC, Ottawa) and Global Affairs Canada, and partial funding from SeCan, the Agricultural Adaptation Council and Grain Farmers of Ontario. In addition, the collaborator for Nepal field experiment Nepal Agricultural Research Council (NARC, Nepal) is highly acknowledged. Finally, I have to thank my wife, Anuja, and my daughter, Aarohi, for their love, support and patience through good and bad times during these five years. At this moment, I would like to remember my parents, brother and sister in Nepal, who had a big dream for me, for their love and patience to finally achieve this.

TABLE OF CONTENTS

Abstract ...... ii

Dedication ...... iv

Acknowledgements ...... v

Table of Contents ...... vii

List of Tables ...... xv

List of Figures ...... xvii

List of Symbols, Abbreviations and nomenclature ...... xix

List of Appendices ...... xxi

1 General Overview ...... 1

2 Review of the Literature ...... 3

2.1 Abstract ...... 3

2.2 Wheat in the context of global food security ...... 3

2.3 Status of wheat research in Nepal ...... 4

2.4 Factors affecting wheat productivity ...... 7

2.4.1 Key biotic and abiotic factors affecting wheat yield ...... 7

2.4.2 Factors affecting wheat productivity in Nepal ...... 8

2.5 Breeding high yielding, stress tolerant wheat varieties ...... 9

2.5.1 The physiological approach to wheat breeding ...... 10

2.5.2 Molecular approaches to wheat breeding ...... 11

2.5.3 Genome-wide association mapping (GWAS) ...... 12

2.5.4 Kompetitive Allele-Specific PCR (KASP) ...... 12

2.5.5 Genetic diversity for breeding better wheat varieties ...... 13

2.6 Physio-morphological traits for breeding high yielding wheat varieties ...... 14 vii

2.6.1 Phenological traits ...... 14

2.6.2 Coleoptile length ...... 14

2.6.3 Chlorophyll content ...... 15

2.6.4 Normalized difference vegetative index ...... 16

2.6.5 Waxiness ...... 17

2.6.6 Grain filling period ...... 17

2.6.7 Awn length ...... 18

2.7 Improving the efficiency of phenotyping physio-morphological traits ...... 18

2.8 Research gap, hypothesis and objectives ...... 19

2.8.1 Research hypotheses ...... 21

2.8.2 Research objectives ...... 21

3 A Physio-morphological Trait-based Approach for Breeding Drought Tolerant Wheat ………………………………………………………………………………………………23

3.1 Abstract ...... 24

3.2 Introduction ...... 24

3.3 Targets for breeding drought-tolerant wheat ...... 31

3.3.1 Selection environment ...... 31

3.3.2 Physio-morphological traits ...... 31

3.4 Growth stage based targets ...... 32

3.4.1 Germination and seedling stages ...... 44

3.4.2 Tillering and stem elongation stages...... 44

3.4.3 Heading and anthesis stages ...... 45

3.4.4 Grain filling stage ...... 46

3.5 Phenotyping above ground physio-morphological traits associated with drought tolerance at different growth stages ...... 47 viii

3.5.2 Germination and seedling growth stages ...... 49

3.5.2.1 Coleoptile length and gibberellic acid (GA3) sensitivity ...... 49

3.5.2.2 Seedling vigour ...... 50

3.5.3 Vegetative growth to post-anthesis growth stages ...... 51

3.5.3.1 Number of Tillers ...... 51

3.5.3.2 Chlorophyll content ...... 52

3.5.3.3 Normalized difference vegetative index ...... 53

3.5.3.4 Chlorophyll fluorescence ...... 54

3.5.3.5 Shoot waxiness ...... 55

3.5.3.6 Carbon isotope discrimination ...... 56

3.5.3.7 Leaf rolling ...... 57

3.5.3.8 Canopy temperature ...... 58

3.5.3.9 Days to heading and anthesis...... 58

3.5.3.10 Flag leaf senescence ...... 59

3.5.3.11 Grain filling rate and duration ...... 60

3.5.3.12 Awn length ...... 61

3.5.3.13 Plant height ...... 62

3.6 Phenotyping root architectural traits ...... 62

3.7 Improving the efficiency of phenotyping physio-morphological traits ...... 65

3.7.1 Application of controlled physical structures ...... 65

3.7.2 High-throughput phenotyping platforms (HTPPs) ...... 66

3.7.2.1 Proximal high-throughput phenotyping platforms ...... 67

3.7.2.2 Remote sensing for high throughput phenotyping ...... 68

3.8 Conclusions and future perspectives ...... 70

3.9 Acknowledgments ...... 74

4 Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought Tolerant Wheat ...... 75

4.1 Abstract ...... 76

4.2 Introduction ...... 76

4.3 Utilization of genetic variation from diverse sources ...... 78

4.4 Molecular breeding of wheat for drought tolerance ...... 90

4.4.1 Identification of QTLs for drought tolerance related traits ...... 90

4.4.2 Genome engineering techniques ...... 97

4.5 Future perspectives ...... 97

4.6 Acknowledgments ...... 98

5 The Population Structure of Nepali Spring Wheat (Triticum aestivum L.) Germplasm Appears to be Governed by Historical Introductions ...... 100

5.1 Abstract ...... 101

5.2 Introduction ...... 101

5.3 Materials and methods ...... 104

5.3.1 Plant materials ...... 104

5.3.2 DNA extraction and genotyping-by-sequencing (GBS) ...... 105

5.3.3 GBS data analysis ...... 105

5.3.4 Analysis of SNP distribution and genetic diversity ...... 106

5.3.5 Population structure analysis ...... 106

5.3.6 Linkage disequilibrium decay analysis ...... 107

5.4 Results ...... 107

5.4.1 The density of polymorphic GBS markers differs among the genomes ...... 107

5.4.2 The populations from different sources vary in genetic diversity ...... 110 x

5.4.3 The subgroup separation did not correlate to the seed source ...... 110

5.4.4 Linkage disequilibrium decayed more rapidly in the whole population ...... 112

5.5 Discussion ...... 115

5.5.1 The population structure may be governed by the extensive introduction of germplasm into Nepal ...... 115

5.5.2 Higher genetic diversity in landraces ...... 118

5.5.3 The D genome is the least polymorphic genome as expected ...... 119

5.5.4 Variation in linkage disequilibrium (LD) patterns may indicate the level of selection pressure ...... 120

5.6 Conclusions and future perspectives ...... 121

5.7 Acknowledgments ...... 121

6 A Genome-wide Association Study Reveals the Genetic Basis for Agro-morphological Traits in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel ...... 123

6.1 Abstract ...... 124

6.2 Introduction ...... 124

6.3 Materials and methods ...... 127

6.3.1 Plant materials ...... 127

6.3.2 Experimental conditions and field trial design ...... 127

6.3.3 Phenotyping of agro-morphological traits ...... 128

6.3.4 Phenotypic data analysis ...... 129

6.3.5 DNA extraction and genotyping of the panel ...... 130

6.3.6 Population structure and LD analysis ...... 130

6.3.7 Marker-trait association analysis ...... 131

6.3.8 Candidate gene identification ...... 131

6.4 Results ...... 132

6.4.1 Assessment of phenotypic traits ...... 132

6.4.2 Genotypic data and population structure ...... 138

6.4.3 GWAS of agro-morphological traits ...... 138

6.5 Discussion ...... 142

6.5.1 Phenotypic variation for agro-morphological traits exists in the NWDP ...... 142

6.5.2 Genomic regions associated with morphological traits ...... 144

6.5.3 Candidate genes in each QTL region ...... 145

6.6 Conclusions ...... 149

6.7 Acknowledgments ...... 149

7 Phenotyping and Identification of Reduced Height (Rht) and Photoperiod Sensitivity (Ppd) Alleles in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel to Enable Seedling Vigour Selection ...... 150

7.1 Abstract ...... 151

7.2 Introduction ...... 151

7.3 Materials and methods ...... 154

7.3.1 Plant materials ...... 154

7.3.2 Phenotyping coleoptile length and seedling root length ...... 155

7.3.3 Phenotyping for height plant ...... 156

7.3.4 DNA extraction ...... 156

7.3.5 Genotyping by PCR amplification of Kompetitive Allele Specific PCR ...... 157

7.3.6 Cluster analysis of genotypic data ...... 159

7.3.7 Phenotypic data analysis ...... 159

7.3.8 Data visualization...... 160

7.4 Results ...... 160

7.4.1 Variation for seedling vigour traits and Rht/Ppd loci ...... 160 xii

7.4.2 Distinct genetic clusters identified based on genotypic data ...... 163

7.4.3 Effect of breeding history on coleoptile and root length ...... 168

7.4.4 Identification of likely semi-dwarf accessions with seedling vigour potential ..... 170

7.4.5 Effect of GA3 treatment by genotype ...... 174

7.4.6 Identification of accessions with GA-responsive coleoptiles ...... 179

7.5 Discussion ...... 181

7.5.1 Allelic variation at Rht and Ppd loci in the NWDP population ...... 181

7.5.2 Effects of dwarfing alleles at Rht loci on coleoptile and seedling root length ..... 182

7.5.3 Effects of alleles at Ppd loci on coleoptile and seedling root length ...... 183

7.5.4 Identification of candidate NWDP accessions for longer coleoptile length and other seedling vigour traits to facilitate breeding ...... 184

7.5.5 The need to genotype other GA-responsive Rht genes ...... 186

7.6 Conclusions and future perspectives ...... 186

7.7 Acknowledgments ...... 187

8 Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat? ...... 188

8.1 Abstract ...... 189

8.2 Introduction ...... 189

8.3 Materials and methods ...... 192

8.3.1 Plant materials ...... 192

8.3.2 Field trials ...... 193

8.3.3 Field data collection ...... 193

8.3.4 Phenotypic data analysis ...... 196

8.4 Results ...... 197

8.4.1 The response of traits to different growing environments ...... 197 xiii

8.4.2 Correlation of chlorophyll-related traits with waxiness and grain yield ...... 197

8.4.3 Principal component analysis ...... 199

8.4.4 Within trial correlation analysis ...... 199

8.5 Discussion ...... 208

8.5.1 Does wax interfere with NDVI? ...... 208

8.5.2 UAV versus NDVI measurements ...... 212

8.5.3 Grain yield and its association with physiological traits ...... 212

8.5.4 Should NDVI be used in future diversity studies for wheat grain yield? ...... 214

8.6 Conclusions ...... 215

8.7 Acknowledgments ...... 215

9 General Summary, Future Perspectives, and Conclusions ...... 217

9.1 A general summary of findings ...... 217

9.2 Future perspectives ...... 219

9.3 Conclusions ...... 221

References ...... 222

Appendices ...... 287

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LIST OF TABLES

Table 3.1. Examples of previous studies that measured wheat yield declines due to drought imposed at different growth stages...... 35

Table 4.1. Recent peer reviewed reports (2011-current) of germplasm evaluation for assessing drought tolerance in wheat...... 79

Table 5.1. Genetic diversity within the three components of the Nepali Wheat Diversity Panel...... 109

Table 6.1. Mean, standard error of the mean, mean ranges and heritability for traits evaluated during the 2016 and 2017 wheat growing seasons in Nepal and Canada...... 133

Table 7.2. Least squares means for coleoptile and root length of genotypes with Rht-B1, Rht-D1 and Ppd-D1 and their interaction without GA treatment...... 162

Table 7.3. The allelic variation observed at the Rht and Ppd loci in the Nepali Wheat Diversity Panel...... 164

Table 7.4. Tukey’s pairwise comparison of mean values within a treatment group at 95% confidence for genotypes segregated into three groups based on seed source...... 171

Table 7.5. List of likely semi-dwarf accessions identified as having potential seedling vigour traits...... 172

Table 7.6. Tukey’s pairwise comparison of the mean response of NWDP seedling traits to GA3 treatment at 95% confidence...... 175

Table 7.7. Tukey’s pairwise comparison of the mean response of seedling traits from different breeding history groups to GA3 treatment (at 95% confidence)...... 175

Table 8.1. Pearson correlation measurements for pairs of phenotypic traits evaluated in four field experiments conducted in 2016 and 2017...... 201

Table 8.4. Discrepancy observed between the correlations for waxiness with AUNC, and waxiness with AUSC...... 211

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LIST OF FIGURES

Figure 2.1. Trend of the area for cultivation and productivity of wheat in Nepal from 1990 to 2017...... 6

Figure 3.1. Global map showing the change in precipitation observed during two different time periods from 1901-2010 (left) and 1951-2010 (right)...... 27

Figure 3.2. Major wheat growing areas around the world...... 28

Figure 3.3. The figure shows different growth stages of wheat along with the associated visible growth events and traits related to drought tolerance...... 33

Figure 3.4. Summary of physio-morphological traits associated with different growth stages and phenotyping methods in wheat...... 48

Figure 3.5. A proposed comprehensive strategy for breeding wheat for drought tolerance...... 72

Figure 5.1. Analysis of SNP markers in the Nepali Wheat Diversity Panel (NWDP). (a) Proportional distribution of SNP markers across genomes A, B, and D in 318 spring wheat genotypes. (b) Distribution of 95,388 single nucleotide polymorphism (SNP) markers in the three wheat sub-genomes (A, B and D) across all 21 chromosomes from the 318 spring wheat genotypes. (c) Distribution of minor allele frequency for 95,388 SNP markers amongst the 318 spring wheat genotypes...... 108

Figure 5.2. Population structure analysis of the NWDP. (a) Estimated population structure of 318 spring wheat genotypes from Nepal on K= 4. Columns represent individual wheat accessions, while the length represents the proportion of each subpopulation (indicated by the colour) belonging to that accession. (b) Dendrogram based on cluster analysis using pairwise genetic distances. (c) Principal component analysis (PCA) using 95K GBS markers...... 111

Figure 6.1. Pearson correlation values for pairs of phenotypic traits evaluated using the trait means calculated from all four field experiments conducted in 2016 and 2017...... 135

Figure 6.3. Manhattan plots obtained from GWAS in the NWDP for: a) awn length at Elora 2016, b) days to anthesis at ABD (Nepal) 2017, c) days to maturity at Elora 2017, d) grain yield

xvii

at Elora 2016, e) grain yield at Elora 2017, f) grain yield at NWRP (Nepal) 2017, g) pre-harvest sprouting at Elora 2016 and h) test weight at NWRP (Nepal) 2017...... 139

Figure 7.1. Dendrogram showing clusters based on accessions carrying the same allele combinations at three loci (Rht-B1, Rht-D1 and Ppd-D1)...... 165

Figure 7.2. Biplots generated to distinguish the effect of alleles at Rht-B1 (a), Rht-D1 (b) and Ppd-D1 (c) loci on NWDP accessions...... 167

Figure 7.3. A biplot generated to distinguish the effect of allele combinations at multiple loci on NWDP accessions in the absence of a GA3 treatment...... 169

Figure 7.4. Effect of GA3 treatment on coleoptile as a factor of alleles at the Rht-B1 and Rht-D1 loci. Shown are GA-response box plots illustrating the frequency distribution of NWDP accessions for coleoptile a separated into groups based on their alleles at the loci indicated. ... 177

Figure 7.5. Effect of GA3 treatment on coleoptile length as a factor of alleles at the Rht-B1 and Rht-D1 loci combined. Shown are GA-response box plots illustrating the frequency distribution of NWDP accessions for coleoptile length, separated into groups based on their alleles at the loci indicated...... 178

Figure 8.1. Pearson correlation values for pairs of phenotypic traits evaluated using the trait means calculated from all four field experiments conducted in 2016 and 2017...... 198

Figure 8.2. A biplot generated for grain yield, waxiness and chlorophyll-related traits evaluated in four field experiments in 2016 and 2017...... 200

Figure 8.3. Scatter plots of AUNC with grain yield from four field experiments conducted in 2016 and 17. Trend line indicates a Pearson Correlation line of best fit...... 202

Figure 8.4. Scatterplots of waxiness ratings and AUNC and AUSC from three field experiments conducted in 2016 and 17 (no data was available for waxiness from the NWRP, Nepal site). .. 203

xviii

LIST OF SYMBOLS, ABBREVIATIONS AND NOMENCLATURE Δ Carbon isotope discrimination ABD Agricultural Botany Division ANOVA Analysis of variance AUNC Area under NDVI curve AUSC Area under SPAD curve AL Awn length

Ci Intercellular partial pressure of CO2

Ca Atmospheric partial pressure of CO2 CIMMYT The International Maize and Wheat Improvement Centre

CO2 Carbon dioxide CT Canopy temperature CV Coefficient of variation DA Days to anthesis DIRT Digital imaging of root traits DM Days to maturity DNA Deoxyribonucleic acid ESV Early season vigour FAO Food and Agriculture Organization

F0 Initial fluorescence

FM Maximum fluorescence

FV Variable fluorescence

GA3 Gibberellic acid G x E Genotype by environment GFP Grain filling period GLM General linear model GWAS Genome-wide association studies GY Grain yield HTP High-throughput phenotyping HTPP High-throughput phenotyping platforms xix

IWGSC International Wheat Genome Sequencing Consortium K Sub-population KASP Kompetitive Allele Specific PCR LD Linkage disequilibrium LiDAR Light Detection And Range LS Lease square MAF Minor allele frequency masl Meter above sea level NAGRC Nepal Agriculture Genetic Resources Centre NARC Nepal Agriculture Research Council NDRE Normalized difference red edge NDVI Normalized differential vegetative index NDVIdr NDVI derived from drone data NIR Near infra-red NWDP Nepali Wheat Diversity Panel NWRP National Wheat Research Program PCA Principle component analysis PCs Principle components PCR Polymerase chain reaction PHS Pre-harvest sprouting Ppd Photoperiod PSII Photosystem II QTL Quantitative trait locus r Pearson correlation coefficient R2 Coefficient of determination RCBD Randomized complete block design

LIST OF APPENDICES

Appendix 1. List of the genotypes in the Nepali Wheat Diversity Panel included in this study...... 287

Appendix 2. Summary of distribution of SNPs in wheat genomes across 21 chromosomes. ... 313

Appendix 3. The frequency of genotypes in the Nepali Wheat Diversity Panel as differentiated into different subpopulations based on Q-matrix obtained from fastSTRUCTURE...... 313

Appendix 4. Summary of weather data for the four field experiments conducted in Nepal and Canada during 2016 and 2017 wheat growing seasons...... 314

Appendix 6. Test of normality for the distribution of nine different agro-morphological traits evaluated in the 2016 and 2016 field experiments at four locations in Canada and Nepal...... 316

Appendix 7. Mean, standard error of the mean and mean range for traits evaluated in four field experiments during the 2016 and 2017 wheat growing seasons in Nepal and Canada...... 317

Appendix 9. List of protein coding genes identified in each QTL region corresponding to the most significant marker-trait associations...... 319

Appendix 10. Analysis of variance of 318 genotypes in the Nepali Wheat Diversity Panel for coleoptile length and seedling root length...... 326

Appendix 13. Pearson correlation of NDVI values and AUNC with grain yield...... 329

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Appendix 14. Frequencies of different haplotype groups for each seed source group...... 329

Appendix 15. Alleles of Rht-B1, Rht-D1 and Ppd-D1 loci present in accessions in the NWDP...... 330

Appendix 16. Analysis of variance for 318 genotypes belonging to the NWDP for phenotypic traits evaluated at four different field experiments conducted in 2016 and 2017...... 338

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1 General Overview Wheat is one of the major cereal crops for global food security. Wheat is also the third most important cereal crop in Nepal both in terms of area and production. Demand for wheat in Nepal is demonstrated by the fact that in the past four decades consumption of wheat has increased by several-fold, and today, the country imports more than 70,000 tons of wheat every year. Altogether 43 varieties released in the past 6 decades are the outcomes of wheat research in Nepal. Despite gradual improvements, wheat yields in Nepal are not on par with other wheat- growing countries. All this information is covered in Chapter 2 of this thesis. In the wheat- growing regions of South Asia including Nepal, drought is one of the challenges in wheat production. Therefore, this thesis also includes two review chapters (Chapter 3 and 4) that focus on physiological and molecular approaches of breeding wheat for drought tolerance. While the use of exotic genetic resources has been the basis of wheat varietal improvement in Nepal, there has been little research to assess the diversity that exists in the Nepali wheat diversity pool. This study was undertaken to assess the diversity existing in Nepali spring wheat. A panel representing Nepali spring wheat diversity (Nepali Wheat Diversity Panel) was assembled for this study. The NWDP was genotyped to assess its genetic diversity and the population structure (Chapter 5). The results revealed two distinct subpopulations with two other slightly scattered smaller subpopulations with a high level of admixture. The diversity panel was phenotyped for different physio-morphological traits in four environments (2 in Canada and 2 in Nepal). The results from the field experiments revealed significant differences among the genotypes for agro-morphological traits of interest, and a significant G x E effect was also observed. Utilizing the genotypic and phenotypic data, a marker-trait association of different agro-morphological traits was assessed (Chapter 6). Generally, breeders shy away from using seedling traits for variety selection considering that selection for yield mostly captures the optimal traits for an environment. However, evaluation of seedling traits may accelerate the variety selection process in wheat breeding. Therefore, a study was conducted to evaluate coleoptile length, a trait that promotes early season drought tolerance. It was found that the dwarfing genes (Rht genes) affect the length of coleoptiles in wheat. Seedling phenotypic traits were evaluated, and the effects of Green

Revolution dwarfing (Rht) and photoperiod response (Ppd) genes on these traits were assessed to identify accessions with potential for greater seedling vigour (Chapter 7). Furthermore, the NWDP panel was phenotyped for important physiological traits contributing to grain yield. Chapter 8 presents the confounding effect of waxiness on the measurement of chlorophyll related traits as observed in this project. Finally, Chapter 9 summarizes the outputs of the project and outlines the future perspectives. In summary, this thesis provides novel information on genetic diversity and population structure of Nepali spring wheat genetic resources. Similarly, the study provides a genetic basis for different agro-morphological traits and also highlights the importance of physiological traits and seedling morphological traits for improving the effectiveness and efficiency of wheat breeding.

2 Review of the Literature 2.1 Abstract Wheat (Triticum aestivum L.) is the third most important cereal crop in Nepal in terms of area and production, contributing ~20% of total cereal production in the country. Wheat cultivation increased in Nepal by ~500 fold from the 1960s to 1990s which was mainly due to the introduction of high yielding semi-dwarf exotic germplasm. The National Wheat Research Programme (NWRP), the commodity research program under the Nepal Agriculture Research Council (NARC), has been instrumental in developing high yielding wheat varieties. Despite gradual yield gains over the past few decades, wheat productivity has not been outstanding especially at the farm level. A number of biotic and abiotic factors affect wheat physio- morphology negatively. The wheat breeding program in Nepal predominantly follows conventional breeding approaches because of technical and financial challenges. There is a need to adopt more efficient physio-morphological approaches coupled with high throughput phenotyping and recent molecular technologies. The current project aims to generate new knowledge on the genetics and phenotypic diversity of Nepali spring wheat in order to contribute to current and future breeding efforts in Nepal. Such research is especially critical in this era of rapid climate change.

2.2 Wheat in the context of global food security The current world population is expected to reach 10 billion by 2050 (Hickey et al., 2019). A big challenge ahead for contemporary plant breeders, therefore, is to breed crop varieties with high yield potential. According to estimates, food production should increase at least by 33% to meet the demand of the growing population (UN, 2014) which is a historic challenge yet also opportunity (Hanson, 2013), especially for plant breeders. Climate change is yet another factor that is affecting global food security (Chhetri et al., 2012; Venton, 2012; Wheeler and von Braun, 2013; Ewert et al., 2014; Myres et al., 2017). Changes in the pattern of precipitation and temperature, increase in crop growth due to a higher concentration of atmospheric carbon dioxide, and changes in the approaches taken at the farm level, are the three major means that global climate change influences agriculture (Rosenzweig and Parry, 1994). In this context,

emphasis on breeding high yielding and climate-resilient crop varieties is a top priority for plant breeders. Bread wheat (Triticum aestivum L.) is the third most important cereal crop in the world in terms of the volume of production, and it has the largest area under cultivation among the major cereals (FAOSTAT, 2019). For this reason, the need to develop high yielding wheat varieties to ensure global food security cannot be overstated. Currently, wheat is grown on ~215 million ha and the total production is ~ 735 million metric tons (FAOSTAT, 2019). Bread wheat is an allohexaploid with three closely related sub-genomes (AA, BB and DD) with 21 pairs of chromosomes (Snape and Pankova, 2006; Marcussen et al., 2014). It was developed by natural hybridization of the tetraploid emmer wheat (AABB, Triticum turgidum ssp. dicoccoides) with the diploid Aegilops tauschii (the donor of the D genome) (Biswas et al., 2008; Haider, 2012; Marcussen et al., 2014). The tetraploid emmer wheat was the result of the natural hybridization of two diploid wheats, Triticum urartu L. (the donor of the A genome) and Aegilops speltoides (the donor of the B genome). Wheat is fundamental to human civilization, and it has been the major source of carbohydrates for a large proportion of the world’s population for thousands of years. Globally, wheat provides 41% of the total cereal calorie intake; wheat is the source of 35% of the cereal calorie in developing countries and 74% in developed countries (Shiferaw et al., 2013). Among the cereals, wheat comes second in terms of dietary intake, while 65 % of the wheat produced is used for food, 17% used for feed and 12% used for industrial purposes including biofuels (FAO, 2014). The demand for wheat is expected to increase by 60% by 2050, and thus, an increase in wheat yield to 1.6% per year from the current 1% is required to meet this demand (GCARD, 2012).

2.3 Status of wheat research in Nepal Wheat is the third-largest cereal crop grown in the country in terms of area and production (Wheat Atlas, 2019, FAOSTAT, 2019). Wheat occupies more than 22% of national cereal coverage, and it contributes 20.3% of cereal production in the country (MoAD, 2017). Before the 1960s wheat was a minor crop with cultivation limited to the mid- and far-western hills of Nepal (Morris et al., 1994; Tripathi, 2010; Vijayaraghavan et al., 2018). It was after the introduction of 4

the green revolution, semi-dwarf Mexican varieties that the area and production of wheat increased multiple fold (Gurung, 2013; Vijayaraghavan et al., 2018). Wheat is grown in three agro-ecological zones: the Terai [75-500 meters above sea level (masl), a southern east-west strip bordering India]; the Hills (Himalayan foothills) up to 2300 masl, and in the mountain region (Himalayas, bordering China/Tibet) up to 4000 m asl (MoAD, 2017). Of these, ~58% of wheat grown in Nepal is in the Terai, 34% is in the Hills and 8% is in the Mountains (MoAD, 2017). Wheat cultivated in the Terai region is more for commercial purposes, while it is grown as a subsistence food crop in the hills and mountains. The trend shows a gradual increase in area under wheat cultivation in Nepal peaking in 2011 at 767 kha and appearing relatively level or slightly decreasing in the last decade (MoAD, 2017; Figure 2.1). The trend in Nepal’s wheat productivity also shows a gradual increase over the years (MoAD, 2017; Figure 2.1) but the current average grain yield is still lower compared to other wheat growing countries like India and the USA (FAOSTAT, 2019).

Figure 2.1. Trend of the area for cultivation and productivity of wheat in Nepal from 1990 to 2017. (Source: MoAD, 2017).

Public wheat breeding started in Nepal from 1951 (Joshi, 2017; Vijayaraghavan et al., 2018). The National Wheat Development Programme (NWDP) was established in 1972 to consolidate the country’s wheat research and extension initiatives. When the Nepal Agriculture Research Council (NARC) was established in 1990, the wheat program was renamed the National Wheat Research Programme (NWRP) and it became one of the commodity programs under NARC. As of 2017, 43 wheat varieties have been released for commercial cultivation in Nepal (Joshi, 2017; MoAD, 2017). Currently, >95% of the wheat area in Nepal is covered by improved wheat varieties (Gurung, 2013; Timsina et al., 2016; Vijayaraghavan et al., 2018). Disease resistance has been one of the major areas of focus for wheat research in Nepal. Varieties that have been released for commercial cultivation are resistant to important diseases such as yellow rust (caused by Puccinia striiformis), leaf rust (caused by P. triticina) and spot blotch (caused by Bipolaris sorokiniana) (Vijayaraghavan et al., 2018). Apart from varietal research, agronomic research initiatives related to resource conservation, fertilizer application, irrigation, etc. are also part of the wheat research program in Nepal (Tripathi, 2010). The findings of a study concerning the investment and return on research and development showed that there was a negative growth rate (-0.59%) of NARC funding from 1998 to 2004, indicating underfunding for major cereal research including wheat (Thakur et al., 2007). Due to different technical challenges as well as financial constraints, conventional breeding approaches still predominate in Nepali wheat improvement programs (Joshi, 2017; Vijayaraghavan et al., 2018). Although the new and efficient breeding methods including molecular/marker technologies have shown tremendous potential in the field of plant breeding (Charmet and Storlie, 2012; Paliwal et al., 2012), Nepal is still at the infant stage in terms of adopting these technologies.

2.4 Factors affecting wheat productivity 2.4.1 Key biotic and abiotic factors affecting wheat yield Globally, different bacterial, fungal and viral diseases have reduced wheat productivity significantly (William et al., 2003; Strausbaugh et al., 2004; Kazan et al., 2012; Miedaner et al., 2012). There are insects that directly hinder wheat productivity and also act as vectors for plant disease and viruses (Morales-Rodriguez and Wanner, 2015; Rogers et al., 2015; Wang et al., 2015; Zhao et al., 2015). Apart from biotic stresses, abiotic stresses such as heat stress (Blum et 7

al., 2001; Joshi et al., 2007; Ortiz et al., 2008), salinity (Shan et al., 2008; Kaydan, 2007) and drought stress (Zhang et al., 2006; Dehbalaei et al., 2013; Anjum et al., 2011) reduce wheat productivity, affecting plants at different growth stages and reducing net assimilation (Blum et al., 2001; Kondo et al., 2004; Yang et al., 2010; Dehbalaei et al., 2013). Abiotic stresses account for more than 50% of crop loss worldwide (Alcázar et al., 2006). Nepal faces many of these challenges that act to constrain wheat yield improvement.

2.4.2 Factors affecting wheat productivity in Nepal The impact of breeding is apparent when considering the progress, albeit gradual, in wheat productivity in Nepal (MoAD, 2017) and also the high adoption of improved wheat cultivars (Joshi, 2017; Viajayaraghavan et al., 2018). However, as in the global context, different biotic, abiotic and management-related factors are associated with comparatively low wheat productivity in Nepal (FAOSTAT, 2019). The rice (Oryza sativa L.) -wheat cropping system is the most common cropping system, practiced in South Asian countries including India, Nepal, Bangladesh, and Pakistan (Ladha et al., 2000), as this cropping system has been accepted to be efficient, productive and profitable (Kataki et al., 2002). This cropping system occupies 8 % of the wheat area and 37% of the rice area in Nepal and constitutes more than 0.5 million ha of cultivated land in the country (Kandel et al., 2011). The key factors involved in low productivity of wheat in this system are frequent water shortages, delays in wheat planting (Shah, 2013) and changes in management practices (Gami et al., 2001; Kataki et al., 2002). In addition, poor fertility management (Regmi et al., 2002; Shah, 2013), diseases and pests, weeds (Shah, 2013), poor irrigation and drought-management facilities constrain wheat production in Nepal (Poudel et al., 2013). Agricultural productivity is fairly low in the hills and mountains of Nepal and wheat yield can be further lowered when precipitation is inadequate at the latter stage of the crop (Bhandari, 2012). A study showed that a large number of weeds were found in irrigated fields (up to 38 species), and farmers’ perception is that up to 33% of the reduction in wheat yield was due to weeds (Subedi et al., 2013). The micro-climatic variation in the country exposes Nepal to a number of biotic as well as abiotic stresses which directly or indirectly impede crop productivity and production (Gurung, 2013). Heat stress, especially during anthesis and the grain filling period has been significant 8

causes of yield reductions (Mondal et al., 2013). At times, cold weather may coincide with flowering time in early maturing wheat varieties resulting in sterility rates as high as 98% (Chakrabarti et al., 2011). Among the diseases, foliar blight diseases caused by Bipolaris sorikiniana (teleomorph Cochliobolus sativus) and Pyrenophora tritici-repentis are major diseases in wheat, causing yield losses of up to 15% (Duveiller et al., 2005). Wheat rust (Puccinia ssp.) is another major disease in South Asia (Bhardwaj et al., 2019) while new diseases such as wheat blast caused by Pyricularia graminis-tritici (Pygt) is threatening wheat production throughout South Asia including Nepal (Mottaleb et al., 2018). Investments in wheat research have always been a major challenge in Nepal. Inconsistencies in investment for research across different production domains may have played some role in constraining the development of target-specific wheat varieties. For example, the western region of Nepal, which is more prone to drought stress, was found to be under-invested compared to the central region and eastern region (Shrestha et al., 2013a). Despite this, many challenges, the priority of wheat research under NARC, Nepal has been to develop high yielding and stress tolerant wheat varieties targeted for different agro-ecological zones (Tripathi, 2010; Gurung, 2013).

2.5 Breeding high yielding, stress tolerant wheat varieties The development of high yielding varieties is a consistent goal of plant breeding. In the context of rapid climate change, breeding varieties with better adaptation to different climate- related factors is an additional challenge for contemporary wheat breeders. For example, genetic gains for improved yield under drought conditions are slow and low as the genetic variation for grain yield is masked by large genotype by environment (G x E) interactions (Mohammadi, 2019). According to Ceccarelli et al. (1998), the efficiency of yield improvement is lower when selected for under optimal conditions, than when selected under stressed conditions; i.e. that direct selection in stressed environments creates more durable and stable gains in yield (Ceccarelli, 1987). Johnson and Frey (1967) reported that varieties selected directly under stressed conditions exhibit low G x E interaction compared to those selected under optimal conditions. Although there are arguments over the empirical approach of breeding for stress 9

tolerance, the majority of plant breeders emphasize selection under both optimal and stressed conditions for yield stability (Bruckner and Frohberg, 1987). In recent years, as many kinds of stresses have been directly shown to affect plant physiology and metabolic processes, a greater emphasis on the physiological approach to breeding has become apparent.

2.5.1 The physiological approach to wheat breeding Grain yield is an outcome of a number of individual physiological processes. Therefore, understanding physiological processes may increase the efficiency of breeding crops for improved stress tolerance. The physiological approach to breeding focuses on the indirect selection of grain yield by pursuing traits contributing to yield. Since grain yield is a complex trait with a low heritability, the G x E interaction can be very large (Zarei et al., 2013; Kosová et al., 2014). Different physiological traits that exhibit higher stability across a range of environments, and have higher heritability than yield itself, can be used as surrogate traits (Reynolds et al., 2005). For example, physiological traits such as early vigour and late senescence have been found to have indirect positive contributions to yield (Kandic et al., 2009). Application of plant physiology for precision phenotyping, along with genetic and molecular approaches combined in the breeding process, have significant potential for improving the efficiency of the breeding program (Mir et al., 2012; Kosová et al., 2014). Furthermore, a physiological trait-based approach to breeding for abiotic stress has merit over breeding for yield per se by increasing the probability of successful crosses resulting from additive gene action. As a result, such approaches have been part of CIMMYT’s work through multidisciplinary research involving genetic resource enhancement and crop physiology (Ortiz et al., 2008). The physiological approach to breeding for stress tolerance has advantages over conventional breeding (Reynolds & Trethowan, 2007) but integration of physiological approaches with the conventional empirical approach potentially improves the efficiency of the breeding program (Nigam et al., 2005). There are clear benefits to the application of physiological approaches to breeding, as physiological trait-based crossing achieves cumulative gene action for yield in drought-prone environments (Reynolds et al., 2009a). While application of physiological approaches in breeding potentially reduce the number of materials to be screened at the advanced stage, advancements in phenotyping methods have made the screening 10

of complex physiological traits much easier and quicker. In the past two decades, more high throughput methods have been adopted in breeding wheat for abiotic stress tolerance which actually incorporates the measurement and analysis of surrogate traits (Reynolds et al., 2007; Cossani and Reynolds, 2012; Lopes and Reynolds, 2012). The increased volume and quality of phenotypic data have opened more opportunities to utilize progress in molecular technologies to further improve efficiency in breeding high yielding wheat varieties (Stuber et al., 1999).

2.5.2 Molecular approaches to wheat breeding The evolution of the SNP markers has taken center stage in molecular-based breeding due to their abundance and availability for high throughput detection formats and platforms (Mammadov et al., 2012). The use of RFLP markers has become obsolete since the detection of RFLPs is expensive, labor extensive and time-consuming (Mammadov et al., 2012). RAPD, AFLP, and SSR markers are still in use especially in molecular genetics research on crops in which little genomic information is available. However, since the discovery of SNP markers in the human genome and subsequently in other species, SNPs have been accepted as the most abundant forms of variation in the genomes of animals and plants (Rafalski, 2002; Mammadov et al., 2012; Song et al., 2013). SNP platforms are also gaining attention because they require low computational effort for downstream processing, are simple to use and less prone to error (Wang et al., 2014b). The availability of high-density SNPs for genome analysis makes it possible to discern the genetic structure of different populations and the effect of selection and recombination on these populations (Chao et al., 2010; Cavanagh et al., 2013; Wang et al., 2014). In addition, the recent next-generation sequencing (NGS) technologies have provided opportunities to establish time-, labour- and cost-effective genotyping methods (He et al., 2014; Ray and Satya, 2014; Bhat et al., 2016). Now, the fully annotated high-quality reference genome for common wheat is available (IWGSC, 2018), and thus the scope of NGS analyses in wheat has broadened. Illumina MiSeq and HiSeq2500 (Bentley et al., 2012), and Roche 454 FLX Titanium (Thudi et al., 2012), are some of the examples of unique NGS platforms available for genome sequencing. Genotype by sequencing (GBS) is one of the latest NGS methods that produces short reads throughout the wheat genome (Poland et al., 2012; Mwadzingeni et al., 2016; Chung et al., 2017). 11

2.5.3 Genome-wide association mapping (GWAS) SNP markers including GBS markers are utilized in genome-wide association studies (GWAS), to identify marker-trait associations (MTAs) for the trait(s) of interest (Corvin et al., 2014; Scherer and Christensen, 2016). GWAS is basically the first step that is used to assess the genetic architecture of a target trait. Assessment of MTAs exploits linkage disequilibrium (LD) resulting from variants at a locus caused by different factors such as historical mutations, natural and artificial selection and other forces (Wang et al., 2012; Huang and Han, 2014; Visscher et al., 2017). The efficiency of GWAS depends upon individual factors such as the number of loci for a trait that segregate in the population, genetic architecture and size of the study population (Visscher et al., 2017). Specifically, GWAS facilitates the exploration of the genetic variation of complex quantitative traits controlled by several genes and their interactions (Kooke et al., 2016) under diverse environments. Diversity panels having a wide genetic background are more suitable for GWAS compared to bi-parental mapping populations as they increase the probability of identifying QTLs of interest (Korte and Farlow, 2013). GWAS may result in some false associations and linkage disequilibrium due to confounding effects of the population structure (Crossa et al., 2007; Burghart et al., 2017). However, corrective statistical methods such as the mixed linear model have been in use to reduce any spurious associations (Osario et al., 2019).

2.5.4 Kompetitive Allele-Specific PCR (KASP) Although genome-wide markers have been in common use recently in different crops including wheat, the biggest challenge lies in the application of these markers for marker-assisted selection (MAS) (Ayalew et al 2019). Especially when there is a need to genotype only a limited number of SNPs in a large population, efficient and low-cost platforms are required for the utilization of trait-specific markers (Thomson, 2014). One such platform is Kompetitive Allele- Specific PCR (KASP) which is cost-effective and efficient for MAS (Semagn et al., 2014). KASP assays are highly convenient in terms of being able to discriminate two alleles of a SNP, are fast, inexpensive, and are statistically friendly (Patterson et al., 2017). This technology has been used in different crops such as potato (Uitdewilligen et al., 2013) and cereal species (Cuenca et al., 2013) including wheat (Whittal et al., 2017, Ayalew et al., 2019). In wheat, KASP

assays have been developed and validated for genes that are associated with different traits such as grain yield, disease resistance, end-use quality, and stress-related traits (Rasheed et al., 2016).

2.5.5 Genetic diversity for breeding better wheat varieties The basic requirement of plant breeding is the availability of genetic variation so that desirable genotypes can be selected. Landraces are one of the major sources of genetic variation in crop breeding including wheat (Mwadzingeni et al., 2017b). Although the use of landraces is not very extensive in wheat breeding, their importance cannot be understated. Landraces have complex agro-morphological diversity as they are mostly grown in low input environments (Padulosi et al., 2012) and this makes them more adapted to stress (Lopes et al., 2015; Padulosi et al., 2012). For example, the Japanese landrace “Aka Komugi” is the novel source of the Rht8c allele, which confers drought tolerance in wheat (Lopes et al., 2015a; Grover et al., 2018). In addition to the landraces, the wild relatives of wheat and other genera such as Aegilops and Secale are also valued as sources of germplasm for breeding drought tolerant varieties. The modern genetic resources or the elite germplasm are other important sources of genetic variation required for the development of new varieties. The enhancement of genetic gain and selection response are rapid in modern genetic materials as they have been already selected for a majority of preferred traits (Mwadzingeni et al., 2017b). However, the reduction in genetic diversity is another reality associated with modern genetic materials which is another area of concern. Stress-adapted landraces and close relatives in breeding programs have been used to develop synthetic hexaploid wheat (SHW). The SHW combines genes from diploid and tetraploid wheat species (Dorostkar et al., 2015; Li et al., 2018). Therefore, the process of development of SHW is itself a very typical example of the deployment of ancestral genetic resources (Reynolds et al., 2007). The SHWs have shown potential as new sources of genes for higher yield gains, biotic and abiotic stress tolerance and also nutrient use efficiency (Li et al., 2018) A diversity of wheat genetic resources is available in many national and international research institutions and programs. These genetic materials include landraces, wild relatives, breeding populations, obsolete varieties, and modern elite varieties. Plant breeders usually search for materials with particular traits such as tolerance to heat and drought, resistance to specific 13

diseases, etc. and the genebanks facilitate the process to make the suitable material available to the breeders. The availability of these genetic materials is based on existing national and international laws on plant genetic resources (Brint and Hintum, 2020). The genebanks are the major centres for ex situ conservation of plant genetic resources including wheat wherein a large proportion of landraces and also wild relatives are maintained. Pre-breeding of wheat genetic materials conserved in the genebanks can create a large diversity for plant breeders to use although this creates a challenge. It is also suggested that genebanks conserve other genetic materials including the pre-breeding materials developed by researchers but this may require different ex situ conservation protocols (Diez et al., 2018). Therefore, genebanks face challenges in adding new genetic materials due to complexities associated with documentation and regulations (Brint and Hintum, 2020).

2.6 Physio-morphological traits for breeding high yielding wheat varieties 2.6.1 Phenological traits In wheat, based on the “critical growth stage” concept, booting/heading, anthesis to grain filling and the vegetative stages, were rated in order of decreasing sensitivity to soil moisture deficit (Singh, 1981). Similarly, many studies (Nezhadahmadi et al., 2013; Farooq et al., 2014; Sarto et al., 2017) reported that heading and anthesis stages are more sensitive to abiotic stresses in wheat (Senapati et al., 2019). Higher yield losses due to moisture stress (Ding et al., 2018) and heat stress (Joshi et al., 2007; Ortiz et al., 2008) during heading stages have been reported. A reduction in the number of grains and increased sterility due to heat stress and drought stress during flowering/anthesis stage is common (Joshi et al., 2007; Ortiz et al., 2008; Su et al., 2013). It has been shown that early heading and anthesis can be escape mechanisms for terminal drought (Shavrukov et al., 2017) especially in the regions that are more prone to terminal heat and drought stress (Joshi et al., 2007).

2.6.2 Coleoptile length The coleoptile is a pointed protective sheath that covers the emerging shoot in monocotyledonous grasses or cereals. When the seed germinates, the coleoptile pushes through the soil and protects the young shoot (Farhad et al., 2014). The coleoptile length varies among 14

different wheat genotypes, and this has special significance with respect to breeding wheat for early season drought tolerance (Rebetzke et al., 2005; Rebetzke et al., 2007; Farhad et al., 2014). Shorter coleoptiles can result in poor plant establishment (Schillinger et al., 1998a) especially in dry environments: longer coleoptiles are useful in improving seedling emergence in wheat that is deeply sown to mitigate the effect of early season moisture stress. The Norin 10 dwarfing genes, namely Rht-B1b (Rht 1) and Rht-D1b (Rht 2), have been used widely in wheat to reduce plant height (Waddington et al., 2010). These commonly deployed dwarfing genes decrease the sensitivity of the vegetative tissues to endogenous gibberellins (Keyes et al., 1989) leading to reduced plant height at maturity but also shorter coleoptiles at the seedling stage as a result of reduced cell elongation (Rebetzke et al., 2001). Dwarfing genes such as Rht8 and Rht24 are sensitive to endogenous gibberellins and these genes have less or no effect on the coleoptile length although they reduce plant height (Rebetzke et al., 1999; Rebetzke et al., 2005; Ellis et al., 2005; Tian et al., 2017; Würschum et al., 2017; Grover et al., 2018). There is an opportunity for wheat breeders to develop semi-dwarf genotypes with longer coleoptiles by combining reduced height genes that are sensitive to endogenous gibberellins.

2.6.3 Chlorophyll content Chlorophyll content in plant leaves is a direct measure of green pigment which is associated with crop productivity, physiological processes such as photosynthesis and the phenological status of plants such as senescence (Lopes et al., 2012; Kira et al., 2015). Chlorophyll content is related to stress tolerance in plants (Lopes et al., 2012) such as drought tolerance (Khayatnezhad et al., 2011). It is reported that any heat stress during flowering and early grain setting stage causes significant reductions in flag leaf chlorophyll content and yield- related traits such as grain number, grain mass and post heading duration (Khayatnezhad et al., 2011; Talukder et al., 2014). Drought tolerant wheat genotypes maintain a high level of greenness during moisture stress in comparison to drought susceptible genotypes (Talebi, 2015; Rehman et al., 2016; Ahmed et al., 2019; Bowne et al; 2012). Therefore, plant breeders are using a quantitative measure of greenness to evaluate the performance of genetic materials under optimal and stressed conditions (Lopes and Reynolds 2012).

The Soil Plant Analysis Development (SPAD) chlorophyll meter is used to measure SPAD values that are taken as an estimate of chlorophyll content in field research. Chlorophyll readily absorbs red light with nearly no absorption in the infrared range. SPAD meters use red and near-infrared emitting diodes, which pass light through the leaf and then measure the difference in the transmittance of red light (650 nm) compared to infrared light (peaking at 940 nm) as a reference (Lopes et al., 2012). Sharma et al. (2014) used a SPAD meter (SPAD-502; Konica Minolta Sensing, Inc. Osaka, Japan) as a proxy for chlorophyll under heat stress conditions.

2.6.4 Normalized difference vegetative index The use of vegetation indices (VIs) as a proxy for vegetative properties associated with chlorophyll in plants is common. These indices are derived from the reflection of visible and near-infrared wavelengths from plant surfaces (Bannari et al., 1995). The VI are simple algorithms to assess vegetation characteristics such as plant cover, dynamics of plant growth and photosynthetic properties both qualitatively and quantitatively (Xue and Su, 2019). Among different VIs, the normalized difference vegetative index (NDVI) is one that is based on the difference between the reflection of near-infrared light (which vegetation strongly reflects) and red light (which vegetation strongly absorbs as noted above) (Sultana et al., 2014). NDVI values range from -1 to +1, and are calculated using the following formula (Tucker, 1979): 푅 − 푅 푁퐷푉퐼 = 푁퐼푅 푅 푅푁퐼푅 + 푅푅 Where: NDVI =Normalized difference vegetative index;

RNIR = Near-infrared radiation;

RR =Visible red spectrum.

In crop research, NDVI is used extensively to evaluate greenness and ground cover, and to infer the photosynthetic strength of the crop canopy, using measurements taken from the ground level up to satellite altitudes (Pietragalla and Vega, 2012). Because of this property of NDVI, it is now widely accepted as a proxy for stress tolerance related traits (Lopes and 16

Reynolds, 2012) such as drought tolerance and heat stress adaptiveness. The association of NDVI with grain yield has been found to be positive (Ramya et al., 2016; Condorelli et al., 2018). Plant breeders have already adopted NDVI to estimate seedling vigour, growth rate and senescence pattern in wheat (Pietragalla and Vega, 2012; Ramya et al., 2016). Both ground- based and unmanned aerial vehicle (UAV) based NDVI assessments are common depending on resource availability. The GreenSeeker spectral sensor (Trimble, California, USA) is a ground- based NDVI tool while UAV platforms for measuring NDVI has been gaining popularity recently due to its precision and resource efficiency (Xue and Su, 2019).

2.6.5 Waxiness Wheat genotypes differ in the amount of wax present on the leaves or on the whole shoot. It has been reported that shoot wax contributes to environmental stress tolerance (Bi et al., 2017). The wax present on the shoot reduces water loss from the cuticular surface making the plant more tolerant to drought and heat stress (Bi et al., 2017; Jäger et al., 2014). Therefore, waxiness is one of the physiological traits considered when selecting for heat and drought tolerant genotypes. Bowne et al. (2012) reported that the wheat genotypes that deposited more wax on the shoot gave the highest yield under mild and severe drought conditions, supporting that waxiness reduces the loss of water from wheat surfaces. Genotypes with a higher composition of β-diketones in the wax are more drought tolerant (Bi et al., 2016; Bi et al., 2017). In the field, genotypes with a very high amount of wax on the shoot appear bluish-white in colour. Commonly, a visual rating involving a 0-10 scale is used, where 0 indicates no or low waxiness, and 10 indicates high waxiness (Torres and Pietragalla, 2012).

2.6.6 Grain filling period The grain filling period is the growth stage which is very sensitive to changes in different environmental factors such as moisture availability (Barnabás et al., 2008; Alghabari and Ihsan, 2018). In arid and semi-arid wheat growing regions, moisture stress is especially a common problem during the grain filling period (Saeidi and Abdoli, 2015). The duration of the grain filling period has a positive association with grain yield in wheat (Monpara, 2011), even under water-limited conditions (Bogale and Tesfaye, 2016; Kilic and Yagbasanlar, 2010). However, 17

grain yield loss also depends on the severity of the stress during this period (Farooq et al., 2014; Saeidi and Abdoli, 2015; Tabassam et al., 2014). In this context, reduction in yield could be the result of a reduction in grain filling duration (Alghabari and Ihsan 2018) which is associated with early senescence (Farooq et al., 2014; Bogale and Tesfaye, 2016). Ihsan et al. (2016) suggest that stress tolerant genotypes have longer grain filling duration while Borrill et al. (2015), and Nass and Reiser (1975), contend that the grain filling capacity is more important than the length of the grain filling period.

2.6.7 Awn length Not only is there variation in wheat for the absence or presence of awns, the length of awns varies significantly among the genotypes having them. Awns are photosynthetically active and awnedness has been demonstrated to effect a small but significant increase in grain yield (Maydup et al., 2010; Taheri et al., 2011; Ali et al., 2015; Towfiq and Noori, 2016; Rebetzke et al., 2016a). Awn length has been shown to have a positive association with other traits such as grain yield and spike length especially under soil moisture deficit conditions (Blum, 2005; Taheri et al., 2011; Khamssi and Najaphy, 2012; Ali et al., 2015). The awns maintain higher relative water content and improved photosynthetic electron transport rate compared to the flag leaf under drought conditions (Maydup et al., 2014) and this highlights the role awns may play in arid and semi-arid wheat-growing regions. Awn length is reported to have a moderate to high level of heritability (Bhatta et al., 2018a).

2.7 Improving the efficiency of phenotyping physio-morphological traits Precise phenotyping bridges the gap between genomics and phenomics. Field phenotyping is very challenging in plant breeding considering the large numbers involved and the diversity of the breeding materials that are handled. In addition, annual variation and the large size of both the environment and especially the GxE effects mandate multi-year experiments (Kant et al., 2017; Hoover et al., 2018). These factors highlight the need for high throughput phenotyping (HTP) methods that are capable of generating precise phenotypic data with reduced time, labour and resources (Rutkoski et al., 2016). The HTP methods have been used in controlled environments using single plant measurements, but in such cases, translation of the data to the 18

field is often not satisfactory (White et al., 2012). Therefore, to evaluate the stress tolerance of genetic materials at the field level, physical structures have been used as they overcome the unpredictability of weather (precipitation in particular) (Kundel et al., 2018; Trillo and Fernández, 2005; Mwadzingeni et al., 2016). Such structures have been used in the evaluation of genetic materials of different crops including wheat (Zu et al., 2017; Kundel et al., 2018; Wimmerová et al., 2018). In recent years, the addition of standard soil moisture sensors, motion cameras, and other tools has improved the precision of data recording (Mwadzingeni et al., 2016; Kant et al., 2017). The progress made in the past few years on remote sensing, aeronautics, and computing has contributed to the development of several phenotyping platforms including both ground-based and aerial systems (White et al., 2012; Araus and Cairns, 2014). The proximal or ground-based HTP platforms include visible/near-infrared (VIS-NIR) spectro-radiometry, infrared thermometry and thermal imaging including conventional digital photography (Araus and Cairns, 2014). These platforms are mostly handheld or may be mounted on vehicles (Qiu et al., 2018). The remote sensing HTPPs include the low-cost unmanned aerial vehicles (UAVs) or drones as these can potentially be used to quantify crop health, and effects of soil moisture and nutrients on crop growth and development (Holman et al., 2016; Khan et al., 2018). These UAVs can provide high-resolution images of small experimental plots from distances as high as 30-100 m above ground (Shi et al., 2016; Tattaris et al., 2016). The use of aerial measurement to derive important physiological traits such as canopy temperature (CT) and NDVI have been successfully performed in wheat (Rutkoski et al., 2016; Tattaris et al., 2016). One of the major challenges associated with these HTP platforms is access, i.e. still many research institutes around the world may not be able to afford these recent methods.

2.8 Research gap, hypothesis and objectives There is a big gap between on-farm yield and the potential yield of wheat varieties developed in Nepal (Rosyara et al., 2010; Bista et al., 2013; Timsina et al., 2018). For more than a decade, >95% of the wheat growing area in Nepal has been planted with improved varieties (Gurung, 2013; Timsina et al., 2016; MoAD, 2017; Vijayaraghavan et al., 2018). Considering the high rate of varietal adoption, the productivity at the farm level is surprisingly low (Timsina et al., 2018). However, there are numerous factors hindering the productivity of wheat in Nepal 19

which mainly include biotic and abiotic stresses along with different management practices such as fertilizer application (Shrestha et al., 2013). Wheat in Nepal is grown during the winter season; sown in November-December and harvested in April-May depending on the variety grown and the altitude of the field location. Terminal heat stress is a serious threat to wheat production in Nepal, and is a stress that is expected to occur more frequently according to current climate change modeling (Joshi et al., 2007a; Mondal et al., 2013). Nepal has approximately 75% of the wheat grown under rain-fed conditions (CIMMYT, 2019). Diminishing winter rainfall is limiting the productivity of winter crops (MoSTE, 2011) including wheat. The climate predictions also indicate that the probability of drought during winter months will increase in the major wheat-growing areas (DHM-MoSTE Nepal, 2013). The majority of Nepali wheat is grown from an altitude of <100 to ~1700 masl, although it is grown in even higher altitudes at a smaller scale (MoAD, 2017; CIMMYT, 2019). Therefore, the range in micro-climatic variation is a major challenge for Nepali wheat breeders as there are many niche-specific environments that must be addressed by small breeding programs (Gurung, 2013). Apart from abiotic stresses, different foliar diseases such as rusts and foliar blight have been constraining wheat productivity in Nepal (Joshi et al., 2007b, Rosyara et al., 2010). Considering the major challenges in wheat productivity, Nepal’s wheat breeding program targets the development of varieties that are drought and heat stress tolerant, resistant to rusts and foliar blight diseases, and high yielding. NWRP is fully committed to breeding appropriate wheat varieties for different growing environments in addition to the development of technologies associated with wheat. However, an analysis of the investment in Nepali wheat research from 2006 to 2016 showed that the wheat research program is under-budgeted (Timsina et al., 2019). Conventional or classical approach to breeding is still predominant in Nepal (Gurung 2013; Joshi, 2017; Vijayaraghavan, 2018). The use of very recent HTPPs and the molecular breeding approaches are still in the infancy stage although a few initiatives have been undertaken in collaboration with different international research partners such as CIMMYT and university partners. The slow or low adoption of recent technologies is likely a result of insufficient financial investment (Timsina et al., 2019).

Nepal has been highly dependent on foreign germplasm for varietal development (Rosyara et al., 2005; Joshi et al., 2006; Vijayaraghavan, 2018). Most of the released wheat varieties in Nepal are the outcome of introduced genetic materials from international research and development organizations like CIMMYT and USAID. Of the 43 wheat varieties released for commercial cultivation in the country, more than 80% have an exotic origin (Joshi, 2017); even those varieties that originated in Nepal also do not have any parents associated with Nepali germplasm. Although Nepal has almost 540 landraces within the country (Joshi et al., 2006; Paudel et al., 2016), they have not been utilized in the breeding program. Investigating these genetic materials and exploiting the existing genetic variability is a large task for Nepali wheat breeders (Paudel et al., 2016). The landraces may have a higher probability of harboring stress adaptive alleles (Lopes et al., 2015). Therefore, it is both appropriate and urgent that local Nepali genetic materials are assessed and appropriately incorporated into existing breeding programs. This research project was developed to generate new knowledge that may contribute to the current and future wheat breeding program in Nepal. The project targeted the characterization of a representative group of Nepali spring wheat using both phenotypic and molecular methods. A diversity panel, named the “Nepali Wheat Diversity Panel” (NWDP), was assembled to include 166 Nepali landraces, 34 commercially released varieties, 115 CIMMYT advanced breeding lines that were previously tested in Nepal for three seasons from 2011-14, and three Canadian varieties (AC Carberry, Norwell and Pasteur).

2.8.1 Research hypotheses 1. Nepali spring wheat germplasm is genetically diverse. 2. The accessions in the NWDP possess quantitative trait loci (QTL) alleles that correlate with agro-morphological traits of importance to Nepal. 3. The NWDP has genetic materials with potential for stress tolerance.

2.8.2 Research objectives 1. To assess the population structure and genetic diversity of the NWDP; 2. To locate the genomic regions in the NWDP associated with agro-morphological traits of interest;

3. To evaluate the effect of height-reducing alleles and photoperiod sensitivity alleles on seedling vigour traits in the NWDP. 4. To evaluate physiological traits associated with grain yield in the NWDP.

3 A Physio-morphological Trait-based Approach for Breeding Drought Tolerant Wheat

This chapter has been accepted for publication as a review article with the following citation:

Kamal Khadka, Hugh J. Earl, Manish N. Raizada, and Alireza Navabi (2020). A Physio- morphological Trait-based Approach for Breeding Drought Tolerant Wheat to Combat Climate Change. Frontiers in Plant Science (submitted, Manuscript ID 511313).

Author contributions: Kamal Khadka: Lead the development of this review paper. Ali Navabi: Provided input to the paper. Hugh J. Earl: Provided input to the paper. Manish N. Raizada: Provided input to the paper and did the comprehensive editing.

3.1 Abstract In the past, there have been drought events in different parts of the world, which have negatively influenced the productivity and production of various crops including wheat (Triticum aestivum L.), one of the world’s three important cereal crops. Breeding new high yielding drought-tolerant wheat varieties is a research priority specifically in regions where climate change is predicted to result in more drought conditions. Commonly in breeding for drought tolerance, grain yield is the basis for selection, but it is a complex, late-stage trait, affected by many factors aside from drought. A strategy that evaluates genotypes for physiological responses to drought at earlier growth stages may be more targeted to drought and time efficient. Such an approach may be enabled by recent advances in high-throughput phenotyping platforms (HTPPs). In addition, the success of new genomic and molecular approaches rely on the quality of phenotypic data which is utilized to dissect the genetics of complex traits such as drought tolerance. Therefore, the first objective of this review is to describe the growth-stage based physio-morphological traits that could be targeted by breeders to develop drought tolerant wheat genotypes. The second objective is to describe recent advances in high throughput phenotyping of drought tolerance related physio-morphological traits primarily under field conditions. We discuss how these strategies can be integrated into a comprehensive breeding program to mitigate the impacts of climate change. The review concludes that there is a need for comprehensive high throughput phenotyping of physio-morphological traits that is growth stage-based to improve the efficiency of breeding drought tolerant wheat.

Keywords: Drought tolerance, physiology, morphology, high throughput phenotyping, wheat, climate change, traits, breeding.

3.2 Introduction Wheat (Triticum aestivum L.) is ranked second among cereal crops in terms of total global production but ranked number one in terms of the total area under cultivation (OECD-FAO, 2018; FAO, 2018a). The global annual production of wheat in 2017 was 757 million metric tons (FAO, 2018a). Globally, wheat is responsible for 41% of total cereal calorie intake, broken down as 35% and 74% in developing and in developed countries, respectively (Shiferaw et al., 2013).

Currently, wheat ranks second after rice in terms of dietary intake volume, with 68% of the wheat produced used for food, and approximately 19% for feed, and the rest for other purposes, including industrial biofuels (FAO, 2018b). Imports of wheat in developing countries, including in the tropics where wheat is not grown, are increasing (FAO, 2018b). For example, 2-3% increases in wheat demand per year have been observed in Sub-Saharan Africa (CIMMYT, 2017). In particular, Sub-Saharan Africa imports wheat not only because of rapid population growth but also due to biotic and abiotic factors that constrain crop production, in addition to climate change, changing food habits of local people (Tadesse et al., 2018) and an inability of farmers to cope with market fluctuations and price shocks for different crops (Antonaci et al., 2014). Global hunger has increased in the past three years (FAO, 2018c), and the current data reveals that we are not in a situation to eradicate hunger by 2030 as targeted by sustainable development goals. The demand for wheat is expected to increase by 60% to feed the human population which is expected to surpass nine billion by 2050; to achieve this, there is a need to accelerate global average wheat yield increases from the current 1% per year to a minimum of 1.6% (GCARD, 2012). Losses in wheat production are mainly due to abiotic factors including drought, salinity, and heat stress rather than biotic factors (Abhinandan et al., 2018). Unfortunately, recurrent drought events have threatened global wheat production which necessitates major attention. The effect of water stress differs at different growth stages of wheat (Daryanto et al., 2016) while the duration and intensity of water stress can affect the development of wheat at different trait levels (Sarto et al., 2017) which ultimately reduces grain yield. Various reports from around the world indicate that limited water availability plays a major role in reducing wheat yield. According to FAO (2018c), global wheat production in 2018 was predicted to decline by 2.7% which is based on predictions of changing weather. A meta-analysis of 60 published studies showed that drought reduced wheat yields by an average of 27.5% (Zhang et al., 2018), and a similar study, which included peer-reviewed articles from 1980 to 2015, showed decreases of 20.6% (Daryanto et al., 2016). For example, in Australia, wheat productivity has been severely affected by water stress due to drought events among many other factors (Curtis and Halford, 2014). Specifically, there were severe drought events in Australia during 1982, 1994, 2002, 2004, and 2006, which resulted in yield reductions of five major field crops including wheat by 25-45% compared to the 25

years with optimum rainfall (Madadgar et al., 2017). In this region, the average wheat yield at field sites was only around 0.8 t/ha due to severe drought over the 2006 crop season (Fleury et al., 2010). Between 2005 to 2007, Australian food prices increased at twice the rate of the consumer price index, and this increase was attributed to the drought events of 2004 and 2006 (Quiggin, 2007). Similarly, inconsistencies in winter wheat production were observed during the 2011 to 2013 drought in Texas, USA: a yield reduction was observed in 2011 and 2014 while the yield increased in 2012 (Ray et al., 2018). Many African countries have also been prone to drought events at different times of the year, including a 2009 drought in Kenya which reduced wheat production by 45% (Rauf et al., 2016). Different climate change studies have developed models that predict changes in the frequency and intensity of precipitation, increases in global temperatures, and a rise in atmospheric CO2 concentrations (Rosenzweig and Parry, 1994; Mahato, 2014). Figure 3.1 shows the change in annual precipitation over the last century. The trend from such historical observations and future climate change models indicates that some regions will receive more precipitation while others will get drier (IPCC, 2013). In general, it is expected that changes in the global climate will have both spatial and temporal impacts on agricultural production (Kulukulasuriya and Rosenthal, 2003).

Figure 3.1. Global map showing the change in precipitation observed during two different time periods from 1901-2010 (left) and 1951-2010 (right). The trends were calculated only for the grid boxes indicated on the maps which had >70% complete data records and more than 20% data availability for the first and last 10% of the time period. Incomplete data sets are indicated by white areas while significant trends are indicated by a black plus sign (+). Source: (IPCC, 2013).

Figure 3.2. Major wheat growing areas around the world. Darker colors show regions where more wheat is grown. Map based on You, L., U. Wood-Sichra, S. Fritz, Z. Guo, L. See, and J. Koo. 2014. Spatial Production Allocation Model (SPAM), 2005 Beta Version, from IFPR.

(Harvest Choice) (Source: wheat.org).

Some of the major wheat growing areas (Figure 3.2) are going to be profoundly affected by drought in the years to come, specifically in South Eastern and South Western Australia, parts of Western China, the Indo-Gangetic plains, the Middle-East, Southern Europe and Western Canada. A recent study (Daryanto et al., 2016) used wheat drought data from 144 studies around the world, published between 1980 to 2015, and showed that drought conditions caused a 20.6% average decrease in wheat yield corresponding to a 40% reduction in water availability at the global level. Similarly, Punjab and Haryana states in the Indo-Gangetic plain experienced a prolonged duration of low wheat yield from 2002 to 2010, mainly attributed to depleted groundwater resulting from an insufficient monsoon, poor surface water irrigation and higher temperature (Figure 3.1; Mukherjee and Wang, 2019). In China, a study using integrated climate assessment models estimated a ~55% yield loss in wheat yield due to drought during 1955-2014 compared to a ~7% yield loss under the baseline irrigation scenario (Yu et al., 2018a). The same study also predicted that the yield loss rate will double based on a current 100-year drought projection for rain-fed environments. Combined, these examples suggest that drought is going to be a critical yield limiting factor for wheat in some wheat growing areas of the world in the years to come. There are a limited number of comprehensive studies that quantify the impacts of drought on agriculture because of the complex nature of drought and its complicated association with other abiotic factors such as heat or the soil profile type (Nicolas et al., 1984; Ali et al., 1999; Eriyagama et al., 2009; Yu et al., 2014). Drought can be divided into three broad inter-related groups: meteorological drought, caused by anomalies in the atmosphere and higher temperatures; agricultural drought, caused by low precipitation and high evapotranspiration rates; and hydrological drought when sources of water fall below their normal average (Dai, 2011). Drought conditions may be defined as mild, moderate or severe drought based on the duration and extent of water availability. However, there is inconsistency in the use of such classifications due to confounding factors such as the soil type and the experimental environment (i.e. greenhouse or field). For example, in a greenhouse experiment intended to evaluate wheat seedling traits under water limitation, Ahmed et al. (2019) defined optimal water as 100% field capacity and stress as 50% field capacity. By contrast, in a field experiment involving rainout

shelters, severe, moderate and optimal water conditions for wheat were defined as 35-40%, 55- 60% and 75-80% field capacity, respectively (Abid et al., 2018). It is clear that drought negatively affects crop growth, development, dry matter production and potential yield (Zhang et al., 2006; Anjum et al., 2011; Zhang et al., 2018). Grain yield is the basis for selection in most breeding programs for drought tolerance. However, grain yield is affected by many factors aside from drought. From a molecular perspective, drought tolerance is a very complex trait involving many drought-responsive genes that differ in expression at different growth stages (Blum, 2011), each of which generally makes a minor contribution to the trait (Sallam et al., 2019). Pyramiding their additive gene action by crossing complementary drought tolerance traits from different growth stages may achieve greater results than direct selection on yield alone. Additionally, selection using earlier proxy traits may be more time efficient than yield. In general, as reviewed below, the extent to which drought impacts plant growth and physiology largely depends upon the growth stage at which it is exposed to drought and the plant species/genotype (Kondo et al., 2004). The first objective of this review is to describe growth-stage based physio-morphological traits that can be targeted by breeders to develop drought-tolerant wheat varieties. The second objective is to describe advances in precision phenotyping of drought tolerance related physio- morphological traits under field conditions. Such phenotyping is required to reveal the genetic basis of these traits which are complex and quantitative (i.e. many minor quantitative trait loci, QTLs) (Fleury et al., 2010; Tuberosa and Maccaferri, 2015) as already noted. There have been related reviews on these subjects. In particular, Monneveux et al. (2012) highlighted the methodological approach for the use of physiological traits in breeding for drought tolerance in wheat, while Sallam et al. (2019) reviewed a wide array of studies pertaining to drought physiology in plants along with advances in wheat breeding for drought tolerance. In this review, we focus on more practical aspects of wheat breeding by focusing on specific physio-morphological traits at different growth stages that are affected by moisture stress. These stage-specific traits may be potential targets for future selection. This paper also highlights advances in phenotyping of these traits and concludes with a comprehensive strategy for breeding drought tolerant wheat by taking into account these approaches.

3.3 Targets for breeding drought-tolerant wheat 3.3.1 Selection environment Considering the dynamic nature of abiotic stresses, the empirical approach to breeding for drought tolerance emphasizes selection under both optimal and stressed conditions to observe yield stability and yield potential (Bruckner and Frohberg, 1987). Plant breeders have been adopting replicated, multi-location, and multi-year variety testing to identify varieties that perform best across a wide range of environments as part of empirical breeding based programs. If the objective is to breed a crop to tolerate a specific stressful environment, then direct selection under such an environment results in higher stability and durability of the crop yield (Ceccarelli, 1987; Ceccarelli et al., 1998). This observation was supported earlier by Johnson and Frey (1967) who reported that varieties selected directly from stressed conditions exhibit a low genotype x environment (G x E) interaction compared to those selected under optimal conditions. However, some studies show that selection under optimal environments also improves grain yield under drought conditions to a limited extent (Araus et al., 2002; Cattivelli et al., 2008; Sserumaga et al., 2018). Mohammadi and Amri (2011) indicate that grain yield improvement may be attained by either selection in low input and stressed environments or by selection in non-stressed conditions followed by selection under stressed conditions. Practically, drought stress may not occur every season. Therefore, the evaluation of traits only under stressed conditions may limit the variety development process by losing potential genetic materials that perform better in a normal wheat-growing environment. Furthermore, a variety that performs better under different environmental conditions may be more stable in terms of grain yield across different years and environments. Therefore, employing a wide range of testing environments including both normal and stressed could be more appropriate and efficient for the development of high yielding, stable varieties adapted to drought-prone environments.

3.3.2 Physio-morphological traits Advances in precision phenotyping, along with combining genetic and molecular approaches in the breeding process, are expected to improve the efficiency of breeding programs (Mir et al., 2012; Kosová et al., 2014; Choudhary et al., 2018). In this scenario, the indirect selection that targets the underlying physiological traits that contribute to yield can be more 31

efficient than direct selection for higher yield (Reynolds et al., 2005; Reynolds and Trethowan, 2007). The reason for this observation is that traditional yield-based breeding relies on yield analysis of tens of thousands of plants at the end of each breeding cycle which in general masks the effect of the trait of interest on grain yield. By contrast, the hope of physio-morphological trait-based breeding is that early season and/or simple surrogate traits can be identified for yield or yield attributing traits (Nigam et al., 2005). This means that the measurement of yield attributing physio-morphological traits independent of grain yield improves the efficiency of selection by reducing the reliance on final grain yield. This approach may increase the possibility of making more successful crosses in a breeding program by exploiting the potential for additive gene action (Reynolds et al., 2009a; Ataei et al., 2017; Dolferous et al., 2019), as already noted above. In addition, it is always an advantage if the physiological trait considered for selection under a harsh environment has heritability higher than yield itself, which confers a greater chance for success for the development of a stress tolerant variety.

3.4 Growth stage based targets Increasing the efficiency of breeding drought-tolerant wheat varieties by targeting physio- morphological traits requires a thorough understanding of the impact of drought at different growth stages. In general, while the intensity and frequency of drought are extremely critical to the overall performance of the crop, the phenological stage at which drought events occur is equally important (Sarto et al., 2017). Wheat plants may be more susceptible to drought at specific critical growth stages, i.e., germination and seedling stages (Akram, 2011); tillering and stem elongation stages (Saeidi et al., 2015; Wang et al., 2015; Ding et al., 2018); and heading, anthesis and grain filling stages (Sarto et al., 2017; Akram, 2011). The morphological traits that contribute to final grain yield differ at each growth stage, and the extent to which these are impacted by drought, determine the seriousness of the stress event (Figure 3.3). Long term droughts such as those starting from stem elongation through to maturity, reduce yield more significantly compared to those starting at later phases through to maturity (Shamsi and Kobraee, 2011).

Figure 3.3. The figure shows different growth stages of wheat along with the associated visible growth events and traits related to drought tolerance. Also shown are the components that contribute to final grain yield that are important during each growth stage. Adapted from Slafer and Rawson (1994). The line graph shows the trend of moisture requirements at different growth stages.

Combined, these observations suggest the need to understand the crop growth stages of wheat, the intensity of water stress at any given growth stage, and also the timing and extent of drought stress in a given target environment, to develop a drought-tolerant variety for any specific environment. The following sub-sections detail the effect of drought at different growth stages of wheat as well as drought tolerance-related physio-morphological traits critical to developing drought-tolerant wheat varieties. It is important to note, however, that severe drought at any phenological stage of wheat may potentially reduce the final grain yield. For example, a study that explored drought tolerance of ten wheat genotypes under different levels of stress treatments showed that all the growth stages were influenced by limited moisture availability (HongBo et al., 2005). More recently, Ihsan et al. (2016) also observed that multiple growth stages, including germination, tillering, booting, heading, anthesis, and maturity, are negatively affected by drought stress, although the effect on heading and grain filling stages was more severe.

Table 3.1. Examples of previous studies that measured wheat yield declines due to drought imposed at different growth stages. Growth stages Stress Grain Yield Notes Source^ level § reduction Germination and Moderate 7%  One variety Zhang et al., 2013 seedling stages  Water withheld for 7 days at spikelet initiation stage (3 leaf stage)  Rapid recovery after re-watering Tillering Severe 6%-16%  One drought tolerant and one drought Abid et al., 2018 Moderate 2%-13% susceptible variety: 6% vs 16% reduction (severe stress), 2% vs 13% reduction (moderate stress)  Drought treatments were 10 days at 35-40% (severe) and 55-60% (moderate) vs 75-80% (control) field capacity  Drought tolerant variety had small yield reduction by maintaining high photosynthetic rate during drought and had rapid recovery Severe 52%  Yield averaged over 10 varieties Mehraban et al., 2019

Growth stages Stress Grain Yield Notes Source^ level § reduction  Water withheld during tillering (duration not reported)  Number of grains significantly reduced Moderate 4-13%  Three local varieties Maqbool et al., 2015  Water withheld at up to 50% of the optimal gravimetric soil water content

(44.32 g H20 /25 Kg dry soil)  Recovery after re-watering Stem elongation Severe 53%  One variety Ding et al., 2018 Mild 0%  Relative soil moisture content at 40% (severe drought), 60% (mild drought) and 75% (control)  Decrease in spikes per plant and kernel weight  Drought reduced kernel number, but compensated by increased kernel weight for mild stress

Growth stages Stress Grain Yield Notes Source^ level § reduction Severe 15%-24%  One drought tolerant and one drought Abid et al., 2018 Moderate 5%-11% susceptible variety: 15% vs 24% reduction (severe stress), 5% vs 11% reduction (moderate stress)  Drought treatments were 10 days at 35-40% (severe) and 55-60% (moderate) vs 75-80% (control) field capacity  Yield observations noted above Severe 2-45%  Five varieties including 2 drought Varga et al., 2015 tolerant varieties  Water withheld for 7-10 days: volumetric water content (v/v %) 3.5% (drought) vs 20-25% (control).  Reduced kernel number Stem elongation to Severe 54%  Yield averaged across four varieties Saeidi et al., 2015 anthesis  Treatments were 50% (drought) vs 100% (control) field capacity

Growth stages Stress Grain Yield Notes Source^ level § reduction  Number of grains per spike Severe 11%-3%  One drought tolerant and one drought Liu et al., 2016 Moderate 7%-10% susceptible variety: 11% vs 13%, 7% Mild 0%-4% vs 10% 0% vs 4% reductions for severe, moderate and mild stress, respectively  Drought treatments were 40-45% (severe), 55-60% (moderate), 65-70% (mild) vs 75-80% (full irrigation) of field capacity  Reduced canopy photosynthesis and translocation Booting, heading and Severe 46-82%  Six varieties Khakwani et al., 2012 anthesis  Grown at 100% field capacity then water withheld for 20 days (drought) after booting and anthesis  Reduction in kernel number Severe 47%  Yield averaged across three varieties Shamsi et al., 2010

Growth stages Stress Grain Yield Notes Source^ level § reduction  Water withheld after the onset of the growth stage (duration not reported)  Reduced kernel number and kernel weight Heading Severe 38%  One variety Ding et al., 2018 Mild 11%  Drought treatments were: relative soil moisture content maintained at 40% (severe), 60% (mild) vs 75% (control)  Reduction in number of spikes per plants, kernel weight and number Severe 25-78%  Five varieties including 2 drought Varga et al., 2015 tolerant varieties  Water withheld for 7-10 days: volumetric water content (v/v %) 3.5% (drought) vs 20-25% (control).  Reduced kernel weight and number

Growth stages Stress Grain Yield Notes Source^ level § reduction Anthesis Severe 69%  Yield averaged acrosss 4 synthetic Pradhan et al., 2012 hexaploids and 2 standard checks  Water withheld for 16 days  Reduction in kernel number and grain weight Moderate 11%  One variety Zhang et al., 2013  Water withheld for 6 days just after heading  Slight reduction in kernel number per spike Moderate 19-42%  Three local varieties Maqbool et al., 2015  Water withheld at up to 50% of the optimal gravimetric soil water content

(44.32 g H20 /25 Kg dry soil)  Reduced number of kernels per spike Grain filling Severe 57%  Yield averaged across 5 varieties Balla et al., 2011  Drought reported as 40-45% of the natural water content vs 60-70% for

Growth stages Stress Grain Yield Notes Source^ level § reduction the control, starting 12 days after heading (duration not reported)  Significant reduction in kernel weight and number Severe 24-87%  Five varieties including 2 drought Varga et al., 2015 tolerant varieties  Water withheld for 7-10 days: volumetric water content (v/v %) 3.5% (drought) vs 20-25% (control).  Reduced kernel weight Severe 15%  Yield averaged over 10 varieties Mehraban et al., 2019  Water withheld during grain filling (duration not reported)  Reduced kernel weight Severe 31%  Yield averaged across three varieties Shamsi et al., 2010  Water withheld after the onset of grain filling stage (duration not reported)

Growth stages Stress Grain Yield Notes Source^ level § reduction  Reduced kernel weight Severe 26%  Yield averaged across 4 synthetic Pradhan et al., 2012 hexaploids and 2 standard checks  Water withheld for 21 days after anthesis  Kernel weight reduced Severe 28.2%  Yield of one variety averaged over Gevrek and Atasoy, 2012 two years  Drought imposed by rain-out shelter after anthesis until maturity; control was rainfed (~47 mm and 95 mm rainfall in two years)  Reduction in kernel weight and number Severe 13%-3%  One drought tolerant and one drought Liu et al., 2016 Moderate 7%-12% susceptible variety: 13% vs 13%, 7%- Mild 0%-1% 12%, 0%-1% reductions for severe, moderate and mild stress, respectively

Growth stages Stress Grain Yield Notes Source^ level § reduction  Drought treatments were 40-45% (severe), 55-60% (moderate), 65-70% (mild) vs 75-80% (full irrigation) of field capacity  Reduced canopy photosynthesis and translocation Moderate 24-48%  Three local varieties Maqbool et al., 2015  Water withheld at up to 50% of the optimal gravimetric soil water content

(44.32 g H20 /25 Kg dry soil)  Reduced kernel weight § The criteria used to define the severity of drought stress was defined by each study and there is inconsistency in this definition between studies. ^All were greenhouse/control environment trials except for Mehraban et al., 2019; Iiu et al., 2016; Gevrek and Atasoy, 2012; and Shamsi et al., 2010 which were under field conditions

It is important to note that in the literature (e.g. Table 3.1), the severity of drought (mild or severe) that is reported can be subjective, dependent on the authors of a particular study. For example, Ding et al. (2018) defined treatments with 75%, 60% and 40% soil moisture content as control, mild drought and severe drought, respectively, while other studies have used different thresholds (Table 3.1). It may be more useful to define drought severity objectively, based on the quantitative frequency and duration of drought stress. In any case, prolonged mild, moderate or severe stress will increasingly reduce final grain yield.

3.4.1 Germination and seedling stages With respect to the impacts of drought at specific growth stages in wheat, it is known that sufficient moisture in the soil, along with optimum temperature, is required for uniform germination and hence may be critical for drought-sensitive varieties (He et al., 2017; Mukharjee et al., 2019). This is because germination-related traits such as emergence index, emergence rate index, the energy of emergence, and relative cell injury (RCI) vary significantly among different wheat varieties under normal and water-limited conditions (Ahmad et al., 2015). Increasing the level of stress during the germination and early seedling phases negatively affect traits such as germination rate, seedling vigour, and lengths of coleoptile, shoot and/or root (Kızılgeçi et al., 2017). There are limited studies that quantify yield losses in wheat due to drought at the germination and seedling stages. However, a few studies have reported a positive association of seedling traits with reproductive traits including grain yield (Kandic et al., 2009; Dodig et al., 2015). These studies highlight the importance of seedling drought tolerance to final plant performance.

3.4.2 Tillering and stem elongation stages After double ridge formation, spikelet initiation begins right at the seedling stage and proceeds until the tillering stage, while floret initiation starts at tillering and continues during the stem elongation period. Thus, these growth stages are important for maintaining the spikelet number per plant and spikes per plant which directly contribute to grain yield. As a result, a severe drought imposed during tillering and stem elongation in wheat reduces the number of grains per spike and ultimately grain yield (Blum et al., 1990; Saeidi et al., 2015; Ding et al., 44

2018) (Table 3.1). For example, Saeidi et al. (2015) observed a 54% reduction in grain yield as a result of water stress at the vegetative stage (stem elongation to flowering). In one study Ding et al. (2018) observed that extreme water stress during the stem elongation period reduced grain yield by up to 72%, which was greater than the effect of extreme water stress during the reproductive period. Similarly, in another study, the greatest decrease in grain yield observed as a result of moisture stress was at the stem elongation stage compared to booting and grain filling stages (Keyvan, 2010). In addition to declines in stem growth and the number of effective tillers, plant height is also reduced by drought at this stage (Blum et al., 1990; Sarto et al., 2017), and overall plant biomass is also negatively affected (Saeidi et al., 2015; Ding et al., 2018). This results in changing source-sink relationships, resulting from an increased fraction of available carbon being allocated to the root system rather than to the shoot when plants are under limited water supply (Palta and Gregory, 1997). Contrary to the above observations, an improvement in canopy structure and also maintenance of photosynthesis at the canopy level was observed when mild water stress was applied at stem elongation without a reduction in yield (Liu et al., 2016) (Table 3.1). It is sometimes argued that mild drought stress during this phase may not be very critical to the final grain yield. One possible explanation for these latter observations is that mild drought stress during tillering and stem elongation stages primes wheat plants to become acclimated to tolerate drought during the grain filling period (Wang et al., 2015); the mechanism involves low accumulation of hydrogen peroxide (H2O2) due to increased activity of H2O2 scavenging enzymes such as ascorbate peroxidase (APX) and guaiacol peroxidase (POX) (Khanna-Chopra and Selote, 2007). Despite some reports (Wang et al., 2015; Liu et al., 2016), the above evidence suggests that drought at tillering and stem elongation stages negatively affect grain yield. Therefore, the evaluation of genotypes for drought tolerance at these growth stages is equally important as other growth stages.

3.4.3 Heading and anthesis stages Many studies suggest that flowering and anthesis are the most susceptible wheat growth stages to drought (e.g., Ji et al., 2010; Fahad et al., 2017; Sarto et al., 2017). Water stress at heading and anthesis stages causes multiple impacts, but amongst these, a decrease in the number 45

of grains per head and grain weight was reported to be the most severe (Varga et al., 2015) (Table 3.1). Drought at these stages reduces pollen viability leading to failures in fertilization, and thus spikelet sterility (Ji et al., 2010; Su et al., 2013). This is also the stage when there is maximum evapotranspiration, which aggravates the situation and leads to severe crop loss. On the other hand, in comparison to severe drought, moderate stress at these stages may improve the translocation of assimilates in some genotypes (Liu et al., 2016; Ding et al., 2018) but this observation may be specific to only a few genotypes.

3.4.4 Grain filling stage It might be expected that drought during grain fill would be extremely critical compared to earlier growth stages because there is less opportunity for recovery compared to earlier stages. However, various studies (Table 3.1 and noted below) suggest that this stage is not more sensitive to drought, suggestive of mitigation strategies. Indeed, at the grain filling stage, though water availability becomes critical for translocating photosynthates to the grain, pre-anthesis storage reserves such as those in the stem can play vital roles in preventing yield loss, to mitigate the negative impact of moisture stress on photosynthate assimilation (Blum, 1998; Liu et al., 2016). One study showed a 5.2% decrease in kernel weight and a 20.7% reduction in kernel numbers as a result of drought imposed after anthesis resulting in a ~28% yield decline (Gevrek and Atasoy, 2012) (Table 3.1), indicating the severe impact of drought during the grain filling period. Furthermore, mild drought during the grain filling stage does not appear to cause a significant reduction in final grain yield (Ding et al., 2018). As noted above, moderate drought during vegetative growth stages may prime plants to acclimate to drought during grain fill; the mechanism involves reduced photo-inhibition in the flag leaves at this later stage associated with increased accumulation of abscisic acid (ABA) (Wang et al., 2015). In addition, drought tolerance during grain fill may be due to the accumulation of dehydrins (Lopez et al., 2002), a family of hydrophilic, thermostable proteins produced during dehydration that provide protection from a yet unknown mechanism (Yu et al., 2018b).

3.5 Phenotyping above ground physio-morphological traits associated with drought tolerance at different growth stages In the previous section, the impact of drought during specific growth stages of wheat was described. For these observations to be used in a breeding program, it is important to identify phenotypic target traits for selection at each of the growth stages. Phenotypic-based selection is already a common practice in breeding for drought-tolerant wheat varieties. To further improve the efficiency of breeding, researchers have identified many more useful physio-morphological traits that influence drought tolerance, and these candidate traits for selection are summarized in Figure 3.4 and detailed below.

Figure 3.4. Summary of physio-morphological traits associated with different growth stages and phenotyping methods in wheat.

3.5.2 Germination and seedling growth stages

3.5.2.1 Coleoptile length and gibberellic acid (GA3) sensitivity The coleoptile of wheat is a pointed protective sheath, which covers the emerging shoot or the first leaf during germination (Farhad et al., 2014). When a seed germinates, the coleoptile pushes through the soil and protects the young shoot. Coleoptile length is measured from the scutellum to the tip of the coleoptile sheath (Rebetzke et al., 2005) when the coleoptile is completely visible, typically within 1-2 weeks after germination. The length of the coleoptile varies among different genotypes, and this has special importance to wheat cultivation in drought environments. The concept of deep planting is more subjective as the depth seeding may be defined based on the soil moisture availability. Although coleoptile length may not be the sole factor responsible for emergence after deep sowing (Mohan et al., 2013), the frequency of emergence following deep sowing, needed to overcome surface desiccation, is greater in genotypes with longer coleoptiles than those with shorter coleoptiles (Rebetzke et al., 2005; Rebetzke et al., 2007; Farhad et al., 2014). Poor plant establishment can, therefore, result from shorter coleoptiles under stressed conditions (Schillinger et al., 1998), especially in dry environments. All of these studies establish that there is potential to use coleoptile length as a surrogate trait to screen drought-tolerant wheat varieties. In fact, it is not appropriate to look coleoptile length only in isolation for early-season drought tolerance. But, the above evidence suggest that it is a trait that carries potential while breeding wheat for early-season drought tolerance. It is noteworthy that the dwarfing genes in wheat have important associations with coleoptile length. Specifically, over the past few decades of modern wheat breeding, the dwarfing genes Rht-B1b (Rht1) and Rht-D1b (Rht2) derived from “Norin 10”, a Japanese wheat variety, have been widely used to develop dwarf and semi-dwarf wheat varieties around the world (Borojevic and Borojevic, 2005; Waddington et al., 2010). The Rht1 and Rht2 genes made the plants insensitive to internal gibberellic Acid (Keyes et al., 1989) leading to shorter coleoptiles and reduced plant height as a result of reduced cell elongation (Rebetzke et al., 2001). There are other dwarfing genes apart from Rht-B1 and Rht-D1 that are sensitive to endogenous gibberellins, which reduce plant height but have less or no effect on coleoptile length and leaf surface area (Rebetzke et al., 1999; Ellis et al., 2005; Rebetzke et al., 2005). The dwarfing gene 49

Rht8, derived from a Japanese variety “Aka Komugi” (Grover et al., 2018), allows for the development of a long coleoptile (Rebetzke et al., 2005). The Rht8 gene has been employed in commercial varieties in some Mediterranean countries including Italy. According to Grover et al. (2018), coleoptile length and plant height were reduced by only 6.75% and 2.84%, respectively, in the lines carrying the Rht8 gene compared to 21.64% and 23.35% reductions in lines containing the Rht1 gene. Growing seeds on filter paper or germination paper under dark conditions (Purchase et al., 1992) is a common practice while “cigar-roll method” (Bai et al., 2013) is another method more into use. The coleoptile measurements are also taken after growing seeds in soil media (Mohan et al., 2013). Measurement of coleoptile length is commonly done manually when the seedlings are 7-10 days old, and it is a time taking activity. Recently, Zhang et al. (2018) analyzed coleoptile images using custom-developed image processing algorithms which was significantly correlated (r=0.69-0.91, P<0.0001) with manual measurement of coleoptile length.

3.5.2.2 Seedling vigour Among phenotypic targets for selection, seedling vigour is considered as a candidate trait for evaluating wheat for drought tolerance at the early growth stage (Rebetzke et al., 1999; Reynolds et al., 2005). The four-leaf stage is the appropriate stage to study seedling vigour (Hafid et al., 1998). This trait is characterized by the extent of ground cover and establishment, and can be measured using a visual rating score (Mullan and Garcia, 2012). Also, the use of digital imaging techniques such as using an RGB camera is also a common way to assess seedling vigour, though this requires technical skills to process the images using different software programs. One of the reasons why this is a promising trait is the large number of genotypes that can be screened quickly and cost effectively (Dhanda et al., 2004). Dodig et al. (2015) found a significant association (r= 0.31, P≤0.01) between a stress tolerance index (STI) at the seedling stage and the STI associated with grain yield in wheat. Furthermore, early seedling vigour has been reported to have a positive correlation (r=0.28-0.63, P<0.01) with grain yield (Kandic et al., 2009). Vigourous, healthy seedlings are water and nutrient use efficient and can compete against weeds (Zhang et al., 2014). A handful of scientists have recently emphasized the use of seedling vigour as part of variety selection: for example, Ahmad et al. (2015) evaluated 50 50

wheat genotypes for different seedling traits including seedling vigour and successfully identified eight potentially drought-tolerant genotypes. Such evidence highlights the importance of this trait in breeding wheat for drought tolerance. The recent studies (Zhang et al., 2018; Walter et al., 2019) showed that digital imaging using an RGB camera that recognizes colour- based traits may be an alternative to visual assessment which is more subjective. These studies have highlighted that digital imaging may increase the throughput rate and precision of measuring seedling traits. The images can be analysed by using open source software such as ImageJ (Schneider et al., 2012). Therefore, application of high throughput imaging technologies can create opportunities to more efficiently use this trait for screening in breeding programs.

3.5.3 Vegetative growth to post-anthesis growth stages 3.5.3.1 Number of Tillers Different studies have reported that drought reduces the number of tillers (Maqbool et al., 2015; Abid et al., 2018). Since each tiller has the potential to initiate a spike, then the tiller number directly affects grain yield in wheat. Tiller initiation in wheat is asynchronous, and the tillering stage encompasses newly initiating tillers as well as older tillers undergoing reproductive transition. Therefore, the tillering stage determines both the tiller number and also the development of reproductive primordia (spike, spikelets, and florets) (Figure 3.3). Indeed, moisture stress during this stage reduces tiller number which is compounded by a reduction in the number of kernels per spike (Blum, 1990). The extent to which moisture stress reduces tiller number varies among genotypes depending on the intensity and duration of stress (Sarto et al., 2017). The genotypes that maintain a higher rate of photosynthesis and other physiological parameters such as leaf water potential, and exhibit rapid recovery after re-supplying moisture, promote drought tolerance at the tillering stage (Abid et al., 2018). In many cases, the number of tillers may not be reduced significantly if the moisture is supplied before the end of the tillering stage, but in such cases, the tillers have a low biomass and kernel weight (Blum, 1990). There are also arguments that the number of tillers may not always be a good indicator of yield in wheat. Duggan et al. (2000) showed that under drought, vigorous tillers each with a high number of large kernels were more important than the numbers of tillers per se in terms of grain yield. Although many researchers do not measure tiller number, it is an important trait that should be 51

considered in breeding for drought tolerance in wheat. This trait is commonly scored at maturity by counting the number of tillers per unit area in a representative part of each plot.

3.5.3.2 Chlorophyll content The green area of plant leaves specifies the photosynthetic capacity and provides valuable information associated with crop productivity, and the physiological and phenological status of the plant (Kira et al., 2015; Ramya et al., 2016). Chlorophyll content positively correlates with grain yield (Talebi, 2011; Kumari et al., 2012). Abiotic stresses lead to changes in the amount of chlorophyll content in plant leaves. Drought susceptible wheat varieties showed losses in chlorophyll content, while tolerant varieties exhibited higher chlorophyll content (Khayatnezhad et al., 2011). Similarly, a 13-15% reduction in chlorophyll content was observed in wheat varieties due to limited water supply (Nikolaeva et al., 2010). This loss in chlorophyll content is due to decreased expression of genes encoding enzymes for chlorophyll biosynthesis (Liu et al., 2018). By contrast, the stay-green trait in which chlorophyll persists, and which signifies the delayed senescence of leaves, is another important indicator of stress tolerance (Lopes et al., 2012; Rosyara et al., 2008) and is discussed in more detail below. Talukder et al. (2014) state that stress during anthesis results in a significant drop in flag leaf chlorophyll content and yield- related traits such as grain number, grain mass and duration of grain filling. Khayatnezhad et al. (2011) observed that the durum wheat varieties that had a significantly higher chlorophyll content during the reproductive phase were higher yielding and more tolerant to end-season drought. Other studies have also revealed that drought-tolerant wheat genotypes maintain higher chlorophyll content during water stress compared to drought susceptible varieties (Talebi, 2011; Bowne et al., 2012; Rehman et al., 2016; Ahmed et al., 2019). All of these examples delineate that crop varieties with higher chlorophyll content and slow chlorophyll degradation, are potentially more drought-tolerant. Therefore, chlorophyll content is an important trait that can be used as a proxy for drought tolerance and higher grain yield, and it is a simple trait to phenotype. The Soil Plant Analysis and Development (SPAD) meter can rapidly measure SPAD values as a proxy for chlorophyll content (Lopes et al., 2012; Rosyara et al., 2008) and is widely used. The SPAD meter has diodes that emit red and near infra-red wavelengths that pass through the leaf; chlorophyll absorbance is determined at 650 nm while non-chlorophyll absorbance is 52

calculated using a wavelength peaking at 940 nm (Lopes et al., 2012). In wheat, SPAD measurements are taken from the flag leaf when evaluating varieties for selection (Paul et al., 2016). SPAD measurements can be taken starting at the emergence of flag leaves until early senescence at a desired time interval (e.g. a week or 10 days). This allows a breeder to assess the association of chlorophyll content at different growth stages with other target traits such as grain yield (Rosyara et al., 2008).

3.5.3.3 Normalized difference vegetative index The normalized difference vegetative index (NDVI) is a metric for vegetation based on the difference between the reflection of near-infrared light (which vegetation strongly reflects) and red light (which vegetation strongly absorbs) (Sultana et al., 2014). Values of NDVI range from - 1 to +1, and are calculated using the following formula: 푅 − 푅 푁퐷푉퐼 = 푁퐼푅 푅 푅푁퐼푅 + 푅푅 Where: NDVI=Normalized difference vegetative index;

RNIR= Near-infrared radiation;

RR=Visible red spectrum.

NDVI is extensively used as a trait to report the greenness and ground cover of the vegetation, and therefore, to infer the photosynthetic strength of the crop canopy, using measurements taken from the ground level up to satellite altitudes (Pietragalla and Vega, 2012). It is now widely accepted as a proxy for drought adaptive traits (Lopes and Reynolds, 2012), having a positive association with grain yield (Ramya et al., 2016; Condorelli et al., 2018), and it has also shown potential for estimating growth rate, seedling vigour and senescence patterns in wheat (Pietragalla and Vega, 2012). For example, selection for yield parameters based on NDVI along with other chlorophyll measurements resulted in a yield increase by 17.1% in a half-sib population of bread wheat (Ramya et al., 2016). The GreenSeeker spectral sensor (Trimble, California, USA) is an NDVI tool that has been used to measure the greenness of a plant and is commonly used to estimate the greenness of an 53

entire field plot. The use of GreenSeeker has been considered more integrative than SPAD as it is mostly helpful in determining the pattern of senescence from the whole crop canopy, while SPAD uses only one leaf at a time to record the greenness. However, GreenSeeker is unique as it only measures the reflectance of the modulated red and NIR light that it provides, not the ambient light. The accuracy of the measurement depends upon the number of NDVI measurements taken (Lopes et al., 2012). Therefore, recording NDVI throughout the crop season at defined intervals is essential to improve the accuracy of the measurements taken and to evaluate the pattern of change in greenness throughout the season. Although NDVI is a commonly used index, its sensitivity to genetic and environmental diversity has been criticized (Christopher et al., 2016). Therefore, caution should be used when using NDVI to predict wheat grain yield when a diverse genetic population is involved and when the environmental conditions are highly variable. While NDVI can be the first choice for many breeders, the use of other vegetative indices such as Normalized Difference Red Edge (NDRE) may be another option. The performance of NDRE is better compared to NDVI, overcoming the limitations of NDVI associated with absorptance by the upper canopy and saturation at its maximum value during later growth stages of the crop (Fu et al., 2020).

3.5.3.4 Chlorophyll fluorescence Chlorophyll fluorescence measurements can be used to non-destructively determine a wide variety of leaf-level parameters related to the functional status of Photosystem II (PSII), the first protein complex in the light-dependent reactions of photosynthesis. For example, in illuminated leaves, chlorophyll fluorometry can be used to calculate the rate of photosynthetic electron transport through PSII (Genty et al., 1989; Earl and Tollenaar, 1998), or to quantify the activity of non-photochemical quenching processes associated with the safe dissipation of excess light energy (Laisk et al., 1997). More commonly, chlorophyll fluorescence is measured on dark-adapted leaves (e.g., pre- dawn, or after placing plants in darkness for a defined period, generally 30 min to a few hours). Dark-adapted measurements provide estimates of maximum efficiency of PSII, which can be reduced due to damage or down-regulation occurring in response to prior stress (Lu et al., 2003; Baker, 2008). Because PSII efficiency is so sensitive to any stress affecting photosynthesis, 54

evaluation of chlorophyll fluorescence can be used as a quick indicator in any crop (Jansen et al., 2014). The parameters of chlorophyll fluorescence measured on dark-adapted leaves, namely the initial fluorescence (F0; the signal when all functional PSII centres are “open”), maximum fluorescence (FM; the signal when all PSII centres are “closed” by a brief pulse of saturating light), variable fluorescence (FV = FM – F0) and efficiency potential (FV/FM) are affected by unfavourable environmental factors like drought (Zhang et al., 2010; Sharma et al., 2014a).

Under drought conditions, F0 may increase or decrease, while FM, FV and FV/FM all decrease. These parameters of chlorophyll fluorescence kinetics (PCFKs) and imaging are considered as very powerful and reliable indicators of the impact of various abiotic stresses, including drought, on plant physiological processes (Paknejad et al., 2007; Khayatnezhad et al., 2011; Jansen et al.,

2014; Sharma et al., 2014b). Under drought conditions, crop varieties that maintain high FV/FM under water-limited conditions are considered to be stress tolerant (Zlatev, 2009), and indicate efficient protection of PSII activity. Chlorophyll fluorescence has been used in a diversity of plant species including wheat (Zlatev, 2009; Khayatnezhad et al., 2011; Rahbarian et al., 2011; Kamanga et al., 2018; Yao et al., 2018). Similarly, analysis of PCFKs in winter wheat seedlings indicated that the variety that maintained FV/FM was tolerant to water stress, able to maintain high photosynthetic activity (Zlatev, 2009). Despite the potential usefulness of PCFKs in drought tolerance studies, there are some limitations associated with the measurement of these parameters. In particular, the high-throughput measurement of PCFKs is mostly useful during the seedling stage and can be a difficult task later in development (Furbank and Tester, 2011). Another issue is that these parameters may have limited applicability to breeding programs if the population size is very large, although emerging high throughput phenotyping technologies may overcome this challenge. The best application of PCFKs in breeding may be at the advanced breeding stages where the number of genotypes may not be a limiting factor.

3.5.3.5 Shoot waxiness Cuticle, the outermost layer of the plant shoot, is made up of cutin and waxes. The amount of wax present on the leaves differs among species and genotypes within a species. Waxiness, also known as glaucousness, is a shoot morphological trait that is vital from the perspective of environmental stress tolerance (Bi et al., 2017). In wheat breeding, waxiness is used to select 55

drought-tolerant genotypes. The composition of the wax and the changes in wax composition influence water loss from the cuticular surface, leading to drought tolerance, though it is not the sole indicator (Jäger et al., 2014; Bi et al., 2017). In a study consisting of three wheat varieties, Bowne et al. (2012) reported that the variety with the most waxiness yielded the highest under both mild and severe drought conditions, supporting that waxiness reduces the loss of water from plant surfaces. Waxiness is usually quantified visually based on the proportion of visible bluish- white coloured wax on the plant shoot surface including spikes when phenotyping in the field. A 0-10 visual rating scale is commonly used, where 0 indicates no or low waxiness, and 10 indicates high waxiness (Torres and Pietragalla, 2012). Qualitatively, analyses of cuticular waxes have shown that genotypes with higher β-diketones, one of the two major components of wax along with alkanes, are more drought-tolerant (Bi et al., 2016; Bi et al., 2017). More recently, it has been shown that considerable changes occur in the composition of carbonyl ester in cuticular wax of wheat leaves when exposed to water stress suggesting the possibility of having a new biochemical marker for drought tolerance (Willick et al., 2018). Considering its influence on drought tolerance, and given that it is easy to score visually, waxiness is a valuable trait to include when breeding wheat for drought tolerance.

3.5.3.6 Carbon isotope discrimination Water use efficiency (WUE), known as transpiration efficiency, is a ratio of above ground (aerial) biomass yield (carbon) to the total amount of water used by a plant (Condon et al., 1992; Rebetzke et al., 2002). Direct measurement of WUE is very complicated and also time exhausting. Therefore, utilization of carbon isotope discrimination (Δ), which is a proxy for the ratio of intercellular (Ci) and the atmospheric (Ca) partial pressure of CO2, i.e. Ci/Ca, has been exploited as an indirect measurement of WUE (Farquhar et al., 1982; Farquhar et al., 1989; 12 Ehdaie et al., 1991). CO2 exists in the atmosphere mostly as CO2, but also as the rarer, heavier 13 isotope CO2. During CO2 fixation, plants discriminate between these two isotopes, favouring 12 12 C since CO2 diffuses more freely into leaves than does the heavier isotope, and also because 12 13 RuBisCO fixes CO2 more readily than CO2. However, the magnitude of discrimination (and therefore the measurable 13C / 12C ratio in the final crop biomass) varies depending on the type of crop, genotype, environment and other factors (Kumar and Singh, 2009). If Ci was in perfect 56

13 equilibrium with Ca, then discrimination against C would be constant. However, because stomatal conductance is not infinite, photosynthesis reduces leaf internal CO2 (i.e. Ci / Ca < 1), and the air inside the leaf becomes more depleted in 12C relative to 13C, which reduces the 12 opportunity for further discrimination in favour of C. Thus, plants that have had lower Ci over 13 12 their lifetimes will have higher C / C in their final biomass. Since lower Ci is associated with higher WUE (Farquhar et al., 1982), carbon isotope discrimination serves as a time-integrated proxy for WUE. Consistent with this well-established theory, Δ has been shown experimentally to be negatively associated with WUE (Ehdaie et al., 1991; Medrano et al., 2015). The Δ metric has been used to assess WUE and its association with grain yield in many different crops including wheat (Austin et al., 1990; Blum, 2005; Haq et al., 2008; Kumar and Singh, 2009; Luckett et al., 2011). Interestingly, Δ has high heritability (the narrow-sense heritability is ~0.63) under limited water conditions (Rebetzke et al., 2002; Rebetzke et al., 2006), and thus, it is a potential indirect method to select high yielding wheat varieties for dry environments (Rebetzke et al., 2006). To determine Δ, finely ground dried shoot tissue is analyzed using an isotope ratio mass spectrometer (Optima, VG Instruments, UK) (Becker and Schmidhalter, 2017; Bachiri et al., 2018). It has been assessed using the flag leaf in durum wheat under drought conditions by Merah and Monneveux (2001) and in Triticale by Munjonji et al. (2016). The throughput rate for determination of Δ may not be high but it is a reliable trait to assess drought tolerance in wheat. The trait may be best evaluated when the wheat breeding lines are at an advanced stage.

3.5.3.7 Leaf rolling When the plants are under limited water conditions, turgor pressure adjustment in the cells is one of the biochemical mechanisms that helps plants acclimate to dry conditions (Price, 2002; Fang and Xiong, 2015). Leaf rolling is one of the consequences of turgor pressure adjustment observed in diverse plants when they are exposed to limited water environments (Kadioglu, 2007; Fleury et al., 2010). During the stress period, leaf rolling reduces the leaf area, which in turn reduces the effective area for evapotranspiration (Fang and Xiong, 2015; Lamaoui et al., 2018) and hence represents a drought acclimation response. In wheat, like other cereals, leaf rolling is a typical symptom when there is water deficit in the soil (Clarke, 1986; Kadioglu et al., 57

2012). Since leaf rolling is associated with water loss in cultivated wheat, it is a potential proxy trait for screening wheat genotypes up to a certain level of water loss from the leaves (Condorelli et al., 2018). Although the heritability of leaf rolling was found to be high (narrow-sense heritability ~0.83), Sirault et al. (2008) suggest further investigation to exploit the usefulness of this trait for breeding. In wheat, leaf rolling can be easily scored visually in the field using qualitative scales (Torres and Pietragalla, 2012; Condorelli et al., 2018) at the vegetative and reproductive growth stages. Despite leaf rolling not being widely used, it has the potential to improve the efficiency of breeding for drought tolerance in wheat.

3.5.3.8 Canopy temperature Canopy temperature (CT) is a physiological trait that indicates crop water status (Mason and Singh, 2014) and is an established proxy trait for stomatal conductance (Deery et al., 2019). CT has the potential to be a very useful tool for indirect selection of tolerant genotypes for heat and drought stress tolerance (Reynolds et al., 2009b). In such environments, genotypes that maintain cooler canopies are more likely to thrive. It has been observed that in deep-rooted genotypes, CT is usually lower, as the crop can extract moisture from a deeper soil depth (Lopes et al., 2012). The CT is affected by various confounding factors such as solar radiation, soil moisture, wind speed, temperature, and relative humidity (Reynolds et al., 2012; Mason and Singh, 2014), and hence caution should be used when interpreting CT data. Canopy temperature can be measured from post-tillering to physiological maturity using an infrared thermometer (Pask et al., 2012), thermal imaging (Costa et al., 2013), and ArduCrop wireless infrared thermometers (Rebetzke et al., 2016b; Deery et al., 2019). Since the frequency of drought and heat stress are expected to increase in the near future, the development of wheat genotypes with cooler canopies should be one of the targets of a wheat breeding program.

3.5.3.9 Days to heading and anthesis According to Singh (1981), there are three very critical phenological stages in wheat, based on the soil moisture requirement: the early vegetative stage, booting to the heading stage, and the flowering and grain filling stage. Many studies suggest that plants are more sensitive to water limitation from heading to flowering than the other stages (Nezhadahmadi et al., 2013; Farooq et 58

al., 2014; Sarto et al., 2017). Evapotranspiration reaches a maximum at this growth phase (Sarto et al., 2017). Therefore, maintenance of optimum soil moisture at this interval is critical to ensure higher wheat yield and yield stability (Senapati et al., 2019). Drought at heading has been found to affect spike weight negatively (Ding et al., 2018). Similarly, in winter wheat, Varga et al. (2015) reported that water stress during the heading period, or the growth stages after heading, resulted in a higher yield penalty. Drought at the onset of flowering results in sterility and a reduced number of grains per spike, which is primarily due to reduced pollen viability and ovule abortion as a consequence of limited moisture (Su et al., 2013). With respect to genetic variation in this trait, in a study that evaluated the response to drought during the reproductive stage, Senapati et al. (2019) observed a 13.4% higher mean yield in drought-tolerant wheat genotypes compared to susceptible genotypes across 13 major wheat growing regions of Europe. One additional study showed that early heading and anthesis can be escape mechanisms for terminal drought (Shavrukov et al., 2017), perhaps more applicable to geographic regions that suffer from late-season water deficit. Phenotyping a genotypically diverse set of plants at heading and anthesis stages is time-consuming, requiring daily visits to a field site, because of the corresponding diversity in flowering times. Recently, an automated phenotyping method for these stages has been tested in wheat, involving computer software analyzing digital images; the method has achieved a >85% accuracy for both stages (Sadeghi-Tehran et al., 2017). Based on its reproducibility and cost effectiveness, such a method has potential for adoption in wheat breeding programs.

3.5.3.10 Flag leaf senescence It is proposed that a delay in flag leaf senescence greatly influences grain yield in cereal crops (Thomas and Howarth, 2000; Borrill et al., 2015). In wheat, early flag leaf senescence has been found to affect grain yield negatively (Gregersen et al., 2013) including under dryland conditions (Liang et al., 2018), while delayed flag leaf senescence is positively associated with higher grain yield (Verma et al., 2004) and harvest index (Carmo-silva et al., 2017). Drought- tolerant varieties have a higher CO2 uptake rate in the flag leaves which contributes to yield stability (Paul et al., 2016). During the senescence phase, protein complexes in chloroplasts including PSII, break down which leads to changes in the structure, metabolism and gene 59

expression of photosynthetic cells, ultimately resulting in loss of cellular chlorophyll content (Thomas and Howarth, 2000; Podzimska-Sroka et al., 2015). Therefore, to maintain translocation of assimilates from leaves and stems to the grain and maintain grain yield, genotypes with delayed leaf senescence are preferred (Hafsi et al., 2013). Delayed senescence and persistence of greenness were introduced above as the stay-green trait and are associated with altered cytokinin and ethylene activities (Thomas and Ougham, 2014). This evidence implies that delayed flag leaf senescence can be a key parameter for selecting genotypes in areas with end season drought stress (Campos et al., 2004). Flag leaf senescence can be recorded visually by using qualitative leaf colour scores (Pask and Pietragalla, 2012). There are some studies with wheat which reveal that the contribution of ear photosynthesis to grain yield is relatively greater than that of the flag leaf in optimal and drought environments (Abbad et al., 2004; Bragado et al., 2010; Yun-qi et al., 2016). Nonetheless, the significance of delayed flag leaf senescence for drought tolerance should not be underestimated. Since flag leaf senescence is easy to record and has close association with chlorophyll decline, the trait can be targeted in wheat breeding programs for drought tolerance. Confounding effects may be observed, however, when the genotypes under study are extremely diverse with respect to days to maturity.

3.5.3.11 Grain filling rate and duration In cereals, the grain filling period is one of the most sensitive growth stages to water stress (Barnabás et al., 2008; Alghabari and Ihsan, 2018) and commonly affects many wheat growing regions of the world including semi-arid regions (Saeidi and Abdoli, 2015). Grain yield loss depends on the severity of the stress during this interval (Farooq et al., 2014; Tabassam et al., 2014; Saeidi and Abdoli, 2015). This loss in grain yield could be due to a reduction in the duration of grain filling (Alghabari and Ihsan, 2018) which is associated with early senescence (Bogale and Tesfaye, 2016; Farooq et al., 2014). Monpara (2011) stated that the grain filling period is positively correlated (r= 0.51, P<0.05) with grain yield in wheat, which was confirmed under water-limited condition (Kilic and Yagbasanlar, 2010; Bogale and Tesfaye, 2016). Ihsan et al. (2016) observed that a drought-tolerant wheat genotype had a 38% longer grain filling period compared to a drought susceptible genotype. Other researchers such as Borrill et al. (2015) and Nass and Reiser (1975) argue that the rate of grain filling or grain filling capacity is 60

more critical than the grain filling duration. Baillot et al. (2018) and Madani et al. (2010) showed that the rate of translocation of photosynthates to the grain contributes to grain weight and is a major component of grain yield in wheat, which supports the statement that grain filling rate is more fundamental to grain yield than grain filling duration. There is also an argument that stressed plants may have a higher rate of grain filling, and this, combined with a shorter grain filling period, cause improper assimilate translocation which ultimately leads to reduced grain yield (Ihsan et al., 2016; Alghabari and Ihsan, 2018). Therefore, it may be useful to screen genotypes that have a longer interval from anthesis to grain maturity and/or a higher rate of grain biomass increase in a time course. Selection for a higher rate of grain biomass increase may be more useful than delayed grain maturity in climates where short duration wheat varieties are grown.

3.5.3.12 Awn length Wheat genotypes vary for having spikes with no awns to those with differing awn lengths. Awn length is measured from the tip of the spike to the tip of the longest awn but can also be scored qualitatively (Torres and Pietragalla, 2012). A significant positive association was observed between awn length and grain yield, and between awn length and spike length, under water deficit conditions (Blum, 2005; Taheri et al., 2011; Khamssi and Najaphy, 2012; Ali et al., 2015). A significant correlation (r= 0.43, P≤0.01) between awn length with drought stress tolerance index was also observed (Taheri et al., 2011). Similarly, it was observed that awns maintained a higher relative water content and photosynthetic electron transport rate compared to the flag leaf under drought, indicating tolerance to soil moisture deficit (Maydup et al., 2014). Awns are green and contribute to the photosynthetic area, and positively influence grain yield (Rebetzke et al., 2016b). Maydup et al. (2010) reported that the contribution to grain yield by spike photosynthesis was up to 42% while the contribution was even greater in varieties having longer awns. The results from another study (Towfiq and Noori, 2016) showed that the awns increased the photosynthetic spike surface area by 36-59% and increased grain yield by 10-16%. Morphologically, moisture stress causes a significant reduction in awn dry weight but not length (Khamssi and Najaphy, 2012). This result may be related to awns competing for assimilates during ovary growth, and represents another issue that requires further investigation. 61

Furthermore, awns are associated with larger but fewer grains, which is another topic that needs to be further investigated (Rebetzke et al., 2016b). Integrating the information discussed above (Taheri et al., 2011; Ali et al., 2015) and its high to moderate level of heritability (broad-sense heritability ~0.95) (Bhatta et al., 2018), awn length is an interesting trait to consider for assessment of wheat genotypes for drought tolerance. Awn length is measured from the tip of the spike to the tip of the longest awn after physiological maturity (Torres and Pietragalla, 2012).

3.5.3.13 Plant height Plant height is one of the critical traits affected by drought in wheat. Low moisture reduces photosynthesis and metabolite/nutrient translocation in wheat, especially during the stem elongation stage, resulting in reduced height (Sarto et al., 2017). The reduction in plant height is due to carbon partitioning as the plants undergo osmotic adjustment under moisture stress (Blum et al., 1997). The proportion of reduction in plant height depends on both the intensity of drought and the genotype. Reductions in wheat plant height of 2-24% and 1-16%, respectively, due to drought stress applied at 30% and 70% field capacity during the stem elongation stage, have been reported (Qadir et al., 2016). Similarly, in another study (Mirbahar et al., 2009), a ~25% reduction in wheat plant height was observed due to drought. The drought tolerant plants tend to maintain shorter plant height and plant area index to reduce the moisture demand and prevent moisture loss due to transpiration (Su et al., 2019). Therefore, proportionally, the growth reduction observed in smaller plants is less than larger plants indicating that smaller plants are less sensitive to moisture stress (Blum et al., 1997). With respect to this concept, selection for a higher root to shoot ratio appears to be a more appropriate strategy for drought-prone environments [e.g. Ahmed et al. (2019)]. Plant height in wheat is commonly measured manually using a ruler after maturity. However, sensor-based tools have been recently tested in other crops for the measurement of plant canopy height (Andrade-Sanchez et al., 2014) which can be transferred to wheat breeding programs to reduce time and labour.

3.6 Phenotyping root architectural traits Aside from the above-ground traits described above, root system architecture (RSA) also plays a vital role in the growth, development and overall productivity of crop plants (Wasaya et 62

al., 2018). In general, RSA of a plant largely depends upon the environment within which it grows and the types of abiotic stresses associated with soil and water that it experiences (Wang and Frei, 2011). When water is the major constraint, then root characters have an equally important role in promoting drought tolerance as shoot traits (Manavalan et al., 2010). Some of the important root traits, such as root angle, primary root length (Comas et al., 2013; Forde, 2014), the number of lateral roots (Zhan and Lynch, 2015), and average root diameter (Comas et al., 2013; Haling et al., 2013), contribute to regulating water uptake which is critical under drought stress. It is stated that plants with root systems having a smaller root diameter and long fine roots are more suited to drought environments. In wheat, the RSA consists of two different types of roots, the early initiating seminal roots, sometimes regarded as embryonic roots, and the later nodal roots, commonly known as a crown or adventitious roots (Kirby, 2002; Manske and Vlek, 2002). The wheat embryo develops ~6 root primordia: during germination, the primary root and 4-5 seminal roots emerge out of the coleorhiza which ultimately develops into the seminal root system (Perry and Belford, 2000; Kirby, 2002). The later nodal roots, which emerge at the nodes, develop as tillering progresses (Kirby, 2002; Manske and Vlek, 2002). The seminal roots can grow up to 2 m deep into the soil, while the nodal roots are thicker and scavenge from the upper surface of the soil in a more horizontal orientation, to the middle layers (Kirby, 2002). Both seminal and nodal roots branch to form lateral roots. Similar to other crops, root traits are also associated with drought tolerance in wheat (Lopes and Reynolds, 2010). Root angle is a trait that explains some mechanisms related to soil- root interactions (Chen et al., 2017; Chen et al., 2018). The seminal and nodal roots of genotypes with a narrow root angle tend to grow deeper compared to those with a wider root angle at the early growth stages (Manschadi et al., 2008; Wasson et al., 2012; Richard et al., 2015). Similarly, a high density of lateral roots at a narrow angle (steeper) are considered better for wheat since such roots have more access to soil moisture at a deeper soil depth (Manschadi et al., 2008; Wasson et al., 2012; Chen et al., 2017; Chen et al., 2018). Root angle in wheat shows high heritability (broad-sense heritability~0.82), and thus, it is an important trait to consider when breeding wheat for drought tolerance (Hassouni et al., 2018). Although evidence suggests that root angle is a vital trait especially for end season drought tolerance in wheat (Gesimba et al., 63

2004), it is important to note that wheat root systems that have a high number of nodal and seminal roots in the crown region located close to the surface of the soil are more drought- tolerant at the early growth stages. The limited number of studies on RSA morphology is primarily due to a lack of efficient high throughput phenotyping methods. Conventional methods of root study include excavation of roots from the field which is labour intensive and time-consuming; such methods are mostly limited to phenotyping young roots under controlled conditions. For example, Goron et al. (2015) established a growth system consisting of fertigation of coarse Turface clay, rather than fine sand or soil which damages roots upon excavation, to phenotype root hairs in wheat, millet, and maize. The study demonstrated that this approach could be an alternative method to characterize finer root traits like root hair number and length. However, as stated above, this method requires considerable time, human and financial resources, which limits its application in plant breeding. Some low-cost and simplified high throughput phenotyping methods have been developed recently. Richard et al. (2015) used two different methods, the clear pot method, and the transparent growth pouch method, to study wheat seedling RSA using two proxy traits, root angle and the number of seminal roots associated with root surface area. In the clear pot method, the pots are transparent, and seeds are sown at the edge of the pots to permit RSA observation; the pots are placed in dark pots to prevent light exposure. Hassouni et al. (2018) also used the clear pot method to study the seminal roots of durum wheat. The use of root digital imaging techniques has also facilitated RSA studies in recent years. Platforms that are capable of automatic washing of the roots are available, as well as machines that can scan the root samples and different software that can analyze the root scanned images (Lobet et al., 2011). Digital imaging of root traits (DIRT) (Bucksch et al., 2014) is an example of such methods, which is useful in characterizing the roots of both dicot and monocot crop species. The use of WinRhizo Tron MF software (Regent Instrument Inc., Quebec, Canada) is another method that has been used to analyze seedling root traits. Tomar et al. (2016) used WinRhizo software to study wheat seedling root traits targeting drought tolerance which included root length, surface area of root and root volume. Recently other rapid methods have been developed such as X-ray tomography that provide 3-D images of root systems, which shows significant potential for studying RSA without 64

disturbing the root system (Wasaya et al., 2018). Furthermore, structural root models are also coming into use to analyze RSA traits while model generated images are used to improve the precision of root trait estimation (Lobet et al., 2017). Studies of root foraging, the process by which RSA adapts to changes in the environment, is also another area that is gaining attention (Tian and Doerner, 2013; Chen et al., 2018). In general, phenotyping mature roots is much more complex and demands high labour than juvenile roots, which limits selection strategies. Selection based on phenotyping seedling roots in a controlled environment may be unreliable, as the traits may not translate into mature RSA under field conditions (Wasson et al., 2012) and hence should be evaluated on a case by case basis (Comas et al., 2013). A method for nodal root phenotyping often referred to as “shovelomics” (Trachsel et al., 2011), where roots are excavated from the soil as already noted above, is in wide use for field phenotyping of root systems, including wheat (Kadioglu, 2007). Thus, despite the above mentioned advancements in root phenotyping, the challenge that persists is the lack of cost-effective, high resolution, efficient, simple, and reproducible high-throughput methods that can be readily employed in plant breeding programs (Chen et al., 2018).

3.7 Improving the efficiency of phenotyping physio-morphological traits To make use of a wide range of physio-morphological traits of interest while selecting drought tolerant wheat varieties, numerous drought screening methods have been developed and tested for use in crop breeding programs (Siddig and El, 2013; Gómez-Luciano et al., 2014; Zu et al., 2017). In the following sections, we review some of the advancements that have been made to improve phenotyping.

3.7.1 Application of controlled physical structures One of the greatest challenges in breeding crops for drought tolerance is the simulation of drought at a larger scale. Conducting drought experiments in open field conditions requires a long-term data set as there is large variation in precipitation and soil moisture conditions (Kant et al., 2017). Additional environmental factors like light intensity, temperature, and canopy cover are also critical in open field experiments, but these factors are highly variable temporally and spatially. Therefore, multi-season, multi-treatment experiments, which enable the understanding 65

of the complex nature of drought, are required (Hoover et al., 2018). Physical structures that overcome the unpredictability of weather while attempting to mimic actual field environments have already shown great potential in drought studies. For example, rainout/rainwater shelters, which are structures established in the field to protect against rainfall, can be used to conduct controlled water experiments over a short span of time (Yahdjian and Sala, 2002; Trillo and Fernández, 2005; Mwadzingeni et al., 2016; Kundel et al., 2018). Rainwater shelters have been successfully tested in different crops including wheat (Zu et al., 2017; Kundel et al., 2018; Wimmerová et al., 2018). Rainwater shelters can be mobile (Wimmerova et al., 2018) or geographically fixed (Kundel et al., 2018) and either manual, semi-automated or fully automated (Ries and Zachmeier, 1985). Custom-designed, automated and portable shelters are very efficient for stress tolerance phenotyping (Kant et al., 2017). For improving the precision of these shelters, it is important that they are equipped with standard soil moisture sensors, motion cameras, and other tools which essentially add value to precise data recording (Mwadzingeni et al., 2016; Kant et al., 2017). One technical issue is that rainwater shelters exclude solar radiation (Wimmerova et al., 2018). Therefore, Vogel et al. (2013) suggested the use of control treatments to assess any confounding effects of these closed structures. The use of these artificial structures for drought tolerance studies is still scrutinized as they do not entirely reflect the real field environment, and very high investments are required to maintain and manage these structures. Despite these limitations, rainout shelters continue to hold promise in efforts to breed for drought tolerance in wheat. This method appears to be the best option available for the study of drought tolerance under field conditions.

3.7.2 High-throughput phenotyping platforms (HTPPs) With recent advances in genomics, efficient phenotyping has become limiting (Salas Fernandez et al., 2017). Although it has been common practice to conduct high throughput phenotyping under environmentally controlled conditions (greenhouses, growth rooms and growth chambers) using single plant measurements, translation of the quantitative trait loci (QTL) identified under such conditions to the final field-level performance of the crop has been low (White et al., 2012). Since the actual field environment is highly heterogeneous, canopy level phenotypic measurements are considered more accurate. In such a situation, utilization of 66

high-throughput phenotyping platforms (HTPPs) are needed that are precise, labor- and cost- effective, to phenotype complex physio-morphological traits associated with biotic and abiotic stresses (Rutkoski et al., 2016) as they bridge the gap between genomics and phenomics (Araus and Cairns, 2013; Fahlgren et al., 2015; Bai et al., 2016, Zhang et al., 2017). Innovations in remote sensing, aeronautics and computing have largely contributed to the development of several phenotyping platforms including both ground-based and aerial systems (White et al., 2012; Araus and Cairns, 2014). Highly sophisticated HTPPs with fully automated functions for canopy level studies are available in a limited but growing number of institutions such as the United States Department of Agriculture (USDA), the Australian Plant Phenomics Facility and European Plant Phenotyping Network (Araus and Cairns, 2014). Recently, many other institutions are investing on HTPPs to improve the efficiency of their programs. However, the current situation at many institutions demands judicious selection and proper use of HTPPs that are easily accessible (Cobb et al., 2013; Junker et al., 2015). Management of the high-volume of data generated by these HTPPs also presents a challenge in analysis and interpretation (Shi et al., 2016; Singh et al., 2016; Coppens et al., 2017). In the following two sections, we discuss two high throughput phenotyping approaches that have been gaining attention recently:

3.7.2.1 Proximal high-throughput phenotyping platforms The use of proximal or ground-level sensors to permit measurements of plant traits at the canopy level has shown potential in terms of replacing current labor-intensive methods. The proximal sensing and imaging approach includes three basic techniques: visible/near-infrared (VIS-NIR) spectroradiometry, infrared thermometry and thermal imaging including conventional digital photography (Araus and Cairns, 2014). These platforms are usually handheld or mounted on vehicles (Qiu et al., 2018). Bai et al. (2016b) tested a manually operated multi-sensor system capable of carrying many sensors to measure more than one trait in a wheat breeding program. A strong correlation was observed between grain yield and early and late growth stage sensor- based traits, indicating the significance of using proximal sensing in plant breeding. Similarly, measurement of vegetative index, NDVI, using a handheld GreenSeeker and a passive bi- directional reflectance sensor, showed similar results in wheat, suggesting that these indices can be used for selection even during early breeding cycles (Walsh et al., 2013; Barmeier and 67

Schmidhalter, 2017). An infrared thermometer has been used to measure canopy temperature in wheat (Lopes and Reynolds, 2010), which is very helpful in drought and heat stress studies. Conventional digital cameras have also been used to estimate green biomass, canopy soil cover and plant color (Casadesus et al., 2007; Araus and Cairns, 2014). Some digital cameras with charge-coupled device (CCD) silicon sensors, capable of detecting not only color but also the texture of the objects, are useful in interpreting vegetative indices as well (Qiu et al., 2018). For example, Casadesus et al. (2007) observed a high correlation between vegetation indices from the pictures taken by the digital camera and grain yield in wheat under drought conditions. Comar et al. (2012) developed and successfully tested a semi-automatic system to study the dynamics of change in vegetation index, based on the green fraction (GF), which calculates the fraction of green area per ground area for wheat cultivars grown in micro-plots under field conditions. This system has a hyperspectral radiometer and two RGB cameras which are used to observe the canopy from approximately 1.5 m from the top of the canopy, and it is supported by a tractor which is driven across the plots to collect data from individual plot canopies. Field-based phenotyping platforms used with other crops may also hold potential for wheat. For example, Andrade-Sanchez et al. (2014) mounted three sensors on a vehicle to measure canopy height, temperature, and reflectance of cotton (Gossypium barbadense L.) varieties grown under irrigated and water-limited conditions. Significant differences were observed among the varieties for the traits recorded by the sensors, indicating the reproducibility of the method. Very recently, Phenobot 1.0, a field-based HTPP, equipped with an auto-steered and self-propelled system, has been developed and tested for crops such as sorghum, which have tall and dense canopies (Salas Fernandez et al., 2017). Phenobot 1.0 has RGB cameras that are used to take measurements of plant height and stem diameter very efficiently. The challenge now is to increase the global accessibility of these new technologies.

3.7.2.2 Remote sensing for high throughput phenotyping Low-cost unmanned aerial vehicles (UAVs) or drones have already demonstrated significant potential for precision agriculture as they can be used to quantify crop health, and effects of soil moisture and nutrients on crop growth and development (Holman et al., 2016; Khan et al., 2018). These UAVs are capable of providing high resolution images of small 68

experimental plots from distances as high as 30-100 m above ground (Shi et al., 2016; Tattaris et al., 2016). In wheat, aerial measurements of secondary traits such as canopy temperature (CT) and NDVI, have been successfully used to improve the precision of genomic prediction models for grain yield (Rutkoski et al., 2016). A highly significant association (r= 0.76, P<0.05) was observed between vegetative indices extracted from an unmanned aerial system with the ground truthing data recorded by a spectroradiometer applied to advanced wheat breeding lines (Haghighattalab et al., 2016). Similarly, Tattaris et al. (2016) observed that CT and NDVI measurements in wheat recorded by UAVs were better correlated with yield and biomass compared to the data recorded by proximal measurement under both irrigated and water limited conditions. Khan et al. (2018) also observed a close association between vegetative indices from aerial images with RGB images in a wheat breeding program. Furthermore, optimized UAVs have been tried in crop growth rate studies. Apart from this, UAVs have been used to assess the growth rate of winter wheat as a response to different rates of fertilizer application (Holman et al., 2016). Very recently, integration of Light Detection And Range (LiDAR), a powerful tool which can acquire precise 3D measurements for crop phenotyping and remote sensing (Madec et al., 2017), has been successfully used in wheat phenotyping studies (Madec et al., 2017; Jimenez-Berni et al., 2018). In China, Crop 3D, an HTP tool developed to integrate LiDAR and UAV, is capable of measuring many plant traits such as plant height, leaf width, leaf length, leaf angle and leaf area (Guo et al., 2018). Development of improved drought monitoring systems such as the remote sensing drought monitoring system (RSDMS) can have a significant impact on future agricultural research as it was devised based on a model that permits flexibility and increased coverage (Dong et al., 2017). The use of satellite data can also be used to prepare hazard maps that display the distribution of drought risk across a specific region and is being explored for its application to drought tolerance research in crops (Skakun et al., 2016). The pace and progress of new remote sensing technologies potentially offer improvements in selection efficiency, but challenges remain such as poor access to these technologies (e.g. cost) and the need to manage large volumes of data.

3.8 Conclusions and future perspectives In this paper, we have presented evidence-based information related to the negative impacts of drought stress on the productivity of wheat, in the past few decades. Drought affects the physiology of wheat plants resulting in reduced grain yield. The paper highlights the importance of the selection environment for developing high yielding, stable wheat varieties for drought-prone regions. Furthermore, the indirect selection of physiological traits contributing to grain yield shows tremendous potential in terms of improving the efficiency of breeding for drought resilience. Drought may prevail at any growth stage, and the intensity and type of drought can vary for different wheat growing regions. Therefore, here we emphasized the benefits of phenotyping growth stage-based physio- morphological traits. We have also reported the availability of different high throughput phenotyping platforms that are capable of measuring these traits. As we have demonstrated, in many wheat growing environments, the development of drought tolerant wheat varieties is going to be critical to address the growing food demand. A major challenge associated with the use of physiological traits in plant breeding is that such traits are sensitive to environmental variation (Zarei et al., 2013; Kosová et al., 2014). As this review has highlighted, a multidisciplinary physio-morphological approach is a very promising way forward to enable breeding of wheat varieties for drought stressed environments (Ortiz et al., 2008). To achieve this goal, a comprehensive effort is needed to establish more efficient platforms for phenotypic selection as described in the sections above, combined with biochemical and marker-assisted and genomic selection. These strategies need to be integrated into a well-organized and comprehensive breeding program (Figure 3.5) to accelerate the development of new high yielding drought tolerant wheat varieties. As indicated in Figure 3.5, utilization of appropriate existing genetic resources, advanced phenotypic and genomic approaches and robust data handling tools potentially improve the efficiency of a breeding program, resulting in a higher genetic gain. To create a breeding program that is capable of generating high yielding and adaptive varieties, smart planning from the beginning is crucial. For example, adopting a “few cross” strategy or smart crossing (Witcombe et al., 2005) could be beneficial; this strategy includes careful selection of parents as per the breeding target and allows opportunities for higher genetic gain. The approach also stresses the benefits of phenotyping 70

physiological traits associated with drought tolerance and testing of breeding materials in diverse environments including under controlled conditions. Dissecting the growth stages further into sub-stages may further improve the genetic gain. As a part of the strategy in Figure 3.5, data management is also a major part of a breeding program. Newly available open-source software programs have been developed for the benefit of biological research including plant breeding databases such as BrAPI (Selby et al., 2019) and Planteome (Cooper et al., 2018). These programs represent the programming interface for plant breeding and the integrated ontology resource, respectively. The integration of such platforms can improve the adeptness of a breeding program further. The target of the breeding strategy discussed here is to improve the genetic gain while also generating and maintaining new diversity created in the breeding program. In summary, Figure 3.5 presents an all-inclusive approach for breeding drought tolerant wheat.

Figure 3.5. A proposed comprehensive strategy for breeding wheat for drought tolerance.

Genomic tools such as genome-wide association studies (GWAS), used to identify QTLs associated with physio-morphological traits, benefit substantially from improved phenotypic data. Further adoption of novel molecular technologies such as genome editing (Wang et al., 2018) may open new avenues with regards to breeding for drought tolerance. For example, to enable the development of stress tolerant varieties, Zafar et al. (2020) proposed targeting of regulatory and structural genes responsible for stress tolerance in plants using the CRISPR/Cas9 system. But as has been emphasized, the success of molecular and genomic technologies lies in the quality, quantity and relevance of phenotypic data available. In other words, the significance of phenomics is ever increasing. More efficient, cost-effective, simple, viable and straightforward platforms for phenotyping morphological, physiological and phenological traits, and those that reduce the impacts of environmental variation, can have a significant impact on drought tolerance research in wheat. One challenge that is mounting is the massive volume of data that is generated from high throughput phenotyping (Araus et al., 2018; Yang et al., 2020). This observation highlights the need for platforms to handle the data generated to facilitate the sustained adoption of these techniques. The Minimum Information About a Plant Phenotyping Experiment (MIAPPE) is an example of such platforms that helps in data standardization to promote standard data management practices (Bolger et al., 2019). Similarly, the Consultative Group for International Agricultural Research (CGIAR) centres developed Crop Ontology, a platform to promote proper use of genotypic and phenotypic data through data annotation (Shrestha et al., 2012). However, greater investments are needed to develop tools as well as expertise in bioinformatics for data management and processing along with technologies that offer secure storage of large volumes of data. Training of experts in phenomics, genomics and data management is as important as technical advancements in these disciplines (Yang et al., 2020). Finally, successful breeding for drought-tolerant wheat varieties will be made possible through collaborations between different institutions or communities across academia, public research institutions, industry, and the Consultative Group for International Agricultural Research (CGIAR) centers. The Heat and Drought Wheat Improvement Consortium (HeDWIC) coordinated by the International Maize and Wheat Improvement Center (CIMMYT), is a great example of a network that includes a 73

wide range of partners and aims to develop climate-resilient wheat varieties. The over 40 year long China-CIMMYT partnership is a notable example of collaboration that has been highly successful in improving wheat (He et al., 2019). However, any such partnerships should be sustainable through long term investments in human resources and visionary project initiatives.

3.9 Acknowledgments We dedicate this review paper to Dr. Alireza Navabi, the lead author’s Ph.D. supervisor who passed away during the preparation of this article due to pancreatic cancer. This work was supported by a grant to MNR from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by Global Affairs Canada and the International Development Research Centre (IDRC, Ottawa) as well as grants to AN from the Agricultural Adaptation Council (Canada), Grain Farmers of Ontario (Canada) and SeCan (Canada). We acknowledge Dr. Mina Kaviani for her input to improve Figure 3.3.

4 Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought Tolerant Wheat

This chapter has been submitted as a review article with the following citation:

Kamal Khadka, Manish N. Raizada, and Alireza Navabi (2020). Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought Tolerant Wheat. Frontiers in Plant Science (submitted, Manuscript ID 511315).

Author contributions: Kamal Khadka: Conceptualized and wrote the manuscript. Manish N. Raizada: Provided inputs and contributed to technical and language editing. Ali Navabi: Contributed to conceptualization of the paper and provided inputs. .

4.1 Abstract There is an urgent need to increase wheat productivity to meet the food demands of the ever-growing human population. However, the development of high yielding varieties at a faster pace is hindered by drought, which is worsening due to climate change. Germplasm diversity is central to the development of new drought tolerant wheat varieties. These genetic resources are conserved in national and international genebanks and are accessible to all researchers. In addition to phenotypic assessments, use of advanced molecular techniques to identify quantitative trait loci (QTLs) for drought tolerance related traits is useful for marker-assisted selection (MAS) based approaches. Here, to assist wheat breeders at a critical time, we performed a search of the recent peer reviewed literature (2011-current), first, to identify wheat germplasm observed to be useful genetic sources for drought tolerance, and second, to report QTLs associated with drought tolerance. Though many breeders limit the parents used in breeding programs to a familiar core collection, the results of this review show that wider germplasm, including wild relatives, landraces, elite commercial cultivars, advanced breeding lines, and synthetic hexaploids, have been sources of useful genes for drought tolerance in wheat. With respect to QTLs, the review demonstrates that QTLs for drought tolerance are associated with diverse physio-morphological traits at different growth stages of wheat that otherwise reduce grain yield. We briefly discuss the potential of genome engineering/editing, high- throughput phenotyping, and global collaborations to improve drought tolerance in wheat.

Keywords: Drought tolerance, Genetic Resources, Landraces, QTL mapping, Wheat, Climate change.

4.2 Introduction Bread wheat (Triticum aestivum L.) is one of the world’s major cereal crops with global production of 756.7 million tons in 2017 (FAO, 2018a). The world’s population is expected to exceed 9 billion by 2050, requiring at least a 60% increase in wheat yield (UN, 2019). An increase in wheat yield from the current level of 1% per year to at least 1.6 % is deemed required to address this challenge (GCARD, 2012). Like many other crops, drought affects wheat at all

growth stages (HongBo et al., 2005; Saeidi et al., 2015; Saeidi and Abdoli, 2015; Wang et al., 2015; Sarto et al., 2017; Ding et al., 2018). However, the degree of impact on final yield depends on the growth stage and the intensity and duration of the stress (Daryanto et al., 2016; Sarto et al., 2017). Some of the growth stage-specific physio-morphological traits associated with drought tolerance in wheat include: early vigour (Rebetzke et al., 1999), coleoptile length leaf (Rebetzke et al., 2007a), chlorophyll content (Khayatnezhad et al., 2011; Kira et al., 2015; Ramya et al., 2016), glaucousness (waxiness) for photo-protection (Merah et al., 2000; Bi et al., 2017), leaf rolling (Kadioglu and Terzi, 2007b), carbon isotope discrimination (Akhter et al., 2008; Kumar and Singh, 2009), flag leaf senescence (Verma et al., 2004; Hafsi et al., 2013), etc. Also, root system architecture (RSA) traits are fundamental targets for breeding drought tolerant wheat varieties (Lopes and Reynolds, 2010). As early as 10,000 years ago, tetraploid durum wheat (Triticum durum desf.) with an AABB genome was domesticated from tetraploid emmer wheat (Triticum turgidum spp. dicoccoides) (Marcussen et al., 2014b; Abrouk et al., 2018). Hybridization between the tetraploid wheat and the diploid wild goatgrass, (Aegilops tauschii Coss.) with a DD genome, formed bread wheat (AABBDD) (Shi and Ling, 2017). As a result, tetraploid and hexaploid wheat contain two or three functional copies of most genes (homoeologs), respectively, with >97% similarity across coding sequences (Uauy, 2017). Therefore, bread wheat has a large size genome, ~17 Gb (Marcussen et al., 2014; Uauy, 2017), >80% of which consists of repetitive DNA elements (Shi and Ling, 2017). This large, complex genome has limited wheat improvement compared to other major cereal crops (Uauy, 2017). Very recently, however, an annotated reference sequence for the bread wheat genome was released, which described 107,891 high-confidence level genes (IWGSC, 2018). This release has provided a significant opportunity to exploit the genome for the development of target-based wheat varieties such as drought tolerant wheat varieties. The world’s wheat breeders have access to up to 320,000 accessions, many of which are adapted to dry environments. In recent years, molecular approaches have led to the discovery of quantitative trait loci (QTLs) associated with drought tolerance in wheat. Therefore, the objectives of this review are: (1) to highlight recent peer-reviewed reports of the use of wheat genetic resources to improve drought tolerance in wheat, and (2) review recent progress in identifying genomic regions that promote this drought tolerance. 77

4.3 Utilization of genetic variation from diverse sources The use of diverse germplasm is key to the development of drought tolerant wheat varieties. Landraces are one of the major groups of genetic resources valuable for breeding drought tolerant wheat (Mwadzingeni et al., 2017b). They have complex morphological diversity and are mostly grown in low input environments (Padulosi et al., 2012) which make them more adapted to stress (Lopes et al., 2015; Padulosi et al., 2012). For example, a group of Creole wheat landraces (the landraces introduced to Mexico from Europe) showed better adaptation to different abiotic stresses, including drought, due to the presence of rare but beneficial alleles (Vikram et al., 2016). Similarly, the Japanese landrace “Aka Komugi” is the source of the Rht8c allele, which confers drought tolerance in wheat (Lopes et al., 2015a; Grover et al., 2018). Furthermore, nine wheat landraces exhibited drought tolerance based on a stress susceptibility index (SSI) in an evaluation by Sareen and Sharma (2014) for drought and heat tolerance. More examples are summarized in Table 4.1. Wild relatives of wheat and other genera such as Aegilops, Secale and Hordeum are additional valued sources of germplasm for breeding drought tolerant varieties. Domesticated wheat species of such as T. compactum Host, T. durum Desf, T. turgidum, T. turanicum Jakubz (‘Kumut’) and T. polonicum L. are also sources of valuable genes. Use of stress-adapted landraces and close relatives in crossing programs is a common practice in synthetic hexaploid wheat (SHW) development. Thus, SHW is an example of deployment of ancestral genomes using interspecific hybridization techniques (Reynolds et al., 2007). The development of modern varieties has reduced the genetic diversity of wheat, and this reduction in diversity shows both spatial and temporal trends (Rauf et al., 2010). Nonetheless, elite germplasm is a convenient source of genetic variation (Table 4.1). Unlike wheat landraces and wild relatives, most of the breeding programs around the world work on elite materials. There is less linkage drag associated with co-inheritance of undesirable and defective genes and rare alleles in these materials (Mwadzingeni et al., 2017b). Enhancement of genetic gain and selection response is rapid in elite genetic resources.

Table 4.1. Recent peer reviewed reports (2011-current) of germplasm evaluation for assessing drought tolerance in wheat. # Collaborating Summary of work Germplasm type Reference institutions 1 Bread Wheat Breeding Pedigree analysis of high yielding genotypes Advanced breeding (Crespo-Herrera et al., Program, CIMMYT using data from 740 semi-arid wheat yield trials lines including SHW 2018) conducted in 66 countries from 2002-2014 was derivatives conducted to determine genetic gain for grain yield. Results showed broader use of Pastor, Baviacora 92, and synthetic hexaploid derivatives to develop stable and drought-tolerant wheat lines. 2 University of A total of 96 lines (including 88 lines from Advanced (Mwadzingeni et al., KwaZulu-Natal CIMMYT’s heat and drought nurseries and 8 CIMMYT lines and 2016b) (UKZN), local checks) were tested under greenhouse and local checks Pietermaritzburg, South field conditions during 2014/15 and 2015/16. Africa Twelve CIMMYT lines were selected as drought Agricultural Research tolerant after the evaluation. Council-Small Grain Institute, Bethlehem, South Africa

# Collaborating Summary of work Germplasm type Reference institutions University of South Africa, Pretoria, South Africa 3 Razi University, Twenty landraces were evaluated under rain-fed Landraces (Farshadfer et al., 2012) Kermanshah, Iran and normal irrigated conditions in 2010-11 season using 13 drought tolerance indices. The landraces WC-4953S, WC-47572, and WC- 47574 were identified as drought tolerant. 4 Kerman University, Forty wheat genotypes previously reported as Landraces and (Abdolshahi et al., Iran drought tolerant, were evaluated for drought modern cultivars 2013) tolerance using nine different drought indices under normal and water stressed conditions for two seasons in 2009-10 and 2010-11. Landrace Mahdavi was identified as the most drought tolerant genotype. 5 Indian Institute of A set of 15 wheat varieties representing major Modern cultivars (Meena et al., 2015) Wheat and Barley wheat growing zones in India were tested for two Research, Karnal, India seasons under different water regimes using the three marked water stress indices. Three 80

# Collaborating Summary of work Germplasm type Reference institutions genotypes, NI-5439, WH-1021 and HD-2733, were identified as the most drought tolerant cultivars. 6 University of Reading, Six varieties were assessed for drought tolerance Modern cultivars (Khakwani et al., 2011) United Kingdom under three watering regimes in an ambient and a local check glasshouse environment (100%, 35%, and 25% capacity) using different drought indices. Hashim-8 was identified as the superior variety for drought tolerance. 7 Mansoura University, Ten wheat varieties were evaluated for the effect Modern cultivars (Mickky and Mansoura, Egypt of two levels of osmotic stress at seedling stage. (released varieties Aldesuquy, 2017) The results showed a negative effect of the stress from 1999 to 2011) on morphological seedling traits. However, variation was observed among the genotypes i.e. Sids 13, one of the tested genotypes, was the most drought tolerant while Shandawel 1 was observed to be the most sensitive genotype. 8 Mansoura University, Seven Hungarian wheat landraces were selected Landraces (Abido and Zsombik, Mansoura, Egypt and tested for different physiological traits at the 2018) 81

# Collaborating Summary of work Germplasm type Reference institutions seedling stage under five levels of water stress University of (0%, 6%, 12%, 18%, and 24%) using PEG-6000. Debrecen, Hungary Two landraces, Leweucei and Mateteleki, were found to be more drought tolerant based on different drought tolerance related parameters such as higher relative water content (RWC), tolerance index (TI) and activities associated with α and β-amylases. 9 Nanjing Agricultural One drought tolerant (Luhan-7) and one drought Modern winter (Abid et al., 2016) University, PR China sensitive (Yangmai-16) variety were evaluated to wheat cultivars assess improved tolerance to water stress during post-anthesis growth phase as a result of pre- drought priming at different stages during the vegetative growth phase. Results showed positive effect of drought priming on both varieties when they were exposed to post-anthesis drought. However, the drought tolerant genotype showed greater response to priming, while the growth

# Collaborating Summary of work Germplasm type Reference institutions stage of priming also contributed to drought tolerance to some extent. 10 Cereal Research Non- A set of 29 wheat varieties was phenotyped for Modern cultivars (Nagy et al., 2018) Profit Ltd., Hungary different root and shoot traits under drought in a (released varieties) glasshouse along with one drought tolerant and Snowy River Seeds Pty one drought susceptible check. Based on final Ltd., Australia grain yield, three varieties were identified as drought tolerant. 11 Punjab Agricultural A group of genotypes consisting of 57 Aegilops Wild relatives (Suneja et al., 2019) University, Ludhiana, tauschii accessions and 26 Triticum dicoccoides India accessions were used to assess adaptive plasticity induced by water stress for different morpho- physiological characters such as root-shoot development, induction of proline and cell membrane injury. Some of the Ae. tauschii accessions such as 9816, 1409 and 14128, and T. dicoccoides accessions 5259 and 7130, exhibited significantly higher adaptive plasticity for water stress. 83

# Collaborating Summary of work Germplasm type Reference institutions 12 The University of Near isogenic lines (NILs) derived from the cross Near isogenic lines (Mia et al., 2019) Western Australia, between C306 and Dharwar, two varieties with (NILs) Perth, Australia spring growth habit, were evaluated targeting a QTL on chromosome 4BS that confers drought The tolerance. Results from both the phenotypic and Bangladesh quantitative RT-PCR analyses suggested that Agricultural Research NILs can be a valuable source of germplasm for Institute, Gazipur, gene mapping and characterisation of drought Bangladesh tolerance related genes. 13 Northwest Agricultural A set of 90 varieties representing important A representative (Chen et al., 2012a) and Forestry wheat growing areas in China was evaluated in collection from 328 University, Yangling, irrigated and water-limited environments using winter wheat China rainout shelters for 14 traits, including morpho- accessions physiological and yield traits to distinguish genotypes tolerant to drought stress. Five genotypes were identified as highly drought. 14 Northwest Agricultural Eighty-two wheat alien chromosome addition Landrace derived (Liu et al., 2015b) and Forestry lines derived using Chinese Spring as the advanced lines common parent, were evaluated using 10 84

# Collaborating Summary of work Germplasm type Reference institutions University, Yangling, important agronomic traits under irrigated and China water-limited conditions. The result showed that 26 out of 82 lines possessed high levels of drought tolerance. 15 China Agricultural Two season (2011 and 2012) evaluation of 145 Local genotypes and (Zhang et al., 2019) University, Beijing, genotypes from the CIMMYT Wheat CIMMYT advanced China Physiological Germplasm Screening Nursery lines (CWPGSN) and seven Chinese local spring Ministry of wheat genotypes was performed to identify the Agriculture, Xinjiang, most stable genotypes across water-stressed and China controlled conditions. Seven lines from CWPGSN and three local varieties (Xinchun 11, Xinjiang Academy of Xinchun 23 and Xinchun 29) were found to be Agricultural Sciences,, the most stable. Xinjiang, China

Chinese Academy of Sciences, Beijing, China 85

# Collaborating Summary of work Germplasm type Reference institutions 16 Bulgarian Academy of Seedlings of six modern semi-dwarf varieties and Modern cultivars (Petrov et al., 2018) Sciences, Sofia, six old tall varieties were subjected to stress by and landraces Bulgaria withholding water for six days. The results indicated that the modern varieties were more Slovak University of drought tolerant, as the water balance Agriculture, maintenance was better compared to the old Nitra, Slovak Republic varieties. 17 University of Idaho, Assessment of 198 winter wheat accessions for Winter wheat (Liu et al., 2017) Aberdeen, USA drought tolerance under irrigated and terminal accessions from the drought environment was done in the 2012-2013 USDA-ARS Northwest Agricultural and 2013-2014 seasons. Based on drought National Small and Forestry susceptibility index and membership function Grains Collection University, Yangling, value of drought tolerance, 23 accessions were China reported to have drought tolerance.

Institute of Water Saving Agriculture in Arid Regions of China, Yangling, China 86

# Collaborating Summary of work Germplasm type Reference institutions

USDA-ARS, Small Grains and Potato Germplasm Research Unit, Aberdeen, USA 18 University of A diversity panel of 105 bread wheat genotypes A panel of (Ahmed et al., 2019) Faisalabad, Punjab, was used to assess the selection criteria for landraces, historical Pakistan drought tolerance at the seedling stage. Seedling Pakistani varieties traits, including root length, fresh weight, dry and advanced University of weight cell membrane thermo-stability and breeding lines Islamabad, Islamabad, chlorophyll b, were suggested to improve genetic Pakistan gain for drought tolerance. Out of these 105 genotypes, 10 drought tolerant genotypes were Yunnan Academy of also identified. Agricultural Sciences, Kunming, China 19 Imam Khomeini A total of 180 accessions of wild relatives of Wild relatives of (Ahmadi et al., 2018) International wheat were tested under irrigated and water- wheat stressed conditions. The result showed that 12 87

# Collaborating Summary of work Germplasm type Reference institutions University, Qazvin, accessions were highly superior in drought Iran tolerance. One remarkable observation was that Ae. speltoides (suggested source of B genome) Ilam University, Ilam, and Ae. tauschii (source of D genome) responded Iran very well to drought stress

The University of Western Australia, Perth, Australia 20 Agricultural Research, A set of landraces with the majority being of Landraces (Arshad et al., 2016) Education and Iranian origin were tested under irrigated and Extension Organization drought treatments. Based on the drought indices (AREEO), Iran used in the evaluation, superior genotypes for drought tolerance were identified.

Development of SHW has widened the genetic variation available in wheat. These SHWs combine genes from diploid and tetraploid wheat species (Dorostkar et al., 2015; Li et al., 2018). There is an opportunity to exploit broad genetic variation in SHWs to improve different agronomic traits in bread wheat (Li et al., 2018). Dorostkar et al. (2015) reported drought tolerant SHW varieties compared to conventionally-bred wheat varieties. An evaluation of 33 Ae. tauschii accessions and corresponding SHW lines revealed a wide range of variability for drought tolerance (Sohail et al., 2011). In another study, six out of 34 SHW lines were found to be drought tolerant (Song et al., 2013). These SHW lines showed high antioxidant activities related to superoxidase and peroxidase activity. The SHWs also demonstrated superior drought tolerance over the parental lines. Lopes and Reynolds (2011) observed that four of the synthetic hexaploid derived lines out-yielded the parental lines by an average of 26%. Additionally, the use of double haploid populations Fleury et al. (2010) may be useful to map different drought tolerance related traits. Mutation breeding can be another avenue to create variants suitable for drought adaptation. Many national and international research institutions and programs maintain extensive collections of wheat landraces, wild relatives, breeding populations, obsolete varieties, and modern elite varieties. Genesys (https://www.genesys-pgr.org/welcome) is an online portal that hosts information about plant genetic resources for food and agriculture conserved in genebanks worldwide. The genetic resources maintained in the genebanks have passport data with images along with genomic data, dependent on the availability of financial and human resources. Out of almost 320,000 wheat accessions documented in Genesys, 23% are landraces, and 7% are wild relatives. The International Maize and Wheat Improvement Centre (CIMMYT) (http://www.cimmyt.org/seed-request/) hosts the most extensive collection of wheat germplasm (102,375 accessions). USDA-ARS genebanks hold 67,615 wheat accessions: most of them are maintained at the National Small Grains Collection (https://www.ars.usda.gov/pacific-west- area/aberdeen-id/small-grains-and-potato-germplasm-research/docs/national-small-grains- collection/) in Aberdeen, ID. Wild species are held at the Wheat Genetic Resource Center (WGRC) (https://www.k-state.edu/wgrc/), and additional materials at the Germplasm Resources Information Network (GRIN) (https://www.ars-grin.gov/). The Australian Grains Genebank (http://www.seedpartnership.org.au/associates/agg) has 42,624 accessions, and the International 89

Center for Agriculture Research in the Dry Areas (ICARDA) (http://www.icarda.org/) has 41,471 accessions. Other organizations maintaining wheat germplasm are: the Svalbard Global Seed Vault in Norway (https://www.croptrust.org/our-work/svalbard-global-seed-vault/), Navadanya in India (http://www.navdanya.org/) and the National Bureau of Plant Genetic Resources (NBPGR) in India (http://www.nbpgr.ernet.in/). However, the challenge associated with the Svalbard, Navadanya and NBPGR collections is their restricted access. Despite these resources, effective utilization of a diverse set of germplasm maintained in genebanks is still lagging in crop breeding programs. However, pre-breeding is one of the activities that genebanks may undertake to ease the proper characterization of the genetic materials and to promote their further use. For example, the USDA-ARS National Plant Germplasm System (NPGS) is placing greater effort on pre-breeding in addition to regular collection and phenotyping of genetic resources (Byrne et al., 2018). Core collections, which include a set of germplasm representing the genetic diversity of crop species and its wild forms, are usually maintained in clusters that make it easy for researchers to gain access to the desired materials in their research projects (Wang et al., 2013).

4.4 Molecular breeding of wheat for drought tolerance 4.4.1 Identification of QTLs for drought tolerance related traits Molecular markers based on single nucleotide polymorphisms (SNPs) are the most abundant forms of variation in the genome (Rafalski, 2002; Mammadov et al., 2012; Song et al., 2013). SNP platforms are gaining attention as they require low computational effort for downstream processing, are simple to use and less prone to error (Wang et al., 2014b). Genotyping arrays including 90K SNPs have been used to genotype winter and spring wheat (Zanke et al., 2014), and also wild emmer wheat and durum wheat (Avni et al., 2014). Advancement of next-generation sequencing (NGS) technologies has provided opportunities to establish time-, labour- and cost-effective genotyping methods (He et al., 2014; Bhat et al., 2016; Ray and Satya, 2014). With the availability of a high-quality reference genome for bread wheat, the scope of NGS analyses in wheat has broadened (Ramirez-Gonzalez et al., 2015). More recently, Illumina MiSeq and HiSeq2500 (Bentley et al., 2012) and Roche 454 FLX Titanium (Thudi et al., 2012), are examples of novel NGS platforms. One of the major applications of 90

NGS technologies with respect to genotyping is fine-tuning of resolution to single base precision (Kilian and Graner, 2012; Ray and Satya, 2014). Genotype by sequencing (GBS), a DNA sequencing method that follows the NGS protocol, does not require prior genome sequence information related to traditional methods and has enormous potential to genotype complex genomes such as in wheat (Poland et al., 2012; Mwadzingeni et al., 2016; Chung et al., 2017). In genome-wide association studies (GWAS), the genetic variants in the genome are used to identify marker-trait associations for the trait(s) of interest (Corvin et al., 2014; Scherer and Christensen, 2016). This is the first step that assesses the genetic architecture of a target trait. It exploits linkage disequilibrium (LD) resulting from variants at a locus caused by different factors such as historical mutations, natural and artificial selection and other forces (Wang et al., 2012; Huang and Han, 2014; Visscher et al., 2017). The efficiency of GWAS depends upon individual factors such as the number of loci for a trait that segregates in the population, genetic architecture which also includes heritability of the target trait, and size of the study population (Visscher et al., 2017). Specifically, GWAS facilitates the exploration of the genetic variation of complex quantitative traits controlled by several genes and their interactions (Kooke et al., 2016) such as drought. The large genome size of wheat and the presence of homeologous genes contribute to the number of epistatic interactions among QTLs; this, in addition to the large number of genes influencing a complex trait, limits successful identification of useful QTLs (Ashraf, 2010). Nonetheless, QTLs for wheat agronomic traits are reported in many studies (Dashti et al., 2007; Kirigwi et al., 2007). Bhatta et al. (2018) identified 90 marker-trait associations (MTAs) related to yield and associated traits under limited water conditions in a GWAS study with GBS markers in 123 synthetic hexaploid wheat lines. Similarly, GWAS conducted on a double haploid mapping population resulted in the identification of 98 QTLs (distributed in all chromosomes except in 4D) for nine agronomic traits, out of which 32 QTLs were associated with a drought sensitivity index (DSI) affecting these nine agronomic traits (Gahlaut et al., 2017). QTLs for drought-related traits such as DSI, NDVI, and leaf traits including green leaf area, leaf senescence, and flag leaf phenotypes were detected on chromosomes 1B, 4A, 6B, 5B, 7A, and 7B (Edae et al., 2014) in another GWAS involving a spring wheat panel. Studies also suggest that A and B genomes harbour the putative QTLs associated with drought tolerance in wheat on 91

chromosomes 2B, 3A, 4A, 4B, 7A and 7B (Mwadzingeni et al., 2016a), though an independent study (Gahlaut et al., 2017) identified QTLs also on the D genome. Similarly, QTLs with heritability ranging from 0.63 to 0.91 across different environments for carbon isotope discrimination (ΔC), a measure of water use efficiency, have been reported on chromosomes 1BL, 2BS, 3BS, 4AS, 4BS, 5AS, 7AS and 7BS (Rebetzke et al., 2008). Some other earlier QTL studies of interest include those QTLs identified for: flag leaf senescence on chromosomes 2B and 2D (Verma et al., 2004); coleoptile length on chromosomes 4B and 6A (Rebetzke et al., 2001); and seedling vigour and plant height on chromosome 6A (Spielmeyer et al., 2007). QTLs for canopy temperature have also been mapped to chromosomes 1B, 2B, 3B, 4A, and 5A (Pinto et al., 2010). A strong association of QTLs with target traits is desired to facilitate selection in order to develop superior cultivars. Quarrie et al. (2006) reported a strong association of a yield QTL with grains per spike under drought as well as under favourable growing conditions. In another study, MTA of canopy temperature with grain yield, stem carbohydrate, and NDVI was significant under high temperature and a drought environment (Pinto et al., 2010). Table 4.2 summarizes QTLs reported for drought tolerance related traits on different chromosomes of the wheat genome, published in the peer reviewed literature from 2011 onwards.

Table 4.2. Recent peer reviewed reports (2011-current) of the important physio-morphological traits associated with drought tolerance and locations of QTLs associated with these traits in wheat. Target trait Chromosome Type of population Testing Reference environment Maturity 2B, 3B, 4B, 4D and 6A Elite mapping population Irrigated (Sukumaran et al., 2015) Grain yield 4A, 3B, and 7A Doubled haploid Rainfed (Gahlaut et al., 2017) population

3B F2:F3 population Drought stressed (Parent et al., 2017)

3B, 5A, 5B and 6A Elite mapping population Irrigated (Sukumaran et al., 2015) Spike length 1B, 2B, 2D, 3A, 4B, 5B, Diversity panel Drought stressed (Mwadzingeni et al., 6A, 6B and 7A 2017b) Spikelet per spike 6B, 2D, 2B, 5D, 1B and 4B Diversity panel Drought stressed (Mwadzingeni et al., 2017b) Number of kernels per 2D and 4A Diversity panel Drought stressed (Mwadzingeni et al., spike 2017b) Thousand grain weight 7A Doubled haploid Rainfed (Gahlaut et al., 2017) population 1B, 2D, 3A, 3B, 3D, 4D, Doubled haploid Rainfed and (Shi et al., 2017) 5A, 6A, 6B, 7A and 7B population irrigated

Target trait Chromosome Type of population Testing Reference environment

2A Core collection Rainfed and (Ahmad et al., 2014) irrigated

1AL, 4AL, 7AS, 7DS F2:F3 population Drought stressed (Nezhad et al., 2012) (Drought stress) Awn length - 1D, 2A, 2B, 3B, 4A, 4B, Synthetic hexaploid Drought stressed -(Bhatta et al., 2018a) 4D, 5A, 5B, 5D, 6B, and 7A wheat (drought stress) Plant height 2B and 7D Double haploid Irrigated (Gahlaut et al., 2017) population

5A Double haploid Rainfed (Gahlaut et al., 2017) population 1A and 2D Diversity panel Drought stressed (Mwadzingeni et al., 2017b) 1A and 6A Elite mapping population Irrigated (Sukumaran et al., 2015) Chlorophyll content -1B, 2A, 2B, 2D, 3A, 3B, Double haploid Rainfed and (Shi et al., 2017) 4B, 4D, 5A, 6B, 6D and 7A population irrigated 3A and 6A Elite mapping population Irrigated (Sukumaran et al., 2015) 94

Target trait Chromosome Type of population Testing Reference environment Normalized difference 1A, 1B, 1D, 2A, 2B, 2D, Double haploid Rainfed and (Shi et al., 2017) vegetative index 3A, 3B, 3D, 4A, 4B, 4D, population irrigated (NDVI) 5A, 6A, 6B, 6D, 7A, and 7B Stay green trait 1AS, 3BS, and 7DS RIL population Rainfed (Kumar et al., 2010)

Flag leaf senescence 1B, 2B, and 3B BC2F2 families Drought stressed (Barakat et al., 2015) Germination ability 5A, 3B, 4B and 6B Mapping population Drought stressed (Czyczyło-Mysza et al., 2014) Coleoptile length 5D -RIL population Osmotic stress (Zhang et al., 2013)

Seedling height 2D RIL population Osmotic stress (Zhang et al., 2013) Root length 4A RIL population Osmotic stress (Zhang et al., 2013) 5B and 5D Mapping population Drought stressed (Czyczyło-Mysza et al., 2014) 2D A population of elite Drought stressed (Ahmad et al., 2017) wheat varieties Root to shoot dry 2D, 5A and 3B RIL population Irrigated and (Zhang et al., 2013) weight Osmotic stress

Photosynthetic rate 3A F2 population Drought stressed (Maqsood et al., 2017)

Target trait Chromosome Type of population Testing Reference environment

Relative water content 4D F2 population Drought stressed (Maqsood et al., 2017)

Cell membrane 2B F2 population Drought stressed (Maqsood et al., 2017) thermo-stability

2B and 6B BC2F2 families Drought stressed (Barakat et al., 2015)

Abscisic acid content 2B, 3B, 5B, 6B and3D BC2F2 families Drought stressed (Barakat et al., 2015) Low excised-leaf water 3A, 3B, 4B, 5B, 5D, 6B, Double haploid Irrigated (Czyczyło-Mysza et al., loss (ELWL) 7A, 7B and 7D hexaploid wheat 2018)

4.4.2 Genome engineering techniques Genome engineering techniques are potential options for gene pyramiding and gene stacking to improve wheat for tolerance to abiotic stress including drought (Budak et al., 2015; Mwadzingeni et al., 2016). Clustered Regulatory Interspaced Short Palindromic Repeats (CRISPR) is a bacterium-derived genome editing system; in this system, a DNA fragment encoding a non-coding RNA sequence is designed to target and then cut a chromosomal DNA target of interest, within the Cas9 protein complex (Budak et al., 2015). Recently, Wang et al. (2018) used CRISPR/Cas9 to increase grain size in wheat. Kim et al. (2017) also successfully established a CRISPR/Cas9 genome editing system in wheat protoplasts to edit the stress- responsive factor genes TaDREB2 and TaERF3. Some essential regulatory genes that control the biosynthesis of metabolites associated with drought tolerance have been identified (Yang et al., 2010) and the CRISPR/Cas9 gene editing technology can be used to target such traits and genes in the future (Singh et al., 2018). There is already evidence that suggests that wheat genetic improvement is possible through genetic engineering. For instance, transfer into wheat of the AISAP gene encoding a stress-associated protein from the halophyte Mediterranean saltgrass (Aeluropus littoralis) has been shown to enhance the germination rate, biomass and grain yield of wheat under osmotic- and salinity stress (Ben-Saad et al., 2012).

4.5 Future perspectives Uncovering the genetic basis of complex traits contributes to the development of stress tolerant crop varieties. The common tradition in breeding for drought tolerance in wheat is to select for grain yield in the target environment (i.e. water deficit) (Khakwani et al., 2011). However, the complex nature of drought (e.g. drought on sandy soil is more severe than on clay soil) complicates our understanding of its impact on the crop (Hoover et al., 2018) and complicates breeding drought tolerant wheat. In addition, breeding for drought tolerant wheat is challenging, considering the large, complex nature of its genome. Adoption of a comprehensive strategy to develop drought tolerant wheat varieties is the way forward. Such a strategy requires precise phenotyping in a water deficit environment (Fleury et al., 2010). The efficiency of genomics also rests on advances in phenomics (Araus and Cairns, 2014; Bai et al., 2016; Fahlgren et al., 2015; Salas Fernandez et al., 2017; Zhang et al., 2017). Many ground-based and 97

aerial phenotyping platforms have been developed, with advances made in remote sensing, aeronautics and computing (Araus and Cairns, 2014; White et al., 2012). A significant challenge associated with high throughput phenotyping (HTP) platforms is the management of a high volume of data (Shi et al., 2016; Singh et al., 2016; Coppens et al., 2017); However, advances in bioinformatics tools and methods have the power to overcome these challenges. Utilization of precise phenotypic data increases the precision of detecting significant QTLs associated with traits of interest or target traits which may be further used in marker assisted selection. Recently, genome editing has shown tremendous potential in improving breeding efficiency. In addition, the use of mutagenesis protocols employing chemical mutagens, ultraviolet light, and high energy radiation to generate diversity is another option that breeders have to identify drought tolerant genes (Chen et al., 2014b). Similarly, to discover the genetic basis of complex traits, transcriptomics, proteomics, and metabolomics can play significant roles (Reddy et al., 2014). The information generated concerning different metabolic pathways add value to modulate the expression of genes associated with stress tolerance (Yang et al., 2010). There are different global initiatives working diligently to develop stress adaptive wheat varieties for major wheat growing areas around the world. CIMMYT is using the concept of mega environments (ME), defined as broad geographic regions with similar biotic and abiotic stresses, agronomic practices and consumer preferences. CIMMYT has defined 12 mega- environments to develop and promote drought tolerant wheat. Other global initiatives include “The Heat and Drought Wheat Improvement Consortium (HeDWIC)” (https://www.hedwic.org), which is a worldwide platform of wheat scientists from more than 90 countries working on drought and heat tolerance. The HeDWIC is making an effort to exploit the wide diversity of genetic resources available in gene banks to generate drought and heat tolerant wheat varieties. It is hoped that such global scientific cooperation in wheat breeding has the potential to generate new drought tolerant varieties to combat climate change.

4.6 Acknowledgments We would like to dedicate this article to Dr. Alireza Navabi, the lead author’s PhD supervisor who passed away during the preparation of this manuscript due to pancreatic cancer. The authors are grateful to Global Affairs Canada, IDRC Canada, Agricultural Adaptation 98

Council, Canada; Grain Farmers of Ontario, Canada; and SeCan, Canada, for financial support. We also acknowledge the intellectual contributions of the Nepal Agriculture Research Council (NARC) and the International Maize and Wheat Improvement Centre (CIMMYT).

5 The Population Structure of Nepali Spring Wheat (Triticum aestivum L.) Germplasm Appears to be Governed by Historical Introductions

This chapter has been submitted as an original research article with the following citation:

Kamal Khadka; Davoud Torkamaneh; Mina Kaviani; Francois Belzile; Manish N. Raizada; Alireza Navabi (2020). The Population Structure of Nepali Spring Wheat (Triticum aestivum L.) Appears to be Governed by Historical Germplasm Introductions. (Submitted).

Author contributions: Kamal Khadka: Conceptualized and wrote the paper. Mina Kaviani: Contributed in plant sampling and DNA isolation. Davoud Torkamaneh: Contributed in data analysis and technical editing. Francois Belzile: Supervised genotyping and provided technical inputs. Manish N. Raizada: Contributed in technical and language editing. Alireza Navabi: Supervised the project and contributed to conceptualizing the paper.

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5.1 Abstract Authentic information about genetic diversity and population structure of germplasm improves the efficiency of plant breeding. The low productivity of Nepali bread wheat (Triticum aestivum L.) is a major concern particularly since Nepal is ranked the 4th most vulnerable nation globally to climate change. The genetic diversity and population structure of Nepali spring wheat have not been reported. We used genotyping-by-sequencing (GBS) to characterize a panel of 318 spring wheat accessions from Nepal including 166 landraces, 115 CIMMYT advanced lines, and 34 Nepali released varieties. We identified 95K high-quality SNPs. The greatest genetic diversity was observed among the landraces, followed by CIMMYT lines, and released varieties. Though we expected only three groupings corresponding to these three seed origins, the population structure revealed two large, distinct subpopulations along with two smaller and scattered subpopulations in between, with significant admixture. This result was confirmed by principal component analysis (PCA) and UPGMA distance-based clustering. The pattern of LD decay differed between subpopulations, ranging from 60-150 Kb. We discuss the possibility that germplasm explorations during the 1970s-1990s may have mistakenly collected introduced germplasm instead of local landraces and/or collected materials that had already cross-hybridized since exotic germplasm was introduced in Nepal starting in the 1950s. We suggest that only a subset of wheat “landraces” in Nepal are authentic, which this study has identified. Targeting these authentic landraces may accelerate local breeding programs to improve the food security of this climate-vulnerable nation. Overall, this study provides a novel understanding of the genetic diversity of wheat in Nepal.

Keywords: Nepal, landraces, genotype-by-sequencing (GBS), genetic diversity, population structure, linkage disequilibrium

5.2 Introduction A significant challenge faced by modern plant breeders is the need to improve crop yield in the wake of the ever-increasing human population while combating the consequences posed by climate change on crop productivity (Raza et al., 2019). The current world population of ~8

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billion in 2019 is expected to exceed 9.6 billion in 2050, which means that food production must increase by at least 33% to meet the growing demand (UN, 2019). However, to eradicate global hunger by 2030 as targeted by the sustainable development goals of the United Nations, the current state of research and development does not seem to be able to meet this important challenge (FAO, 2018a). As there is limited scope for increasing the area under food production, yield increase through genetic improvement is the key means to overcome issues related to future food crises. Current studies related to climate change outline the increasing occurrence of heat, cold and drought stresses that are detrimental to agricultural crops (IPCC, 2013). This situation poses numerous challenges for improving the production of crops including bread wheat (Triticum aestivum L.). Bread wheat is the third most important staple cereal crop in the world with a global production of 757 million metric tonnes in 2017 (FAO, 2018b). Globally, wheat provides 41% of the total cereal calorie intake, constituting 35% of the cereal calorie intake in developing countries, and 74% in developed countries (Shiferaw et al., 2013). Overall, wheat ranks second globally in terms of dietary intake, and a large majority of the crop (68%) is used as food while approximately 19% is for feed and biofuels (FAO, 2018c). Similar to the global context, wheat is also one of the major cereals in Nepal. The area under wheat cultivation increased >500 folds in Nepal since 1960 (Bhattarai and Pandey, 1997) and today constitutes 1/5th of the nation’s cereal acreage (MoAD, 2017). The current yield of wheat in Nepal is 2.7 t/ha (FAOSTAT, 2019), compared to 3.1 t/ha in the United States (FAOSTAT, 2019) and 3.2 t/ha in nearby India (FAOSTAT, 2019). The demand for wheat in Nepal is expected to grow by ~890 thousand tonnes by 2030 (Prasad et al., 2011). At the same time, Nepal is ranked as the 4th most vulnerable nation globally to climate change, making it especially vulnerable to drought and other climate-related hazards (UN, 2018). This scenario suggests that Nepal in particular can benefit from further advances in wheat breeding. To accelerate such efforts, a thorough understanding of genetic diversity and population structure of available wheat germplasm can potentially aid in the more efficient deployment of available genetic resources (Mwadzingeni et al., 2017a). Molecular characterization of germplasm can point to unique sources of alleles in the population and prevent genetically redundant germplasm from being used as parents in breeding programs (Nielsen et al., 2014). In this context, the

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analysis of the genetic diversity of the existing wheat population in Nepal can be valuable. The population structure of Nepali spring wheat germplasm has not previously been reported. Bread wheat, also known as common wheat, is an allohexaploid (2n=6x=42) developed as a result of two natural hybridizations of diploid grass species, Triticum urartu (source of the A genome), Aegilops speltoides (most probable source of the B genome) and Aegilops tauschii or goat grass (source of the D genome). Therefore, bread wheat has three distinct genomes (A, B, and D) (Peng et al., 2011; Marcussen et al., 2014). According Marcussen et al. (2014), allotetraploid wheat (T. turgidum; AABB) was the product of hybridization between the close relatives of Ae. speltoides (BB) and T. urartu (AA) approximately 800,000 years ago and subsequent hybridization between T. turgidum and Ae. tauschii gave rise to bread wheat (hexaploid, AABBDD) less than 400,000 years ago (Marcussen et al., 2014). Li et al. (2014) state that the evolution of modern bread wheat through the hybridization of donor species with narrow genetic variation created a genetic bottleneck leading to narrow genetic diversity. Narrow genetic variation is also the result of bottlenecks during the domestication process combined with intensive breeding efforts in the past few decades (Fu 2015; Zhang et al., 2017). Therefore, wheat breeders are always interested in opportunities to diversify and widen the genetic diversity of the crop. Landraces, which are the genetic resources grown and selected by local farmers but without the efforts of professional breeders, are mostly adapted to low input conditions and are regarded as major sources of germplasm diversity (Lopes et al., 2015b). Locally adapted elite germplasm, the result of modern breeding programs, can be used for targeted introduction of specific alleles (Lopes et al., 2015; Mwadzingeni et al., 2017a). Synthetic hexaploid wheat (SHW) developed through interspecific hybridization of ancestral genomes may also contribute an additional and promising source of genetic diversity in wheat (Reynolds et al., 2007; Li et al., 2018). In Nepal, hundreds of landraces are available in the National Agricultural Genetic Resources Centre (NAGRC), the national genebank for Nepal, belonging to the Nepal Agriculture Research Council (NARC). Since the 1950s, Nepal has been introducing elite germplasm from The International Maize and Wheat Improvement Center (CIMMYT, Mexico), which have formed the foundation of varieties released by The National Wheat Research Program (NWRP) of Nepal.

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Genetic diversity characterization at the molecular level benefits from a genome sequence. Bread wheat has a large genome (~17,000 Mb) of which ~80% is comprised of repetitive sequences (Brenchley et al., 2012; Shi and Ling 2017). The annotated reference genome sequence of hexaploid bread wheat was released in 2018 (IWGSC, 2018), covering all 21 chromosomes and included 107,891 high-confidence genes. The presence of a high-quality reference genome has opened avenues to exploit available wheat genetic resources using modern tools and approaches. The genome sequence has enabled the development of high-density genome-wide markers (Torkamaneh et al., 2018). Genotyping-by-sequencing (GBS) is one such next-generation sequencing (NGS) based techniques that are capable of producing high-density genome-wide markers (Elshire et al., 2011; Fang and Xiong 2015; Torkamaneh et al., 2018). Among the various methods used to achieve complexity reduction, the GBS method is more efficient considering the cost, ease of handling, and lower number of purification steps (Davey et al., 2011; Poland et al., 2012). It reduces the genome complexity significantly and creates more homogenous libraries for sequencing. Since allopolyploidy and large genome size are the two key factors that have deterred the development of molecular markers in wheat, the use of GBS markers presents a step forward to achieve current and future genomic exploration. GBS markers include SNP markers which are widely used in genetic studies requiring large sets of markers such as the determination of population structure, QTL mapping, marker-trait association, genomic selection and map-based cloning (Kumar et al., 2012; Huq et al., 2016). This study aims to contribute to Nepali plant breeding efforts by appraising the genetic diversity and population structure of Nepali spring wheat. Here, we used GBS derived SNPs to evaluate a panel of 318 spring wheat accessions including landraces, released varieties, and advanced breeding lines. The major objectives of the study were: (i) to characterize the panel for genetic diversity and linkage disequilibrium (LD), and (ii) to identify the underlying basis of the population structure of the Nepali wheat genetic materials.

5.3 Materials and methods 5.3.1 Plant materials A panel of 318 spring wheat accessions from different sources was assembled for the study, and it was named the Nepali Wheat Diversity Panel (NWDP) (Appendix 1). The panel includes 104

166 Nepali landraces representing 29 districts of Nepal; these were collected during different germplasm expeditions during 1970s to 1990s. The seeds of these Nepali landraces were provided by the National Agricultural Genetic Resources Centre (NAGRC) of Nepal. The International Maize and Wheat Improvement Center (CIMMYT, Mexico) contributed 115 advanced breeding lines: these lines were selected based on performance (including diseases and grain yield) following three years of field testing (2011-12 season to the 2013-14 seasons) in Nepal. The National Wheat Research Program (NWRP), NARC, Nepal, also contributed 34 varieties released in Nepal for commercial cultivation until 2014. The objective was to make the diversity panel representative of spring wheat genetic resources available in Nepal. Out of interest, three Canadian spring wheat genotypes were also included in the panel, available from the wheat breeding laboratory, University of Guelph, Canada.

5.3.2 DNA extraction and genotyping-by-sequencing (GBS) Genomic DNA of each wheat accession was extracted from leaf tissues collected in the field, using DNeasy Plant Mini Kits (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. SNP genotyping was performed using a GBS approach, and PstI/MspI libraries were prepared as per Poland et al. (2012). Single-end sequencing of multiplex GBS libraries (two PI chips per 96-plex GBS library) was performed on an Ion Proton sequencer at the Plateforme d’Analyses Génomiques [Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval (Quebec, QC, Canada)].

5.3.3 GBS data analysis Ion Torrent sequence reads (50-160 bp) were processed using the Fast-GBS pipeline (Torkamaneh et al., 2017). In brief, FASTQ files were demultiplexed based on barcode sequences. Demultiplexed reads were trimmed and then mapped against the wheat reference genome (Torkamaneh et al., 2016). This was followed by the identification of nucleotide variants from mapped reads. Then, variants were removed if they met any of the following criteria: (i) they had more than two alleles; (ii) the overall read quality (QUAL) score was <32; (iii) the mapping quality (MQ) score was <30; (iv) read depth was <2; (v) heterozygosity was >50%; or

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the missing data was >80%. Missing data imputation was performed with BEAGLE v4.1 (Browning and Browning, 2016) as described by Torkamaneh and Belzile (2015).

5.3.4 Analysis of SNP distribution and genetic diversity The distribution of SNPs in the genome was visualized and analyzed by using TASSEL v.5.2.48 (Bradbury et al., 2007). Genetic diversity was characterized by estimating nucleotide diversity (π) and divergence (Tajima’s D) using VCFtools 0.1.12b (Danecek et al., 2011). For these analyses, we used SNPs with a minor allele frequency (MAF) of ≥0.01 and in a window size of 1000 bp. The average π and Tajima’s D across all windows were computed to obtain a genome-wide average for each source population.

5.3.5 Population structure analysis The model-based Bayesian clustering software fastSTRUCTURE v 1.0 (Raj et al., 2014) was used to infer the number of subpopulations in the study panel. In order to test the number of subpopulations (K), fastSTRUCTURE was run using the default settings with 100-fold cross- validation, varying the value of K from K=1 to 10 on 318 accessions. The number of subpopulations that maximized the marginal likelihood was selected using a Python script included in the fastSTRUCTURE package. The structure plot was ordered based on the Q-values generated by fastSTRUCTURE. In addition, TASSEL v.5.2.48 (Bradbury et al., 2007) was used to generate principal components (PCs) from the same SNP data using the covariance method. The proportion of variation explained by each PC was determined by the eigenvalues estimated in the program. Then the PC plots were created using only the first three PCs. Colour coding of the accessions was based on the estimated population structure computed using fastSTRUCTURE. TASSEL v.5.2.48 (Bradbury et al., 2007) was also used to estimate the marker-based genetic distance matrix for all pair-wise combinations using data from 95,388 markers. Hierarchical cluster analysis was performed in R (R Core Team 2016) by using the hclust function (Müllner, 2013). The unweighted Pair Group Method with Arithmetic Mean (UPGMA) was used to conduct the clustering. Dendrograms were produced using the as.dendrogram function, and customization of the dendrograms was performed with the dendextend (Galili, 2015) and circlize 106

packages in R (Gu et al., 2014). Finally, colour coding given to each accession was based on the estimated population structure computed using fastSTRUCTURE.

5.3.6 Linkage disequilibrium decay analysis To study linkage disequilibrium (LD) decay in the study population, squared allele frequency correlations (r2) were obtained by using 1000 permutations with comparison-wise significance in TASSEL v.5.2.48 (Bradbury et al., 2007). Then, LD decay was plotted as the relationship between r2 values and the physical distance of the SNP markers in the genomes. LD decay was measured both in the whole population and in the four sub-populations derived from population structure analysis.

5.4 Results 5.4.1 The density of polymorphic GBS markers differs among the genomes We performed genotyping-by-sequencing (GBS) of the 318 accessions (Appendix 2) and obtained ~800 million reads, for an average of 2.5 million reads per accession. Raw reads were processed with the Fast-GBS pipeline to call SNPs. After the imputation of missing data, we obtained a final dataset of 95,388 polymorphic markers across the A, B and D genomes (Appendix 2, Figure 5.1a, b). The highest proportion of SNP markers (51%) was derived from the B genome followed by the A genome (39%) and the remaining 10% from the D genome. The number of SNP markers per chromosome ranged from 550 (4D) to 8,374 (2B) (Appendix 2, Figure 5.1b). In each of the genomes, the lowest and highest number of markers identified per genome, respectively, ranged from 3,979 (6A) to 7,578 (7A), 2648 (4B) to 8,374 (2B) and 550 (4D) to 2046 (2D), respectively (Appendix 2, Figure 5.1b). Around 26% of the SNPs (25,820) in this catalog had a minor allele that could be called rare (MAF < 0.1) (Figure 5.1c).

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Table 5.1. Genetic diversity within the three components of the Nepali Wheat Diversity Panel. Population (#accessions) SNPs per accession SNP count π Tajima's D Landraces (166) 571 94794 6.11E-04 2.14E-03 CYMMYT lines (115) 798 91794 5.60E-04 1.90E-03 Released varieties (37)* 2184 80822 5.34E-04 1.41E-03

*Note: The released varieties also include three Canadian varieties

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5.4.2 The populations from different sources vary in genetic diversity The genetic diversity within the NWDP was assessed using two different indices: nucleotide diversity (Nei and Li, 1979) and Tajima’s D (Tajima, 1989). The nucleotide diversity in a population indicates the mean nucleotide differences between DNA sequences per site in every possible pairing, while Tajima’s D is a test statistic for a population calculated as the differences in mean numbers of pairwise differences and segregating sites. In the case of Tajima’s D, any values greater than zero indicate lesser abundance of rare alleles in the population. The nucleotide diversity of the panel was measured in each of the three components of the NWDP (the three Canadian genotypes were included in the group “released varieties”). As expected, the highest genetic diversity was observed among the landraces (166) followed by CIMMYT lines (115) and released varieties (37) (Table 5.1). However, Tajima’s D statistic showed that non-of the component groups in the NWDP had abundance of rare alleles.

5.4.3 The subgroup separation did not correlate to the seed source The fastSTRUCTURE analysis determined that four subpopulations (K=4) was the optimal number of clusters for the 318 accessions in the NWDP using 95K high-quality SNP markers (Figure 5.2a). Among these four subpopulations, subpopulations 2 and 3 were distinct and large while the other two (1 and 4) were found to be smaller. Results revealed that subpopulations 1, 2, 3 and 4 include 22, 154, 99 and 43 accessions, respectively. However, neither the number of subpopulations nor the assignment of accessions perfectly reflected the existence and composition of the three types of accessions included in the NWDP (Figure 5.2.a, Appendix 1).

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Clustering analysis was then performed using UPGMA and based on the estimates of relatedness or kinship. The resulting dendrogram (Figure 5.2.b) was obtained using the estimated genetic distance that provided the placement of each accession in a certain cluster and order. The clusters were in high agreement with the population structure described above (Figure 5.2.a), although there were some instances where an accession assigned to a subgroup was not grouped with other individuals of the same subgroup (indicated by a colour label different than the respective subpopulation in Figure 5.2.a, Figure 5.2.b; Appendix 3). The PCA conducted to validate the results obtained above confirmed the existence of two large and distinct subpopulations as well as two smaller and more overlapping subpopulations (Figure 5.2.c). Three PCs were used for this analysis, where the three PCs accounted for approximately 14% of the variation. The result showed that the biplots of PC1, PC2, and PC3 separated the genotypes into two distinct groups (subpopulations 2 and 3) while the other two groups (subpopulations 1 and 4) were observed scattered somewhere in between. The accessions in subpopulation 3 appeared more distinct whereas subpopulations 2 and 4 appeared slightly closer to one another despite subpopulation 2 being distinct. The accessions appeared tightly clustered within subpopulations 2 and 3, while subpopulations 1 and 4 were smaller and more scattered. The smallest cluster, i.e. subpopulation 1, appeared to be highly scattered compared to the other subpopulations (Figure 5.2.c).

5.4.4 Linkage disequilibrium decayed more rapidly in the whole population The analysis of LD decay was performed for the whole panel and separately for each subpopulation derived from the population structure analysis. The estimated allele frequency correlations (r2) were plotted against the physical distance of the loci pairs to assess the pattern of LD decay. On average, the LD (r2= 0.72) had decayed by half (r2= 0.36) at a physical distance of ~60 Kb for the whole population (Figure 5.3.a). The result also showed variable LD patterns for the four different subpopulations (Figure 5.3.b). The LD observed was highest for subpopulation 1 (r2= 0.85) followed by subpopulation 3 (r2= 0.75), subpopulation 4 (r2= 0.75) and then subpopulation 2 (r2= 0.6). For subpopulation 1, the LD declined to half at a physical distance greater than 150 Kb. Similarly, for subpopulation 2, the LD declined to half of its value (r2= 0.3) at a physical distance of around ~75 Kb. In 112

subpopulation 3, LD decayed to half at a physical distance of ~90 Kb. The pattern of LD decay in subpopulation 4 was similar to that of subpopulation 3, i.e. ~90 Kb. Thus, the results evidently exhibit differences in r2 values among the whole population and the identified subpopulations. On average, subpopulations 1 and 4 had higher r2 values while the whole population and subpopulation 2 had the lower r2 values. Similarly, LD decay of the whole population was lower (~60 Kb) compared to all the four subpopulations.

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Figure 5.3. Linkage disequilibrium (LD) analysis of the NWDP. (a) The LD decay (r2) over physical distance for the whole population. (b) The LD decay (r2) over physical distance for the whole population where: Group-1= subpopulation 1, Group-2= subpopulation 2, Group-3= subpopulation 3 and Group-4= subpopulation 4.

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5.5 Discussion 5.5.1 The population structure may be governed by the extensive introduction of germplasm into Nepal The wheat accessions used in this study are representative of spring wheat diversity in Nepal. The analysis of population structure assigned the 318 wheat accessions of the Nepal Wheat Diversity Panel (NWDP) into four subpopulations (two large and two small subpopulations scattered in between). All the three methods used (population structure, UPGMA clustering, and PCA) consistently led to this grouping. The consistency of grouping using these methods has also been observed in earlier studies on other germplasm panels (Chen et al., 2012; Tascioglu et al., 2016; Ya et al., 2017). The differentiation of the population into different subpopulations by fastSTRUCTURE is based on frequencies of relatedness of the genotypes to each of the subpopulations as hypothesized (Chao et al., 2007, 2010; Nielsen et al., 2014). Similarly, the UPGMA clustering separates the population into different subpopulations based on genetic distances (Liu et al., 2018b) while PCA illustrates the subpopulation differentiation based on genetic distances (Khan et al., 2015). A priori, we expected two or three groupings, corresponding to the seed origin, with the first one comprising the landraces from Nepal, and the others including CIMMYT lines and commercially released Nepali varieties (the majority of released varieties are CIMMYT introductions). This expectation was based on the conviction that the landraces grown in diverse areas of Nepal, a mountainous nation with diverse ethnic groups, are genetically more diverse than newly introduced modern cultivars (Lopes et al., 2015; Wingen et al., 2017). However, contrary to our expectation, the result obtained did not yield such distinct clusters based on the sources of the accessions, but rather admixture (Ward et al., 2019) was observed among all four subpopulations. Substantial admixture in the population was indicated by the first principal component explaining only 6.3% of the total genotypic variation. We observed that subpopulation 2 had the highest proportion of CIMMYT lines (60%) and released varieties (65%), whereas subpopulation 3 had the highest proportion of landraces (42%) (Appendix 3). Similarly, observations from the Q-matrix, an output of Bayesian clustering in fastSTRUCTURE, showed that there are many accessions in each of the four subgroups which are not related (Bayesian probability of belonging to a subpopulation >99% or accessions 115

without admixed ancestry) to other individuals from other groups. Specifically, among 99 accessions grouped into subpopulation 3, 69 accessions are not related to the individuals from other groups, and 49 of these 69 accessions are the landraces. However, in subpopulation 2, among 154 accessions, 56 are not related to accessions from other subpopulations, and 28 of them are CIMMYT lines with remainder being landraces and the released Nepali varieties (Appendix 3). The unexpected population structure (four subpopulations not two or three; admixture) results may be due to the numerous factors that influence the structure of a germplasm population such as age of the variety, activities of plant breeders (Brbaklić et al., 2015; Benson et al., 2012), geographical origin (Chao et al., 2007; Zhang et al., 2010; Khan et al., 2015; Bhatta et al., 2018), market class (Chao et al., 2007; Zhang et al., 2010) and ploidy level (Khan et al., 2015). According to Joshi et al. (2006), wheat has been growing in Nepal (mostly in the western hills) from ancient times. But formal wheat breeding by the public sector started in Nepal in 1951; Lerma 52 was the first wheat variety released in Nepal for commercial cultivation in 1960 (Vijayaraghavan et al., 2018). Until 1960, wheat was a minor crop in Nepal; mostly the landraces and some Indian varieties were predominant in about 100,000 ha area under wheat cultivation (Morris et al., 1994; Vijayaraghavan et al., 2018). But the scenario changed dramatically in the following decades as a large number of semi-dwarf modern varieties were introduced into Nepal from the beginning of the mid-1960s simultaneous to the government promoting the cultivation of the improved varieties (Morris et al., 1994). Wheat germplasm was sourced in Nepal mainly from CIMMYT in Mexico and India (Morris et al., 1994; Joshi et al., 2006; Vijayaraghavan et al., 2018) and USAID (Joshi et al., 2006). Considering India’s restrictions on sharing germplasm, it is possible that the wheat germplasm introduced into Nepal from India were also CIMMYT lines. The wheat area expanded by over 500% between 1960 and 1990 (Bhattarai and Pandey, 1997) which is also known as the “Green Revolution” (Morris et al., 1994). Bhattarai and Pandey (1997) also state that some of the popularly grown wheat varieties before the mid-1960s were also the modern varieties developed by Indian and Mexican wheat breeding programs. Thus, from a minor crop in the 1950s (Morris et al., 1994), wheat has now become one of the major cereals in Nepal, contributing to 22% of the national cereal coverage (MoAD 2013, 2017). This situation likely resulted in a heavy decline in the cultivation of Nepali landraces. 116

Examining the 43 varieties released in the country since 1960, Nepal has been highly dependent on foreign germplasm for variety development (Vijayaraghavan et al., 2018). On average, the current adoption of improved varieties in Nepal is approximately 97% (Timsina et al., 2016; Vijayaraghavan et al., 2018). Thirty-five wheat varieties released in Nepal until 2006 used a total of 89 ancestors from 22 countries while these did not include any Nepali germplasm (Joshi et al., 2006). Among the released wheat varieties in Nepal until 2016, approximately 80% of them do not have Nepali origin but are of foreign origin, such as USA (13%), India (13%) and France (12%) notably (Joshi, 2017). This evidence clearly highlights the dominance of foreign materials in the wheat germplasm pool of Nepal. The NAGRC was established in 2010 and it has been working on maintaining agricultural genetic resources through characterization, evaluation, and identification of valuable traits. There are about 1700 wheat accessions collected and maintained by NAGRC (Paudel et al., 2016). Altogether 18 germplasm collection programs were carried out to collect different crop species including wheat, while only two explorations were conducted to collect wheat genetic resources from Western Nepal (Joshi et al., 2006), which is potentially the major source of diverse Nepali landraces. These genetic resources were collected from different altitudes ranging from 720 to 3353 meters above sea level. A large portion of these collections was not from standing crops but from the farmers’ granaries due to difficulties associated with the rugged terrain and inaccessible remote agro-ecological conditions of Nepal (Paudel et al., 2016). Therefore, the collection process, by chance, could have resulted in the duplication of genetic resources, and furthermore, some of the passport information collected during the germplasm exploration could be misleading, i.e. some of the collections may not have been the “authentic” landraces. Here, we define the “authentic” landraces as only those distinct accessions grown and selected solely by local farmers without efforts from professional breeders. The argument is that there is a high probability that germplasm explorations during the 1970s, 1980s, and 1990s could have mistakenly collected and labelled exotic germplasm as local landraces because the farmers had been growing them for decades (after the introduction of exotic materials in 1950s and early 1960s). There is a second potential cause: though Nepal is not one of the centres of origin for wheat, a study by Balfourier et al. (2019) revealed that wheat germplasm spread and evolved collinearly with human migration. In this context, one of the sources of exotic wheat genetic 117

diversity in Nepal could be associated with seasonal migration of farmers from far-western and mid-western Nepal back and forth to/from North-western India, which continues for at least two to three generations for many families (Bruslé, 2008). Historical farmer-led sharing of seeds between India and Nepal, combined with formal introduction of CIMMYT materials, may be multiplying the distortions within the Nepali wheat population structure, because there has been extensive collaboration and use of Indian wheat genetic resources in international wheat breeding programs (e.g. CIMMYT) (CIMMYT 1992, 1996), enabled by the reasonable assumption that traits adapted to northern Indian would also be beneficial to Nepal. The latter statement is evidenced by the observation that out of 35 wheat varieties released in Nepal until 1997, 16 of them were shown to have an Indian origin (Joshi et al., 2006). In terms of the extensive admixture between the “landraces” and exotic germplasm, since the germplasm collections occurred from the 1970s-1990s, whereas improved germplasm was introduced starting in the 1950s, the simplest explanation is that cross-hybridization occurred prior to the collections. All of these arguments need verification.

5.5.2 Higher genetic diversity in landraces We compared nucleotide diversity among the groups of accessions based on their seed source and we observed that the landraces were genetically more diverse than the other two groups (elite and advanced lines). There is a greater chance that these landraces possess some valuable alleles associated with biotic and abiotic stress tolerance. Compared to modern elite cultivars, landraces typically exhibit higher genetic diversity (Wingen et al., 2017). The genetic diversity of the landraces is basically shaped by the activities of farmers, environmental factors and also due to evolutionary forces including random mutations, gene flow between populations and genetic drift (Mercer and Perales, 2010). Contrary to this, the selection pressure, rate of recombination and some segregation distortion in modern cultivars during cultivar improvement lead to the loss of certain genes resulting in low genetic diversity (Cavanagh et al., 2013; Lopes et al., 2015; Wingen et al., 2017). In addition, landraces are subjected to less severe selection compared to the modern elite germplasm and thus, there is a greater chance for maintenance of higher genetic diversity (Monteagudo et al., 2019). The landraces harbor some valuable genes associated with adaptation to different stresses as they have been grown under extremely low 118

input environments for years allowing natural selection for alleles associated with adaptive traits (Lopes et al., 2015). For example, Zhou et al. (2017) observed high genetic divergence in a Chinese wheat landrace population due to environmental stresses and individual selection efforts. Landraces promote the introduction of new genetic variation from distant relatives to enable crop improvement in the long run (Cavanagh et al., 2013) including for stress-tolerance related traits such as drought (Mwadzingeni et al., 2017b). For example, the group of Creole wheat landraces (the landraces which were introduced from Europe to Mexico) has been utilized as a source of alleles for different abiotic stresses including drought (Vikram et al., 2016). Although the current results did not show the presence of a high level of rare alleles (Tajima’s D), the landraces could have been adapted and selected for some specific traits relevant to Nepali wheat growing environments.

5.5.3 The D genome is the least polymorphic genome as expected The application of high density SNP markers is now widely used to assess genetic diversity, population structure, and various evolutionary questions (Oliveira et al., 2014). In this study, we identified 95,388 SNP markers across the three wheat genomes. The highest number of polymorphic markers (~51%) was found in the B genome similar to Eltaher et al. (2018). Furthermore, the highest number of SNPs in the B genome was observed in different synthetic hexaploid wheat (SHWs) populations (Nielsen et al., 2014; Bhatta et al., 2018; Gordon et al., 2018). The highest number of SNPs was found on chromosome 2B followed by chromosomes 7B and 6B. This result is also in accordance with other studies performed on different wheat populations (Nielsen et al., 2014; Yu et al., 2014). The proportion of D genome SNPs (~10%) observed was low but in agreement with Rimbert et al. (2018). The low level of polymorphism within the D genome could be due to the proposed genetic bottleneck that occurred upon the hybridization of the donor D genome (i.e. Ae. tauschii) into the hexaploid genome compared to that of the tetraploid wheat ancestor (AABB) (Dubcovsky and Dvorak, 2007). In other words, hexaploid wheat contains a lower percentage of the diversity present in the wild Ae. tauschii genome compared to the wild A and B genomes. An alternative explanation is that the higher proportion of rare alleles in the D genome compared to the A and B genomes may have been more susceptible to genetic drift during modern cultivar development, creating a recent genetic 119

bottleneck in the D genome (Chao et al., 2009). The relative contributions of the different genomes to SNP diversity as obtained using GBS-derived SNP markers are also in agreement with the results of past studies performed on hexaploid wheat using DArT markers (Zhang et al., 2011; Nielsen et al., 2014; El-Esawi et al., 2018) and SSR markers (Brbaklić et al., 2015; Khan et al., 2015; Abbasabad et al., 2017; Ya et al., 2017).

5.5.4 Variation in linkage disequilibrium (LD) patterns may indicate the level of selection pressure The LD decay distance can indicate the rate of recombination which determines the precision of association and QTL mapping (Chao et al., 2010; Zhang et al., 2010; Abbasabad et al., 2017). Various factors affect the random assortment of alleles in a population leading to variation in LD patterns including selection, non-random mating, mutation, admixtures, population size, and genetic drift (Flint-Garcia et al., 2003; Vos et al., 2017). Similarly, the type and number of markers also affect LD measurements (Chao et al., 2010), i.e., limited number of markers give the limited resolution of LD distribution across the genome (Benson et al., 2012). More recently, large sets of SNP markers have been used to generate high-resolution maps that result in precise estimations of LD to facilitate the exploitation of genetic resources (Zhang et al., 2010a). In this study, the estimated LD distances for the whole population and the subpopulations indicate shorter LD decay blocks compared to a recent study by Ward et al. (2019); this study demonstrated that the LD decayed at a distance of approximately 1 Mb in a population of 322 soft red winter wheat which is much higher than what we observed in this study despite the population sizes being approximately similar. This result could be due to the presence of higher genetic diversity in the Nepali wheat germplasm. Furthermore, we observed lower LD and faster LD decay for the whole study population compared to the subpopulations, certainly due to the larger size of the former (Wang et al., 2014); smaller populations usually have higher LD (Zhang et al., 2010; Nielsen et al., 2014). Consistent with this observation, the LD for subpopulation 1 (the smallest with only 22 accessions) was the largest despite ~50% of the accessions in this group being landraces. As observed by Hao et al. (2011), landraces have higher allelic diversity, and the LD decay distance is shorter, compared to elite lines. Here, subpopulation 3 had higher LD and slow LD decay compared to subpopulation 2 despite having 120

a higher proportion of landraces (~71%) in the group. Here, the variation in the extent of LD values and LD decay may also be due to genetic drift and/or selection pressure employed on the genetic materials (Flint-Garcia et al., 2003; Chao et al., 2007) especially CIMMYT lines and Nepali released varieties which form ~50% of the population.

5.6 Conclusions and future perspectives This study provides a novel understanding of the genetic diversity of spring wheat in Nepal. In particular, the relatedness of many accessions regarded as landraces with CIMMYT advanced lines has been a surprising result. The finding suggests that the introduction of genetic resources from the 1950s to recent years significantly altered the population structure of what is currently labeled as native Nepali spring wheat through cross-hybridization and/or collection error. The information generated in this study, including genetic diversity, population structure, and LD, can guide future breeding programs in Nepal. In particular, as more than 42% of the agricultural land in Nepal is rainfed (MoAD 2017) and drought alone contributes 20-30% of yield loss (Li et al., 2011), which is expected to worsen due to climate change (Dahal et al., 2016; Khatiwada and Pandey, 2019), Nepal is prioritizing the development of new stress tolerant wheat varieties which will be dependent on exploiting germplasm diversity. In this context, this study has shown that only a subset of the wheat “landraces” in Nepal are actually authentic. Utilization of these authentic landraces could be a way forward to utilize useful adaptive traits to improve the future food security of this climate vulnerable nation.

5.7 Acknowledgments We dedicate this paper to our mentor, colleague and friend, the late, Professor Alireza Navabi, who passed away suddenly during the preparation of this manuscript. The authors wish to thank Nick Wilker and Kat Bosnic (University of Guelph) for assistance with seed handling and field planting. We thank Dr. A.K. Joshi, Mr. Madan Bhatta and Mr. Deepak Pandey for facilitating seed acquisition from Nepal and Dr. Thomas Payne from CIMMYT, Mexico. This research was generously supported by a grant to MNR from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by the International Development Research Centre

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(IDRC, Ottawa) and Global Affairs Canada, in addition to grants to AN from SeCan, the Agricultural Adaptation Council and Grain Farmers of Ontario. Nepali landrace seeds were generously provided by the National Genetic Resources Centre (NGRC, Nepal) and the Agricultural Research Council (NARC, Nepal). Seeds of released varieties were provided by the National Wheat Research Program (NWRP belonging to NARC, Nepal). CIMMYT breeding lines were provided by CIMMYT, Mexico.

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6 A Genome-wide Association Study Reveals the Genetic Basis for Agro-morphological Traits in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel

This chapter is intended as an original research article for publication with the following citation:

Kamal Khadka, Davoud Torkamaneh, Harwinder Singh Sidhu, Dhruba Bahadur Thapa, Deepak Pandey, Mina Kaviani, Andrew J. Burt, Francois Belzile, Manish N. Raizada, Ali Navabi (2020). A Genome-wide Association Study Reveals the Genetic Basis for Agro-morphological Traits in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel. (In preparation).

Author contributions Kamal Khadka: Conceptualized the project and wrote the manuscript. Mina Kaviani: Contributed to DNA isolation. Davoud Torkamaneh: Contributed to data analysis and technical editing. Harwinder Singh Sidhu: Provided technical inputs. Francois Belzie: Supervised the genotyping. Andrew J. Burt: Contributed to technical editing. Dhruba Bahadur Thapa: One of the collaborators in the Nepal field experiment. Deepak Pandey: One of the collaborators in the Nepal field experiment. Manish N. Raizada: Provided technical and language editing. Ali Navabi: Conceptualized and supervised the project.

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6.1 Abstract Bread wheat (Triticum aestivum L.) is the third most important cereal crop in Nepal both in terms of area and production. Despite many efforts to improve the productivity of wheat varieties, the national average productivity of wheat in Nepal is low compared to many other countries. Here a set of 318 spring wheat genotypes termed the “Nepali Wheat Diversity Panel (NWDP)”, representing the wheat diversity in Nepal, was evaluated for 9 agro-morphological traits in four field experiments in Nepal and Canada. The phenotypic assessment showed a significant difference (P≤0.0001) among the genotypes for the agro-morphological traits. The diversity panel was also characterized using genotype by sequencing (GBS). The phenotypic and genotypic data were used in a genome-wide association study (GWAS) to assess the marker-trait associations (MTAs). The GWAS results were used to identify candidate genes in the QTL regions defined in this study. Of nine agro-morphological traits evaluated, 15 MTAs and QTL regions for six traits were detected (days to anthesis, days to maturity, awn length, pre-harvest sprouting, grain yield and test weight) on six different chromosomes. Each QTL explained 5-8% of the phenotypic variation. More than 40 candidate genes were identified. This study identifies the chromosomal regions for target traits which may be of interest to wheat breeders, in particular, Nepali wheat breeders. In addition, the GBS SNP markers used in this study may be further used in marker-assisted selection or genomic selection.

Keywords: Wheat, agro-morphological traits, genome-wide association study, marker-trait associations, Nepal

6.2 Introduction Wheat (Triticum aestivum L.) is the third most important crop for global food security with an annual production of ~735 million metric tons (FAOSTAT, 2019). Globally, wheat provides 41% of the total cereal calorie intake and accounts for 35% of the cereal calorie intake in developing countries and 74% in developed countries (Shiferaw et al., 2013). As the world’s population is estimated to grow beyond 9.6 billion by 2050 (UN, 2019), the demand for wheat to feed this ever-increasing population is expected to increase by 60% (GCARD, 2012). Global

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wheat production is threatened by different biotic and abiotic factors which are also associated with rapid changes in the climate (Ewert et al., 2014; Abhinandan et al., 2018). These stresses affect crop growth and potential yield (Zhang et al., 2006; Dehbalaei et al., 2013). Therefore, the development of high yielding and stress tolerant wheat varieties is a major challenge for current and future wheat breeders. Among the major food crops grown in Nepal, wheat is the third most important cereal crop in terms of area and production (MoAD, 2017). Wheat was not a major crop in Nepal before the 1960s (Tripathi, 2010). After the introduction of semi-dwarf green revolution varieties, wheat area and production had multi-fold increases (Tripathi, 2010; Gurung, 2013; Vijayaraghavan et al., 2018). The national average wheat productivity in Nepal is low (2.7 t/ha) (FAOSTAT, 2019) compared to its adjoining neighbour India (3.2 t/ha) (FAOSTAT, 2019). Wheat in Nepal is grown during the winter season which is the dry period of the year and hence access to irrigation is critical to achieving higher grain yield. Unfortunately, micro-climatic variation exposes Nepal to a number of stresses that impede crop production (Gurung, 2013; Shrestha et al., 2013b; Pokharel et al., 2015), in addition to poor fertility, diseases, pests and weeds (Poudel et al., 2013; Shah, 2013). Climatic predictions for Nepal show that the probability of winter droughts is likely to increase in the major wheat growing areas (DHM-MoSTE Nepal, 2013). In particular, Western Nepal is affected severely by drought events (Miyan, 2015). There is a dire need to develop climate-resilient wheat varieties for Nepal. Breeding high yielding crop varieties requires the evaluation of agro-morphological traits. Genetic dissection of different traits makes it possible to select and improve the crop varieties for specific traits that need special attention and are difficult to phenotype, for example, drought tolerance (Choudhary et al., 2018). Many physio-morphological, as well as phenological traits that influence the performance of a wheat variety, have been identified (Nigam et al., 2005; Reynolds and Trethowan 2007). Often these traits are both quantitative in nature and have low heritability, making breeding for high-yielding, stress-tolerant varieties a complex and challenging task (Kumar et al., 2014; Cobb et al., 2019). To address these challenges, cost- effective, labor-efficient and precise high throughput phenotyping methods have been emphasized in recent years (Kosová et al., 2014; Rutkoski et al., 2016; Mwadzingeni et al., 2016; Kant et al., 2017). 125

In addition to grain yield and grain yield-related traits such as thousand grain weight and test weight, some agro-morphological traits such as days to anthesis, days to maturity, awn length, and plant height are important for Nepal. Considering the challenges associated with late season heat stress (Joshi et al., 2007a), early flowering and early maturing varieties are suitable for the Nepali wheat growing environment. The presence of awns has been reported to have a positive influence on grain yield especially under stress conditions including drought and heat (Ali et al., 2015; Taheri et al., 2011). Current climate change situations in South Asia including Nepal make awn length an important trait for improving abiotic stress tolerance in wheat. The introduction of dwarfing alleles (Rht-B1b and Rht-D1b) in wheat has contributed to increases in wheat grain yield by at least 10%, with one reason being the reduction in lodging (Worland et al., 2001) which makes plant height an important agro-morphological trait for evaluation. Many GWAS studies have successfully identified genomic regions in wheat associated with different qualitative and quantitative traits (Liu et al., 2018; Qaseem et al., 2018; Sheoran et al., 2019; Ye et al., 2019). The fully annotated reference genome recently released by the International Wheat Genome Sequence Consortium (IWGSC, 2018) has widened the scope for identification of markers associated with different genomes and associated candidate genes. Novel tools and techniques for genotyping that are capable of producing high-density genome- wide markers such as Genotyping-by-sequencing (GBS) have become available (Elshire et al., 2011; Fang and Xiong, 2015; Torkamaneh et al., 2018). Among the various methods used to achieve complexity reduction, the GBS method is more efficient considering the cost, ease of handling, and low number of purification steps (Davey et al., 2011; Poland et al., 2012). Allopolyploidy and large genome size are two key factors that have slowed the development of molecular markers in wheat, however, the use of GBS markers presents a significant opportunity for current and future marker development work (He et al., 2014). Nepali wheat breeders are making efforts to develop high-yielding, climate-resilient wheat varieties using phenotype-based selection. Nepali spring wheat diversity has not been characterized using GWAS. Here, we evaluated a diversity panel of 318 spring wheat genotypes, termed as the “Nepali Wheat Diversity Panel (NWDP)”, by GWAS analysis. It is expected that the outcomes of the study will contribute to the on-going breeding endeavors in Nepal. The objectives of this study were: (1) to evaluate the NWDP for different agro-morphological traits; 126

(2) to identify the marker-trait associations in the NWDP; and (3) to identify candidate genes in the marker/QTL regions.

6.3 Materials and methods 6.3.1 Plant materials A diversity panel of 318 spring wheat genotypes termed the “Nepali Wheat Diversity Panel” (NWDP) was assembled for the study and included 166 Nepali landraces, 115 CIMMYT advanced breeding lines, and 34 Nepali commercial varieties (Appendix 1). As two major field experiments were conducted in the Canadian environment, three Canadian spring varieties were also included in the diversity panel. The landraces in the diversity panel represented 29 districts of Nepal and these were collected during different germplasm expeditions during the 1970s to 1990s. The landraces were provided by National Agriculture Genetic Resource Centre (NAGRC), Nepal Agriculture Research Council (NARC) of Nepal. The National Wheat Research Program (NWRP), NARC, Nepal generously supported the seed of 34 commercially released varieties. Similarly, the advanced breeding lines were provided by International Maize and Wheat Improvement Centre (CIMMYT), Mexico. These advanced breeding lines from CIMMYT were selected based on their performance in Nepal from the 2011-12 to 2013-14 seasons. The seed of the three Canadian varieties were obtained from the Wheat Breeding Laboratory, University of Guelph, Canada.

6.3.2 Experimental conditions and field trial design Four field experiments were conducted in 2016 and 2017 both in Canada and Nepal to evaluate the diversity panel for different agro-morphological traits. The first field trial was conducted during the 2016 growing season, at the Elora Research Station of the University of Guelph, Canada (43°38'23.0"N 80°24'11.0"W). The seeds were planted on May 11 and harvested on September 5. Similarly, the second field trial was conducted during the 2017 growing season at the same research station (43°38'10.4"N 80°24'07.6"W). During this season, planting was done on May 16 and the plots were harvested on August 29. Both of these experiments were conducted under a rain-fed environment. The remaining two field trials were conducted in Nepal during the 2016-17 wheat growing seasons. One of these field trials was conducted at the Nepal 127

Agriculture Research Council (NARC) Research Station located at Khumaltar, Lalitpur, Nepal (27°39'12.3"N 85°19'33.7"E) at an altitude of 1360 masl in collaboration with the Agricultural Botany Divison (ABD) of NARC. Similarly, the second field trial in Nepal was conducted at the National Wheat Research Program (NWRP), NARC, located at Bhairahawa, Rupandehi, Nepal (27°31'53.5"N 83°27'32.2"E) at an altitude of 107 masl in collaboration with NWRP. The planting and harvesting dates for the trial at the NARC station in Khumaltar were November 23, 2016, and May 5, 2017. Similarly, seed planting in NWRP, Bhairahawa, was done on November 30, 2016, while the plots were harvested on April 19, 2017. The experiments were conducted using an alpha lattice design (Patterson and Williams, 1976) with two complete blocks and 20 incomplete blocks with 32 accessions (two accessions were removed from the analysis as high seed mixture was observed). At the Elora Research Station, each experimental plot consisted of a six-row plot (1 m x 3 m) with 17.8 cm row spacing, and the plot-to-plot and range distances were 0.5 m and 1 m, respectively. In the Nepal sites, due to limitations in the availability of seed, 2 m long 2-row plots with 20 cm row-to-row spacing were used. The summary of weather data for all the four experimental sites is presented on Appendix 4.

6.3.3 Phenotyping of agro-morphological traits The diversity panel was evaluated for nine agro-morphological traits: days to anthesis (DA), days to maturity (DM), awn length (AL), plant height (PH), pre-harvest sprouting (PHS), grain filling period (GFP), grain yield (GY), test weight (TW) and thousand-grain weight (TGW). The data was recorded on the phenological traits DA and DM in all the four field trials. The DA was estimated as the number of days from sowing until ~75% of the spikes in the plot reached the anthesis stage. Similarly, data on DM was recorded when ~75% of the peduncles in the plot lost their green color and turned brown or yellow. The GFP was calculated by subtracting DA from DM. A ruler was used to measure AL from the tip of the spike to the end of the longest awn. The PH was measured from the base of the plant to the tip of the spike (excluding the awns) on a handful of tillers from the middle of the plot. At the Elora Research Station, research plots were harvested using a Wintersteiger plot combine harvester (Wintersteiger AG; Ried im Inkreis, Austria). Grain yield data for each plot was recorded using a HarvestMaster Grain Gauge (Juniper Systems, Inc., Logan, UT) adjusted 128

on the combine. However, to confirm that there was no error, the grain was manually weighed after completing the harvest. In both trials conducted at the Nepal field sites, harvesting, threshing, drying and measurement of grain yield were all done manually. Subsequently, grain yield (t/ha) was calculated using the total weight of the grain harvested from each plot. Finally, test weight (TW, kg/Hl,) and thousand-grain weight (TGW, g) were measured postharvest. In 2016, due to unfavorable weather (heavy rain) during the late maturity and harvesting stage, pre- harvest sprouting (PHS) was distinctly observed. Therefore, data on PHS was recorded based on the number of grains with sprouts (germinated seeds with radicle and coleoptile emerged) out of 200 randomly sampled grains.

6.3.4 Phenotypic data analysis PROC MIXED and PROC GLIMMIX in SAS version 9.4 (SAS Institue, Cary, NC) were used to analyze the phenotypic data. The Shapiro-Wilk test was performed in PROC UNIVARIATE to test the normality of residuals. To test the homogeneity of error variances, studentized-residual by predictor plots were constructed using PROC SGPLOT to ensure that the data points were random and independent. Any studentized residuals >3.5 and <-3.5 were regarded as outliers for any genotype by treatment combination and they were excluded from the analysis (Bowley, 2015). Pair-wise Pearson correlation analysis was done by using PROC CORR function in SAS and in R (RCore Team, 2017). Principle component analysis was performed in SAS and the bipot was generated using the scatterplot function in Microsoft Excel (Microsoft Office, 2017). Broad-sense heritability (H2) was estimated using a mixed model in SAS version 9.4 (SAS Institute, Cary, NC) which estimates heritability from multiple environments using multiple replications per environment (Holland et al., 2003). These values were estimated using the formula (Falconer and Mackay, 1996) shown below:

푉 퐻2 = 퐺 푉퐺 + 푉퐺퐸 + 푉푒

Where, 129

VG= genetic variance

VGE= genetic x environment variance

Ve= error variance

6.3.5 DNA extraction and genotyping of the panel The DNA extraction was performed using leaf samples of the 318 spring wheat genotypes in the NWDP collected from the field plots when the plants were at the 3-4 leaf stage. These samples were freeze-dried at -800C, and genomic DNA was isolated using a DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) using the manufacturer's protocol. The SNP genotyping was performed using a genotype by sequencing (GBS) approach (with PstI/MspI digestion) and libraries were prepared following Poland’s protocol. Single-end, 50-160 bp sequencing of multiplex GBS libraries (one 96-plex GBS library per two PI chips) was performed on an Ion Proton Sequencer (Poland et al., 2012) at the Plateforme d’Analyses Génomiques [Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval (Quebec, QC, Canada)]. Processing of Ion Torrent sequence reads were processed using the Fast-GBS pipeline (Torkamaneh et al., 2017). The FASTQ files were demultiplexed based on barcode sequences. Demultiplexed reads were trimmed and then mapped against the wheat reference genome (Torkamaneh et al., 2016). This was followed by identification of nucleotide variants from mapped reads. Then, the variants were removed if they met any of the following criteria: i) they had two or more alternate alleles, ii) the overall read quality (QUAL) score was ≤32, iii) the mapping quality (MQ) score was ≤30, or iv) read depth was ≤2. Missing data imputation was performed with BEAGLE v4.1 (Browning and Browning, 2016) as previously described (Torkamaneh and Belzile, 2015).

6.3.6 Population structure and LD analysis Population structure analysis was performed using the model-based Bayesian clustering software fastSTRUCTURE v 1.0 (Raj et al., 2014) (unpublished-Chapter 5). The number of subpopulations that maximized the marginal likelihood was selected using a Python script included in the fastSTRUCTURE package. The structure plot was ordered based on the Q-values generated by fastSTRUCTURE and these Q-values were utilized in marker-trait association 130

analysis in this study. For the linkage disequilibrium (LD) decay analysis, squared allele frequency correlations (r2) were obtained by using 1000 permutations with comparison-wise significance in TASSEL v.5.2.48 (Bradbury et al., 2007). Then, the LD decay was plotted as the relationship between r2 values and the physical distance of the SNP markers in the genomes (unpublished-Chapter 5). The LD decay distance for the smallest sub-population (the largest distance) was used in defining the QTL region in this study (unpublished-Chapter 5).

6.3.7 Marker-trait association analysis Marker-trait association (MTA) between the agro-morphological traits evaluated in the four field experiments and the GBS markers was assessed using the Genome Association and Prediction Tool (GAPIT), a R software program. Marker-trait association analysis was performed for agro-morphological traits using 80249 GBS markers (minor allele frequency (MAF) >0.05) and a mixed linear model (MLM). The kinship matrix (K-matrix) derived from the population structure analysis of the NWDP (unpublished-Chapter 5) was used while running GAPIT for the marker-trait association study. To appraise marker-trait association results, multiple models were generated: K, K+PCs and K+Q. The Q-Q plots based on observed and expected p values were compared to determine the model appropriate for the study. An adjusted p value (q value) to ensure a false discovery rate (FDR) <0.05 was used to establish a significance threshold (Wang et al., 2012). The Manhattan plots were generated using the rVMP package (https://github.com/XiaoleiLiuBio/rMVP) in R (R Core Team, 2017).

6.3.8 Candidate gene identification The results from GWAS using the GAPIT platform was used to identify the significant SNP markers associated with the agro-morphological traits evaluated in the four field experiments. The region of the chromosome within 150 kb on both sides of the physical position of a significant marker was defined as a QTL region. This was based on the linkage block derived by linkage disequilibrium (LD) decay analysis of the NWDP (unpublished-Chapter 5) in a separate study. Followed by this, the “JBrowse” search tool in the IWGSC website was used to identify candidate genes in each QTL region defined in this study based on cross-referencing to the published literature. 131

6.4 Results 6.4.1 Assessment of phenotypic traits The NWDP consisting of 318 spring wheat genotypes was evaluated for nine different agro-morphological traits including: days to anthesis (DA), days to maturity (DM), awn length (AL), plant height (PH), pre-harvest sprouting (PHS), grain filling period (GFP), grain yield (GY), test weight (TW) and thousand-grain weight (TGW) in four different field experiment conducted during the 2016 and 2017 wheat growing seasons in Canada and Nepal. A normal distribution was observed for all traits except AL and DA (Figure 6.1 and Appendix 6). A normal distribution was observed for DM in three locations except at Elora 2017, while GY was not normally distributed at the NWRP (Nepal) site. However, TGW was normally distributed only at Elora 2016. Analysis of variance (ANOVA) of 318 genotypes in the NWDP was performed for 9 different agro-morphological traits (AL, DA, DM, GFP, GY, PH, PHS, TGW, and TW) to assess the variation among the genotypes and also to test the effects of genotypes (G) and environments (G) and their interactions (G x E). The result showed a significant difference (P≤0.0001) among the genotypes for all the 9 traits evaluated in the study (Appendix 5). Similarly, a significant location (environment) effect and a significant effect of the interactions (P≤0.0001) between genotype and environment (G x E) were observed. The significant variation among the genotypes in the diversity panel for the traits evaluated in the study was corroborated with the range of least-square means (Appendix 7). Furthermore, broad-sense heritability (H2) was assessed for all the nine traits across all the four field experiments (except for PHS, which was recorded only at the Elora Research Station during the 2016 season) (Appendix 8). The traits including AL, PH, PHS, and TGW had moderate to high heritability. For the combined analysis, high heritability estimates were observed for AL and TGW, at 87.4% and 65.7%, respectively (Table 6.1). Heritability estimates ranged from low (~9%) for GFP, to high (~87%) for AL.

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Table 6.1. Mean, standard error of the mean, mean ranges and heritability for traits evaluated during the 2016 and 2017 wheat growing seasons in Nepal and Canada. Traits Mean SEM Minimum Maximum Heritability (%) Awn length (AL) (cm) 3.28 0.13 0.00 6.72 87.38 Days to anthesis (DA) 86.97 0.14 81.35 95.82 24.03 Days to maturity (DM) 121.13 0.13 114.75 129.17 13.54 Grain filling period (GFP) 34.30 0.13 27.91 40.08 8.93 Grain yield (GY) (kg/ha) 3083.98 32.71 1433.45 4399.23 18.85 Plant height (PH) (cm) 87.45 0.58 60.86 109.18 62.44 Pre-harvest sprouting (PHS)* 42.75 1.51 0.00 95.25 72.35 Thousand grain weight (TGW) (g) 38.26 0.29 23.64 48.19 65.68 Test weight (TW) (kg/hL) 72.06 0.14 58.13 77.08 19.76 *Pre-harvest sprouting was evaluated only at Elora Research Station in the 2016 field experiment.

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The least-square means from the combined analysis were used to perform correlation analysis among different traits under study (Figure 6.1). The results from the correlation analysis showed some trait pairs showed significant positive associations: GY and TGW (r=0.66***), GY and TW (r=0.46***), and GY and AL (r=0.64***). However, other trait pairs were negatively correlated: GY and PH (r=-0.63***), GY and DA (r=-0.39***), GY and DM (r=-0.26***). Furthermore, a significant positive association between GY and PHS (r=0.53***) was observed. The GFP also had a significant positive association (r=0.18*) with GY. Another observation from the correlation analysis was that GFP and DA were negatively associated (r=-54***) while GFP and DM were positively associated (r=0.39***) (Figure 6.1).

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Figure 6.1. Pearson correlation values for pairs of phenotypic traits evaluated using the trait means calculated from all four field experiments conducted in 2016 and 2017. For each pair of traits indicated, the panel at the lower left intersection is the raw data, while the panel at the upper left intersection is the Pearson correlation value. The panel with the trait label indicates the distribution of the data.

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Abbreviations: AL= Awn length, DA= Days to anthesis, DM= Days to maturity, GFP= Grain filling period, GY= Grain yield, PH= Plant height, PHS= Pre-harvest sprouting, TGW= Thousand grain weight, TW= Test weight

Furthermore, a biplot was constructed using two principal components (PCs) generated from the principal component analysis (PCA) using the combined phenotypic data (Figure 6.2). The two PCs explained ~55% of the phenotypic variability which was in agreement with the correlation analysis and a similar pattern of variability was observed on each side of the biplot. Based on the biplot, GY, TGW and TW were closely associated. Similarly, a positive association was observed among GFP, GY, TW, TGW, PHS and AL while a negative association of these traits was detected with DA, DM, PH and DA. As shown in Figure 6.2, most of the CIMMYT lines and the released varieties showed higher GY compared to the landraces. The landraces were low yielding and had a longer DA and DM period. Combined, we conclude that the phenotypic data analysis show distinct variation among the accessions for the nine different traits evaluated.

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Figure 6.2. A biplot of the traits evaluated in four field trials for 318 spring wheat genotypes belonging to the NWDP evaluated during the 2016 and 2017 wheat growing seasons in Nepal and Canada. Abbreviations: AL= Awn length, DA= Days to anthesis, DM= Days to maturity, GFP= Grain filling period, GY= Grain yield, PH= Plant height, PHS= Pre-harvest sprouting, TGW= Thousand grain weight, TW= Test weight.

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6.4.2 Genotypic data and population structure Genotype-by-sequencing (GBS) on 318 accessions of the NWDP resulted in 95,388 polymorphic SNPs across the A, B and D genomes (unpublished-Chapter 5). After removing rare SNPs [minor allele frequency (MAF) < 5%], 80,249 high-quality SNPs remained for the GWAS analysis. The population structure of the NWDP was determined (K = 4) to consist of two large and distinct subpopulations, and two other subpopulations, using the complete catalogue of SNPs (95K) and fastSTRUCTURE (Figure 5.2 in Chapter 5). The PCA and distance-based clustering also supported the result from fastSTRUCTURE (Figure 5.2 in Chapter 5). The LD decay analysis showed that LD for the whole population decayed at a shorter distance of ~60 kb.

6.4.3 GWAS of agro-morphological traits The GWAS analysis was performed for nine agro-morphological traits using 80K SNPs and a multi-linear model (MLM) implemented in GAPIT. Significant associations were detected for only six traits (AL, DA, DM, GY, PHS, and TW) (Figure 6.3). A total of 15 significant MTAs were identified for these six different traits. These MTA regions explained only 5 to 8 % of the total phenotypic variation for their respective trait (Table 6.2, Appendix 9).

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Figure 6.3. Manhattan plots obtained from GWAS in the NWDP for: a) awn length at Elora 2016, b) days to anthesis at ABD (Nepal) 2017, c) days to maturity at Elora 2017, d) grain yield at Elora 2016, e) grain yield at Elora 2017, f) grain yield at NWRP (Nepal) 2017, g) pre-harvest sprouting at Elora 2016 and h) test weight at NWRP (Nepal) 2017. 139

Out of these 15 significant MTAs (P≤0.00001) detected, four markers were on chromosome 4A for DA which accounted for 6-7% of the phenotypic variability (Table 6.2, Figure 6.3). The most significant SNP for DA among these four markers was physically located at position 715914785 bp (P≤0.00001). Similarly, two significant markers (P≤0.00001) were identified for DM on chromosome 4B, and both of these SNP markers explained 8% of the phenotypic variability. Among these two markers, the most significant marker was physically located at 9053615 bp. Furthermore, one significant (P≤0.00001) MTA, which explained 6% of the phenotypic variation, was detected for AL on chromosome 5A. The marker was located at physical position 504769103 bp. Three MTAs for GY were observed on chromosomes 1B, 4A and 3D and explained 6-7% of the total phenotypic variability. These significant markers on chromosomes 1B, 3D and 4A were located at physical positions 496056408 bp, 726962465 bp, and 580224738 bp, respectively. In addition, a significant (P≤0.00001) MTA was observed for PHS on chromosome 7B. This marker accounted for 6% of the phenotypic variation in PHS and it was detected at physical position 611092780 bp on the chromosome. Finally, four significant (P≤0.00001) MTAs were identified for TW on chromosome 7B; each of these four markers accounted for 6% of the phenotypic variation. Using the “JBrowse” search tool available on the IWGSC website, the high-confidence candidate gene that resided within the QTL regions (150 kb on both sides of the physical position of the significant marker) were identified. Altogether 42 genes that were annotated with high confidence by IWGSC were identified (Tables 6.2 and Appendix 9).

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Table 6.2. Genome-wide significant marker trait associations identified for agro-morphological traits evaluated in the NWDP during the 2016 and 2017 wheat growing seasons in Canada and Nepal. Traits Locations Chr Position (bp) MAF P-value R2 MSS No of SNPs Awn length (AL) Elora 2016 5A 504769103 0.085 8.25E-06 0.06 504769103 1 Days to anthesis (DA) ABD (Nepal) 4A 715914785 0.091 1.06E-05 0.06 715914785 4 Days to maturity (DM) Elora 2017 4B 9053615 0.423 1.47E-06 0.08 9053615 2 Grain yield (GY) Elora 2016 1B 496056408 0.055 5.60E-06 0.07 496056408 1 Grain yield (GY) NWRP (Nepal) 3D 580224738 0.121 3.37E-06 0.07 580224738 1 Grain yield (GY) Elora 2017 4A 726962465 0.237 1.63E-05 0.06 726962465 1 Preharvest sprout (PHS) Elora 2016 7B 611092780 0.349 1.77E-05 0.06 611092780 1 Test weight (TW) NWRP (Nepal) 7B 733797767 0.049 1.05E-05 0.06 733797767 4 Abbreviations: Chr= Chromosome, MAF= Minor allele frequency, R2= Phenotypic variation explained, MSS= Most significant SNP

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6.5 Discussion 6.5.1 Phenotypic variation for agro-morphological traits exists in the NWDP One of the basic requirements for the development of new high yielding crop varieties is the diversity within the target species (Louwaars, 2018). Plant breeders take into account the diversity that exists in the germplasm pool to create new genotypic combinations. In this study, significant phenotypic variation for nine different agro-morphological traits was observed, which indicated that genetic diversity exists in Nepali wheat germplasm. Significant location and G x E effects showed that the performance of the accessions in the panel was affected by location, and also the result of interaction between the accession and the environment. For example, the mean GY was as low as ~2070 kg/ha at the Elora Research Station in 2017 while it was ~4388 kg/ha at NWRP (Nepal). The low mean GY at Elora in 2017 could have been the result of a high prevalence of Fusarium head blight (FHB) due to higher moisture availability (McMullen et al., 2012; Appendix 4). The location effect and G x E results was as expected as most of the accessions in the NWDP are adapted to and/or bred for semi-arid wheat growing regions including Nepal. Furthermore, the biplot showed that a large portion of the Nepali landraces are tall, mature late and are less productive, whereas the modern genetic materials, including the released varieties and CIMMYT advanced lines, are shorter, mature early and are high yielding. Nevertheless, the landraces may be harboring some important adaptive traits (Lopes et al., 2015b) which may be useful in the context of current climate change. Many traits that interest plant breeders show quantitative inheritance. In this study, the distribution of DM, GFP, GY, TGW and TW was normal in at least three or more locations indicating that these traits were quantitative in nature. Such continuous variation is the result of genetic complexity which arises from segregating alleles at multiple loci (MacKay, 2009). Therefore, the effect of a large number of QTLs, epistatic effects of the QTLs, environmental sensitivity and the QTL-environment interaction, all control the variation in the quantitative traits. As a result, in the case of quantitative traits, genotypic values are only a very minor part of phenotypic expression (Semagn et al., 2010) which creates a challenge for selection. In this study, the distribution of AL, PHS and TGW indicated that these traits may have been governed by some major genes. The inheritance of traits governed by major genes is usually higher as they are less influenced by the environment. 142

Broad sense heritability of a quantitative trait is highly affected by the environment. Heritability of such traits is mostly low, as these are controlled by many small-effect genes subject to high environmental influence (Semagn et al., 2010). In this study, heritability estimates of AL and TGW were more stable than the other traits across environments while the estimate for GY heritability was very low in one experiment (Elora, 2017) but moderate for the other three experiments. Many earlier studies have reported low heritability of GY (Iqbal et al., 2006, Bhanu and Mishra, 2018). By contrast, PH has moderate to high heritability (Novoselovic et al., 2004; Charmet et al., 2014; Ataei et al., 2017) similar to this study. In many studies (Iqbal et al., 2006; Bhanu et al., 2018), heritability of DA has been found to be moderate to high in agreement with the current study. Moderate heritability estimates were observed earlier for TGW (Ataei et al., 2017; (Iqbal et al., 2006; Bhanu et al., 2018) but high estimates were observed in this study. Charmet et al. (2014) found moderate heritability estimates for TW similar to this study. However, Ahmed et al. (2018) observed very high heritability (>95%) for DA, DM, GFP, PH, TGW while one of the lowest heritability scores in this study was for GY at ~86%. Fowler et al. (2016) also observed very high heritability (>90%) for GY, DH and TGW: the high heritability was the result of the stable environments that were sampled for the study and also the insignificant genotype by location year interaction. All these observations concerning variation in heritability estimates indicate that many quantitative traits are highly influenced by the environment since they are controlled by many genes with small effects. Genetic factors along with environment, and the interaction between environment and genotype, is the key to efficient selection. It is obvious that solely phenotypic selection may be insufficient to maximize genetic gains especially in the case of quantitative traits with low heritability (Collard and Mackill, 2008; Xu et al., 2017). Therefore, plant breeders are interested in assessing the extent of genetic variation and the heritability of the genetic variation to help identify genetic markers in order to improve the efficiency of breeding. Utilization of this knowledge in molecular approaches to breeding may contribute to the identification of genomic regions associated with the traits that plant breeders are more interested in.

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6.5.2 Genomic regions associated with morphological traits Quantitative traits are governed mostly by numerous small effect genes. For most quantitative traits, significant MTAs have been detected on different wheat chromosomes (Jamil et al., 2019). For quantitative traits, detection of minor QTLs is important for heritability estimation and for improving the predictive accuracy in breeding programs (Oladzad-Abbasabadi et al., 2018). Many association mapping studies have reported low phenotypic variation explained by MTAs for quantitative traits in wheat. For example, in a study involving spring wheat, phenotypic variation explained by MTAs identified for traits such as GY, GFP, PH and TKW was only up to 13% on average (Jamil et al., 2019). Sheoran et al. (2019) observed that phenotypic variation explained by MTAs for DM ranged from 5-41% while Ye et al. (2019) observed ~5-9% phenotypic variation explained for TKW. In the current study, the phenotypic variation explained by MTAs was 6-8%, consistent with these earlier studies. Here, the diversity panel was phenotyped for nine different agro-morphological traits. However, 15 MTAs were identified in total but only for six of the traits (Table 6.2): AL, DA, DM, GY, PHS and TW. The reasons for detecting only a few significant MTAs for these six traits or no MTAs for other traits (PH, GFP and TGW) could be due to the stringent threshold and large number (80K) of SNPs used in the analysis. Among the 80K SNPs, many are certainly in high LD and do not represent independent tests. Thus, by using a stringent significance threshold and large number of SNPs over-estimates the true number of independent tests, making it too difficult to identify the significant MTAs. Furthermore, only one model (i.e. MLM) was used for all the traits in the analysis, and MLM is powerful for quantitative traits with normal distribution, but not for traits governed by major QTLs (such as PH). For such traits, significant MTAs could be explored by testing different models with different covariates. In this study, among these 15 QTLs, six belonged to the A genome, eight belonged to the B genome and one belonged to the D genome (Table 6.2). The QTLs for GY and DA were both identified on chromosome 4A, ~10 mbp apart. Such QTLs may co-segregate and display pleiotropic effects (Fan et al., 2019). This result was shown in a study involving a doubled haploid population of wheat, wherein QTLs for DA and GY were co-located on chromosomes 2D and 7D, and pleiotropy was observed (Bogard et al., 2011). Co-located QTLs may affect the phenotypic associations between traits (Bogard et al., 2011). Indeed, here GY and DA were significantly 144

negatively associated. Given this observation, it may be that a delay in reaching the reproductive stage (i.e. DA) may be shortening the grain fill stage and/or placing this stage into a suboptimal environment for grain fill (Bogard et al., 2011). It is not understood if this is the case of linkage or pleiotropy which may be constraining varietal improvement. These findings could be of interest to future breeding programs (for example, if linkage is playing a role, it may be useful to break the linkage between these two QTLs).

6.5.3 Candidate genes in each QTL region Within the QTL region defined for each MTA, the genes annotated with high confidence by IWGSC were then identified (Appendix 9) and summarized:

Grain yield candidate genes: Although MTAs for GY were observed at three of four locations (Elora 2016, Elora 2017 and NWRP, Nepal), each was located on different chromosomes: 1B, 3D, and 4A, respectively. Jamil et al. (2019) identified 33 MTAs associated with wheat GY on different chromosomes including 1B and 3D. In addition, Ward et al. (2019) identified MTAs for wheat GY on chromosomes 6A, 6B, 7A and 7D; Rahimi et al. (2019) on 1A, 6B and 7A; and Qaseem et al. (2018) on chromosomes 2A, 2B, 3B, 5A, and 7B. Here, a total of 10 high-confidence genes were identified in the three QTL regions for GY. The genes TraesCS1B02G285100 and TraesCS1B02G285200, found in the QTL region for grain yield in Elora 2016, code the NAC domain superfamily which has been documented to contribute to the development of the embryo and seed (Nuruzzaman et al., 2013). These protein domains are related to the expression of plant responses to different abiotic stresses such as heat and drought (Borrill et al., 2017; Lv et al., 2016; Shao et al., 2015). The wheat NAC transcription factor TaNAC2L was found to regulate heat stress in transgenic Arabidopsis (Guo et al., 2015). TraesCS1B02G285300 a transcriptional activator and a member of the zinc finger family Which has reported influencer of seed size in Medicago truncatula (Radkova et al., 2019) conferring resistance to abiotic stresses in maize (Peng et al., 2012). Of two genes identified in the QTL region for GY at the NWRP site in Nepal, gene TraesCS3D02G482700 is associated with glutathione S-transferase (GST). GST and its substrates have functions associated with flower primordia and embryo development and have an effect on grain yield (Gallé et al., 2019; Wei et 145

al., 2019). By contrast, the QTL region associated with the SNP marker identified for GY at Elora 2017 harbored five high confidence genes. Three of these genes (TraesCS4A02G462900, TraesCS4A02G463000 and TraesCS4A02G463300) code P-loop containing nucleoside triphosphate hydrolases. These genes were identified as associated with carbohydrate metabolism in maize cobs (Pan et al., 2015).

Days to anthesis candidate genes: Out of the eight genes that were found in the QTL region associated with DA on chromosome 4A, only five genes have a known functions. Of these genes, TraesCS4A02G450300 codes the malectin domain which is involved in plant senescence, apart from seed development and plant defense in Arabidopsis (Robatzek and Somssich 2002), barley (Rajaraman et al., 2016), and cotton (Gossypium hirsutum) (Yuan et al., 2018). Also, it has been reported to play a role in pollen and seed development in Arabidopsis (Galindo-Trigo et al., 2020). The role of this protein domain in pollen development might have a contributing role in metabolic pathways related to anthesis in wheat. TraesCS4A02G450400 encodes acyltransferase/acyl hydrolase/lysophospholipase and patatin-like phospholipase peptide domains. A protein associated with phospholipid metabolism accelerates flowering in Brassica napus (Li et al., 2015). Two genes (TraesCS4A02G450500 and TraesCS4A02G450800) are associated with pectinesterase/pectin methylesterase inhibitor proteins which control the breakdown of the cell wall by regulating methyl esterification of pectin in Arabidopsis (Lionetti et al., 2017) and barley (Marzin et al., 2016). More importantly, a orthologue of this gene has already been found to be associated with flower development, specifically, pollen and anther development in wheat (Rocchi et al., 2012; Wormit and Usadel 2018).

Days to maturity candidate genes: A significant marker for DM in the current study was identified on chromosome 4B only. But, previous studies have reported genomic regions associated with wheat DM on different chromosomes such as 3B, 5A, 5B and 6A (Sheoran et al., 2019) and 2A (Sharma and Pandey, 2016). In this QTL region, we identified TraesCS4B02G013100 a homologue of methyltransferase which has been found to be associated with aging in Arabidopsis (Ogneva et al., 2016).

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Awn length candidate genes: Only one MTA for AL was observed on chromosome 5A in the current study. However, Sheoran et al. (2019) found several MTAs associated with awn presence on chromosomes 2D, 3D, 4D, 5D, 6A and 7D. Sheoran et al. (2019) also detected a 5A QTL but it was for the awn attitude. Previous studies have reported a significant positive association between AL and GY (Blum, 2005; Taheri et al., 2011; Khamssi and Najaphy, 2012; Ali et al., 2015) similar to the current study and also documented that long awns can increase the photosynthetic spike surface area by 36-59% resulting in enhanced grain yield (Towfiq and Noori, 2016; Rebetzke et al., 2016a). A total of 10 high confidence genes were found in the QTL region identified for AL including TraesCS5A02G297000 which codes for phospholipids, diacylglycerol acyltransferase (Dahlqvist et al., 2000; Li-Beisson et al., 2013). The acyl lipids increase the thickness of the cuticle layers and promote waxiness, which protects the awn and reduces moisture loss (Li-Beisson et al., 2013) to grain yield. TraesCS5A02G296700 annotated as Sas10/Utp3/C1D protein which contributes to seed development in Arabidopsis (Chen et al., 2016). TraesCS5A02G296800 codes for a pentatricopeptide repeat (PPR) protein localized in mitochondria and chloroplasts, and it has a role in cell division, embryogenesis and development of endosperm in seeds (Sosso et al., 2012; Liu et al., 2016; Wang et al., 2019). The PPR-DYW protein, EMP21, reported in maize mitochondria, contributes to seed development (Wang et al., 2019). This is an interesting candidate as awn being considered to play a role in photosynthate accumulation during the grain filling period (Yoshioka et al., 2017).

Test weight candidate genes: Although four significant SNPs for TW were discovered on chromosome 7B from the NWDP (Nepal) trial, however, Cabral et al. (2018) identified QTLs for TW at the Rht-D1 locus in wheat on chromosome 4D, and it had a negative contribution to the trait. Similarly, other studies observed MTAs for TW on chromosome 2A (Wang et al., 2014), chromosome 2D (Brbaklić et al., 2015), chromosomes 1A, 1B, 1D, 3A, 3B, 4D, 5A, 6B and 7D (Reif et al., 2011) and 1A, 3A, 6A and 6B (Ward et al., 2019). In this study, 12 high-confidence genes were identified in the QTL region for TW including TraesCS7B02G478600 that encodes a UDP-glucuronosyl/UDP glucosyltransferase. This protein has a role in ABA homeostasis that affects seed germination and tolerance to moisture stress (Liu et al., 2015). In rice, the target of a micro RNA (miR1435), specifically Os04g44354, which encodes a UDP-glucuronosyl 147

transferase protein, is suggested to be involved in pathways that regulate grain development (Lan et al., 2012). Two other genes, TraesCS7B02G478900 and TraesCS7B02G479000, code for the Jacalin-like lectin domain superfamily and Dirigent (DIR) protein, respectively. The plant lectins enhance plant growth and, development, and promote resistance to environmental stress and expression of phenotypic responses (Esch and Schaffrath, 2017; Li et al., 2017; Han et al., 2018). The DIR genes are hypothesized to be important for stress defense responses including pathogen resistance (Li et al., 2017). Finally, the gene TraesCS7B02G478700, which codes for a member of the MATE gene family (Multi-Antimicrobial Extrusion), mediates transport of metabolites through the membrane system in tomato (dos Santos et al., 2017) and alfalfa (Min et al., 2019). BIG EMBRYO1 (a MATE transporter protein) was found to be associated with enlargement of the embryonic scutellum in maize, indicating the significance of the MATE gene family protein in seed/grain development. Among many plant organs, a significantly higher amount of MATE gene transcripts were accumulated in seed tissue both in rice and Arabidopsis which also signifies its role in seed/grain development (Wang et al., 2016).

Pre-harvest sprouting candidate genes: PHS is a trait that can be expressed by certain wheat genotypes when the weather becomes cool and wet during the late maturity and harvesting stage (Vetch et al., 2019). In contrary to the previous studies (Ogbonnaya et al., 2007; Lin et al., 2017; Martinez et al., 2018; Wang et al., 2019), a total of 4 MTAs for PHS were identified on chromosome 7B in this study encompassing two genes (X and Y) with unknown functions.

Together these findings show that there are a number of high-confidence genes that code for proteins that have metabolic significance and/or contribute to these traits directly or indirectly. Many of the protein functions discussed are related to carbohydrate metabolism, growth and development, and stress tolerance. Apart from protein coding genes, the relevant genetic factors associated with each QTL might be a non-coding regulatory gene (e.g. lnc-RNA). Targeted mutagenesis or use of synteny may be helpful to identify the exact molecular basis of each QTL identified in this study which could assist in marker assisted selection.

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6.6 Conclusions In this study, 318 accessions belonging to the Nepali spring wheat diversity panel (NWDP) was evaluated for nine agro-morphological traits in four different fields in Canada and Nepal. The phenotypic diversity for each trait in this population suggests high genetic variation in Nepali wheat germplasm indicating untapped potential for varietal improvement. Furthermore, this study shows that currently available technologies such as GBS and GWAS can facilitate the identification of chromosomal regions that are of interest to plant breeders. Here, 15 MTAs for six agro-morphological traits were identified. Additionally, a number of high confidence candidate genes within the QTL regions were identified. The QTL regions and the genes identified may be of interest to current and future wheat breeders, especially Nepali wheat breeders. Since there are limited molecular studies on Nepali wheat diversity, it is believed that this study has opened a new avenue for further analysis of Nepali spring wheat diversity.

6.7 Acknowledgments We dedicate this paper to our mentor, colleague and friend, the late Professor Alireza Navabi, who passed away suddenly during the preparation of this manuscript. The authors wish to thank Nick Wilker, Katarina Bosnic and Melinda Drummond (University of Guelph) for assistance with seed handling and field planting. We thank Dr. A.K. Joshi, Mr. Madan Bhatta and Mr. Deepak Pandey for facilitating seed acquisition from Nepal, and Dr. Thomas Payne from CIMMYT, Mexico. This research was generously supported by a grant to MNR from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by the International Development Research Centre (IDRC, Ottawa) and Global Affairs Canada, in addition to grants to AN from SeCan, the Agricultural Adaptation Council and Grain Farmers of Ontario. Nepali landrace seeds were generously provided by the National Genetic Resources Centre (NGRC, Nepal) and the Agricultural Research Council (NARC, Nepal). Seeds of released varieties were provided by the National Wheat Research Program (NWRP belonging to NARC, Nepal). CIMMYT breeding lines were provided by CIMMYT, Mexico.

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7 Phenotyping and Identification of Reduced Height (Rht) and Photoperiod Sensitivity (Ppd) Alleles in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel to Enable Seedling Vigour Selection

This chapter is intended as an original research article with the following citation:

Kamal Khadka, Mina Kaviani, Manish N. Raizada, Alireza Navabi (2020). Phenotyping and Identification of Reduced Height (Rht) and Photoperiod Sensitivity (Ppd) Alleles in a Nepali Spring Wheat (Triticum aestivum L.) Diversity Panel to Enable Seedling Vigour Selection. (In preparation).

Author contributions Kamal Khadka: Conceptualized the project and wrote the manuscript. Mina Kaviani: Contributed to DNA isolation, molecular marker data collection, data analysis and technical editing. Manish N. Raizada: Assisted with the analysis, and technical and language editing. Alireza Navabi: Contributed to project conceptualization and also supervised the project.

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7.1 Abstract Breeding in bread wheat (Triticum aestivum L.) has reduced plant height by introgression of reduced height (Rht) alleles. However, these alleles reduce coleoptile length and growth plasticity, mediated by gibberellic acid (GA), making improved varieties less able to be deep- seeded to combat early season drought. Larger/deeper seedling roots are also desirable traits to mitigate early season drought, but potentially reduced by Rht alleles. Photoperiod insensitivity has also been bred into wheat using the Ppd loci to permit north-south breeding, but there are reports that it alters stem height, and hence possibly coleoptile/root length. Nepal is expected to face more intense early season moisture stress associated with climate change. With the long term objective of breeding wheat for improved early season drought (longer coleoptiles and seedling roots), here 318 Nepali spring wheat accessions were assembled. The objectives of this study were to characterize the panel for: (1) allelic variation at the Rht/Ppd-D1 loci; (2) variation in coleoptile/seedling root traits and the impact of the Rht/Ppd alleles on these traits; and (3) to identify accessions with longer and/or more GA-responsive coleoptiles as candidate parents for future breeding programs. Kompetitive Allele Specific PCR was used for genotyping, and results analyzed at the population level. The cigar roll method was used to grow/phenotype seedlings in the presence/absence of GA. Plant height at maturity was measured under field conditions. The results showed that Nepali landraces had a significantly higher frequency of Rht-B1a and Ppd- D1b alleles compared to the introduced germplasm. The Rht-B1b and Rht-D1a dwarfing alleles had negative effects on coleoptile length and positive effects on the length of the longest seedling root. Forty semi-dwarf accessions with long and/or more plastic coleoptiles were identified to permit deep sowing. These accessions have potential as parents in future breeding programs to mitigate early season drought in Nepal.

Keywords: Rht, Ppd, coleoptile length, seedling root length, drought, deep seeding

7.2 Introduction The coleoptile is the structure that covers the first leaf before it emerges from the soil surface during germination. In cereal crops including wheat (Triticum aestivum L.), the

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coleoptile is important for supporting and protecting the protruding leaf as it rises to the soil surface (Amram et al., 2015). The length of the coleoptile determines seed sowing depth (Rebetzke et al., 2007a; b; Li et al., 2017a). Longer coleoptiles allow deep sowing which helps in the exploitation of soil moisture (Würschum et al., 2018; Bovill et al., 2019) and facilitates early establishment as well as weed suppression (Murphy et al., 2008). Therefore, wheat accessions having longer coleoptiles are better suited to areas with moisture stress or drought at the early growth stage of the crop (Bovill et al., 2019; Li et al., 2017; Farhad et al., 2014). In this context, breeding wheat for longer coleoptiles is a strategy to cope with these challenges (Akram, 2011; Farhad et al., 2014; Li et al., 2017). In addition to the coleoptile, different root architectural traits including longer roots contribute to improving moisture stress tolerance in wheat (Lopes and Reynolds, 2010; Richard et al., 2015) . Longer seedling roots contribute to plant establishment at the early stage (Dhanda et al., 2004). Therefore, the phenotypic assessment of wheat root traits can be useful when selecting accessions for moisture stress tolerance (Wasaya et al., 2018). Genetic variation for wheat coleoptile length has been reported in many studies (Schillinger et al., 1998b; Li et al., 2017a; Bovill et al., 2019). The trait is governed by many genes with a strong additive effect and high heritability (Rebetzke et al., 2007a; Murphy et al., 2008). Short coleoptiles in wheat are the result of pleiotropic effects associated with mutations in alleles at the reduced height genes, Rht-B1b (Rht1) and Rht-D1b (Rht2), that encode DELLA proteins in the gibberellic acid (GA3) signaling pathway (Rebetzke et al., 2007a; Wilhelm et al 2013). Several Rht loci have been discovered (Ellis et al., 2005; Chen et al., 2013; Würschum et al., 2018; Würschum et al., 2018; Vikhe et al., 2019), but among these Rht-B1b and Rht-D1b are the most commonly used dwarfing alleles in modern wheat, derived from “Norin 10”, a Japanese variety, at the start of the Green Revolution (Borojevic and Borojevic, 2005b). Rht-B1b and Rht- D1b have contributed at least a 10% increase in wheat yield (Nagel et al., 2013). Today, most modern semi-dwarf varieties have Rht-B1b and Rht-D1b alleles (Murphy et al., 2008). These varieties are insensitive to endogenous gibberellins, have shorter coleoptiles, and if sown deep have poor emergence (Rebetzke et al., 2007a; Schillinger et al., 1998; Li et al., 2017). Coleoptile response is also influenced by the environmental conditions which may be influenced by GA3. The response of different wheat varieties was shown to vary based on the

GA3 concentration (Pavlista et al., 2013). Exogenous application of GA3 was shown to increase 152

coleoptile length, and this response did not differ significantly between tall lines and lines with GA-responsive dwarfing alleles (Chen et al., 2014a). However, the increase in coleoptile length in response to GA3 application was highly reduced at higher temperatures (Pavlista et al., 2013) and also under the Mediterranean environment (Amram et al., 2015) in accessions with dwarfing alleles. These results suggest that the coleoptile response to GA3 may an indicator of environmental plasticity. The International Center for Maize and Wheat Improvement (CIMMYT) has been breeding high yielding wheat varieties for wider geographic adaptation (Braun et al., 1996). For this, shuttle breeding has been a successful approach, facilitated by photoperiod insensitivity (Rajaram and Hettel, 1995) which allows breeders to induce flowering in wheat irrespective of daylength (Langer et al., 2014). Photoperiod insensitivity has contributed to the spread of spring wheat genotypes grown in lower latitudes to higher latitudes above 480 N (Worland, 1994). Photoperiod insensitivity in wheat has been mediated by the photoperiod loci, Ppd-D1, Ppd-D2 and Ppd-D3, of which the photoperiod-insensitive allele Ppd-D1a is considered the most widespread (Shaw et al., 2013). Interestingly photoperiod insensitivity in wheat was unknowingly selected in the semi-dwarfs (Rajaram and Hettel, 1995). Chen et al. (2018) reported an additive interaction between Ppd-D1a and the Rht12 gene on stem height. Similarly, Ppd-D1 was found to contribute to reducing height, and a combination of Ppd-D1 and Rht5 produce shorter plants with improved lodging resistance (Würschum et al., 2017). The interaction is complex because along with the Ppd loci, Rht-B1 might also be affecting flowering time (Wilhelm et al., 2013). As already noted, seedlings with longer roots are more adapted to moisture stress. Studies on wheat seedling roots (Wojciechowski et al., 2009; Li et al., 2011) have shown that Rht-B1b and Rht-D1b alleles have a pleiotropic effect on root traits. Manske et al. (2002) indicated only a slight effect of RHT loci on root length and density. Nagel et al. (2013) reported that Rht-B1b and Rht-D1b negatively affect seedling root length. Similarly, Vikhe et al. (2019) showed that Rht-B1b was associated with reduced coleoptile length and reduced seedling root length. In contrast, Chen et al. (2016) showed that the selection of Rht-B1b decreased coleoptile length but increased the root length. Therefore, Li et al. (2019) suggest that the relationship between dwarfing alleles and root traits should be cautiously reported. In general there are a lack of 153

studies on the effect of Rht genes on root length. Studies on the effects of the photoperiod insensitivity Ppd genes on root length are highly limited. There is a need to develop semi-dwarf or dwarf wheat varieties with longer coleoptiles and longer seedling roots targeting wheat growing regions that face early moisture stress. One of these regions is Nepal. Wheat is the third most important cereal crop in Nepal, covering almost 22% of the total land acreage (MoAD, 2017). Almost 75% of the wheat in the country is rain-fed (CIMMYT, 2019), and climate change predictions suggest that early season moisture stress will become more frequent and intense (DHM-MoSTE Nepal, 2013). With the long-term objective of breeding Nepali spring wheat with longer coleoptiles and longer seedling roots, here a panel of 318 spring wheat accessions was assembled from Nepal, termed the Nepali Wheat Diversity Panel (NWDP). Prior to phenotyping the accessions, it was critical to genotype them at the Rht-B1 and Rht-D1 loci to avoid focusing on accessions that had longer coleoptiles simply because they were not semi-dwarf; genotyping for the photoperiod insensitivity locus Ppd-D1 would also facilitate north-south breeding efforts. Furthermore, since these loci have been targets of modern wheat breeding, it was of interest to know to what extent the relevant Green Revolution varieties have affected the Nepali wheat germplasm population. For these reasons, the 3 objectives of this study were: (1) to measure allelic variation at the Rht- B1, Rht-D1 and Ppd-D1 loci in the NWDP; (2) to assess the NWDP for variation in the seedling vigour traits associated with early season moisture stress and the impact of the Rht/Ppd alleles on these traits; and (3) to identify candidate accessions with longer and/or more GA-responsive coleoptiles (as a ratio of stem height at maturity) as potential parents for future breeding programs.

7.3 Materials and methods 7.3.1 Plant materials A diversity panel of 318 spring wheat genotypes termed the Nepali Wheat Diversity Panel (NWDP) was assembled for the study (Appendix 1). The panel included 166 Nepali landraces, 116 CIMMYT advanced breeding lines tested in Nepal for three seasons from 2011-12 to 2013- 14 wheat growing seasons, and 34 varieties released for commercial cultivation in Nepal. As the two major field experiments were conducted in Canada, three Canadian spring varieties, 154

“Norwell”, “Pasteur” and “AC Carberry”, were also included in the panel. The landraces in the diversity panel represented 29 districts of Nepal and these were collected during different germplasm expeditions during the 1970s to 1990s. The National Agriculture Genetic Resource Centre (NAGRC, Nepal) provided the seeds of the landraces; the Nepal Agriculture Research Council (NARC) and the National Wheat Research Program (NWRP, Nepal) of NARC provided the seeds of the released varieties. The seeds of the advanced breeding lines were acquired from CIMMYT, Mexico, and the seeds of the Canadian varieties were obtained from the Wheat Breeding Laboratory at the University of Guelph, Canada.

7.3.2 Phenotyping coleoptile length and seedling root length The experiment to phenotype coleoptile length was conducted in the Crop Science Building at the University of Guelph using a two factorial randomized complete block design (RCBD) with three replications. The two factors were the genotype and treatment [application of

Gibberellic Acid (GA3) (C19H22O6)]. The “cigar-roll” method was used to grow seedlings to assess coleoptile length and seedling root length (Bai et al., 2013; Amram et al., 2015). Seeds were sterilized with 70% (v/v) ethanol for 30 seconds, rinsed with distilled water three times and further sterilized for 10 minutes in a 4% sodium hypochlorite solution prepared from commercially available bleach and then rinsed with distilled water three times. Seven seeds from a single genotype were placed ~4 cm distance apart and ~4 cm from the top of a moistened sheet of germination paper (25 x 38 cm, Anchor Paper Company, St. Paul, MN, USA), and covered with a second germination paper. The papers were rolled with a ~2 cm diameter and placed in a tray (32 cm x 24 cm x 14 cm), bending the lower part by ~5 cm to make it stable, where it was also supported in the vertical position by a ~3 cm by ~3 cm wire mesh in the tray. There were 40 rolls in each tray with each cigar roll representing a different genotype. Of the 7 seedlings, the randomly selected 5 seedlings per cigar roll were counted as 1 replicate (averaged), and the three replications per +/- GA3 treatment were placed in three separate randomized trays. All six trays relevant for each genotype were in the growth chamber at the same time, but to accommodate the large number of genotypes, they were staggered at different times.

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The trays with the control treatment were supplied with two liters of deionized water, and the trays with the GA3 treatment were filled with two liters of 50-ppm GA3 solution (dissolved in deionized water) (Würschum et a., 2017). All trays were kept at 4 0C in a vernalization chamber for 48 hours to achieve uniform germination. Finally, the trays were transferred to a Percival growth chamber for a period of seven days with a 16/8 hour day-night using Sylvania Cool White F20T12/CW 20 watt fluorescent bulbs at 65-80 µmol m-2 sec-1 (at the top of the trays), set at a constant temperature of 200C. After seven days, the seedlings were removed from the growth chamber to measure coleoptile length. Out of the seven seedlings, five were chosen randomly to measure the coleoptile length and seedling root length using a ruler. The seedling roots and shoots were dried at 600C for three days, and the dry weight was recorded. The dry weight was recorded as the mean of five plants for each genotype per replicate.

7.3.3 Phenotyping for height plant For the field experimental description, please see Chapter 6 (GWAS). The mean plant height at maturity was averaged across four field experiments conducted in 2016 and 2017: two at Elora Research Station, University of Guelph, in 2016 and 2017, and two in Nepal during the 2016/17 wheat growing season. Plant height was measured using a ruler from the base of each plant to the tip of the spike excluding the awns on a handful of tillers from the middle of each plot. The average plant height across the four experiments was used to normalize seedling traits (coleoptile length and seedling root length) in this study.

7.3.4 DNA extraction The 318 spring wheat genotypes of the NWDP were also grown in the field in 2016 for DNA collection (Elora Research Station, Elora, ON). Leaf samples were collected from the field plots when the plants were at the 3-4 leaf stage. These samples were freeze-dried at -800C, and genomic DNA extraction was done by using a DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) using the manufacturer's protocol. The assessment of the quality of DNA was performed by determination of A260 nm and A280 nm using a NanoDrop (ND-1000) spectrophotometer (Nano-Drop Technologies, Washington, DE, USA).

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7.3.5 Genotyping by PCR amplification of Kompetitive Allele Specific PCR The diversity panel was genotyped by using Kompetitive Allele Specific PCR (KASP) markers (Table 7.1). The KASP assay was carried out in a 10 µl volume containing 5 µl of KASP Master Mix with low ROX (LGC group, Hoddesdon, UK), 0.14 µl of KASP assay mix, 1 µl of g DNA (concentration of 10-20 ng/µl) and 4 µl of water. PCRs and fluorescent readings were taken according to the LGC recommended protocol in 96-well plates using the QuantStudio™ 6 Flex Real-Time PCR machine with a fast reading block (Applied Biosystems, CA, USA).

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Table 7.1. Kompetitive allele-specific markers for alleles of the reduced height and photoperiod response genes with primer sequences and citations for the markers used in genotyping the panel of accessions. Marker Genes primer Primer sequence (5’-3’) Alleles References type KASP Rht-B1 FAM CCCATGGCCATCTCCAGCTG FAM (wt) (Ellis et al., 2002) HEX CCCATGGCCATCTCCAGCTA HEX (dwarf) (Rasheed et al., 2016) Common reverse TCGGGTACAAGGTGCGGGCG Rht-D1 FAM CATGGCCATCTCGAGCTGCTC FAM (wt) (Ellis et al., 2002) HEX CATGGCCATCTCGAGCTGCTA HEX (dwarf) (Rasheed et al., 2016) Common reverse CGGGTACAAGGTGCGCGCC Ppd-D1 FAM CAAGGAAGTATGAGCAGCGGTT FAM (Beales et al., 2007) (insensitive) (Rasheed et al., 2016) HEX AAGAGGAAACATGTTGGGGTCC HEX (sensitive) Common reverse GCCTCCCACTACACTGGGC

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7.3.6 Cluster analysis of genotypic data The 14 accessions having heterogenous or undetermined alleles at any locus were not considered in the cluster analysis. Genotypic data were imported into the software Graphical Genotype (GGT2.0 (van Berloo, 2008). A matrix of the pair-wise genetic distances was generated. This matrix was then saved as a MEGA file. The dissimilarity matrix was then exported to the MEGA X software (Tamura et al., 2007), where an unweighted pair group method with arithmetic mean dendrogram (UPGMA) was calculated, and a dendrogram was generated.

7.3.7 Phenotypic data analysis The coleoptile and seedling root length data were analyzed using PROC GLIMMIX in SAS version 9.4 (SAS Institute, Cary, NC, USA). The model contained data from each plant (sample) nested in genotype by treatment due to the nature of the experiment. The Shapiro-Wilk test was conducted in PROC UNIVARIATE to test the normality of the residuals and to ensure that all the data points were independent and random; PROC SGPLOT was used to construct studentized-residuals by predictor plots. The studentized residuals produced by the genotype-by- treatment combinations were considered outliers when they were >3.5 and <-3.5. These outliers were removed from the data set after confirming that they were true outliers. The least-square (LS) means were generated for each genotype-by-treatment where genotype was treated as the fixed effect. In addition, variance and correlation were analyzed using PROC ANOVA and PROC CORR. Tukey’s test was used to compare phenotypic means. Principal component analysis was performed using PROCPRINCOMP and PRINQUAL in SAS to examine the effect of the relationship between observed traits as well as the effect of combined allelic variation on the coleoptile and root length traits. The root to shoot biomass ratio was calculated as the ratio of mean root and shoot dry weight for each genotype. The change in coleoptile length as a result of GA3 treatment was calculated using the following equation:

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퐶퐿 − 퐶퐿 ∆ 푐ℎ푎푛𝑔푒 𝑖푛 퐶퐿 = ( 퐺퐴3 퐶) 푋 100 퐶퐿퐶 Where, CL= Coleoptile length

CLGA3= Coleoptile length after GA3 treatment

CLC= Coleoptile length under control conditions.

A similar equation was used to calculate the change in seedling root length as a result of

GA3 treatment.

7.3.8 Data visualization The graphs were generated in RStudio (RCore Team, 2015) using ggplot2, factoextra and FactoMineR packages (Lê et al., 2008; Wickham, 2011; Kassambara and Mundt, 2017).

7.4 Results 7.4.1 Variation for seedling vigour traits and Rht/Ppd loci A significant difference (P≤0.0001) was observed among the genotypes in the NWDP for coleoptile length and seedling root length (Appendix 10). Different allelles at the Rht-B1 and Ppd-D1 loci had significant effects on coleoptile length and seedling root length. Different alleles at the Rht-D1 locus had a significant effect on coleoptile length but not for seedling root length at the Rht-D1 locus. The mean comparison showed the genotypes with alleles Rht-B1b, Rht-D1b and Ppd-D1a had shorter coleoptiles indicating that dwarfing alleles affected coleoptile length (Table 7.2). The interaction between the two Rht loci indicated that the presence of either of the dwarfing alleles (Rht-B1b and Rht-D1b) reduces coleoptile length significantly compared to the wild type. The interaction between the two Rht loci (Rht-B1, Rht-D1) with the Ppd-D1 locus when analyzed separately showed that the dwarfing allele Rht-B1b is responsible for the reduction in coleoptile length but effect of the other dwarfing allele Rht-D1b and the Ppd locus was not significant. However, the interaction among the three loci (Rht-B1, Rht-D1 and Ppd-D1) demonstrated that the reduction in coleoptile length was mainly due to the presence of both dwarfing alleles. The result also showed that Rht-B1b and Ppd-D1a influenced the longest 160

seedling root length positively. However, the results from the interactions between and among the three loci showed that neither of the alleles at Rht-B1, Rht-D1 and Ppd-D1 loci influence the longest seedling root length significantly (Table 7.2).

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Table 7.2. Least squares means for coleoptile and root length of genotypes with Rht-B1, Rht-D1 and Ppd-D1 and their interaction without GA treatment. Genes Alleles Traits Coleoptile length (mm) Seedling root length (mm) Mean* SD Mean* SD Rht-B1 A 52.85 a ±6.98 102.23 b ±20.60 B 39.56 b ±4.36 107.59 a ±17.82 Rht-D1 A 47.34 a ±9.22 104.02a ±19.73 B 43.59 b ±4.11 109.62 a ±17.59 Ppd-D1 A 42.04 b ±6.03 107.77 a ±19.74 B 53.18 a ±8.01 100.61 b ±18.67 Gene Interactions Rht-B1/Rht-D1 AA 53.43 a ±6.23 104.41 a ±20.75 AB 44.88 a ±3.10 108.40 a ±17.41 BA 39.97 b ±4.23 112.09 a ±17.85 BB 38.49 b ±7.56 100.38 a ±16.89 Rht-B1/Ppd-D1 AA 47.90 a ±5.36 111.14 a ±23.84 AB 50.42 a ±6.47 101.67 a ±17.71 BA 40.80 b ±4.26 102.96 a ±17.54 BB 37.66 b ±5.04 109.51 a ±17.60 Rht-D1/Ppd-D1 AA 45.54 a ±6.34 108.55 a ±20.10 AB 47.86 a ±7.88 107.94 a ±18.81 BA 43.14 a ±3.91 105.94 a ±18.10 BB 40.22 a ±7.75 103.24 a ±2.66 Rht-B1/Rht-D1/Ppd-D1 AAA 51.72 a ±4.28 110.60 a ±29.08 AAB 55.15 a ±6.44 98.22 a ±17.77 ABA 44.07 b ±3.14 111.69 a ±17.71 BAA 39.37 b ±4.11 106.52 a ±17.51 BAB 40.58 b ±4.99 117.67 a ±17.70 *Mean values within the same trait and genotype category having different letters are significantly different based on t-tests at the significance threshold of P= 0.01. Abbreviation: SD= Standard deviation.

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The NWDP has high allelic variation for Rht-B1 and Ppd-D1 loci compared to the Rht- D1 locus (Table 7.3). 173 accessions (54.40%) carried the wild type allele, Rht-B1a, while 145 accessions (45.60%) carried the dwarfing allele, Rht-B1b. 283 accessions (88.99%) carried the wild type allele, Rht-D1a, while only 35 accessions had the dwarfing allele, Rht-D1b. 171 accessions (53.46%) in the NWDP having the insensitive Ppd-D1a allele, and 134 accessions (42.14%) carrying the photoperiod-sensitive Ppd-D1b allele.

7.4.2 Distinct genetic clusters identified based on genotypic data Eight genetic clusters within the 318 accessions of the NWDP were identified using allele- specific markers based on the allele combinations present in each accession at the major Rht and Ppd loci (Figure 7.1). The largest cluster, consisting of primarily CIMMYT lines (in green with 117 accessions), carried Rht-B1b (dwarfing), Rht-D1a (wild type) and Ppd-D1a (photoperiod insensitive) while the second largest cluster of primarily Nepali landraces (in blue with 116 accessions) had the alleles Rht-B1a (wild type), Rht-D1a (wild type) and Ppd-D1b (photoperiod sensitive). Two other clusters (indicated in red and cyan) each had 26 accessions; one of these clusters carried wild type alleles at both Rht loci and the photoperiod insensitive allele Ppd-D1a; the other cluster carried both dwarfing alleles (Rht-B1b, Rht-D1b) and the photoperiod sensitive allele Ppd-D1b. The fifth distinct group (indicated in purple) had 16 accessions with both Rht dwarfing alleles (Rht-B1b and Rht-D1b) and the photoperiod insensitive allele Ppd-D1a. The remaining three clusters had five or fewer accessions in each cluster (Figure 7.1). These results show that introduced accessions into Nepal possess dwarfing and photoperiod-insensitivity alleles compared to native germplasm but that some apparently native landraces also possess dwarfing alleles.

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Table 7.3. The allelic variation observed at the Rht and Ppd loci in the Nepali Wheat Diversity Panel. Number of genotypes (%) Locus Wild types (allele a) Dwarf (allele b) Heterozygous Rht-B1 173 (54.40%) 145 (45.60%) 0 Rht-D1 283 (88.99%) 35 (11.01%) 0 Insensitive (allele a) Sensitive (allele b) Ppd-D1 170 (53.46%) 134 (42.14%) 14 (4.40%)

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Figure 7.1. Dendrogram showing clusters based on accessions carrying the same allele combinations at three loci (Rht-B1, Rht-D1 and Ppd-D1).

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Principal component analysis (PCA) was performed separately for each locus (Rht-B1, Rht-D1, and Ppd-D1) to assess if the alleles at these loci differentiate the accessions in the diversity panel (Figure 7.2). Approximately 98% of the phenotypic variation was accounted for by the first two principal components. The biplots showed that for height controlling loci, Rht-B1 had a greater effect on the accessions for coleoptile and root length traits compared to the Rht-D1 locus, with distinct clusters for the former but overlapping clusters for the latter (Figure 7.2). Alleles at the photoperiod sensitivity locus Ppd-D1 caused the accessions to form distinct clusters based on these morphological traits (Figure 7.2).

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Figure 7.2. Biplots generated to distinguish the effect of alleles at Rht-B1 (a), Rht-D1 (b) and Ppd-D1 (c) loci on NWDP accessions. The large points indicate the average of each allele group.

The letters “A” and “B” indicate the alleles for Rht-B1, Rht-D1 and Ppd-D1 loci.

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The biplots revealed distinct clusters based on the combined effects of alleles at Rht-B1, Rht-D1 and Ppd-D1 loci (Figure 7.3). Accessions with the reduced height allele Rht-B1b combined with the photoperiod insensitive allele Ppd-D1a clustered distinctly from accessions with wild type height alleles at both Rht loci (Rht-B1a, Rht-D1a) and having the photoperiod sensitive allele Ppd-D1b (dark blue dot in Figure 7.3). However, despite the clustering, coleoptile length appeared to be mostly driven by the effect of the Rht-B1 locus (Figure 7.2a) compared to that of the Rht-D1 locus (Figure 7.2b); the mean comparison of interactions revealed that the effect was not significant (Table 7.2). As demonstrated in Table 7.2, the biplots also showed that neither of the Rht loci had significant influence on seedling root length. Conversely, Ppd-D1 has a very small effect on coleoptile length (in Rht-B1a background, but not in Rht-B1b background) and a small effect on root length independent of the Rht genes (Figure 7.3).

7.4.3 Effect of breeding history on coleoptile and root length The genotype analysis suggested that the majority of Nepali landraces did not have Rht- B1b or Rht-D1b, the dwarfing alleles (dark blue in Figure 7.1 and Figure 7.3, Appendix 14), opposite to the majority of CIMMYT lines (green in Figure 7.1 and Figure 7.3). Consistent with this result, Tukey’s pairwise comparison of the mean values (Table 7.4) revealed that CIMMYT lines and released varieties had significantly shorter coleoptiles than the landraces. Interestingly, the landraces had significantly shorter roots than the CIMMYT lines and released varieties. Therefore, the root to shoot biomass ratio was also calculated as an indicator of stress tolerance. The result showed that the CIMMYT lines and the released varieties had a significantly higher root to shoot biomass ratio compared to the landraces.

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Figure 7.3. A biplot generated to distinguish the effect of allele combinations at multiple loci on NWDP accessions in the absence of a GA3 treatment.

The letters “A” and “B” indicate the allelic combinations for Rht-B1, Rht-D1 and Ppd-D1 loci.

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7.4.4 Identification of likely semi-dwarf accessions with seedling vigour potential An objective of this study was to identify semi-dwarf accessions that nevertheless retained seedling vigour potential, given that Rht dwarfing alleles have previously been shown to reduce coleoptile length. The diversity panel was evaluated for seedling vigour traits. As breeders are interested in shorter plants at maturity but larger seedlings, only accessions with one or both Rht dwarfing alleles were analyzed, and the traits were normalized for stem length at maturity: the ratio of seedling coleoptile length to plant height at maturity (CL/PH), longest seedling root length to plant height at maturity (RL/PH), seedling root dry biomass to plant height at maturity (RootB/PH). In addition, the seedling root biomass to seedling shoot dry biomass was calculated. The results are shown in Table 7.5, but only for those accessions having mean values greater than the mean of the entire NWDP population (318 accessions) for all four traits. In total, two landraces, six released varieties and 21 CIMMYT lines were identified as candidates with high seedling vigour potential. Most of these accessions were photoperiod insensitive (i.e. had the Ppd-D1a allele), with the exception of four accessions (two photoperiod sensitive and two heterozygous at the Ppd-D1 locus).

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Table 7.4. Tukey’s pairwise comparison of mean values within a treatment group at 95% confidence for genotypes segregated into three groups based on seed source. § Seed source groups Coleoptile length Root length Root to shoot (control) (mm)* (control) (mm)* biomass ratio^ Landraces (166) 52.7 a 100.2 a 0.59 a CIMMYT lines (115) 39.7 b 107.9 b 0.72 b Released varieties (34) 43.5 c 115.5 b 0.74 b *Means that do not share a letter within a column are significantly different. Significance level: α= 0.05 §The three Canadian varieties were excluded from this analysis. ^Based on dry weights of shoot and total root.

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Table 7.5. List of likely semi-dwarf accessions identified as having potential seedling vigour traits. Rht loci Ppd locus Ratio Accession ID Accession Group Rht_B1 Rht_D1 Ppd-D1 CL/PH RL/PH RootB/PH Root/shoot biomass NGRC02450 Landrace Rht-B1a Rht-D1b Ppd-D1a 0.054 0.142 0.000101 0.743 NGRC04400 Landrace Rht-B1b Rht-D1a Ppd-D1a 0.051 0.159 0.000095 0.820 Vaskar Released variety Rht-B1b Rht-D1a Ppd-D1a 0.053 0.155 0.000098 0.832 Annapurna1 Released variety Rht-B1b Rht-D1a Ppd-D1a 0.052 0.156 0.000120 0.754 BL1022 Released variety Rht-B1b Rht-D1a Ppd-D1a 0.068 0.180 0.000097 0.739 BL1473 Released variety Rht-B1a Rht-D1b Ppd-D1a 0.055 0.204 0.000118 0.859 WK1204 Released variety Rht-B1a Rht-D1b Ppd-D1a 0.054 0.153 0.000129 1.029 Tilottama Released variety Rht-B1b Rht-D1a Ppd-D1a 0.058 0.158 0.000108 0.727 BW43354 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.059 0.193 0.000093 0.723 BW45593 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.054 0.155 0.000110 0.835 BW45587 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.059 0.152 0.000106 0.869 BW48133 CIMMYT line Rht-B1b Rht-D1a Heterozygous 0.055 0.161 0.000095 0.727 BW48139 CIMMYT line Rht-B1a Rht-D1b Ppd-D1a 0.061 0.159 0.000112 0.793 BW48162 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.058 0.188 0.000106 0.929 BW49235 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.060 0.161 0.000122 0.763 BW49079 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.053 0.177 0.000093 0.721 BW49089 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.057 0.143 0.000112 0.766 BW49112 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.052 0.168 0.000099 0.742 172

Rht loci Ppd locus Ratio Accession ID Accession Group Rht_B1 Rht_D1 Ppd-D1 CL/PH RL/PH RootB/PH Root/shoot biomass BW49927 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.051 0.151 0.000121 0.805 BW49931 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.058 0.162 0.000121 0.933 BW49936 CIMMYT line Rht-B1b Rht-D1a Heterozygous 0.061 0.207 0.000112 0.802 BW49943 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.056 0.155 0.000134 0.976 BW49949 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.058 0.172 0.000112 0.940 BW49954 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.051 0.146 0.000112 0.926 BW49957 CIMMYT line Rht-B1b Rht-D1a Ppd-D1b 0.054 0.153 0.000114 0.860 BW49958 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.055 0.174 0.000102 0.818 BW49325 CIMMYT line Rht-B1b Rht-D1a Ppd-D1b 0.058 0.179 0.000097 0.802 BW49392 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.056 0.173 0.000101 0.723 BW49394 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 0.053 0.150 0.000102 0.736 Abbreviations: CL= Coleoptile length, RL= Root length, PH= Plant height, RootB= Root biomass.

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7.4.5 Effect of GA3 treatment by genotype Apart from long coleoptiles, we were interested to determine if members of the NWDP have coleoptiles which are GA3 responsive as this is a proxy for being environmentally responsive, i.e. emerging from deep seeding to escape early seedling drought. The effect of GA3 treatment was significant for the entire NWDP (P≤0.0001) (Appendix 10). Furthermore, a significant (P≤0.0001) genotype×treatment interaction was observed, which indicated an effect of the GA3 application on coleoptile length as well seedling root length. The interaction between

Rht-B1 and GA3 treatment, and Ppd-D1 and GA3 treatment, was significant for both study traits.

However, only the interaction between Rht-D1 and GA3 treatment was significant for seedling root length, not for coleoptile length (Appendix 10). Although multi-factor analysis may have provided a better explanation of this result, a possible reason could be that the majority of the accessions have Rht-B1a or Rht-B1b segregating in the background, which might have reduced the effect of alleles at the Rht-D1 locus. A small, but significant difference in mean coleoptile length in response to GA3 was observed for the entire NWDP (Table 7.6), which suggested that at least a subset of accessions may be candidates for coleoptile plasticity. When the data was divided based on breeding history (Table 7.7), coleoptiles from the landrace group were found to be significantly more responsive to GA3 treatment compared to CIMMYT lines and released varieties, but even these latter groups showed some GA3-responsiveness.

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Table 7.6. Tukey’s pairwise comparison of the mean response of NWDP seedling traits to GA3 treatment at 95% confidence. Treatment Coleoptile length (mm)* Root length (mm)* Control 47.0 a 104.8 a

GA3 48.7 b 98.2 b *Means that do not share a letter are significantly different. Significance level: α= 0.05

Table 7.7. Tukey’s pairwise comparison of the mean response of seedling traits from different breeding history groups§ to GA3 treatment (at 95% confidence).

Effect of GA3 (% change) Seed source groups ∆Coleoptile length * -∆Root length * Landraces (166) 5.25 a 5.10 a CIMMYT lines (115) 0.32 b 8.35 b Released varieties (34) 1.21 b 5.77 ab *Means that do not share a letter within a column are significantly different. Significance level: α= 0.05 §The three Canadian varieties were excluded from this analysis.

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The landraces mostly contain the Rht-B1a allele, while the CIMMYT lines/released varieties contain the Rht dwarfing alleles (Rht-B1b) (Figure 7.1, Appendix 15). When the NWDP accessions were divided based the allele present at each Rht locus, the effect on coleoptile length of the GA3 treatment was significantly larger in the wild type height allele group (either Rht-B1a or Rht-D1a) than in the dwarfing allele (Rht-B1b or Rht-D1b) group (Figure 7.4). However, amongst the accessions that contained either or both Rht dwarfing alleles, some accessions appeared to be more GA3 responsive (boxed, Figure 7.5), suggesting a more detailed examination of these accessions should be pursued. In the case of the length of the longest seedling root, both Rht-B1 allelic forms (wild type and dwarf) responded to GA3 treatment and they were significantly shorter compared to the control treatment, with similar trends at Rht-D1 (albeit not significant for the Rht-D1b allele) (Appendix 11).

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The symbols * and *** indicate significant differences at 0.05 and 0.001, respectively. The solid dots are the outliers, and each box represents the interquartile range representing 50% of the values. Abbreviations: GA= Gibberellic acid.

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*** indicates significant difference at 0.001. The solid dots are the outliers (contained in the large box), and each coloured box represents the interquartile range representing 50% of the values. Abbreviations: C= Control, GA= Gibberellic acid.

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In terms of the photoperiod response alleles, GA3 treatment did not significantly affect coleoptile length among genotypes having the photoperiod-insensitive Ppd-D1a allele, but genotypes carrying the photoperiod-sensitive Ppd-D1b allele had significantly longer coleoptiles after GA3 treatment (Appendix 10). In the case of seedling root length, the opposite response was observed: GA3-treated genotypes having Ppd-D1a responded by having longer seedling roots, while genotypes carrying Ppd-D1b showed no significant difference (Appendix 12).

7.4.6 Identification of accessions with GA-responsive coleoptiles The above analysis showed that amongst accessions with one or two dwarfing alleles, a small subset appeared to be GA-responsive for coleoptile length. To identify the accessions with the most GA-responsive coleoptiles in the NWDP, and hence best candidates for deep seeding, the percentage change in coleoptile length and percentage change in the ratio of coleoptile length to plant height were calculated for each accession. As a filter, only those accessions with dwarfing allele at the Rht-B1 and/or Rht-D1 loci were selected since only these genotypes would be the high-yielding semi-dwarfs. Furthermore, only accessions with values greater than a 10% threshold change for both traits were considered. These criteria resulted in twelve candidate accessions (Table 7.8). The findings showed that among the candidate landraces, NGRC04443, NGRC04466 and NGRC04427, were promising while Nepal 297 appeared to be the best accession among the released varieties. Among the candidate CIMMYT lines, BW48136, BW48139, BW49333, BW49093 and BW48314, were observed to the best GA-responsive accessions (Table 7.8). One accession was noteworthy, the landrace NGRC04443, because it had dwarfing alleles at both the Rht-B1 and Rht-D1 loci, whereas all the others had the wild type allele at one of these loci.

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Table 7.8. The accessions in the NWDP exhibiting the greatest change in coleoptile length in response to GA3 application for those accessions with at least one dwarfing allele at the Rht1-B1 or Rht-D1 loci. % change % change in coleoptile in Coleoptile Plant Accession Accession Rht_B1 Rht_D1 Ppd-D1 length:plant coleoptile length height ID group locus locus locus height ratio length (control) (cm) with GA3 with GA3 (mm) treatment treatment NGRC04427 Landrace Rht-B1a Rht-D1b Ppd-D1a 11.7 10.1 49.3 86.1 NGRC04443 Landrace Rht-B1b Rht-D1b Ppd-D1a 12.5 12.0 53.9 96.1 NGRC04466 Landrace Rht-B1a Rht-D1b Heterozygous 12.1 11.2 63.7 92.9 Nepal297 Released variety Rht-B1b Rht-D1a Ppd-D1a 15.1 11.0 38.9 72.6 BW48136 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 30.6 24.3 29.8 79.3 BW48139 CIMMYT line Rht-B1a Rht-D1b Ppd-D1a 17.1 12.8 45.7 74.7 BW48155 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 12.8 10.8 34.3 84.7 BW48158 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 12.3 10.0 39.4 80.8 BW48166 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 12.9 10.0 36.6 77.6 BW49093 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 13.4 11.2 36.2 83.8 BW48314 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 13.6 10.3 34.6 75.8 BW49333 CIMMYT line Rht-B1b Rht-D1a Ppd-D1a 20.3 14.7 34.0 72.5

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7.5 Discussion 7.5.1 Allelic variation at Rht and Ppd loci in the NWDP population The Nepali Wheat Diversity Panel (NWDP) was characterized by using diagnostic molecular markers for the alleles at Rht-B1, Rht-D1, and Ppd-D1 loci. The marker analysis revealed that ~ 46% of the population carried the dwarfing allele Rht-B1b and only ~11% had the Rht-D1b allele. After the Green Revolution, dwarfing alleles at the Rht-B1 and Rht-D1 loci have been the most commonly used genetic variants in wheat improvement programs globally (Botwright et al., 2001; Amram et al., 2015; Dowla et al., 2018). It is estimated that ~90% of wheat in the world are now semi-dwarf wheats carrying Rht-B1b or Rht-D1b alleles (Worland et al., 2001; Worland et al., 1998). The Rht-B1b and Rht-D1b alleles were introduced from “Norin 10”, a Japanese wheat variety, during the 1960s in the CIMMYT and USA wheat breeding programs to develop semi-dwarf commercial cultivars (Ellis et al., 2002). Improvement of yield in semi-dwarf wheat was due to the positive pleiotropic effect of the dwarfing alleles, resulting in an increased number of grains per spike (Börner et al., 1997; Flintham et al., 1997), partitioning of photosynthetic assimilated to grains and also reduced lodging (Evans, 1993). The low proportion of accessions in the NWDP having the reduced height alleles are likely due to the nature of the diversity panel since ~52% of the accessions in the panel were landraces. The current study, however, showed that there were some landraces with reduced height alleles suggesting that some of the accessions regarded as landraces may not be the authentic landraces. Introduction of foreign wheat genetic materials started in Nepal from the 1950s (Morris et al., 1994; Vijayaraghavan et al., 2018) and the semi-dwarf wheat varieties from CIMMYT, India and USAID were introduced starting in the 1960s (Morris et al., 1994; Joshi et al., 2006). Currently, >95% of the cultivated area for wheat in Nepal contains semi-dwarf improved varieties (Timsina et al., 2016; MoAD, 2017; Vijayaraghavan et al., 2018). As was detailed in Chapter 6, perhaps the germplasm expeditions performed during the 1970s and 1980s collected some exotic germplasm as landraces since these were grown by the farmers for more than a decade. This is supported by the fact that some of the germplasm collections were directly from farmers' granaries, not the field, due to logistical challenges during these expeditions (Paudel et al., 2016). Low variation at the Rht-D1 locus suggests that most of the CIMMYT genetic materials tested in Nepal were selected for the Rht-B1b allele. 181

The findings of the marker analysis also revealed a slightly higher population bias for the photoperiod insensitive allele Ppd-D1a (53.46%) compared to the photoperiod sensitive allele Ppd-D1b. The commercially released varieties and CIMMYT lines (except for a few accessions) were photoperiod insensitive. Photoperiod insensitivity was used by CIMMYT to facilitate selection for early grain maturity during the 20th century, resulting in a large proportion of modern wheat germplasm now carrying photoperiod insensitive alleles (Royo et al., 2018). Conversely, the majority of Nepali landraces were found here to be photoperiod sensitive based on the presence of the Ppd-D1b allele, while a few landraces were photoperiod insensitive. Possibly these photo-insensitive landraces were the early introduced materials either before or after the Green Revolution, or are the mistaken local germplasm as noted above. The concept of breeding varieties for wider north-south adaptation has been enabled by the introduction of photoperiod insensitive genes. CIMMYT has extensive collaborations globally, and CIMMYT genetic materials are distributed worldwide to wheat breeding programs (Bentley et al., 2013) increasing the proportion of photoperiod insensitive genotypes.

7.5.2 Effects of dwarfing alleles at Rht loci on coleoptile and seedling root length The landrace accessions had significantly longer coleoptiles than modern germplasm (CIMMYT lines and released varieties). With the exception of a small number of accessions, all the landraces had the wild type alleles (Rht-B1a and Rht-D1a) at the Rht loci. The mean change in coleoptile length after GA3 treatment was also significantly higher in the landrace group indicating GA-responsiveness. On average, those accessions with the dwarfing alleles Rht-B1b and Rht-D1b were not responsive to GA3 application. Combined, these results show that alleles at Rht-B1 and Rht-D1 loci have highly influenced coleoptile length in the NWDP. These results support earlier studies that reported reduced coleoptile length associated with these dwarfing alleles (Rebetzke et al., 2001; Ellis et al., 2005). Cell division in the intercalary meristem and cell elongation are affected by these dwarfing alleles, leading to reduced length of the epidermal cells and as a result, a shorter coleoptile sheath (Keyes et al., 1989; Liatukas and Ruzgas, 2011; Rebetzke et al., 2014; Li et al., 2017). In this study, only the interaction between Rht-B1 and treatment was significant indicating that the Rht-B1b allele may have more influence on the reduction of coleoptile length than the Rht-D1b allele in the NWDP. This result was supported 182

by the population level clustering data (Figure 7.1), where the Rht-B1b allele was found to be more influential than the Rht-D1b allele. However, a mean comparison of the accessions with Rht-B1b and Rht-D1b did not exhibit a significant difference indicating that the observation could be the artifact of the larger number of accessions with the Rht-B1b allele. A longer coleoptile is one of the targets in wheat breeding for drought resistance. Therefore, those few accessions that possessed dwarfing alleles but which showed potential for longer coleoptiles are of high interest for future breeding efforts. The current study showed that the dwarfing allele Rht-B1b did not influence the longest seedling root length significantly. However, the dwarfing allele caused roots to be more responsive (shorter) to GA3 compared to the wild type allele at the Rht-B1 locus. Earlier studies on Coleus blumei (Bostrack and Struckmeyer, 1967) and Eucalyptus species (Bachelard, 1968) similarly reported reduced root growth with 50 mg/L GA3 treatment. The root and coleoptile results are in agreement with an earlier study using a wheat RIL population that showed pleiotropic effects associated with the Rht-B1 locus, specifically reduced coleoptile length but increased root length (Li et al., 2011). Seedling root length, like plant height and coleoptile length, is also a GA-dependent process (Bai et al., 2013b) and hence there may be a direct effect of Rht-B1b and Rht-D1b on seedling root growth, rather than a secondary partitioning effect

(Wojciechowski et al., 2009).

7.5.3 Effects of alleles at Ppd loci on coleoptile and seedling root length Photoperiod insensivity has significantly contributed to wider geographic adaptability of wheat and has been an important trait for selecting modern high yielding wheat lines (Worland et al., 1998). Photoperiod sensitivity in wheat is governed by mutations in Ppd-1 genes (Ppd-A1, Ppd-B1, Ppd-D1) that are located on chromosomes 2A, 2B and 2D, respectively (Nadolska- Orczyk et al., 2017; Shi et al., 2019). Mutations at Ppd-D1 are relatively common and widespread (Shaw et al., 2013). The findings of this study showed that the coleoptile length was not influenced significantly by the photoperiod insensitive Ppd-D1a allele, which also reduced GA-sensitivity, consistent with previous results (Chen et al., 2016). In earlier studies, a significant negative effect of Ppd-D1a on plant height was been observed (Bakuma et al., 2018; Chen et al 2018a). Other studies reported that the photoperiod sensitive Ppd-D1b allele 183

contributes to a longer culm (Börner et al., 1997; Worland et al., 1998; Cho et al., 2015), and so the Ppd-D1a allele may have had dual advantages for breeders: it facilitated photoperiod insensitivity (the primary target) and its negative influence on plant height was an additional advantage. The dominant Ppd-D1a allele also advances flowering time in wheat (Wilhelm et al., 2013; Chen et al., 2018a), which indicates its positive effect on increased grain filling period. The challenge now rests on wheat breeders to achieve photoperiod insensitivity and semi- dwarfness but longer coleoptiles (to mitigate early season moisture stress). Chen et al. (2016) observed that genotypes containing both the dwarfing Rht-B1b allele and photoperiod-insensitive Ppd-D1a allele have even shorter coleoptiles, consistent with this study, suggesting a negative synergistic interaction on coleoptile length. Therefore, co-selection for both Ppd-D1a and Rht- B1b and/or Ppd-D1a and Rht-D1b may not be a good choice to overcome early season moisture stress. However, based on this study, the haplotypes (e.g. Rht-B1b/Rht-D1b/Ppd-D1a) did not seem to affect the coleoptile length. Recently the dwarfing gene Rht8 and Ppd-D1a together were found to substantially reduce plant height (Zhang et al., 2019b). However, Rht8 is GA- sensitive (Worland et al., 2001) and thus, a combination of GA-sensitive genes such as Rht8 with the Ppd-D1a allele may be useful in generating short, photoperiod insensitive genotypes with longer coleoptiles. The studies related to the effect of photoperiod genes on root length in wheat are rare. Shaw et al. (2012) stated that the photoperiod genes were not expressed in roots. Detailed studies are needed in the future to clarify the effect and mechanism by which photoperiod genes may be affecting root traits in wheat. The result from this study also clearly showed no influence of photoperiod genes on the longest seedling root although some indication of influence was observed in haplotype combinations.

7.5.4 Identification of candidate NWDP accessions for longer coleoptile length and other seedling vigour traits to facilitate breeding It is has been established that longer coleoptiles are important for better plant establishment and better shoot development (Bovill et al., 2019). Shorter coleoptiles prevent deep sowing which is important for wheat growing regions with frequent moisture stress (Amram et al., 2015; Dowla et al., 2018; Vikhe et al., 2019). Coleoptile length is reduced under warmer soil 184

temperature conditions (Rebetzke et al., 2014), which may have negative implications for semi- arid wheat growing regions including the Terai region of Nepal. Here, four traits were used to identify a total of 29 candidate accessions as promising for breeding improved seedling vigour including longer coleoptiles (Table 7.4). First, since wheat breeders commonly target reduced height in breeding programs, only accessions with at least one dwarfing allele were considered for future selection. Semi-dwarf genotypes with Rht-B1b and Rht-D1b alleles have increased grain yield due to reduced lodging (Casebow et al., 2016; Zhao et al., 2019). However, the reduction in plant height has been achieved at the expense of shorter coleoptiles (Ellis et al., 2005; Rebetzke et al., 2007a). Therefore, the second trait used was a combination of the absolute coleoptile length (i.e. longer coleoptiles) and a high coleoptile-length to plant height ratio. In addition, root length is an important trait for drought tolerance in wheat (Narayanan et al., 2014), hence root related traits (i.e. high root length/biomass to plant height ratio) were the third criteria used here. The final trait selected was a high root to shoot biomass ratio which has also been shown to be positively associated with drought tolerance (Narayanan et al., 2014). Based on these four criteria, among the 29 selected accessions, 21 were CIMMYT lines, six were released varieties and the remaining two were landraces. Complementary to the above approach, we also screened for accessions with the greatest GA-dependent percentage increase in the coleoptile length and the greatest percentage increase in coleoptile length per cm of plant height. Here, only accessions having at least one of the dwarfing alleles (Rht-B1b and Rht-D1b) were included. In total, 12 potential candidates were identified that met these two criteria (Table 7.8). It was noteworthy that, of the 12 candidates identified, 3 of them were landraces. The landrace NGRC04443 requires further analysis, as unexpectedly, it was found to carry both the dwarfing alleles Rht-B1b and Rht-D1b and hence would have been predicted to have a coleoptile that was very unresponsive to GA3. However, an earlier study reported that at least in some genotypes, plant height and coleoptile length can be under different genetic control in GA-sensitive wheat (Rebetzke et al., 1999). Furthermore, we are interested in the length of the coleoptile along with the plasticity of the coleoptiles on GA3 treatment. In this context, the three landraces (NPGR 04427, NPGR 04443 and NPGR 04466) and BW48139, which have longer coleoptile length among the 12 candidate accessions for plastic coleoptiles, may be of greater interest. The CIMMYT line BW48139 is especially 185

noteworthy, as it appeared in both sets of assessments: for seedling vigour (Table 7.4), and GA- responsive coleoptiles (Table 7.8). Among the 40 accessions selected for potential seedling vigour and/or having the most GA-responsive coleoptiles, with the exception of five accessions, all possessed the Ppd-D1a allele and hence are expected to be photoperiod insensitive, a trait desired by wheat breeders.

7.5.5 The need to genotype other GA-responsive Rht genes Additional dwarfing genes may offer opportunities to breed for longer coleoptiles. In particular, the GA3-responsive gene Rht8 has been shown to reduce plant height with a minimal effect on coleoptile length (Worland et al., 2001; Rebetzke et al., 2005, 2007a; Landjeva et al., 2011; Asplund et al., 2012). Approximately a 7% decrease in coleoptile length was observed in genotypes with Rht8 compared to a ~22% reduction for genotypes with Rht-D1b in a study of Indian elite wheat cultivars (Grover et al., 2018). Along with Rht-B1b and Rht-D1b, Rht8 is the most abundantly used dwarfing gene among many Rht genes that have been identified (Grant et al., 2018). Additional options are Rht-B1e (Rht11) and Rht-B1p (Rht17) that restrict plant height but have lesser effects on coleoptile length (Bazhenov et al., 2015; Bovill et al., 2019). Similarly, Rht5 (Ellis et al., 2005), Rht12 (Chen et al., 2013), Rht13 (Ellis et al., 2005), Rht14 (Vikhe et al., 2019) and Rht24 (Tian et al., 2017; Würschum et al., 2017, 2018) are the other

GA3-sensitive reduced height genes that may have offer commercial potential in future wheat breeding efforts, though each may have its own limitations (e.g. Chen et al., 2013, 2016).

7.6 Conclusions and future perspectives Nepal’s wheat breeding programs have suffered from a lack of resources. 318 accessions from Nepal were collected to comprise the Nepali Wheat Diversity Panel (NWDP). These accessions were characterized for two Rht and one Ppd loci to better understand the genetic mechanism by which Nepali wheat germplasm has been selected for dwarfing and photoperiod insensitivity. The impact of this allelic diversity on early seedling vigour traits was analyzed. These data were used to identify 40 semi-dwarf accessions with long and/or more plastic coleoptiles to permit deep sowing to mitigate early season drought. Some of these accessions may be utilized as parents in future breeding programs (e.g. BW48139). 186

7.7 Acknowledgments We dedicate this paper to our mentor, colleague and friend, the late Professor Alireza Navabi, who passed away suddenly during the preparation of this manuscript. The authors wish to thank Elijah Dalton and Kaitlyn Ranft (University of Guelph) for assistance with the data recording. We thank Dr. A.K. Joshi, Mr. Madan Bhatta and Mr. Deepak Pandey for facilitating seed acquisition from Nepal, and Dr. Thomas Payne from CIMMYT, Mexico. This research was generously supported by a grant to MNR from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by the International Development Research Centre (IDRC, Ottawa) and Global Affairs Canada, in addition to grants to AN from SeCan, the Agricultural Adaptation Council and Grain Farmers of Ontario. Nepali landrace seeds were generously provided by the National Genetic Resources Centre (NGRC, Nepal) and the Agricultural Research Council (NARC, Nepal). Seeds of released varieties were provided by the National Wheat Research Program (NWRP belonging to NARC, Nepal). CIMMYT breeding lines were provided by CIMMYT, Mexico.

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8 Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat?

This chapter is intended as an original research article with the following citation:

Kamal Khadka, Andrew Burt, Hugh J. Earl, Manish N. Raizada, Alireza Navabi (2020). Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat? (In preparation).

Author contribution statement Kamal Khadka: Conceptualized the paper and wrote the manuscript. Andrew J. Burt: Provided technical inputs and editing the manuscript. Huge J. Earl: Contributed technical expertise and editing. Manish N. Raizada: Contributed technical advice, and technical and language editing. Alireza Navabi: Supervised the project and provided technical expertise.

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8.1 Abstract Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a SPAD meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (P<0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods.

Keywords: NDVI, SPAD, chlorophyll, epicuticular wax, grain yield.

8.2 Introduction Wheat (Triticum aestivum L.) is the source of ~20% of global calories (Shiferaw et al., 2013) demonstrating its importance for global food security. As the world’s population is approaching ~10 billion in the next 30 years (Hickey et al., 2019), wheat breeders have a tremendous challenge ahead to develop high yielding wheat varieties at a greater pace. However, as a consequence of climate change, factors such as drought can hinder or reverse progress in improving wheat grain yield (Abhinandan et al., 2018). The intensity, frequency, and duration of drought (Sarto et al., 2017) along with the growth stage at which the drought events occur (Daryanto et al., 2016) are responsible for losses in grain yield. Globally ~37% of wheat is

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grown in semi-arid regions with limited soil moisture (Chen et al., 2012; Elshafei et al., 2013). Understanding the physiological traits associated with stress tolerance plays a critical role in generating new high-yielding, climate-resilient wheat varieties (Elshafei et al., 2013). Photosynthesis promotes growth and development (Evans, 2013; Simkin et al., 2019). Although significant yield improvement in crops in the past few decades has been achieved without improved photosynthesis, improving photosynthesis is expected to contribute significantly to further increases in crop productivity (Simkin et al., 2019). Drought, among other environmental factors, limits photosynthesis (Pinheiro and Chaves, 2011; Zait and Schwartz, 2018); photosynthetic efficiency is considered one of the key indicators of the plant response to water stress and other physiological stresses (Rungrat et al., 2016). Different abiotic stresses including drought adversely affect the photosystem II reaction center by disrupting electron transport which results in reduced CO2 assimilation (Roach and Krieger-Liszkay, 2014; Schwarz et al., 2018). Developing crop varieties with higher photosynthetic potential can improve crop yield and resistance to drought and other abiotic factors. Chlorophyll has been used to characterize photosynthetic potential and responses to biotic and abiotic stresses (Rosyara et al., 2010; Kira et al., 2015; Shah et al., 2017). Changes in the photosynthetic capacity usually parallel changes in chlorophyll content (Guendouz and Maamari, 2012). Compared to measurements of gas exchange, measurements of chlorophyll and chlorophyll parameters have been found to be more practical when assessing photosynthetic responses in plants (Earl and Davis, 2003; Rosyara et al., 2010). Therefore, chlorophyll parameters such as chlorophyll content (Rosyara et al., 2010), Normalized Difference Vegetative Index (NDVI) (Guan et al., 2019) and chlorophyll fluorescence (Earl and Davis, 2003) have been used to appraise the contribution of chlorophyll to improved photosynthetic efficiency. At present, many plant phenotyping methods and facilities have been established globally. With the advancements in high throughput phenotyping platforms (HTPPs), characterization of a large number of genotypes for chlorophyll-related parameters using non-destructive, reflectance and transmittance based methods have become feasible (Condorelli et al., 2018; Sid’ko et al., 2017). Chlorophyll content as estimated using the Soil-Plant Analysis Development (SPAD) meter, has been widely used in wheat (Condorelli et al., 2018; Rosyara et al., 2010). Studies have shown that SPAD estimates are positively associated with increased photosynthesis (Rosyara et 190

al.,2010), resulting in a positive association with grain yield (Lopresti et al., 2015). Similarly, evaluation of wheat genotypes for variation in NDVI has been commonly used to assess the efficiency of photosynthetic parameters and grain yield (Gracia-Romero et al., 2017). NDVI has also been used for water stress assessment at different growth stages of various crops (Silva et al., 2016). In a research context, NDVI is often measured using a handheld or tractor-mounted equipment such as GreenSeeker; more recently Unmanned Aerial Vehicles (UAVs) are becoming popular (Fernández et al., 2019; Guan et al., 2019). Many studies reveal a close positive association between NDVI and photosynthetic parameters such as chlorophyll content index and also grain yield (Fernández et al., 2019). Apart from these chlorophyll related traits, shoot waxiness is another physiological trait that is regarded as important for abiotic stress tolerance and grain yield (Bi et al., 2017). Deposition of wax on the cuticle gives a light bluish-gray or bluish-white color to plant tissues and it is one of the distinct cuticular properties of some plant species including wheat (Cossani and Reynolds, 2012). Although cuticular wax deposition may not be the sole factor, increased waxiness decreases water loss through transpiration, contributing to drought tolerance (Shepherd and Griffiths, 2006; Buschhaus and Jetter, 2012; Jäger et al., 2014; Huggins et al., 2018; Mohammed et al., 2018; Pereira et al., 2019). A study reported significantly higher epicuticular wax in a recombinant inbred line (RIL) population when grown under moisture deficit conditions (Mohammed et al., 2018). Also, leaf waxes may reduce leaf temperatures, resulting in cooler canopies (Shepherd and Griffiths, 2006). Many studies in the past have shown a positive association between cuticular wax and grain yield in different crops including wheat (Bowne et al., 2012; Xue et al., 2017; Mohammed et al., 2018; Willick et al., 2018). To investigate the above traits, here a diversity panel of Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP), was assembled including landraces, released varieties, and advanced breeding lines. Wheat is a very important crop for food security in Nepal: it is the third most important cereal crop in Nepal in terms of area and production (MoAD, 2017) and covers almost 22% of the total acreage under production (MoAD, 2013). Moisture stress is a common problem for wheat cultivation in Nepal (Gurung, 2013) because ~42% of the crop is grown under rain-fed conditions (MoAD, 2017) and during the dry season which receives less than 10% of the annual rainfall (DHM-MoSTE Nepal, 2013). Climatic 191

predictions suggest that drought is one of the key challenges to improving wheat production in most of the wheat-growing areas in the country (DHM-MoSTE Nepal, 2013; Gurung, 2013; Shrestha et al., 2013). Therefore, assessment of the NWDP for traits related to drought tolerance is relevant. The results obtained from these analyses can potentially provide information on genetic variation for target traits for further utilization in breeding programs. This study was conducted to evaluate 318 accessions from the NWDP for: (i) variation for the above physiological traits and grain yield; and (ii) the association among these physiological traits and also with grain yield. This paper focuses on one unexpected result, namely a negative association between NDVI and leaf wax which has implications for the use of NDVI for measuring leaf chlorophyll in wheat diversity panels.

8.3 Materials and methods 8.3.1 Plant materials A diversity panel of 318 spring wheat genotypes, the Nepali Wheat Diversity Panel (NWDP) (Appendix 1), was assembled for the study. The panel includes166 Nepali landraces, 115 CIMMYT advanced breeding lines, and 34 commercially released Nepali wheat varieties. The landraces in the diversity panel represented 29 districts of Nepal and these were collected during different germplasm expeditions during the 1970s to 1990s. The seeds of the landraces were provided by National Agriculture Genetic Resource Centre (NAGRC), a body under Nepal Agriculture Research Council (NARC), Nepal. Similarly, the seed of the Nepali released varieties was provided by the National Wheat Research Program (NWRP), NARC, Nepal and the advanced breeding lines were provided by CIMMYT, Mexico. These three seed sources also correspond to the breeding history of the genotypes included in the NWDP. Three high-latitude spring wheat varieties (Norwell, a bread wheat cultivar developed in Eastern Canada, Pasteur, a high yielding cultivar developed in the Netherlands and grown in Canada, and AC Carberry (Depauw et a., 2011), a high-quality bread wheat developed in Western Canada) (Statistics Canada, 2020) available at the wheat breeding laboratory, University of Guelph, Canada, were also included in the panel as the two major field experiments were conducted in the Canadian environment.

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8.3.2 Field trials Four field trials were conducted to evaluate the diversity panel. In the 2016 growing season, field trials were conducted at the University of Guelph Research Station at Elora, Ontario (43°38'23.0"N 80°24'11.0"W). The trial was planted on May 11 and harvested on September 5. Similarly, the second field trial was conducted during the 2017 growing season at the same research station (43°38'10.4"N 80°24'07.6"W). During this season, planting was done on May 16 and the plots were harvested on August 29. The remaining two field trials were conducted in Nepal during the 2016 and 2017 wheat growing seasons at the Nepal Agriculture Research Council (NARC) Research Station located at Khumaltar, Lalitpur, Nepal (27°39'12.3"N 85°19'33.7"E) and the National Wheat Research Program (NWRP) station located at Bhairahawa, Rupandehi, Nepal (27°31'53.5"N 83°27'32.2"E). The field experiment at Khumaltar, Lalitpur, Nepal was collaborated with Agricultural Botany Divison (ABD) of NARC, while NWRP, NARC supported the field experiment at NWRP, Bhairahawa. The planting and harvesting dates for the trial at the NARC station in Khumaltar were November 23, 2016, and May 5, 2017. Similarly, seed planting in NWRP, Bhairahawa, was done on November 30, 2016, while the plots were harvested on April 19, 2017. The experiments were conducted using an alpha lattice design (Patterson and Williams, 1976) with two complete blocks and 20 incomplete blocks with 32 accessions (two accessions were excluded from the analysis due to a high level of seed mixture observed in the field trials). At the Elora Research Station, each of the experimental plots was a six-row plot (1 m x 3 m) with 17.8 cm row spacing, and the plot to plot distance was maintained at 0.5 m, with 1 m between the ranges. At the Nepal sites, due to limitations in the availability of seed, 2 m long 2-row plots with 20 cm row-to-row spacing were used.

8.3.3 Field data collection Phenotyping of chlorophyll parameters was done using both reflectance- and transmittance-based methods. At the Elora Research Station, data on chlorophyll parameters were collected using handheld machines for both 2016 and 2017 field trials and UAV only in the 2017 trial. The SPAD estimates were recorded using a SPAD 502Plus Chlorophyll Meter (Spectrum Technologies, Plainfield, Illinois, USA) in all the four field experiments. The operation of the SPAD meters is based on the illuminating system which has diodes emitting Red 193

(650 nm) and infrared (940 nm) radiation that passes through a leaf to a photodiode receptor. The SPAD values were measured in the central part of three randomly selected representative flag leaves in each plot: the three readings were averaged into one reading. The first data was recorded when the genotypes reached Zadoks stage 31 (Zadoks et al., 1974) once a week until three weeks prior to maturity (for most of the genotypes) at the Elora Research Station with a major focus on vegetative and reproductive growth stages. In the NARC Research Station at Khumaltar, Nepal, two reading were taken 10 days apart at the vegetative stage (Zadoks stage 37 and 45 approximately), and one reading during the reproductive phase (Zadoks stage 50), while at NWRP Research Station at Bhairahawa, one reading was taken during the vegetative stage (Zadoks stage 39). The area under the SPAD curve (AUSC) was generated for three experiments (excluded NWRP which had only one reading) using the following formula: 푛=1 푆( ) + 푆 퐴푈푆퐶 = ∑[( 푖+1 푖)](푇 − 푇 ) 2 (푖+1) 푖 푖=1 Where, th Si =SPAD value estimate on the i date th Ti =i day n = number of dates of recording the SPAD value

NDVI was computed using the handheld GreenSeekerTM (NTech Industries, Inc. Boulder, CO), a reflectance-based multispectral sensor unit that measures the reflectance of red and infrared radiation from the plot. The reading was taken by holding the device at ~1.5 m from the ground level from the middle of the plot. NDVI has values between 0 and 1.0, and is calculated as the difference in infrared and red reflectance, divided by their sum (Tucker, 1979): 푅 − 푅 푁퐷푉퐼 = 푁퐼푅 푅 푅푁퐼푅 + 푅푅 Where: NDVI =Normalized difference vegetative index

RNIR = Near-infrared radiation

RR =Visible red spectrum

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A total of five readings were taken until three weeks before maturity at one-week intervals at the Elora Research Station in both the 2016 and 2017 growing seasons. The NDVI readings were taken the day following the SPAD readings. Four NDVI readings, two before anthesis (at an approximate average of Zadoks stages 37 and 45) and two after anthesis (at an approximate average of Zadoks stages 50 and 58), were taken at the NARC Research Station at Khumaltar, while three readings, two before (Zadoks stages 37 and 45) and one after anthesis (Zadoks stage 50), were taken at the NWRP Research Station at Bhairahawa. The area under the NDVI curve (AUNC) was generated using the following formula: 푛=1 푁( ) + 푁 퐴푈푁퐶 = ∑[( 푖+1 푖)](푇 − 푇 ) 2 (푖+1) 푖 푖=1 Where, th Ni =NDVI estimate on the i date th Ti =i day N= number of dates of recording the NDVI value

Deveron UAS (Toronto, Canada) performed an unmanned aerial vehicle (UAV) flight on July 26, 2017, over the experiment at the Elora Research Station using the platform DJI Matrice 100 (DJI, Shenzhen, China). The maturity of the trial was between Zadoks stages 50 and 58. The UAV flight was at an altitude of 30 m to capture the spectral reflectance by a RedEdgeTM narrow-band multispectral camera (MicaSense, Washinton, United States). The multispectral camera captured five bands including blue (480 nm), green (560 nm), red (670 nm), red edge (720 nm), and near-infrared (840 nm). Pix4d software (Pix4d, Lausanne, Switzerland) was used to process the images from each of the wavelengths, and five geo-referenced ortho-mosaics of the flight for each wavelength was generated. An image of a calibration panel with a known reflectance (blue 0.70, green 0.71, red 0.71, near-infrared 0.66 and red edge 0.70) was taken before the UAV flight to adjust the variation in light conditions. ArcGIS (Esri, California, United States) software was used to generate the NDVI map by using the Map Algebra Tool in ArcGIS. Similarly, to avoid the reflectance generated by the background soil, a threshold of >0.3 was used. The Python code as described by Haghighattalab et al. (2016) was used to generate 195

shapefiles for extracting the plot-level data. To ensure better plot coverage, the shapefiles were manually curated after importing them into ArcGIS software. NDVI sum values were extracted for each unit plot using Zonal Statistic using the following formula: 푁 NDVIdr = ∑ NDVI𝑖 푖=1

Here, the NDVIdr (NDVI measured using a UAV/drone) indicates the sum of all NDVI values from the pixels in each experimental unit that exceeded the threshold value 0.3, and this value represents the greenness of the vegetation within each experimental unit. Data on waxiness was recorded by visual assessment using the CIMMYT protocol (Torres and Pietragalla, 2012) after all genotypes reached Zadoks stage 50. Wax deposition on the plants (leaves, stems, and spikes) was observed on the whole plot. To train the eyes, plots with the highest and least waxiness were identified. Based on this observation, individual plots were rated using a scale from 0 (none) to 10 (total cover), thus providing a relative scale for each site and season. Data could not be collected from the NWRP site in Nepal due to technical reasons. At the Elora Research Station, harvesting was done using a Wintersteiger plot combine harvester (Wintersteiger AG; Ried im Innkreis, Austria). The grain yield data were recorded at the harvest time with a HarvestMaster Grain Gage (Juniper Systems, Inc., Logan, UT) fixed on the combine. The grains were dried and the grain weight was taken again to confirm the quality of the data taken from the combine. In the Nepal field trials, plots were harvested and threshed manually. The grain was dried, and the yield per plot was recorded. In all field experiments, grain yield was recorded as kg/plot and later converted into kg/ha.

8.3.4 Phenotypic data analysis The phenotypic data collected from the field were analyzed using PROC MIXED in SAS version 9.4 (SAS Institute, Cary, NC). The Shapiro-Wilk test was conducted in PROC UNIVARIATE to test the normality of the residuals. To ensure that all the data points were independent and random, PROC SGPLOT was used to construct studentized-residuals by predictor plots. The studentized residuals produced by genotype x treatment combinations were

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considered outliers when >3.5 and <-3.5. These outliers were removed from the data set after confirming that they were true outliers. Least-square (LS) means were generated for each genotype. Analysis of variance and correlation were analyzed using PROC ANOVA and PROC CORR commands, respectively. The correlation plots were generated using the R platform and Minitab 19 trial version.

8.4 Results 8.4.1 The response of traits to different growing environments The combined ANOVA analysis showed a significant difference (P≤0.0001) among the genotypes in the NWDP for AUNC, NDVIdr, AUSC, waxiness, and grain yield (Appendix 16). The interaction effect of genotype by environment (G x E) was significant for all of the above traits except for NDVIdr which was recorded only at the Elora Research Station during the 2017 season. These results demonstrated that the genotypes in the study performed differently in the four environments in which the experiments were conducted.

8.4.2 Correlation of chlorophyll-related traits with waxiness and grain yield Correlation analysis was performed for each trait using trait means calculated using the data from all four field experiments. The results showed a significant positive association between grain yield (P≤0.0001) and AUSC (r=0.40), and grain yield and waxiness (r=0.49) (Figure 8.1). However, a significant negative association (P≤0.05) was observed between grain yield and AUNC, and waxiness and AUNC which was unexpected. Similarly, NDVIdr showed a significant negative association with AUSC (P≤0.001), grain yield (P≤0.05) and waxiness (P≤0.001), which was also not expected.

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Figure 8.1. Pearson correlation values for pairs of phenotypic traits evaluated using the trait means calculated from all four field experiments conducted in 2016 and 2017. For each pair of traits indicated, the panel at the lower left intersection is the raw data, while the panel at the upper left intersection is the Pearson correlation value. The panel with the trait label indicates the distribution of the data. Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. Abbreviations: AUNC=Area under NDVI curve, AUSC=Area under SPAD curve, GY= Grain yield, NDVIdr=NDVI derived from UAV/drone data.

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8.4.3 Principal component analysis Principal component analysis (PCA) was performed using the data on chlorophyll-related traits, waxiness and grain yield recorded for all field experiments to assess the association among these traits. It was found that the first two principal components accounted for 44.73% of the variation (Figure 8.2). The bi-plot generated using these first two principal components showed that component 1, which explained 34.18% of the variation, was positively associated with grain yield, AUSC and waxiness. In contrast to this, AUNC (except AUNC for Elora 2017), was negatively correlated with component 1. This result supports the results from correlation analysis related to the unexpected negative association of AUNC with grain yield and waxiness; and the negative association of NDVIdr with grain yield, AUSC and waxiness. The bi-plot also shows that there is little overlap of landraces and the modern varieties/advanced lines with the CIMMYT lines and the released cultivars being more associated with high yield, while the majority of the landraces occupy space in the lower yielding area of the biplot (Figure 8.2).

8.4.4 Within trial correlation analysis Due to the high G x E interaction observed, correlation analysis was performed separately for each individual field experiment (Table 8.1). Positive correlations were observed for waxiness and AUSC, and waxiness and grain yield, across all three sites where waxiness ratings were performed (no data was available for waxiness at the NWRP, Nepal site). The 2016 Elora Research Station (ERS) results showed a negative correlation between AUNC and grain yield, and between AUNC and waxiness. Whereas waxiness and AUNC were positively correlated in 2017 at the Elora Research Station, there was no significant correlation between AUNC and grain yield. Surprisingly NDVIdr was negatively correlated with AUNC, AUSC, and waxiness (Table 8.1). In the case of the data obtained from the ABD (Nepal) site, a significant positive association of AUNC with grain yield was observed, but again, the correlation between AUNC and waxiness was negative. For the NWRP (Nepal) site, grain yield did not show any association with AUNC (Table 8.1); specifically, the association of AUNC with grain yield (Figure 8.3), and the association of waxiness with AUNC and AUSC (Figure 8.4), indicated the need for further analysis. 199

Figure 8.2. A biplot generated for grain yield, waxiness and chlorophyll-related traits evaluated in four field experiments in 2016 and 2017. Abbreviations: AUNC= Area under NDVI curve, AUSC= Area under SPAD curve, NDVIdr= NDVI derived from UAV/drone data, WAX= Waxiness, GY= Grain yield, EL= Elora, NW= National Wheat Research Program (NARC, Nepal), AB= Agricultural Botany Division (NARC, Nepal). The numbers 16 and 17 correspond to the year of experimentation.

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Table 8.1. Pearson correlation measurements for pairs of phenotypic traits evaluated in four field experiments conducted in 2016 and 2017. Traits Location AUNC AUSC GY WAX Elora 2016 AUSC 0.06 GY -0.21*** 0.33*** WAX -0.18** 0.66*** 0.34*** Elora 2017 AUSC 0.57*** GY 0.03 0.06 WAX 0.42*** 0.74*** 0.12* NDVIdr -0.18** -0.31*** -0.03 -0.21*** ABD 2017 AUSC -0.14* GY 0.25*** 0.20** WAX -0.21** 0.48*** 0.44*** NWRP GY 0.05 2017 Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. Abbreviations: AUNC= Area under NDVI curve, AUSC= Area under SPAD curve, GY= Grain yield.

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Figure 8.3. Scatter plots of AUNC with grain yield from four field experiments conducted in 2016 and 17. Trend line indicates a Pearson Correlation line of best fit. Abbreviations: AUNC= Area under NDVI curve.

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Figure 8.4. Scatterplots of waxiness ratings and AUNC and AUSC from three field experiments conducted in 2016 and 17 (no data was available for waxiness from the NWRP, Nepal site). Abbreviations: AUNC= Area under NDVI curve, AUSC= Area under SPAD curve.

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Due to the confounding results observed, further detailed correlation analysis was performed for the data from the two experiments at the Elora Research Station. The data from the ABD (Nepal) and NWRP (Nepal) sites were excluded from this analysis due to fewer NDVI measurements and lack of waxiness data from the NWRP (Nepal) site. For this analysis, the data were segregated into three groups based on waxiness scores as follows: group 1 (no or low waxiness with scores <2; group 2 (medium waxiness) with scores ≥2 and <5; group 3 (high waxiness) with scores above ≥5 (Tables 8.2, 8.3). The low waxiness group 1 was composed mainly of landraces: in 2016, 82 of 88 genotypes were landraces; in 2017, 60 of 69 genotypes were landraces. In contrast, CIMMYT lines dominated the high waxiness group 3; in this group, there were only 28 landraces out of 130 genotypes for the 2016 trial, and 38 landraces out of 144 genotypes in the 2017 trial. The results from the 2016 data analysis showed no association of AUNC with grain yield or waxiness in groups 1 and 3, but AUNC had a negative correlation with grain yield and waxiness for group 2 (Table 8.2), which contains a mixture of genotypes from all three breeding history groups. Using the 2017 data, no association was observed between AUNC with grain yield or waxiness or NDVIdr with grain yield or waxiness (Table 8.3). As an alternative approach, the data from two field experiments at the Elora Research Station were segregated into three groups based on the breeding history (3 Canadian varieties excluded): landraces, commercial varieties, and CIMMYT lines. The results from the 2016 data showed a negative association between grain yield and waxiness in the landrace group, while no association was observed in the other two groups (Table 8.2). In the landrace group, a negative association between waxiness with AUNC and NDVIdr was observed from the results of the 2017 experiment (Table 8.3). There was no association between AUNC or NDVIdr with grain yield and waxiness in the commercial variety and CIMMYT groups except for a positive association between AUNC and waxiness for the CIMMYT lines. These results suggest that the confounding effect may be coming from the landraces which consist of a large number of genotypes with no or low waxiness and relatively low yield. In addition, a positive association between waxiness and AUSC was consistently observed both for the combined analysis or when the data were analyzed by site or stratified by waxiness

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scores or breeding history (Tables 8.1, 8.2, 8.3). Also, grain yield was found to be positively correlated with AUSC in three of the four test environments.

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Table 8.2. Pearson correlations between pairs of phenotypic traits evaluated in field experiments conducted at the Elora Research Station (Canada) in 2016 (a dry year), after segregating the data based on waxiness scores and breeding histories. Traits Groups based on waxiness scores Groups based on breeding history Group 1 – Low wax (N=88) Landraces (N= 166) GY (kg/ha) (mean±SEM)= 2720.1±69.97 GY (kg/ha) (mean±SEM)= 2705.7±48.43 WAX (mean±SE)= 1.2±0.05 WAX (mean±SE)= 2.3±0.10

AUNC AUSC GY AUNC AUSC GY

AUSC 0.23* 0.15

GY -0.16 0.19 -0.24* 0.11 WAX 0.19 0.22* -0.23* -0.23* 0.47*** 0.11 Group 2 – Medium wax (N=100) Commercial varieties (N=34) GY (kg/ha) (mean±SEM)=3037.3±78.38 GY (kg/ha) (mean±SEM)=3608.3±108.12 WAX (mean±SE)=2.9±0.04 WAX (mean±SE)= 2.9±0.19

AUSC 0.10 0.61***

GY -0.34** 0.11 0.48* 0.14 WAX -0.22* 0.35** 0.22* 0.18 0.38* 0.12 (check) Group 3 – Highest wax (N=130) CIMMYT lines (N=115) GY (kg/ha) (mean±SEM)=3413.3±54.53 GY (kg/ha) (mean±SEM)= 3474.7±53.89 WAX (mean±SE)= 5.2±0.11 WAX (mean±SE)= 4.9±0.15

AUSC 0.45*** 0.30**

GY 0.14 0.15 0.09 0.06 WAX 0.17 0.46*** 0.03 0.18 0.45*** 0.00 Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. Abbreviations: AUNC=Area under NDVI curve, AUSC=Area under SPAD curve, GY= Grain yield.

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Table 8.3. Pearson correlations between pairs of phenotypic traits evaluated in field experiments conducted at the Elora Research Station (Canada) in 2017 (a wet year), after segregating the data based on waxiness scores and breeding histories. Traits Groups based on waxiness scores Groups based on breeding history Group 1-Low wax (N=69), Landraces (N=166) GY (kg/ha) (mean±SEM)= 1917.9±87.31 GY (kg/ha) (mean±SEM)= 1987.3±53.07 WAX (mean±SE)= 1.3±0.05 WAX (mean±SE)= 2.9±0.13 AUNC AUSC GY WAX AUNC AUSC GY WAX AUSC 0.50*** 0.51*** GY -0.13 -0.24 -0.03 -0.01 WAX -0.14 0.04 -0.28* -0.30*** 0.67*** 0.03 NDVIdr -0.30* -0.37* 0.16 0.21 0.19* -0.42*** -0.01 -0.25** Group 2-Medium Wax (N=105) Commercial varieties (N=34) GY (kg/ha) (mean±SEM)= 1977.6±61.45 GY (kg/ha) (mean±SEM)= 2240.3±120.29 WAX (mean±SE)= 2.9±0.05 WAX (mean±SE)= 4.1±0.25 AUSC 0.41*** 0.55** GY -0.12 -0.10 -0.10 -0.09 WAX 0.17 0.43*** 0.01 0.22 0.74*** 0.24 NDVIdr 0.07 -0.24* -0.09 -0.18 -0.13 -0.10 0.10 -0.11 Group 3-Highest wax (N=144) CIMMYT lines (N=115) GY (kg/ha) (mean±SEM)= 2126.8±63.58 GY (kg/ha) (mean±SEM)= 3474.7±53.89 WAX (mean±SE)= 5.6±0.09 WAX (mean±SE)= 5.1±0.15 AUSC 0.35*** 0.33** GY 0.09 0.12 0.08 0.15 WAX 0.10 0.39*** 0.08 0.28* 0.64*** 0.20* NDVIdr -0.16 -0.17* -0.01 -0.02 -0.16 -0.20* -0.06 -0.17 Significance: *P≤0.05, **P≤0.001, ***P≤0.0001 Abbreviations: AUNC= Area under NDVI curve, AUSC= Area under SPAD curve, GY= Grain yield, NDVIdr= NDVI derived from UAV/drone data.

8.5 Discussion Though the original purpose of this study was simply to characterize the NWDP for physiological traits to help future breeding efforts, there was an unexpected finding pertaining to the relationship between wax and NDVI as a measure of leaf chlorophyll. Based on the literature, it was expected that leaf wax and chlorophyll would positively correlate across the Nepali Wheat Diversity Panel. Epicuticular wax has been shown to protect the photosynthetic apparatus (photosystem II/chlorophyll) from high radiation and high temperature damage (Shepherd and Griffiths, 2006; Buschhaus and Jetter, 2012; Guo et al., 2016; Medeiros et al., 2017; Huggins et al., 2018; Pereira et al., 2019). Indeed, consistent with this expectation, leaf chlorophyll as measured using SPAD (AUSC) was positively and consistently associated with waxiness across all the field trials. This association between waxiness and SPAD was consistent even when the panel was grouped by waxiness score or by breeding history). The relationship between AUNC and waxiness varied between environments, showing a weakly negative correlation in Elora2016 and ABD2017, but a weakly positive correlation in Elora2017. Within the sub-groups for waxiness or breeding history the relationship was not any more consistent. In Elora 2017, where the overall correlation between waxiness and AUNC was positive, no significant correlation was found within the waxiness sub-groupings, and a negative relationship in the Landrace sub-group and a positive relationship in the CIMMYT lines sub-group. In 2016 where the overall correlation between waxiness and AUNC was negative, a similar trend to that of 2017 was seen within the sub-groupings, however, only the correlation in the Landrace group was statistically significant. Could cuticular wax confound the NDVI measurements in at least a subset of wheat genotypes?

8.5.1 Does wax interfere with NDVI? Plants absorb ~70% of the solar radiation that they receive (Gates et al., 1965). The radiation reflected is affected by various leaf properties such as pubescence and epicuticular waxiness, leaf angle and leaf moisture content along with other optical and biochemical properties (Walter-Shea et al., 1992; Ballester et al., 2019). According to Holmes and Keiller (2002), leaves with higher waxiness reflected more UV and longer wavelength radiation as compared to less waxy counterparts in a study that included a range of species. Another study 208

conducted on forty-four plant species reported that plant leaf epicuticular properties and different environmental stress factors affect reflectance (Mohammed et al., 2000). Since NDVI is based on reflectance, epicuticular wax and other traits may be confounding. The other factors that potentially complicate NDVI measurements are calibration, atmospheric transmission and canopy architecture (Xue and Su, 2017); differences in types of genotypes and growth stages (Morgounov et al., 2014); water regimes and nitrogen fertilization (Araus, 1996; Morgounov et al., 2014); leaf properties such as the age of leaf (Walter-Shea et al., 1992), side of the leaf (Vergara-Díaz et al., 2018) and timing of NDVI measurements (Gracia-Romero et al., 2017; Fernández et al., 2019). The limitation of this current study is that the other environmental factors were not assessed except for the weather records at each study site. The findings of this study suggest that waxiness scores may be affecting the NDVI reading although it was not possible to quantify the size of the effect. Similar to this result, a previous study on winter wheat also suggested that epicuticular wax could be one of the genotype-specific characters that potentially affect spectral reflectance resulting in unexpected NDVI scores (Samborski et al., 2015). Wheat grain yield was previously shown to be positively associated with waxiness (Bowne et al., 2012; Xue et al., 2017; Mohammed et al., 2018; Willick et al., 2018), NDVI (Gracia- Romero et al., 2017) and SPAD measurements (Rosyara et al., 2007, 2010; Lopresti et al., 2015), and the expectation was that similar results would be observed in the current study. Here, although waxiness and AUSC had significant positive associations with grain yield in both field experiments during 2016 and 2017 seasons at the Elora Research Station, negative and positive associations between waxiness and AUNC during the 2016 and 2017 seasons, respectively, demanded further analysis. The other question raised was, why did the association between waxiness and NDVI vary between years? One possible explanation is related to moisture since moisture availability affects wax deposition (Xue et al., 2017; Mohammed et al., 2018). Wax deposition is usually highest during drought-stress, contributing to improved water conservation by reducing the heat load on plant leaves (Sreeman et al., 2018). There was a severe summer drought in the 2016 summer season which extended until the beginning of the reproductive stage [(Perdeaux, 2017; Appendix 4 (weather data)], perhaps resulting in more wax deposition in ‘waxy genotypes’. By contrast, in 2017, Elora received a high amount of precipitation (uniform 209

throughout the season), which perhaps decreased epicuticular wax on all genotypes including the “waxy genotypes”. Foley et al. (2006) revealed that the near infra-red (NIR) wavelength was affected immediately by small changes in leaf water content, in a study where they examined the foliar spectra (350-2500 nm) of five tree species. While moisture availability affects spectral reflectance, the differential deposition of wax suggested in this study in response to moisture stress appears to be further complicating the relationship. Both NDVI (AUNC) and SPAD (AUSC) are proxies for chlorophyll content. Therefore, the extent to which epicuticular wax may be interfering with NDVI was further validated by calculating the discrepancy in the association between waxiness and AUNC, and waxiness and AUSC (Table 8.4). The lowest discrepancy was in the low wax group 1 compared higher wax groups 2 and 3, consistent with wax interfering with NDVI measurements. However, the discrepancy was highest among landraces compared to commercial and CIMMYT lines in both years (Table 8.4). This observation is consistent with Morgounov et al. (2014) who stated that differences in types of genotypes in a diversity panel may affect NDVI measurements; indeed the landrace group was more genetically diverse than the other groups based on a molecular marker based population structure analysis of the NWDP (refer to Chapter 5). It is hypothesized that waxiness along with other associated factors such as seasonal precipitation and diversity of the genetic resources confounded the correlations in this study.

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Table 8.4. Discrepancy observed between the correlations for waxiness with AUNC, and waxiness with AUSC. Elora 2016 Traits Groups based on waxiness scores Groups based on breeding history Group 1 – Low wax (N=88) Landraces (N= 166) AUNC AUSC *Discrepancy AUNC AUSC *Discrepancy WAX 0.19 0.22* 0.03 -0.23* 0.47*** 0.70 Group 2 – Medium wax (N=100) Commercial varieties (N=34) WAX -0.22* 0.35** 0.57 0.18 0.38* 0.20 Group 3 – Highest wax (N=130) CIMMYT lines (N=115) WAX 0.17 0.46*** 0.29 0.18 0.45*** 0.27 Elora 2017 Traits Groups based on waxiness scores Groups based on breeding history Group 1 (N=69) Landraces (N=166) AUNC AUSC Discrepancy AUNC AUSC Discrepancy WAX -0.14 0.04 0.18 -0.30*** 0.67*** 0.97 Group 2 (N=105) Commercial varieties (N=34) WAX 0.17 0.43*** 0.26 0.22 0.74*** 0.52 Group 3 (N=144) CIMMYT lines (N=115) WAX 0.10 0.39*** 0.29 0.28* 0.64*** 0.36 Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. *The discrepancy was calculated as r (WAX with AUSC) – r (WAX with AUNC). Abbreviations: AUNC= Area under NDVI curve, AUSC= Area under SPAD curve, WAX= Waxiness.

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8.5.2 UAV versus NDVI measurements In the Elora 2017 experiment, AUNC and NDVIdr exhibited a negative association, even though these are similar reflectance measurements but taken by different instruments. The expectation was to observe a positive association between these two readings. It has been shown that the positive correlation between NDVI and grain yield becomes stronger when UAVs are used compared to ground-based platforms such as Greenseeker NDVI (Condorelli et al., 2018; Kyratzis et al., 2017) and that UAVs are more precise (Tattaris et al., 2016; Shi et al., 2016). The advantage of using UAVs over ground-based platforms may be that it overcomes the confounding effects of short term environmental variation (since handheld NDVI measurements take time for many plots), while UAVs allow measurements from many plots in a short span of time (Condorelli et al., 2018; Haghighattalab et al., 2016). Although the UAV data in this study was scant, it did not noticeably improve the correlation with grain yield compared to handheld NDVI data. Earlier studies have shown that plant growth stage and time of NDVI measurement may affect the correlation between NDVI and grain yield (Gracia-Romero et al., 2017; Fernández et al., 2019). Specifically, in wheat, the sensitivity of ground-based sensors such as in GreenSeeker has been found to be higher at early growth stages and at the senescence stage, while the sensitivity of UAV-based platforms has not been appraised (Condorelli et al., 2018). In this study, NDVI was recorded 5 times during the crop season beginning at an early vegetative stage while the UAV was flown over the field only once after all the genotypes had begun heading. When NDVI readings from each of the five measurements using GreenSeeker were correlated with NDVIdr data, a significant negative association was observed between NDVIdr and the last three readings, inlcuding the final handheld NDVI reading which was taken only two days after the UAV flight (Appendix 13). This needs further evaluation to explain the unexpected association between NDVIdr and ground-based NDVI measurement using the GreenSeeker.

8.5.3 Grain yield and its association with physiological traits Chlorophyll related traits are measured when characterizing germplasm to identify potential genotypes for breeding programs since chlorophyll has been shown to positively correlate with rates of photosynthesis and yield (Guendouz and Maamari, 2012; Simkin et al., 212

2019). SPAD values are used to estimate chlorophyll content, and a positive association between SPAD (AUSC) and grain yield has been reported in many studies (Rosyara et al., 2007, 2010; Islam et al., 2014; Zhang et al., 2019a). Here, chlorophyll measurements using SPAD (AUSC) were positively correlated with grain yield across two field experiments [Elora 2016 and ABD (Nepal) sites] but no correlation was observed at Elora 2017. The key factor confounding this association at Elora 2017 may have been the high Fusarium head blight (FHB) disease pressure during that year compared to 2016 (data is not shown), likely since 2017 was a wetter year which is more conducive to this disease. FHB will directly reduce yield and tends to affect the wheat heads only without impact on the leaf tissue where the SPAD measurements were taken. Waxiness is an important trait that protects plants from different biotic and abiotic stresses, particularly waxiness contributes to drought stress tolerance by minimizing water loss through transpiration (Shepherd and Griffiths, 2006; Buschhaus and Jetter, 2012; Huggins et al., 2018; Mohammed et al., 2018; Pereira et al., 2019). Guo et al., 2016; Medeiros et al., 2017; Many studies in the past have shown a positive association between epicuticular waxiness and grain yield (Mohammed et al., 2018; Xue et al., 2017). A significant positive correlation between waxiness and grain yield was observed in the present study as expected. NDVI is considered a reliable tool for estimating crop biomass and grain yield by assessing photosynthetically active radiation at the crop canopy level (Lopresti et al., 2015; Rebetzke et al., 2016b; Kyratzis et al., 2017; Condorelli et al., 2018; Tan et al., 2018; Zhang et al., 2019). (Rebetzke et al., 2016b). Therefore, the significant negative association observed between NDVI (AUNC) and grain yield at Elora 2016 was unexpected. NDVIdr measurements taken with a UAV at Elora 2017 also did not show any significant association with grain yield. These results are incongruous with many earlier studies which reported NDVI to be positively correlated with grain yield (Fernández et al., 2019, Condorelli et al., 2018; Rebetzke et al., 2016b; Lopresti et al., 2015). However, some studies have also reported a negative association between NDVI and wheat grain yield, consistent with the current study (Morgounov et al., 2014; Rutkoski et al., 2016; Kyratzis et al., 2017). When the study population was segregated into three groups based on waxiness scores and seed source (breeding history), the correlation between NDVI and grain yield was not significant except for the medium wax group (group 2) in 2016 (where a negative association was observed). Since wax groups positively correlated with grain yield, here also the 213

argument is that epicuticular wax along with other environmental factors affected the NDVI readings.

8.5.4 Should NDVI be used in future diversity studies for wheat grain yield? Many studies have shown the superiority of NDVI and other vegetative indices over SPAD in terms of wheat grain yield predictions especially under stressed conditions (Kyratzis et al., 2017; Lopes and Reynolds, 2012). Despite this, positive associations of vegetative indices such as NDVI with SPAD values have been reported (Kyratzis et al., 2017). Therefore, based on the availability of machines/tools, SPAD or NDVI measurements are both common when evaluating a set of germplasm. However, the results in the present study suggest that NDVI may not be always superior to SPAD in terms of its ability to predict grain yield in wheat. Prasad et al. (2009) also indicated that vegetation based NDVI may not be an effective breeding tool to identify higher biomass producing wheat genotypes compared to other spectral reflectance indices such as water-based indices and pigment-based indices. Furthermore, NDVI may not be appropriate for screening genotypes under extreme environmental conditions. For example, despite observing a strong positive association between NDVI and wheat grain yield under favorable conditions, the association was poor under severe drought conditions (Thapa et al., 2019). Also, the NDVI platforms may differ in their sensitivity and capacity to discriminate between diverse wheat genotypes (Christopher et al., 2016). Samborski et al. (2015) suggested that developing correction coefficients may not be very helpful for individual genotypes considering the practical challenges. A shortage of quality hyperspectral data can hinder accurate assessment and prediction of yield or any other important traits (Tan et al., 2018). Combined, the literature and the current findings suggest caution should be used when using NDVI to predict wheat grain yield when a diverse genetic population is involved and when the environmental conditions are highly variable. On the other hand, while NDVI still is a commonly used tool to identify improved yield with a narrower genetic base, such as a breeding population use of other vegetative indices such as Normalized Difference Red Edge (NDRE) may be another option. Performance of NDRE has been found to be better to NDVI considering its limitations associated with absorptance of NDVI by the upper canopy and also its saturation at its maximum value during latter growth stages of the crop (Fu et al., 2020). 214

8.6 Conclusions Chlorophyll-related parameters such as SPAD readings and spectral reflectance indices such as NDVI are commonly used to predict grain yield, appraise fertilizer requirements and also to evaluate germplasm in breeding programs for abiotic stress tolerance. The findings from this study suggest that NDVI may not always be an effective predictor of wheat grain yield and that caution should be used when using this trait to evaluate diversity panels. Waxiness, high genetic diversity in the study panel, and environmental factors such as differential precipitation may confound NDVI measurements, raising valid questions as to the reliability of NDVI for predicting grain yield under these circumstances. It is already accepted that leaf epicuticular properties such as waxiness affect reflectance measurements although the extent of the effect is not clearly understood. This should be an area of further investigation particularly since selection for higher waxiness in wheat has become common considering its advantages in abiotic stress tolerance and grain yield. Segregating the genotypes in a breeding population based on maturity, plant architecture, traits such as waxiness, and the target environment may help to identify and reduce confounding effects.

8.7 Acknowledgments We dedicate this paper to our mentor, colleague and friend, the late, Professor Alireza Navabi, who passed away suddenly during the preparation of this manuscript. The authors wish to thank Nick Wilker and Katarina Bosnic (University of Guelph) for assistance with seed handling and field planting. We thank Dr. A.K. Joshi, Mr. Madan Bhatta and Mr. Deepak Pandey for facilitating seed acquisition from Nepal and Dr. Thomas Payne from CIMMYT, Mexico. For the Nepal trials, we thank our research assistants, Ms. Sapana Ghimire and Niraj Ghimire, for their support in field data recording. This research was generously supported by a grant to MNR from the Canadian International Food Security Research Fund (CIFSRF), jointly funded by the International Development Research Centre (IDRC, Ottawa) and Global Affairs Canada, in addition to grants to AN from SeCan, the Agricultural Adaptation Council and Grain Farmers of Ontario. Nepali landrace seeds were generously provided by the National Genetic Resources

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Centre (NGRC, Nepal) and the Agricultural Research Council (NARC, Nepal). Seeds of released varieties were provided by the National Wheat Research Program (NWRP belonging to NARC, Nepal). CIMMYT breeding lines were provided by CIMMYT, Mexico.

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9 General Summary, Future Perspectives, and Conclusions 9.1 A general summary of findings The genetic structure of Nepali spring wheat diversity has not been reported previously. Therefore, in my thesis, I assembled the Nepali Wheat Diversity Panel (NWDP), consisting of 318 accessions, representing the wheat diversity in Nepal. The NWDP consists of landraces, released varieties, and CIMMYT advanced breeding lines which were previously tested in Nepal from the 2011-12 to 2013-14 seasons. The overall goal of my thesis was to evaluate the NWDP for genotypic and important phenotypic traits of interest. The first hypothesis of my thesis was that Nepali spring wheat germplasm is genetically diverse. To test this hypothesis, in Chapter 5, the NWDP was genotyped using the genotyping- by-sequencing (GBS) method (Poland et al., 2012; Torkamaneh et al., 2017) and the genetic diversity and population structure were analyzed. The genetic diversity of the NWDP, separated based on the seed source or breeding history, showed that the Nepali wheat landrace population is genetically more diverse compared to the Nepali CIMMYT lines and released varieties which represent the modern genetic materials. Based upon the population structure analysis, it was expected that subpopulations would be separated based on this breeding history but surprisingly the result showed 4 distinct subpopulations, not 3, with considerable admixtures within the landrace group. I discussed potential reasons for this surprising result, which has implications for long-term use of this germplasm resource. In Chapter 7, I further explored the genetic diversity of the NWDP by genotyping the population at 3 loci that have been important targets for wheat breeding since the Green Revolution. Specifically, Kompetitive Allele-Specific PCR (KASP) was used to genotype the reduced height Rht loci (Rht -D1 and Rht -D1) and a photoperiod sensitivity locus (Ppd-D1). It was observed that the NWDP had significant variation at Rht -B1 and Ppd -D1 loci but low variation at the Rht -D1 locus. The allelic variation at these Rht and Ppd loci separated the NWDP into different distinct clusters. The first distinct and largest group (117 accessions) contained the majority of landraces with wild type alleles at both the Rht -B1 and Rht -B1 loci, and all these accessions were photoperiod sensitive. The second-largest group (116 accessions) contained the majority of CIMMYT lines with additional released varieties and some landraces, and the accessions in this group were photoperiod insensitive.

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The second hypothesis of my thesis was that the accessions in the NWDP possess quantitative trait loci (QTL) alleles that positively correlate with agro-morphological traits of importance to Nepal. In Chapters 6, the NWDP was phenotyped for traits such as awn length (AL), days to anthesis (DA), days to maturity (DM), grain yield (GY), plant height (PH), harvest sprouting (PHS), test weight and thousand-grain weight (TGW) across four field experiments conducted in Canada and Nepal. The findings revealed that the NWDP accessions differed significantly for these traits. The GBS markers and the results of the population structure analysis along with the phenotypic data were then used to identify QTLs associated with the agro-morphological traits. A total of 15 significant QTLs were detected for six agro- morphological traits: AL, DA, DM, GY, PHS, and TW. To enable future mapping of drought tolerance alleles in the NWDP, I also wrote a review chapter (Chapter 4) on known drought tolerance QTLs in the literature from other populations. The third hypothesis of my thesis was that the NWDP is an important source of useful alleles for future breeding of improved wheat varieties. As already noted, analysis of the GBS data had revealed genetic diversity in the NDWP population, and the Nepali landrace group was observed to be genetically more diverse compared to the other two groups having modern genetic materials. As also noted, the diversity panel was also observed to be genetically diverse for alleles at Rht and Ppd loci. Consistent with these results, in Chapters 5, 6, 7 and 8, analysis of the phenotypic data including, phenological, morphological and physiological traits, indicated the existence of diversity in the NWDP. Seedling vigour traits such as coleoptile length and the length of the longest seedling root differed significantly among the genotypes in the study as did the coleoptile response to gibberellic acid. I used these multi-trait data, combined with the dwarfing allele data, to identify genotypes that are shorter at maturity (and hence desirable), but potentially have longer or more environmentally-responsive coleoptiles to tolerate seed seeding in order to mitigate early season drought. The NWDP was also evaluated for important physiological traits such as SPAD values and NDVI, which are commonly used proxies for chlorophyll content in crop research. Also, the NWDP was evaluated for abiotic stress tolerance traits such as shoot waxiness, known to be involved in drought tolerance, and the ratio of seedling root to shoot dry biomass. Here there was a surprising result which suggested that epicuticular wax might interfere with reflectance-based NDVI measurements. Nevertheless, low 218

wax/high yield accessions were identified, as potential targets for breeding more drought tolerant varieties for Nepal. In general, the significant variation observed among the genotypes for these physiological traits indicated that the NWDP is genetically diverse and it may be a good source of some desired alleles useful for future breeding including for abiotic stress tolerance. To catalyze such breeding for Nepal, in particular for drought tolerance, I wrote a review chapter (Chapter 3) to identify the benefits of using high throughput phenotyping combined with specific physiological traits at different stages of development in wheat that are associated with drought tolerance.

9.2 Future perspectives Wheat is the third most important cereal crop in Nepal in terms of both area and production. Despite >35% increase in wheat yield in the past two decades (Timsina et al., 2018), the average national wheat productivity (2.7 t/ha) is still less low compared to other wheat- growing nations such as India (3.37 t/ha), Bangladesh (3.13 t/ha) and the United States (3.19 t/ha) (FAOSTAT, 2019). Wheat imports into Nepal during the 2016 and 2017 fiscal year was greater than 70K tonnes per year (MoALD, 2018) indicating that demand exceeds local production nationally. These facts highlight the need for improving wheat productivity in Nepal. While various management practices support yield improvement, the development of high yielding varieties is a major way to raise the productivity of a crop. However, abiotic stresses such as drought and heat (Mondal et al., 2016; Sangwan et al., 2018) along with micro-climatic variation, in addition to biotic stresses, constrain wheat productivity in Nepal (Gurung, 2013). Therefore, the development of high yielding stress tolerant wheat varieties is a top priority among wheat breeders in Nepal. The availability of genetic diversity is the basis for successful variety development. The findings from the genetic diversity and population structure analysis showed that the landraces possess higher genetic diversity compared to the modern genetic resources including CIMMYT lines and the released varieties. However, population structures revealed admixtures in the subpopulation, and the relatedness observed between the landraces and modern lines was surprising. The introduction of genetic resources from the 1950s (Joshi et al., 2014; Vijayaraghavan et al., 2018) may have influenced the Nepali spring wheat diversity at a large 219

scale. Also, considering the methods adopted in some of the germplasm expeditions (Paudel et al., 2018), some of the early introductions which were grown by the farmers for decades could have been collected as landraces. Therefore, one of the immediate future tasks could be authentication of the landraces in the genebank. This may be done by tracing the history of germplasm collection, field evaluations and utilization of DNA-based diagnostic tools. The authentic landraces could be further utilized in a pre-breeding program to create new genetic diversity in the elite germplasm pool. Given that Nepal has more than 500 wheat landraces (Joshi et al., 2006), and no landraces were used in the development of the 43 wheat varieties released in Nepal as of 2017 (Joshi, 2017), the findings from this study could be the first step to start integrating landraces into the local wheat improvement program. While the information generated from the population structure and genetic diversity study may guide future breeding programs in Nepal, the genomic regions associated with different agro-morphological traits identified in this project could also be of interest to Nepali wheat breeders. As there are limited molecular studies on Nepali wheat diversity, the results generated from this project may be the starting step to further analyze Nepali spring wheat diversity. Furthermore, the allelic variation existing in Nepali spring wheat in terms of reduced height (Rht) and photoperiod sensitivity (Ppd) loci could be utilized in future breeding efforts. The candidate accessions carrying at least one dwarfing allele at the Rht loci may be directly used as parents in breeding programs in combination with other target traits such as longer coleoptiles, more robust seedling roots, greater epicuticular wax or improved photosynthetic capacity. Indeed, apart from the morphological and seedling vigor related traits, the NWDP was found to be diverse for physiological traits such as waxiness and chlorophyll-related traits. These traits contribute to stress adaption and improve grain yield in wheat (Rosyara et al., 2010; Kira et al., 2015; Bi et al., 2017). Therefore, there is also now the possibility to cross-breed accessions for combined higher waxiness and chlorophyll content, along with early seedling vigour traits, to develop multi-stress tolerant genotypes. Since the findings also showed confounding effects pertaining to leaf waxiness on spectral reflectance, my thesis suggests that caution should be used when using spectral reflectance during the variety selection process at least for this population.

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9.3 Conclusions This thesis evaluated a diversity panel of 318 spring wheat accessions termed the “Nepal Wheat Diversity Panel (NWDP). The outcome of the genetic diversity and population structure analysis using GBS data was surprising, indicating relatedness between many landraces and CIMMYT lines and revealed novel information about the genetic diversity of Nepali spring wheat. The results also suggest the need to verify the authenticity of some of the landraces. Identification of genomic regions associated with different agro-morphological traits using genome-wide association analysis suggested that a genetic basis exists for these traits of interest. These genomic regions or QTLs were found to be associated with protein-coding genes contributing to different molecular functions related to the physiology and development of the target traits. Furthermore, the existence of high allelic variation for reduced height (Rht) and photoperiod sensitivity (Ppd) loci in the NWDP has widened the opportunities for combining the target alleles into new varieties. The candidate accessions identified based on early-seedling vigour traits may be used as parents to develop varieties for early season stress tolerance including moisture stress. The phenotypic variation observed for physiological traits such as waxiness and chlorophyll-related traits shows the potential application of the physiological approach to breeding. However, the confounding results obtained especially with a physiological trait such as NDVI indicated the need for extra caution while measuring complex physiological traits such as shoot waxiness in a population as diverse as the NWDP. On the whole, it is expected that the results obtained from this project will significantly contribute to the on-going wheat breeding initiatives in Nepal.

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APPENDICES

Appendix 1. List of the genotypes in the Nepali Wheat Diversity Panel included in this study. Entry Accessions Pedigree Source Remarks 1 NGRC 00176 Local landrace NAGRC NEPAL Achham 2 NGRC 00177 Local landrace NAGRC NEPAL Achham 3 NGRC 00179 Local landrace NAGRC NEPAL Achham 4 NGRC 00180 Local landrace NAGRC NEPAL Achham 5 NGRC 00181 Local landrace NAGRC NEPAL Bajura 6 NGRC 00199 Local landrace NAGRC NEPAL Gulmi 7 NGRC 00202 Local landrace NAGRC NEPAL Baitadi 8 NGRC 00204 Local landrace NAGRC NEPAL Dandeldhura 9 NGRC 00205 Local landrace NAGRC NEPAL Dandeldhura 10 NGRC 02448 Local landrace NAGRC NEPAL Dandeldhura 11 NGRC 02449 Local landrace NAGRC NEPAL Baglung 12 NGRC 02450 Local landrace NAGRC NEPAL Baglung 13 NGRC 02451 Local landrace NAGRC NEPAL Gorkha 14 NGRC 02452 Local landrace NAGRC NEPAL Solukhumbu 15 NGRC 02455 Local landrace NAGRC NEPAL Solukhumbu 16 NGRC 02456 Local landrace NAGRC NEPAL Khotang 17 NGRC 02457 Local landrace NAGRC NEPAL Baglung 18 NGRC 02458 Local landrace NAGRC NEPAL Myagdi 19 NGRC 02459 Local landrace NAGRC NEPAL Mustang 20 NGRC 02460 Local landrace NAGRC NEPAL Manang 21 NGRC 02461 Local landrace NAGRC NEPAL Manang 22 NGRC 02462 Local landrace NAGRC NEPAL Manang 23 NGRC 02465 Local landrace NAGRC NEPAL Mugu 24 NGRC 02466 Local landrace NAGRC NEPAL Mugu 25 NGRC 02467 Local landrace NAGRC NEPAL Kalikot

287

Entry Accessions Pedigree Source Remarks 26 NGRC 02470 Local landrace NAGRC NEPAL Dandeldhura 27 NGRC 02471 Local landrace NAGRC NEPAL Jumla 28 NGRC 02472 Local landrace NAGRC NEPAL Darchula 29 NGRC 02496 Local landrace NAGRC NEPAL Darchula 30 NGRC 02544 Local landrace NAGRC NEPAL Rolpa 31 NGRC 02546 Local landrace NAGRC NEPAL Sallyan 32 NGRC 02547 Local landrace NAGRC NEPAL Rukum 33 NGRC 02548 Local landrace NAGRC NEPAL Sallyan 34 NGRC 02549 Local landrace NAGRC NEPAL Rukum 35 NGRC 02550 Local landrace NAGRC NEPAL Sallyan 36 NGRC 02551 Local landrace NAGRC NEPAL Sallyan 37 NGRC 02552 Local landrace NAGRC NEPAL Sallyan 38 NGRC 02553 Local landrace NAGRC NEPAL Rukum 39 NGRC 02554 Local landrace NAGRC NEPAL Bajhang 40 NGRC 02556 Local landrace NAGRC NEPAL Mustang 41 NGRC 02557 Local landrace NAGRC NEPAL Mustang 42 NGRC 02558 Local landrace NAGRC NEPAL Mustang 43 NGRC 02559 Local landrace NAGRC NEPAL Mustang 44 NGRC 02560 Local landrace NAGRC NEPAL Baglung 45 NGRC 02561 Local landrace NAGRC NEPAL Baitadi 46 NGRC 02563 Local landrace NAGRC NEPAL Darchula 47 NGRC 02564 Local landrace NAGRC NEPAL Darchula 48 NGRC 02565 Local landrace NAGRC NEPAL Bajura 49 NGRC 02566 Local landrace NAGRC NEPAL Bajura 50 NGRC 02567 Local landrace NAGRC NEPAL Bajura 51 NGRC 02568 Local landrace NAGRC NEPAL Bajura 52 NGRC 02569 Local landrace NAGRC NEPAL Bajura 53 NGRC 02570 Local landrace NAGRC NEPAL Bajhang

288

Entry Accessions Pedigree Source Remarks 54 NGRC 02571 Local landrace NAGRC NEPAL Bajhang 55 NGRC 02572 Local landrace NAGRC NEPAL Bajhang 56 NGRC 02574 Local landrace NAGRC NEPAL Jajarkot 57 NGRC 02575 Local landrace NAGRC NEPAL Dandeldhura 58 NGRC 02576 Local landrace NAGRC NEPAL Dandeldhura 59 NGRC 02578 Local landrace NAGRC NEPAL Dandeldhura 60 NGRC 02579 Local landrace NAGRC NEPAL Dandeldhura 61 NGRC 02580 Local landrace NAGRC NEPAL Dandeldhura 62 NGRC 02581 Local landrace NAGRC NEPAL Dandeldhura 63 NGRC 02582 Local landrace NAGRC NEPAL Dandeldhura 64 NGRC 02584 Local landrace NAGRC NEPAL Taplejung 65 NGRC 02585 Local landrace NAGRC NEPAL Doti 66 NGRC 02586 Local landrace NAGRC NEPAL Baitadi 67 NGRC 02587 Local landrace NAGRC NEPAL Baitadi 68 NGRC 02589 Local landrace NAGRC NEPAL Baitadi 69 NGRC 02590 Local landrace NAGRC NEPAL Baitadi 70 NGRC 02591 Local landrace NAGRC NEPAL Darchula 71 NGRC 02593 Local landrace NAGRC NEPAL Dandeldhura 72 NGRC 02594 Local landrace NAGRC NEPAL Dandeldhura 73 NGRC 02595 Local landrace NAGRC NEPAL Doti 74 NGRC 02596 Local landrace NAGRC NEPAL Doti 75 NGRC 02599 Local landrace NAGRC NEPAL Dolakha 76 NGRC 02602 Local landrace NAGRC NEPAL Kanchanpur 77 NGRC 02603 Local landrace NAGRC NEPAL Doti 78 NGRC 02604 Local landrace NAGRC NEPAL Dadeldhura 79 NGRC 02605 Local landrace NAGRC NEPAL Baitadi 80 NGRC 02606 Local landrace NAGRC NEPAL Baitadi 81 NGRC 02607 Local landrace NAGRC NEPAL Baitadi

289

Entry Accessions Pedigree Source Remarks 82 NGRC 02608 Local landrace NAGRC NEPAL Dadeldhura 83 NGRC 02609 Local landrace NAGRC NEPAL Baitadi 84 NGRC 02610 Local landrace NAGRC NEPAL Dolkha 85 NGRC 02611 Local landrace NAGRC NEPAL Dolkha 86 NGRC 02612 Local landrace NAGRC NEPAL Dolkha 87 NGRC 02613 Local landrace NAGRC NEPAL Dolkha 88 NGRC 02614 Local landrace NAGRC NEPAL Dolkha 89 NGRC 02615 Local landrace NAGRC NEPAL Dolkha 90 NGRC 02617 Local landrace NAGRC NEPAL Dolkha 91 NGRC 02619 Local landrace NAGRC NEPAL Dolkha 92 NGRC 02620 Local landrace NAGRC NEPAL Dolkha 93 NGRC 02621 Local landrace NAGRC NEPAL Rasuwa 94 NGRC 02622 Local landrace NAGRC NEPAL Dolkha 95 NGRC 02623 Local landrace NAGRC NEPAL Dolkha 96 NGRC 02624 Local landrace NAGRC NEPAL Dolpa 97 NGRC 02625 Local landrace NAGRC NEPAL Dolkha 98 NGRC 02629 Local landrace NAGRC NEPAL Dolkha 99 NGRC 02630 Local landrace NAGRC NEPAL Dolkha 100 NGRC 02631 Local landrace NAGRC NEPAL Rasuwa 101 NGRC 02632 Local landrace NAGRC NEPAL Nuwakot 102 NGRC 02633 Local landrace NAGRC NEPAL Nuwakot 103 NGRC 04399 Local landrace NAGRC NEPAL Ramechhap 104 NGRC 04400 Local landrace NAGRC NEPAL Ramechhap 105 NGRC 04401 Local landrace NAGRC NEPAL Ramechhap 106 NGRC 04402 Local landrace NAGRC NEPAL Ramechhap 107 NGRC 04404 Local landrace NAGRC NEPAL Ramechhap 108 NGRC 04405 Local landrace NAGRC NEPAL Ramechhap 109 NGRC 04406 Local landrace NAGRC NEPAL Ramechhap

290

Entry Accessions Pedigree Source Remarks 110 NGRC 04408 Local landrace NAGRC NEPAL Ramechhap 111 NGRC 04409 Local landrace NAGRC NEPAL Salyan 112 NGRC 04410 Local landrace NAGRC NEPAL Myagdi Kavreplanch 113 NGRC 04413 Local landrace NAGRC NEPAL ok Kavreplanch 114 NGRC 04414 Local landrace NAGRC NEPAL ok 115 NGRC 04416 Local landrace NAGRC NEPAL Salyan 116 NGRC 04417 Local landrace NAGRC NEPAL Salyan 117 NGRC 04418 Local landrace NAGRC NEPAL Salyan 118 NGRC 04419 Local landrace NAGRC NEPAL Salyan 119 NGRC 04421 Local landrace NAGRC NEPAL Salyan 120 NGRC 04422 Local landrace NAGRC NEPAL Surkhet 121 NGRC 04423 Local landrace NAGRC NEPAL Surkhet 122 NGRC 04424 Local landrace NAGRC NEPAL Ramechhap 123 NGRC 04425 Local landrace NAGRC NEPAL Surkhet 124 NGRC 04426 Local landrace NAGRC NEPAL Surkhet 125 NGRC 04427 Local landrace NAGRC NEPAL Surkhet 126 NGRC 04428 Local landrace NAGRC NEPAL Dolakha 127 NGRC 04429 Local landrace NAGRC NEPAL Dolakha 128 NGRC 04430 Local landrace NAGRC NEPAL Dolakha 129 NGRC 04431 Local landrace NAGRC NEPAL Dolakha 130 NGRC 04432 Local landrace NAGRC NEPAL Dolakha 131 NGRC 04433 Local landrace NAGRC NEPAL Dolakha 132 NGRC 04434 Local landrace NAGRC NEPAL Dolakha 133 NGRC 04436 Local landrace NAGRC NEPAL Dolakha 134 NGRC 04437 Local landrace NAGRC NEPAL Dolakha 135 NGRC 04439 Local landrace NAGRC NEPAL Dolakha

291

Entry Accessions Pedigree Source Remarks 136 NGRC 04440 Local landrace NAGRC NEPAL Dolakha Kavreplanch 137 NGRC 04443 Local landrace NAGRC NEPAL ok Kavreplanch 138 NGRC 04444 Local landrace NAGRC NEPAL ok Kavreplanch 139 NGRC 04445 Local landrace NAGRC NEPAL ok Kavreplanch 140 NGRC 04446 Local landrace NAGRC NEPAL ok Kavreplanch 141 NGRC 04447 Local landrace NAGRC NEPAL ok 142 NGRC 04448 Local landrace NAGRC NEPAL Surkhet 143 NGRC 04449 Local landrace NAGRC NEPAL Surkhet 144 NGRC 04450 Local landrace NAGRC NEPAL Surkhet 145 NGRC 04451 Local landrace NAGRC NEPAL Dailekh 146 NGRC 04452 Local landrace NAGRC NEPAL Dailekh 147 NGRC 04453 Local landrace NAGRC NEPAL Dailekh 148 NGRC 04454 Local landrace NAGRC NEPAL Dailekh 149 NGRC 04455 Local landrace NAGRC NEPAL Dailekh 150 NGRC 04456 Local landrace NAGRC NEPAL Dailekh 151 NGRC 04457 Local landrace NAGRC NEPAL Dailekh 152 NGRC 04458 Local landrace NAGRC NEPAL Dailekh 153 NGRC 04459 Local landrace NAGRC NEPAL Dailekh 154 NGRC 04460 Local landrace NAGRC NEPAL Dailekh 155 NGRC 04461 Local landrace NAGRC NEPAL Dailekh 156 NGRC 04462 Local landrace NAGRC NEPAL Dailekh 157 NGRC 04463 Local landrace NAGRC NEPAL Dailekh 158 NGRC 04464 Local landrace NAGRC NEPAL Dailekh

292

Entry Accessions Pedigree Source Remarks 159 NGRC 04465 Local landrace NAGRC NEPAL Dailekh 160 NGRC 04466 Local landrace NAGRC NEPAL Dailekh 161 NGRC 04467 Local landrace NAGRC NEPAL Dailekh 162 NGRC 04468 Local landrace NAGRC NEPAL Dailekh 163 NGRC 04470 Local landrace NAGRC NEPAL Dailekh 164 NGRC 04471 Local landrace NAGRC NEPAL Dailekh 165 NGRC 04472 Local landrace NAGRC NEPAL Dailekh 166 NGRC 04473 Local landrace NAGRC NEPAL Dailekh 167 NGRC 04474 Local landrace NAGRC NEPAL Mugu Released 168 Lerma 52 MENTANA/KENYA 324 NWRP NEPAL variety Released 169 Kalyansona PJ"S"/GB55 (S227) NWRP NEPAL variety Released 170 Pitic 62 YT54/N10B126.IC NWRP NEPAL variety 1154- Released 171 RR21 388/AN/3/YT54/NIOB/RL64 NWRP NEPAL variety Released 172 NL 30 HD832-5-5-OY/RB NWRP NEPAL variety Released 173 UP 262 S308/BAJIO-66 NWRP NEPAL variety Released 174 Lumbini E4871/PJ62 NWRP NEPAL variety Released 175 Tribeni HD1963/HD1931 NWRP NEPAL variety Released 176 Vinayak LC55 NWRP NEPAL variety

293

Entry Accessions Pedigree Source Remarks Released 177 Siddhartha HD2092/HD1982//E4870/K65 NWRP NEPAL variety Released 178 Vaskar TZPP/PL//7C NWRP NEPAL variety Released 179 Nepal 297 HD2173/HD2186//HD2160 NWRP NEPAL variety Released 180 NL 251 WH147/HD2160//WH147 NWRP NEPAL variety KBZ/BUHO//KAL/BB=(VEE Released 181 Annapurna 1 "S") NWRP NEPAL variety NPO/TOB"S"//8156/3/KAL/B Released 182 Annapurna 2 B NWRP NEPAL variety KBZ/BUHO//KAL/BB=(VEE Released 183 Annapurna 3 "S") NWRP NEPAL variety Released 184 BL 1022 PVN/BUC NWRP NEPAL variety CMT/COC75/3/PLO//FURY/ Released 185 Bhrikuti ANA75 NWRP NEPAL variety Released 186 BL 1135 QTZ/TAN"S" NWRP NEPAL variety KBZ/3/CC/INIA//CNO/ELGA Released 187 Annapurna 4 U/SN64 NWRP NEPAL variety Released 188 Achyut CPAN168/HD2204 NWRP NEPAL variety Released 189 Rohini PRL"S"/TONI//CHIL"S" NWRP NEPAL variety Released 190 Kanti LIRA/FUFAN17//VEE#5"S" NWRP NEPAL variety

294

Entry Accessions Pedigree Source Remarks Pasang Released 191 Lhamu PGO/SERI NWRP NEPAL variety Released 192 BL 1473 NL297/NL352 NWRP NEPAL variety SIDDHARTH/NING8319/NL Released 193 Gautam 297 NWRP NEPAL variety Released 194 WK1204 SW89-3064/STAR NWRP NEPAL variety MRNG/BUC//BLO/PVN/3/PJ Released 195 NL 971 B81 NWRP NEPAL variety Aditya (BL Released 196 3264) GS348/NL746//NL748 NWRP NEPAL variety Vijay (BL Released 197 3063) NL 748/NL 736 (Ug99 Res.) NWRP NEPAL variety Gaura (BL Released 198 3235) NL 872/NL 868 NWRP NEPAL variety Dhaulagiri Released 199 (BL 3503) BL 1961/NL 867 NWRP NEPAL variety Danphe (NL KIRITATI//2*PBW65/2*SER Released 200 1064) I.1B NWRP NEPAL variety Tilottama (NL Released 201 1073) WAXWING*2/VIVITSI NWRP NEPAL variety CM85836-4Y-0M- CIMMYT 202 BW30655 PBW343 0Y-8M-0Y-0IND MEXICO CGSS97Y00034M- 099TOPB-027Y- 099M-099Y- CIMMYT 203 BW35623 PRL/2*PASTOR 099M-27Y-0B MEXICO

295

Entry Accessions Pedigree Source Remarks CGSS01B00054T- 099Y-099M- 099M-099Y- CIMMYT 204 BW43945 MUNAL #1 099M-13Y-0B MEXICO CGSS01B00063T- 099Y-099M- 099M-099Y- CIMMYT 205 BW45161 BECARD 099M-18WGY-0B MEXICO CGSS02Y00153S- 099M-099Y- CIMMYT 206 BW43354 SUPER 152 099M-46Y-0B MEXICO CMSS02Y00615S- 47Y-0M-099Y- CIMMYT 207 BW44829 PFUNYE #1 4M-0WGY-0B MEXICO CGSS03B00149S- 099M-099Y- CIMMYT 208 BW44908 HUHWA 099M-5WGY-0B MEXICO CGSS04Y00002T- 099M-099Y- TRCH*2/3/C80.1/3*QT4118// 099ZTM-099Y- CIMMYT 209 BW45568 3*PASTOR 099M-8WGY-0B MEXICO CGSS04Y00076S- WHEAR//INQALAB 099Y-099M-099Y- CIMMYT 210 BW45152 91*2/TUKURU 099M-5WGY-0B MEXICO CGSS04Y00106S- 099Y-099M-099Y- CIMMYT 211 BW45573 WHEAR/KRONSTAD F2004 099M-13WGY-0B MEXICO

296

Entry Accessions Pedigree Source Remarks CMSS04Y00201S- 099Y-099ZTM- 099Y-099M- CIMMYT 212 BW45173 WHEAR/SOKOLL 11WGY-0B MEXICO SHA7/VEE#5/5/VEE#8//JUP/ CMSS04Y01158S- BJY/3/F3.71/TRM/4/2*WEA 099Y-099ZTM- VER/6/SKAUZ/PARUS//PAR 099Y-099M- CIMMYT 213 BW45578 US 6WGY-0B MEXICO PFAU/SERI.1B//AMAD/3/IN CGSS04B00024T- QALAB 099Y-099ZTM- 91*2/KUKUNA/4/WBLL1*2/ 099Y-099M- CIMMYT 214 BW45590 KURUKU 26WGY-0B MEXICO CGSS04B00027T- 099Y-099ZTM- 099Y-099M- CIMMYT 215 BW45592 NELOKI 7WGY-0B MEXICO HUW234+LR34/PRINIA//IN QALAB CGSS04B00034T- 91*2/KUKUNA/5/FRET2*2/4 099Y-099ZTM- /SNI/TRAP#1/3/KAUZ*2/TR 099Y-099M- CIMMYT 216 BW45593 AP//KAUZ 9WGY-0B MEXICO SNB//CMH79A.955/3*CNO7 CMSS04M00060S- 9/3/ATTILA/4/CHEN/AEGIL 0Y-099ZTM- OPS SQUARROSA 099Y-099M- CIMMYT 217 BW45587 (TAUS)//BCN/3/2*KAUZ 2WGY-0B MEXICO KAUZ//ALTAR CMSS04M01386S- CIMMYT 218 BW45165 84/AOS/3/PASTOR/4/MILA 0TOPY-099ZTM- MEXICO

297

Entry Accessions Pedigree Source Remarks N/CUPE//SW89.3064/5/KIRI 099Y-099M- TATI 2WGY-0B GRSS04B00010T- MELON//FILIN/MILAN/3/FI 099Y-099B-099Y- CIMMYT 219 BW45595 LIN 099M-6WGY-0B MEXICO CGSS05Y00206T- 099M-099Y- 099M-099Y- MARCHOUCH*4/SAADA/3/ 099ZTM-7WGY- CIMMYT 220 BW48132 2*FRET2/KUKUNA//FRET2 0B MEXICO CGSS05Y00363S- WAXWING/6/PVN//CAR422 0B-099Y-099M- /ANA/5/BOW/CROW//BUC/ 099NJ-099NJ- CIMMYT 221 BW48133 PVN/3/YR/4/TRAP#1 6WGY-0B MEXICO CMSS05Y00671T- 099TOPM-099Y- 099M-099Y- ATTILA*2//CHIL/BUC*2/3/ 099ZTM-2WGY- CIMMYT 222 BW48135 KUKUNA 0B MEXICO CMSS05B00053S- 099Y-099M-099Y- WBLL1/KUKUNA//TACUPE 099ZTM-19WGY- CIMMYT 223 BW48136 TO F2001/3/BAJ #1 0B MEXICO CMSS05B00054S- 099Y-099M-099Y- 099ZTM-8WGY- CIMMYT 224 BW48137 WBLL1//UP2338*2/VIVITSI 0B MEXICO

298

Entry Accessions Pedigree Source Remarks CMSS05B00480S- FRET2*2/4/SNI/TRAP#1/3/K 099Y-099M-099Y- AUZ*2/TRAP//KAUZ/5/PFA 099ZTM-22WGY- CIMMYT 225 BW48139 U/WEAVER//BRAMBLING 0B MEXICO CMSS05B00574S- 099Y-099M-099Y- 099ZTM-10WGY- CIMMYT 226 BW48171 TRCH//PRINIA/PASTOR 0B MEXICO CMSS05B00579S- KAUZ//ALTAR 099Y-099M-099Y- 84/AOS/3/MILAN/KAUZ/4/S 099ZTM-10WGY- CIMMYT 227 BW48140 AUAL 0B MEXICO CMSS05B00581S- 099Y-099M-099Y- 099ZTM-2WGY- CIMMYT 228 BW48141 KACHU/SAUAL 0B MEXICO ATTILA/3*BCN//BAV92/3/T ILHI/5/BAV92/3/PRL/SARA/ CMSS05B00663S- /TSI/VEE#5/4/CROC_1/AE.S 099Y-099M-099Y- QUARROSA 099ZTM-13WGY- CIMMYT 229 BW48144 (224)//2*OPATA 0B MEXICO CGSS05B00121T- ROLF07/YANAC//TACUPET 099TOPY-099M- CIMMYT 230 BW48145 O F2001/BRAMBLING 099NJ-4WGY-0B MEXICO FRET2*2/4/SNI/TRAP#1/3/K CGSS05B00132T- AUZ*2/TRAP//KAUZ*2/6/P 099TOPY-099M- CIMMYT 231 BW48148 VN//CAR422/ANA/5/BOW/C 099NJ-6WGY-0B MEXICO

299

Entry Accessions Pedigree Source Remarks ROW//BUC/PVN/3/YR/4/TR AP#1 CGSS05B00137T- FRET2*2/4/SNI/TRAP#1/3/K 099TOPY-099M- AUZ*2/TRAP//KAUZ/5/PAR 099Y-099ZTM- CIMMYT 232 BW48149 US/6/FRET2*2/KUKUNA 11WGY-0B MEXICO CGSS05B00137T- FRET2*2/4/SNI/TRAP#1/3/K 099TOPY-099M- AUZ*2/TRAP//KAUZ/5/PAR 099NJ-099NJ- CIMMYT 233 BW48150 US/6/FRET2*2/KUKUNA 9WGY-0B MEXICO CGSS05B00141T- FRET2*2/4/SNI/TRAP#1/3/K 099TOPY-099M- AUZ*2/TRAP//KAUZ*2/5/KI 099NJ-099NJ- CIMMYT 234 BW48151 RITATI 11WGY-0B MEXICO FRET2/KUKUNA//FRET2/3/ PASTOR//HXL7573/2*BAU/ CGSS05B00162T- 5/FRET2*2/4/SNI/TRAP#1/3/ 099TOPY-099M- CIMMYT 235 BW48154 KAUZ*2/TRAP//KAUZ 099NJ-13WGY-0B MEXICO CGSS05B00174T- 099TOPY-099M- WBLL1*2/KUKUNA*2//WH 099NJ-099NJ- CIMMYT 236 BW48155 EAR 5WGY-0B MEXICO CGSS05B00189T- 099TOPY-099M- 099NJ-099NJ- CIMMYT 237 BW48156 TRCH/SRTU//KACHU 7WGY-0B MEXICO

300

Entry Accessions Pedigree Source Remarks CGSS05B00198T- SERI.1B//KAUZ/HEVO/3/A 099TOPY-099M- CIMMYT 238 BW48158 MAD*2/4/KIRITATI 099NJ-14WGY-0B MEXICO CGSS05B00220T- WAXWING*2/6/PVN//CAR4 099TOPY-099M- 22/ANA/5/BOW/CROW//BU 099NJ-099NJ- CIMMYT 239 BW48172 C/PVN/3/YR/4/TRAP#1 2WGY-0B MEXICO CGSS05B00256T- 099TOPY-099M- PBW343*2/KUKUNA//PAR 099NJ-099NJ- CIMMYT 240 BW48174 US/3/PBW343*2/KUKUNA 3WGY-0B MEXICO CGSS05B00256T- 099TOPY-099M- PBW343*2/KUKUNA//PAR 099NJ-099NJ- CIMMYT 241 BW48161 US/3/PBW343*2/KUKUNA 5WGY-0B MEXICO CGSS05B00258T- 099TOPY-099M- PBW343*2/KUKUNA*2//YA 099Y-099ZTM- CIMMYT 242 BW48162 NAC 12WGY-0B MEXICO CGSS05B00258T- PBW343*2/KUKUNA*2//YA 099TOPY-099M- CIMMYT 243 BW48163 NAC 099NJ-1WGY-0B MEXICO CGSS05B00261T- 099TOPY-099M- PBW343*2/KUKUNA//SRTU 099NJ-099NJ- CIMMYT 244 BW48166 /3/PBW343*2/KHVAKI 8WGY-0B MEXICO PBW343*2/KUKUNA//SRTU CGSS05B00261T- CIMMYT 245 BW48165 /3/PBW343*2/KHVAKI 099TOPY-099M- MEXICO

301

Entry Accessions Pedigree Source Remarks 099NJ-099NJ- 6WGY-0B ATTILA*2/PBW65/6/PVN//C CGSS05B00290T- AR422/ANA/5/BOW/CROW/ 099TOPY-099M- /BUC/PVN/3/YR/4/TRAP#1/7 099Y-099ZTM- CIMMYT 246 BW48169 /ATTILA/2*PASTOR 20WGY-0B MEXICO ATTILA*2/PBW65/6/PVN//C CGSS05B00290T- AR422/ANA/5/BOW/CROW/ 099TOPY-099M- /BUC/PVN/3/YR/4/TRAP#1/7 099Y-099ZTM- CIMMYT 247 BW48168 /ATTILA/2*PASTOR 17WGY-0B MEXICO CMSS06Y00582T- 099TOPM-099Y- ATTILA*2/PBW65*2//KACH 099ZTM-099Y- CIMMYT 248 BW49069 U 099M-10WGY-0B MEXICO CMSS06Y00582T- 099TOPM-099Y- ATTILA*2/PBW65*2//KACH 099ZTM-099Y- CIMMYT 249 BW49227 U 099M-13WGY-0B MEXICO CMSS06Y00605T- 099TOPM-099Y- 099ZTM-099Y- CIMMYT 250 BW49235 REEDLING #1 099M-11WGY-0B MEXICO CMSS06Y00778T- 099TOPM-099Y- KACHU 099ZTM-099NJ- CIMMYT 251 BW49072 #1/KIRITATI//KACHU 099NJ-6WGY-0B MEXICO PBW343*2/KUKUNA*2//FR CMSS06Y00831T- CIMMYT 252 BW49075 TL/PIFED 099TOPM-099Y- MEXICO

302

Entry Accessions Pedigree Source Remarks 099ZTM-099NJ- 099NJ-5WGY-0B CMSS06Y00885T- WBLL1*2/4/BABAX/LR42// 099TOPM-099Y- BABAX/3/BABAX/LR42//B 099ZTM-099NJ- CIMMYT 253 BW49077 ABAX 099NJ-26WGY-0B MEXICO CMSS06Y00920T- 099TOPM-099Y- ATTILA*2/PBW65*2//MUR 099ZTM-099Y- CIMMYT 254 BW49078 GA 099M-36WGY-0B MEXICO CMSS06Y00926T- ROLF07*2/5/REH/HARE//2* 099TOPM-099Y- BCN/3/CROC_1/AE.SQUAR 099ZTM-099Y- CIMMYT 255 BW49079 ROSA (213)//PGO/4/HUITES 099M-5WGY-0B MEXICO CMSS06Y01061T- 099TOPM-099Y- ROLF07*2/5/FCT/3/GOV/AZ 099ZTM-099Y- CIMMYT 256 BW49082 //MUS/4/DOVE/BUC 0FUS-7WGY-0B MEXICO CMSS06Y01171T- 099TOPM-099Y- SAUAL/3/ACHTAR*3//KAN 099ZTM-099Y- CIMMYT 257 BW49344 Z/KS85-8-4/4/SAUAL 099M-8WGY-0B MEXICO CMSS06B00013S- 0Y-099ZTM- 099Y-099M- CIMMYT 258 BW49084 FRNCLN/ROLF07 2WGY-0B MEXICO

303

Entry Accessions Pedigree Source Remarks CMSS06B00109S- QUAIU/5/FRET2*2/4/SNI/TR 0Y-099ZTM- AP#1/3/KAUZ*2/TRAP//KA 099NJ-099NJ- CIMMYT 259 BW49088 UZ 13WGY-0B MEXICO CMSS06B00142S- TACUPETO 0Y-099ZTM- F2001*2/BRAMBLING//WB 099Y-099M- CIMMYT 260 BW49089 LL1*2/BRAMBLING 30WGY-0B MEXICO CMSS06B00169S- 0Y-099ZTM- 099Y-099M- CIMMYT 261 BW49391 BECARD/KACHU 15WGY-0B MEXICO CMSS06B00169S- 0Y-099ZTM- 099Y-099M- CIMMYT 262 BW49092 BECARD/KACHU 21WGY-0B MEXICO CMSS06B00169S- 0Y-099ZTM- 099Y-099M- CIMMYT 263 BW49093 BECARD/KACHU 28WGY-0B MEXICO ALTAR 84/AE.SQUARROSA CMSS06B00201S- (221)//3*BORL95/3/URES/JU 0Y-099ZTM- N//KAUZ/4/WBLL1/5/MUTU 099Y-099M- CIMMYT 264 BW49399 S 8WGY-0B MEXICO CMSS06B00314S- CIMMYT 265 BW49095 TRCH/HUIRIVIS #1 0Y-099ZTM- MEXICO

304

Entry Accessions Pedigree Source Remarks 099NJ-099NJ- 21WGY-0B CMSS06B00411S- 0Y-099ZTM- 099Y-099M- CIMMYT 266 BW49097 BECARD/AKURI 12WGY-0B MEXICO CMSS06B00485S- 0Y-099ZTM- KINGBIRD #1//INQALAB 099Y-099M- CIMMYT 267 BW49099 91*2/TUKURU 7WGY-0B MEXICO CMSS06B00640S- 0Y-099ZTM- 099Y-099M- CIMMYT 268 BW49102 QUELEA 7WGY-0B MEXICO CMSS06B00848T- 099TOPY- UP2338*2/VIVITSI/3/FRET2/ 099ZTM-099NJ- CIMMYT 269 BW49108 TUKURU//FRET2/4/MISR 1 099NJ-12WGY-0B MEXICO CMSS06B00915T- 099TOPY- 099ZTM-099Y- CIMMYT 270 BW49109 WAXBILL 099M-8WGY-0B MEXICO BAV92//IRENA/KAUZ/3/HU CMSS06B00918T- ITES/4/GONDO/TNMU/5/BA 099TOPY- V92//IRENA/KAUZ/3/HUITE 099ZTM-099Y- CIMMYT 271 BW49110 S 0FUS-6WGY-0B MEXICO WBLL1*2/TUKURU//FN/2*P CMSS06B00926T- CIMMYT 272 BW49111 ASTOR/3/FRET2/KIRITATI 099TOPY- MEXICO

305

Entry Accessions Pedigree Source Remarks 099ZTM-099Y- 0FUS-18WGY-0B CMSS06B00975T- PVN/5/2*REH/HARE//2*BC 099TOPY- N/3/CROC_1/AE.SQUARRO 099ZTM-099Y- CIMMYT 273 BW49112 SA (213)//PGO/4/HUITES 099M-9WGY-0B MEXICO CMSS06B01005T- 099TOPY- 099ZTM-099NJ- CIMMYT 274 BW49113 KFA/2*KACHU 099NJ-31WGY-0B MEXICO CMSS07Y00066S- CHIBIA//PRLII/CM65531/3/ 0B-099Y-099M- CIMMYT 275 BW49922 MISR2, EGY/4/MUNAL #1 099Y-38M-0WGY MEXICO CMSS07Y00124S- 0B-099Y-099M- KACHU//WBLL1*2/BRAMB 099NJ-099NJ- CIMMYT 276 BW49923 LING 9WGY-0B MEXICO CMSS07Y00127S- 0B-099Y-099M- 099NJ-099NJ- CIMMYT 277 BW49924 KACHU/KIRITATI 6WGY-0B MEXICO CMSS07Y00195S- 0B-099Y-099M- CIMMYT 278 BW49927 SUP152/BAJ #1 099Y-16M-0WGY MEXICO CMSS07Y00197S- 0B-099Y-099M- 099NJ-099NJ- CIMMYT 279 BW49929 SUP152/BECARD 21WGY-0B MEXICO

306

Entry Accessions Pedigree Source Remarks CMSS07Y00348S- WBLL4/KUKUNA//WBLL1/ 0B-099Y-099M- CIMMYT 280 BW49931 3/WBLL1*2/BRAMBLING 099Y-19M-0WGY MEXICO CMSS07Y00441S- 0B-099Y-099M- 099NJ-099NJ- CIMMYT 281 BW49932 ITP40/AKURI 4WGY-0B MEXICO CMSS07Y00847T- 099TOPM-099Y- KIRITATI/WBLL1//MESIA/3 099M-099Y-53M- CIMMYT 282 BW49933 /KIRITATI/WBLL1 0WGY MEXICO CMSS07Y00849T- 099TOPM-099Y- KIRITATI/WBLL1//2*BLOU 099M-099Y-17M- CIMMYT 283 BW49934 K #1 0WGY MEXICO CMSS07Y00965T- 099TOPM-099Y- 099M-099Y-23M- CIMMYT 284 BW49936 SUP152/AKURI//SUP152 0WGY MEXICO CMSS07Y01083T- 099TOPM-099Y- 099M-099Y-40M- CIMMYT 285 BW49939 MUTUS*2/AKURI 0WGY MEXICO CMSS07B00129S- PBW343*2/KUKUNA/3/PAS 099M-099Y- CIMMYT 286 BW49943 TOR//CHIL/PRL/4/GRACK 099M-5WGY-0B MEXICO

307

Entry Accessions Pedigree Source Remarks CMSS07B00235S- 099M-099Y- CIMMYT 287 BW49948 BECARD/FRNCLN 099M-25WGY-0B MEXICO CMSS07B00374S- WBLL1*2/BRAMBLING//C 099M-099NJ- CIMMYT 288 BW49949 HYAK 099NJ-8WGY-0B MEXICO CMSS07B00377S- 099M-099NJ- CIMMYT 289 BW49950 BECARD//ND643/2*WBLL1 099NJ-23WGY-0B MEXICO CMSS07B00577T- KAUZ*2/MNV//KAUZ/3/MI 099TOPY-099M- LAN/4/BAV92/5/AKURI/6/M 099Y-099M- CIMMYT 290 BW49953 UTUS 2WGY-0B MEXICO CMSS07B00580T- 099TOPY-099M- KACHU/BECARD//WBLL1* 099Y-099M- CIMMYT 291 BW49954 2/BRAMBLING 10WGY-0B MEXICO CMSS07B00585T- 099TOPY-099M- KAUZ/PASTOR//PBW343/3/ 099Y-099M- CIMMYT 292 BW49955 KIRITATI/4/FRNCLN 11WGY-0B MEXICO CMSS07B00614T- 099TOPY-099M- 099Y-099M- CIMMYT 293 BW49956 SUP152*2/TECUE #1 49WGY-0B MEXICO FRANCOLIN #1/AKURI CMSS07B00655T- CIMMYT 294 BW49957 #1//FRNCLN 099TOPY-099M- MEXICO

308

Entry Accessions Pedigree Source Remarks 099Y-099M- 11WGY-0B CMSS07B00825T- 099TOPY-099M- ND643/2*TRCH//MUTUS/3/ 099Y-099M- CIMMYT 295 BW49958 SUP152 11WGY-0B MEXICO CMSS05B00061S- WBLL1*2/4/YACO/PBW65/3 099Y-099M-099Y- /KAUZ*2/TRAP//KAUZ/5/K 099ZTM-3WGY- CIMMYT 296 BW48314 ACHU #1 0B MEXICO ALTAR 84/AE.SQUARROSA (221)//3*BORL95/3/URES/JU CMSS06Y00117S- N//KAUZ/4/WBLL1/5/REH/ 0B-099Y- HARE//2*BCN/3/CROC_1/A 099ZTM-099Y- CIMMYT 297 BW49217 E.SQUARRO 099M-7WGY-0B MEXICO WBLL1*2/KURUKU*2/5/RE CMSS06Y00933T- H/HARE//2*BCN/3/CROC_1/ 099TOPM-099Y- AE.SQUARROSA 099ZTM-099Y- CIMMYT 298 BW49307 (213)//PGO/4/HUITES 099M-30WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 299 BW49325 /NKT//CBRD/3/CBRD 099M-1WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 300 BW49326 /NKT//CBRD/3/CBRD 099M-6WGY-0B MEXICO

309

Entry Accessions Pedigree Source Remarks CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 301 BW49327 /NKT//CBRD/3/CBRD 099M-9WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 302 BW49328 /NKT//CBRD/3/CBRD 099M-11WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 303 BW49329 /NKT//CBRD/3/CBRD 099M-18WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 304 BW49330 /NKT//CBRD/3/CBRD 099M-23WGY-0B MEXICO CMSS06Y01026T- 099TOPM-099Y- ATTILA*2/PBW65*2/4/BOW 099ZTM-099Y- CIMMYT 305 BW49331 /NKT//CBRD/3/CBRD 099M-28WGY-0B MEXICO CMSS06Y01040T- 099TOPM-099Y- BAV92//IRENA/KAUZ/3/HU 099ZTM-099Y- CIMMYT 306 BW49333 ITES*2/4/GONDO/TNMU 0FUS-28WGY-0B MEXICO CMSS06Y01115T- 099TOPM-099Y- 099ZTM-099Y- CIMMYT 307 BW49342 KACHU*2//CHIL/CHUM18 0FUS-4WGY-0B MEXICO

310

Entry Accessions Pedigree Source Remarks CMSS06Y01282T- 099TOPM-099Y- 099ZTM-099Y- CIMMYT 308 BW49351 KACHU #1*2/WHEAR 099M-3WGY-0B MEXICO CMSS06B00154S- 0Y-099ZTM- FRANCOLIN 099Y-099M- CIMMYT 309 BW49385 #1//WBLL1*2/BRAMBLING 2WGY-0B MEXICO CMSS06B00169S- 0Y-099ZTM- 099Y-099M- CIMMYT 310 BW49392 BECARD/KACHU 25WGY-0B MEXICO CMSS06B00169S- 0Y-099ZTM- 099Y-099M- CIMMYT 311 BW49394 BECARD/KACHU 23WGY-0B MEXICO ALTAR 84/AE.SQUARROSA CMSS06B00201S- (221)//3*BORL95/3/URES/JU 0Y-099ZTM- N//KAUZ/4/WBLL1/5/MUTU 099Y-099M- CIMMYT 312 BW49094 S 14WGY-0B MEXICO ALTAR 84/AE.SQUARROSA CMSS06B00201S- (221)//3*BORL95/3/URES/JU 0Y-099ZTM- N//KAUZ/4/WBLL1/5/MUTU 099Y-099M- CIMMYT 313 BW49400 S 15WGY-0B MEXICO KINGBIRD #1//INQALAB CMSS06B00485S- CIMMYT 314 BW49099 91*2/TUKURU 0Y-099ZTM- MEXICO

311

Entry Accessions Pedigree Source Remarks 099Y-099M- 7WGY-0B CMSS06B00676T- 099TOPY- ATTILA*2/PBW65*2//TOBA 099ZTM-099Y- CIMMYT 315 BW49448 97/PASTOR 099M-2WGY-0B MEXICO CMSS06B00726T- WBLL1*2/VIVITSI//PRINIA/ 099TOPY- PASTOR/3/WBLL1*2/BRAM 099ZTM-099Y- CIMMYT 316 BW49456 BLING 099M-6WGY-0B MEXICO CMSS06B00734T- NAC/TH.AC//3*PVN/3/MIR 099TOPY- LO/BUC/4/2*PASTOR/5/KA 099ZTM-099Y- CIMMYT 317 BW49458 CHU/6/KACHU 099M-13WGY-0B MEXICO 318 Pasteur Pasteur Pasteur Check 319 AC Carberry AC Carberry AC Carberry Check 320 Norwell Norwell Norwell Check

312

Appendix 2. Summary of distribution of SNPs in wheat genomes across 21 chromosomes. Genome Chromosomes Genome A Genome B Genome C Total 1 5377 6544 1883 13804 2 6422 8374 2046 16842 3 4881 7806 1247 13934 4 4808 2648 550 8006 5 4494 6647 1013 12154 6 3979 7952 1343 13274 7 7578 8264 1532 17374 Total 37539 48235 9614 95388

Appendix 3. The frequency of genotypes in the Nepali Wheat Diversity Panel as differentiated into different subpopulations based on Q-matrix obtained from fastSTRUCTURE. SP 1 SP 2 SP 3 SP 4 Total Landraces 12 (7) 62 (37) 70 (42) 22 (13) 166 (100) CIMMYT lines 8 (7) 69 (60) 25 (22) 13 (11) 115 (100) Released varieties 2 (6) 22 (65) 4 (12) 6(18) 34 (100) Canadian varieties 0 1 (33) 0 2 (67) 3 (100) Values in parentheses indicate the frequency within each seed source category. Abbreviations: SP 1= Subpopulation 1, SP 2= Subpopulation 2, SP 3= Subpopulation 3, SP 4= Subpopulation 4.

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Appendix 4. Summary of weather data for the four field experiments conducted in Nepal and Canada during 2016 and 2017 wheat growing seasons. Location Year Month Maximum Minimum Average Precipitation temperature temperature temperature (mm) (0C) (0C) (0C) Elora 2016 May 18.9 6.3 12.6 57.3 2016 June 24.1 10.0 16.9 53.0 2016 July 26.8 13.3 20.1 102.4 2016 August 26.8 14.1 20.4 152.6 Elora 2017 May 14.9 5.7 10.0 120.5 2017 June 22.7 11.9 17.4 117.8 2017 July 24.2 13.2 18.7 35.6 2017 August 23.1 10.8 16.9 68.1 ABD 2016 November 22.2 10.7 16.5 0.0 2016 December 20.7 8.9 14.8 0.0 2017 January 19.7 4.01 11.9 14.4 2017 February 22.0 5.9 14.0 0.0 2017 March 22.4 9.0 15.7 47.4 2017 April 26.9 13.1 20.0 87.6 2017 May 27.6 16.3 22.0 148.6 NWRP 2016 November 30.4 14.4 22.4 0.0 2016 December 23.1 12.2 17.7 0.5 2017 January 22.4 9.1 15.8 14.8 2017 February 26.2 11.4 18.8 0.0 2017 March 30.6 15.0 22.8 48.2 2017 April 36.3 21.1 28.7 3.5

314

Appendix 5. Combined analysis of variance of 318 genotypes in the NWDP for different agro-morphological traits evaluated in four different field experiments conducted in the 2016 and 2017 wheat growing seasons in Nepal and Canada. AL DA DM GFP GY PH PHS TGW TW F-value F-value F-value F-value F-value F-value F-value F-value F-value Fixed effect Entry 36.30*** 2.53*** 1.70*** 1.49*** 2.40*** 9.90*** - 10.07*** 2.22*** Estimate Estimate Estimate Estimate Estimate Estimate Estimate Estimate Random effects Location 0.2192* 991.16** 985.70** 43.6754* 939674** 142.48*** 15.5609* 51.6909* Block (location) 0.001817 0.03116 0.4029 0.1595 0.0 8.1984 - 0.05105 0.3519 iBlock 0.01158* 0.5211*** 0.9481*** 0.8594*** 81323*** 8.8138*** - 0.5384*** 0.4657** (location*block) * Entry*location 0.43260** 7.4721*** 10.4665** 9.7421*** 239926** 27.6610** - 8.1721*** 7.8926** * * * * Residual 0.3697*** 4.2664*** 3.1182*** 6.6094*** 617325** 30.6781** - 4.0604*** 5.3609** * * * Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. Abbreviations: AL= Awn length, DA= Days to anthesis, DM= Days to maturity, GFP= Grain filling period, GY= Grain yield, PH= Plant height, PHS= Pre-harvest sprouting, TGW= Thousand grain weight, TW= Test weight. PHS was recorded at only one site (Elora Research Station, 2016).

315

Appendix 6. Test of normality for the distribution of nine different agro-morphological traits evaluated in the 2016 and 2016 field experiments at four locations in Canada and Nepal. Shapiro-Wilk statistic Trait Elora 16 Elora 17 ABD NWRP Awn length (AL) W=0.990, P=0.0003 W=0.983, P<0.0001 W=0.915, P<0.0001 W=0.947, P<0.0001 Days to anthesis (DA) W=0.977, P<0.0001 W=0.994, P=0.0150 W=0.991, P=0.0007 W=0.979, P=<0.0001 Days to maturity (DM) W=0.999, P=0.9037 W=0.972, P<0.0001 W=0.998, P=0.8023 W=0.998, P=0.5847 Grain filling period W=0.996, P=0.0775 W=0.997, P=0.2328 W=0.996, P=0.0740 W=0.997, P=0.3919 (GFP) Grain yield (GY) W=0.998, P=0.5874 W=0.999, P=0.9075 W=0.998, P=0.6513 W=0.993, P=0.0029 Plant height W=0.999, P=0.9508 W=0.981, P<0.0001 W=0.996, P=0.1255 W=0.997, P=0.3773 Pre-harvest sprouting* W=0.983, P<0.0001 (PHS) Thousand grain weight W=0.997, P=0.2928 W=0.983, P<0.0001 W=0.993, P=0.0038 W=0.965, P<0.0001 (TGW) Test weight (TW) W=0.991 P=0.0007 W=0.998, P=0.6975 W=0.99 8 P=0.7246 W=0.998, P=0.6681 *Pre-harvest sprouting was evaluated only at the Elora Research Station in the 2016 field experiment.

316

Appendix 7. Mean, standard error of the mean and mean range for traits evaluated in four field experiments during the 2016 and 2017 wheat growing seasons in Nepal and Canada. Elora 2016 Elora 2017 ABD 2017 NWRP 2017 Mean Mean Mean Mean Traits Mean range Mean range Mean range Mean range ±SEM ±SEM ±SEM ±SEM AL (cm) 3.0±0.12 0.0-6.75 3.5±0.13 0.0-7.6 3.9±0.17 0.0-8.5 2.9±0.13 0.0-6.5 DA 66.9±0.22 59.9-80.2 58.1±0.24 49.3-69.3 128.1±0.21 120.8-143.7 93.4±0.14 87.3-101.2 DM 97.8±0.20 89.6-108.5 92.5±0.21 80.1-100.0 165.4±0.15 157.7-177.7 118.9±0.27 108.6-137.9 GFP 40.9±0.18 31.8-53.2 34.4±0.27 13.5-43.9 37.2±0.19 20-5-44.4 25.4±0.21 16.1-40.5 GY (kg/ha) 3098.9±41.5 1257.6-5272.9 2070.0±30.3 1063.6-3653.7 2825.0±46.9 695.2-5956.9 4388.5±64.9 309.6-7772.2 PH (cm) 76.9±0.44 49.5-95.2 81.2±0.67 59.5-112.5 86.0±0.59 65.2-118.9 104.4±0.88 68.0-138.5 PHS 42.8±1.51 0.0-95.3 ------TGW (g) 37.3±0.31 19.7-50.5 43.5±0.28 34.1-55.8 38.4±0.33 18.0-47.3 33.9±0.38 16.5-48.9 TW 69.7±0.19 58.8-77.5 62.9±0.28 46.2-74.2 76.8±0.18 62.7-83.9 78.7±0.17 61.0-84.3 (kg/hL) Abbreviations: SEM= Standard error of mean, AL= Awn length, DA= Days to anthesis, DM= Days to maturity, GFP= Grain filling period, GY= Grain yield, PH= Plant height, PHS= Pre-harvest sprouting, TGW= Thousand grain weight, TW= Test weight.

317

Appendix 8. Broad-sense heritability of the nine different agro-morphological traits in four field experiments in Canada and Nepal during the 2016 and 2017 wheat growing seasons in Nepal and Canada. Broad-sense heritability (H2) (%) Traits Elora 2016 Elora 2017 ABD 2017 NWRP 2017 Awn length (AL) 92.2 84.1 84.5 88.1 Days to anthesis (DA) 78.1 75.5 33.8 82.1 Days to maturity (DM) 80.5 76.9 69.4 69.8 Grain filling period (GFP) 51.7 69.7 45.9 70.2 Grain yield (GY) 46.4 9.2 60.8 44.4 Plant height (PH) 68.2 80.1 80.6 62.7 Pre-harvest sprouting (PHS)* 72.3 ND ND ND Thousand grain weight (TGW) 90.4 80.7 84.7 86.4 Test weight (TW) 80.7 66.5 61.1 61.6 *Pre-harvest sprouting was evaluated only at Elora Research Station in the 2016 field experiment.

318

Appendix 9. List of protein coding genes identified in each QTL region corresponding to the most significant marker-trait associations. Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) TraesCS5A02G296100 No information avaliable Armadilo-type fold, Nucleotide factor Fes1, TraesCS5A02G296200 Twin-arginine translocation pathaway, signal sequence, Armadilo-like helical. TraesCS5A02G296300 DNA repair RAD52-like protein TraesCS5A02G296400 No information available Elora Awn TraesCS5A02G296500 P-loop containing nucleoside triphosphate 5A 504769103 2016 length (3 genes) hydrolase and NB-ARC. 1) P-loop containing nucleoside triphosphate hydrolase and NB-ARC and Leucine-rich TraesCS5A02G296600 repeat domain superfamily 2) and 3) P-loop (3 genes) containing nucleoside triphosphate hydrolase and NB-ARC. TraesCS5A02G296700 Sas10/Utp3/C1D

319

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) Pentatricopeptide repeat, Pentatricopentide TraesCS5A02G296800 repeat, DYW domain and Tetratricopeptide- like helical domain superfamily Ankyrin repeat-containing domain superfamily, TraesCS5A02G296900 Ankyrin repeat, STI1 domain Alpha/Beta hydrolase fold, TraesCS5A02G297000 Lecithin:cholesterol/phospholipid id:diacylglycerol acyltransferase. Associated with PGG domain Its function is TraesCS4A02G450000 not known. TraesCS4A02G450100 PGG domain (2 genes) ABD Days to TraesCS4A02G450200 PGG domain 4A 715914785 Nepal anthesis Malectin domain, Leucine-rich repeat domain TraesCS4A02G450300 superfamily Acyl transferase/Acly TraesCS4A02G450400 hydrolase/lysophopholipase, Patatin-like phospholipase domain 320

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) Pectinesterase inhibitor domain, TraesCS4A02G450500 Invertase/pectin methylesterase inhibitor domain superfamily P-loop containing nucleoside triphosphate TraesCS4A02G450600 hydrolase, Rx, N-terminal, NB-ARC, Virus X resistance protein-like, coiled-coil domain Pecinesterase inhibitor domain, TraesCS4A02G450800 Invertase/pectin methylesterase inhibitor domain superfamily 1) S-adenosyl-L-methionine-dependent Elora Days to TraesCS4B02G013100 methyltransferase, Methyltransferase domain 4B 9053615 2017 maturity (2 genes) 25, SAM-binding methyl transferase MPBQ/MBSQ. TraesCS1B02G285100 NAC domain superfamily and NAC domain TraesCS1B02G285200 NAC domain superfamily and NAC domain Elora Grain 1B 496056408 von Willebrand factor A-like domain 2016 yield TraesCS1B02G285300 superfamily, Sec23/Sec24 helical domain (2 genes) superfamily, Sec23/Sec24, trunk domain, 321

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) Sec23/Sec24, helical domain, Zinc finger, Sec23/Sec24-type super family, Zinc finger, Sec23/Sec24-type P-loop containing nucleoside triphosphate TraesCS4A02G462900 hydrolase, NB-ARC and Leucine-rich repeat domain superfamily P-loop containing nucleoside triphosphate TraesCS4A02G463000 hydrolase, NB-ARC, Rx, N-terminal and Virus Elora Grain X resistance protein-like, coiled-coil domain. 4A 726962465 2017 yield TraesCS4A02G463100 No information available TraesCS4A02G463200 No information available P-loop containing nucleoside triphosphate hydrolase, AAA+ ATPase domain, ATPase, TraesCS4A02G463300 AAA-type, core, AAA ATPase, AAA+ lid domain, ATPase, AAA-type, conserved site 3D 580224738 TraesCS3D02G482600 Transcription factor GRAS

322

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) Glutathione S-transferase, C-terminal domain superfamily, Tioredoxin-like superfamily, NWRP Grain TraesCS3D02G482700 Glutathione transferase family, Glutathione S- Nepal yield transferase, N-terminal, Glutathione S- transferase, C-terminal Pre- TraesCS7B02G353300 No information available Elora harvest 7B 611092780 2016 TraesCS7B02G353400 No information available sprouting TraesCS7B02G478600 UDP-glucuronosyl/UDP glucosyltransferase Multi-Antimicrobial Extrusion or MATE gene TraesCS7B02G478700 family P-loop containing nucleoside triphosphate NWRP Test 7B 733797767 hydrolases superfamily protein (P-loop- Nepal weight TraesCS7B02G478800 NTPase), NB-ARC domin of plant resistance, and leucine-rich repeats (LRRs) Jacalin-like lectin domain superfamily, Jacalin- TraesCS7B02G478900 like lectin domain, Dirigent protein

323

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) Jacalin-like lectin domain superfamily, Jacalin- TraesCS7B02G479000 like lectin domain, Dirigent protein This gene is associated with P-loop containing TraesCS7B02G479100 nucleoside triphosphate hydrolase and NB- ARC P-loop containing nucleoside triphosphate hydrolase and NB-ARC it is also associated TraesCS7B02G479200 with Winged helix-like DNA-binding domain superfamily and Leucine-rich repeat domain super family P-loop containing nucleoside triphosphate hydrolase and NB-ARC it is also associated TraesCS7B02G479300 with Winged helix-like DNA-binding domain superfamily and Leucine-rich repeat domain super family P-loop containing nucleoside triphosphate TraesCS7B02G479400 hydrolase and NB-ARC it is also associated

324

Physical Candidate gene in the Location Trait Chromosome position Protein encoded by candidate gene QTL region (bp) with Winged helix-like DNA-binding domain superfamily

In addition to association with P-loop containing nucleoside triphosphate hydrolase TraesCS7B02G479500 and NB-ARC it is also associated with Winged helix-like DNA-binding domain superfamily P-loop containing nucleoside triphosphate hydrolase and NB-ARC it is also associated TraesCS7B02G479600 with Winged helix-like DNA-binding domain superfamily Leucine-rich repeat domain superfamily

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Appendix 10. Analysis of variance of 318 genotypes in the Nepali Wheat Diversity Panel for coleoptile length and seedling root length. Coleoptile length Seedling root length F-Value F-Value Fixed effect Genotype 163.18*** 81.23*** Random effects Treatment 343.39*** 685.08*** *** *** Genotype*GA3 Treatment 6.12 4.70 Residual 56.98*** 56.97*** Loci Rht-B1 706.43*** 8.56* ** * Rht-B1*GA3 Treatment 7.74 8.36 Rht-D1 13.51** 1.26n.s

n.s ** Rht-D1*GA3 Treatment 2.57 8.41 Ppd-D1 360.08*** 12.53** ** ** Ppd-D1*GA3 Treatment 4.68 8.16 Significance: *≤0.05, **≤0.001, ***≤0.0001.

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Appendix 11. Effect of GA3 treatment on root length as a factor of alleles at the Rht-B1 and Rht- D1 loci. Shown are GA-response box plots illustrating the frequency distribution of NWDP accessions for longest seedling root a separated into groups based on their alleles at the loci indicated. The symbols *, ** indicate significant difference at 0.05, and 0.01. The solid dots are the outliers and each box represents the interquartile range representing 50% of the values. Abbreviations: GA= Gibberellic acid.

327

Appendix 12. Effect of GA3 treatment on coleoptile and root length as a factor of alleles at the Ppd-D1 locus. Shown are GA-response box plots illustrating the frequency distribution of NWDP accessions for coleoptile and root length separated into groups based on their alleles at the loci indicated. *, ** indicate significant difference at 0.05, and 0.01. The solid dots are the outliers and each box represents the interquartile range representing 50% of the values. Abbreviations: GA= Gibberellic acid.

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Appendix 13. Pearson correlation of NDVI values and AUNC with grain yield. AUNC GY NDVI1 NDVI11 NDVI3 NDVI4 NDVI5

GY -0.00094

0.9867

NDVI1 0.55343 -0.12476

<.0001 0.0261

NDVI11 0.75542 -0.1156 0.74474

<.0001 0.0394 <.0001

NDVI3 0.8878 0.03 0.3164 0.51548

<.0001 0.5941 <.0001 <.0001

NDVI4 0.84906 0.06898 0.19954 0.41421 0.77071

<.0001 0.2199 0.0003 <.0001 <.0001

NDVI5 0.50557 0.0472 0.13917 0.20462 0.37866 0.38032

<.0001 0.4015 0.013 0.0002 <.0001 <.0001 NDVIdr -0.18493 -0.04832 0.09345 0.06841 -0.26638 -0.23937 -0.20379 0.0009 0.3905 0.0962 0.2238 <.0001 <.0001 0.0003 Abbreviations: GY= Grain yield, NDVI= Normalized difference vegetative index, AUNC= Area under NDVI curve.

Appendix 14. Frequencies of different haplotype groups for each seed source group. Haplotypes Landraces CIMMYT lines Released varieties Rht-B1a/Rh-D1aPpd-D1b 113(70.2) 1 (0.9) 2 (6.5)* Rht-B1b/Rh-D1a/Ppd-D1a 15 (9.3) 93 (80.2) 9 (29.0) Rht-B1a/Rh-D1a/Ppd-D1b 19 (11.8) 1 (0.9) 6 (19.4) Rht-B1a/Rh-D1b/Ppd-D1b 11 (6.8) 11 (1.7) 13 (41.9) Rht-Bb/Rh-D1b/Ppd-D1a 2 (1.2) 3 (2.6) 0.0 Rht-B1a/Rh-D1b/Ppd-D1b 1 (0.6) 0.0 0.0 Rht-B1b/Rh-D1a/Ppd-D1a 0.0 15 (12.9) 1 (3.2)* Rht-B1b/Rh-D1a/Ppd-D1b 0.0 1 (0.9) 0.0 Figures in the parentheses indicate frequencies. * indicate the Canadian varieties.

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Appendix 15. Alleles of Rht-B1, Rht-D1 and Ppd-D1 loci present in accessions in the NWDP. Entry Accessions Rht-B1 locus Rht-D1 locus Ppd-D1 locus 1 NGRC00176 Rht-B1a Rht-D1a Ppd-D1b 2 NGRC00177 Rht-B1a Rht-D1a Ppd-D1b 3 NGRC00179 Rht-B1a Rht-D1a Ppd-D1b 4 NGRC00180 Rht-B1a Rht-D1a Ppd-D1b 5 NGRC00181 Rht-B1a Rht-D1a Ppd-D1b 6 NGRC00199 Rht-B1a Rht-D1a Ppd-D1b 7 NGRC00202 Rht-B1a Rht-D1a Ppd-D1b 8 NGRC00204 Rht-B1a Rht-D1a Ppd-D1b 9 NGRC00205 Rht-B1a Rht-D1a Ppd-D1b 10 NGRC02448 Rht-B1a Rht-D1a Ppd-D1b 11 NGRC02449 Rht-B1a Rht-D1a Ppd-D1b 12 NGRC02450 Rht-B1a Rht-D1b Ppd-D1a 13 NGRC02451 Rht-B1a Rht-D1b Ppd-D1a 14 NGRC02452 Rht-B1a Rht-D1a Ppd-D1b 15 NGRC02455 Rht-B1a Rht-D1a Ppd-D1b 16 NGRC02456 Rht-B1a Rht-D1a Ppd-D1b 17 NGRC02457 Rht-B1a Rht-D1b Ppd-D1a 18 NGRC02458 Rht-B1a Rht-D1a Ppd-D1a 19 NGRC02459 Rht-B1a Rht-D1b Ppd-D1a 20 NGRC02460 Rht-B1a Rht-D1a Ppd-D1b 21 NGRC02461 Rht-B1a Rht-D1a Ppd-D1a 22 NGRC02462 Rht-B1a Rht-D1a Ppd-D1b 23 NGRC02465 Rht-B1a Rht-D1a Ppd-D1b 24 NGRC02466 Rht-B1a Rht-D1a Ppd-D1b 25 NGRC02467 Rht-B1a Rht-D1a Ppd-D1b 26 NGRC02470 Rht-B1a Rht-D1a Ppd-D1b 27 NGRC02471 Rht-B1a Rht-D1a Ppd-D1b 28 NGRC02472 Rht-B1a Rht-D1b Ppd-D1a 29 NGRC02496 Rht-B1a Rht-D1a Ppd-D1b 30 NGRC02544 Rht-B1a Rht-D1a Ppd-D1b 31 NGRC02546 Rht-B1b Rht-D1a Ppd-D1a 32 NGRC02547 Rht-B1a Rht-D1a Ppd-D1b 33 NGRC02548 Rht-B1a Rht-D1a Ppd-D1b 34 NGRC02549 Rht-B1a Rht-D1a Ppd-D1b 35 NGRC02550 Rht-B1a Rht-D1b Ppd-D1a 36 NGRC02551 Rht-B1a Rht-D1a Ppd-D1b 37 NGRC02552 Rht-B1a Rht-D1a Ppd-D1b 38 NGRC02553 Rht-B1a Rht-D1a Ppd-D1b

330

39 NGRC02554 Rht-B1a Rht-D1b Ppd-D1a 40 NGRC02556 Rht-B1a Rht-D1a Ppd-D1b 41 NGRC02557 Rht-B1a Rht-D1a Ppd-D1b 42 NGRC02558 Rht-B1a Rht-D1a Ppd-D1b 43 NGRC02559 Rht-B1a Rht-D1a Ppd-D1b 44 NGRC02560 Rht-B1a Rht-D1a Ppd-D1b 45 NGRC02561 Rht-B1a Rht-D1a Ppd-D1b 46 NGRC02563 Rht-B1b Rht-D1a Ppd-D1a 47 NGRC02564 Rht-B1b Rht-D1a Ppd-D1a 48 NGRC02565 Rht-B1a Rht-D1a Ppd-D1b 49 NGRC02566 Rht-B1a Rht-D1a Ppd-D1b 50 NGRC02567 Rht-B1a Rht-D1a Ppd-D1b 51 NGRC02568 Rht-B1a Rht-D1a Ppd-D1b 52 NGRC02569 Rht-B1a Rht-D1a Ppd-D1b 53 NGRC02570 Rht-B1a Rht-D1a Ppd-D1b 54 NGRC02571 Rht-B1a Rht-D1a Ppd-D1a 55 NGRC02572 Rht-B1a Rht-D1a Ppd-D1b 56 NGRC02574 Rht-B1a Rht-D1a Ppd-D1b 57 NGRC02575 Rht-B1a Rht-D1a Ppd-D1b 58 NGRC02576 Rht-B1a Rht-D1a Ppd-D1b 59 NGRC02578 Rht-B1a Rht-D1a Ppd-D1b 60 NGRC02579 Rht-B1a Rht-D1a Ppd-D1b 62 NGRC02581 Rht-B1a Rht-D1a Ppd-D1b 63 NGRC02582 Rht-B1a Rht-D1a Ppd-D1b 64 NGRC02584 Rht-B1a Rht-D1b Ppd-D1a 65 NGRC02585 Rht-B1b Rht-D1a Ppd-D1a 66 NGRC02586 Rht-B1a Rht-D1a Ppd-D1b 67 NGRC02587 Rht-B1b Rht-D1b Ppd-D1a 68 NGRC02589 Rht-B1b Rht-D1a Ppd-D1a 69 NGRC02590 Rht-B1a Rht-D1a Ppd-D1b 70 NGRC02591 Rht-B1a Rht-D1a Ppd-D1b 71 NGRC02593 Rht-B1a Rht-D1a Ppd-D1b 72 NGRC02594 Rht-B1a Rht-D1a Ppd-D1b 73 NGRC02595 Rht-B1a Rht-D1a Ppd-D1b 74 NGRC02596 Rht-B1b Rht-D1a Ppd-D1a 75 NGRC02599 Rht-B1a Rht-D1a Ppd-D1a 76 NGRC02602 Rht-B1b Rht-D1a Ppd-D1a 77 NGRC02603 Rht-B1a Rht-D1a Ppd-D1b 78 NGRC02604 Rht-B1a Rht-D1a Ppd-D1b 79 NGRC02605 Rht-B1a Rht-D1a Ppd-D1b 80 NGRC02606 Rht-B1a Rht-D1a Ppd-D1b 331

81 NGRC02607 Rht-B1a Rht-D1a Ppd-D1b 83 NGRC02609 Rht-B1a Rht-D1a Ppd-D1b 84 NGRC02610 Rht-B1a Rht-D1a Ppd-D1a 85 NGRC02611 Rht-B1a Rht-D1a Ppd-D1a 86 NGRC02612 Rht-B1a Rht-D1a Ppd-D1b 88 NGRC02614 Rht-B1a Rht-D1a Ppd-D1a 89 NGRC02615 Rht-B1a Rht-D1a Ppd-D1a 90 NGRC02617 Rht-B1a Rht-D1a Ppd-D1a 91 NGRC02619 Rht-B1a Rht-D1a Ppd-D1b 92 NGRC02620 Rht-B1a Rht-D1a Ppd-D1a 93 NGRC02621 Rht-B1a Rht-D1b Ppd-D1a 94 NGRC02622 Rht-B1a Rht-D1a Ppd-D1b 95 NGRC02623 Rht-B1a Rht-D1a Ppd-D1a 96 NGRC02624 Rht-B1a Rht-D1a Ppd-D1b 97 NGRC02625 Rht-B1a Rht-D1a Ppd-D1a 98 NGRC02629 Rht-B1a Rht-D1a Ppd-D1b 99 NGRC02630 Rht-B1a Rht-D1a Ppd-D1a 100 NGRC02631 Rht-B1a Rht-D1a Ppd-D1a 101 NGRC02632 Rht-B1a Rht-D1a Ppd-D1a 102 NGRC02633 Rht-B1a Rht-D1b Ppd-D1a 103 NGRC04399 Rht-B1a Rht-D1a Ppd-D1b 104 NGRC04400 Rht-B1b Rht-D1a Ppd-D1a 105 NGRC04401 Rht-B1b Rht-D1a Ppd-D1a 106 NGRC04402 Rht-B1a Rht-D1a Ppd-D1b 107 NGRC04404 Rht-B1a Rht-D1b Ppd-D1b 109 NGRC04406 Rht-B1a Rht-D1a Ppd-D1a 110 NGRC04408 Rht-B1a Rht-D1a Ppd-D1b 111 NGRC04409 Rht-B1a Rht-D1a Ppd-D1b 112 NGRC04410 Rht-B1a Rht-D1a Ppd-D1b 113 NGRC04413 Rht-B1a Rht-D1a Ppd-D1b 114 NGRC04414 Rht-B1b Rht-D1a Ppd-D1a 115 NGRC04416 Rht-B1a Rht-D1a Ppd-D1b 116 NGRC04417 Rht-B1a Rht-D1a Ppd-D1b 117 NGRC04418 Rht-B1a Rht-D1a Ppd-D1b 118 NGRC04419 Rht-B1a Rht-D1a Ppd-D1b 119 NGRC04421 Rht-B1a Rht-D1a Ppd-D1b 120 NGRC04422 Rht-B1a Rht-D1a Ppd-D1b 121 NGRC04423 Rht-B1a Rht-D1a Ppd-D1b 122 NGRC04424 Rht-B1a Rht-D1a Ppd-D1b 123 NGRC04425 Rht-B1a Rht-D1a Ppd-D1b 124 NGRC04426 Rht-B1a Rht-D1a Ppd-D1b 332

125 NGRC04427 Rht-B1a Rht-D1b Ppd-D1a 126 NGRC04428 Rht-B1a Rht-D1a Ppd-D1b 127 NGRC04429 Rht-B1a Rht-D1a Ppd-D1b 128 NGRC04430 Rht-B1a Rht-D1a Ppd-D1a 129 NGRC04431 Rht-B1a Rht-D1a Ppd-D1b 130 NGRC04432 Rht-B1a Rht-D1a Ppd-D1b 131 NGRC04433 Rht-B1a Rht-D1a Ppd-D1a 132 NGRC04434 Rht-B1a Rht-D1a Ppd-D1b 133 NGRC04436 Rht-B1a Rht-D1a Ppd-D1b 134 NGRC04437 Rht-B1b Rht-D1a Ppd-D1a 135 NGRC04439 Rht-B1a Rht-D1a Ppd-D1a 136 NGRC04440 Rht-B1a Rht-D1a Ppd-D1b 137 NGRC04443 Rht-B1b Rht-D1b Ppd-D1a 138 NGRC04444 Rht-B1a Rht-D1a Ppd-D1b 139 NGRC04445 Rht-B1a Rht-D1a Ppd-D1b 140 NGRC04446 Rht-B1a Rht-D1a Ppd-D1b 141 NGRC04447 Rht-B1b Rht-D1a Ppd-D1a 142 NGRC04448 Rht-B1a Rht-D1a Ppd-D1b 143 NGRC04449 Rht-B1b Rht-D1a Ppd-D1a 144 NGRC04450 Rht-B1a Rht-D1a Ppd-D1b 145 NGRC04451 Rht-B1a Rht-D1a Ppd-D1b 146 NGRC04452 Rht-B1a Rht-D1a Ppd-D1b 147 NGRC04453 Rht-B1a Rht-D1a Ppd-D1b 148 NGRC04454 Rht-B1a Rht-D1a Ppd-D1b 149 NGRC04455 Rht-B1a Rht-D1a Ppd-D1b 150 NGRC04456 Rht-B1b Rht-D1a Ppd-D1a 151 NGRC04457 Rht-B1a Rht-D1a Ppd-D1b 152 NGRC04458 Rht-B1b Rht-D1a Ppd-D1a 153 NGRC04459 Rht-B1a Rht-D1a Ppd-D1b 154 NGRC04460 Rht-B1a Rht-D1a Ppd-D1b 155 NGRC04461 Rht-B1a Rht-D1a Ppd-D1b 156 NGRC04462 Rht-B1a Rht-D1a Ppd-D1b 157 NGRC04463 Rht-B1a Rht-D1a Ppd-D1b 158 NGRC04464 Rht-B1a Rht-D1a Ppd-D1b 159 NGRC04465 Rht-B1a Rht-D1a Ppd-D1b 161 NGRC04467 Rht-B1a Rht-D1a Ppd-D1b 163 NGRC04470 Rht-B1a Rht-D1a Ppd-D1b 164 NGRC04471 Rht-B1a Rht-D1a Ppd-D1b 165 NGRC04472 Rht-B1a Rht-D1a Ppd-D1b 166 NGRC04473 Rht-B1a Rht-D1a Ppd-D1b 167 NGRC04474 Rht-B1a Rht-D1a Ppd-D1b 333

168 Lerma52 Rht-B1b Rht-D1a Ppd-D1a 169 Kalyansona Rht-B1b Rht-D1a Ppd-D1a 170 Pitic62 Rht-B1a Rht-D1b Ppd-D1a 171 RR21 Rht-B1a Rht-D1b Ppd-D1a 172 NL30 Rht-B1a Rht-D1b Ppd-D1a 173 UP262 Rht-B1a Rht-D1b Ppd-D1a 174 Lumbini Rht-B1b Rht-D1a Ppd-D1a 175 Tribeni Rht-B1b Rht-D1a Ppd-D1a 176 Vinayak Rht-B1a Rht-D1b Ppd-D1a 177 Siddhartha Rht-B1a Rht-D1a Ppd-D1a 178 Vaskar Rht-B1b Rht-D1a Ppd-D1a 179 Nepal297 Rht-B1b Rht-D1a Ppd-D1a 180 NL251 Rht-B1a Rht-D1b Ppd-D1a 181 Annapurna1 Rht-B1b Rht-D1a Ppd-D1a 182 Annapurna2 Rht-B1b Rht-D1a Ppd-D1a 183 Annapurna3 Rht-B1b Rht-D1a Ppd-D1a 184 BL1022 Rht-B1b Rht-D1a Ppd-D1a 185 Bhrikuti Rht-B1b Rht-D1a Ppd-D1a 186 BL1135 Rht-B1a Rht-D1b Ppd-D1a 187 Annapurna4 Rht-B1a Rht-D1a Ppd-D1a 188 Achyut Rht-B1b Rht-D1a Ppd-D1a 189 Rohini Rht-B1a Rht-D1a Ppd-D1a 190 Kanti Rht-B1a Rht-D1a Ppd-D1a 191 PasangLhamu Rht-B1a Rht-D1a Ppd-D1a 192 BL1473 Rht-B1a Rht-D1b Ppd-D1a 193 Gautam Rht-B1a Rht-D1b Ppd-D1a 194 WK1204 Rht-B1a Rht-D1b Ppd-D1a 196 Aditya Rht-B1a Rht-D1b Ppd-D1a 197 Vijay Rht-B1a Rht-D1b Ppd-D1a 198 Gaura Rht-B1a Rht-D1a Ppd-D1a 199 Dhaulagiri Rht-B1a Rht-D1b Ppd-D1a 201 Tilottama Rht-B1b Rht-D1a Ppd-D1a 202 BW30655 Rht-B1b Rht-D1a Ppd-D1a 203 BW35623 Rht-B1b Rht-D1a Ppd-D1a 204 BW43945 Rht-B1b Rht-D1a Ppd-D1b 205 BW45161 Rht-B1b Rht-D1a Ppd-D1a 206 BW43354 Rht-B1b Rht-D1a Ppd-D1a 207 BW44829 Rht-B1b Rht-D1a Ppd-D1a 208 BW44908 Rht-B1b Rht-D1a Ppd-D1a 209 BW45568 Rht-B1b Rht-D1a Ppd-D1a 210 BW45152 Rht-B1a Rht-D1a Ppd-D1a 334

211 BW45573 Rht-B1a Rht-D1a Ppd-D1b 212 BW45173 Rht-B1b Rht-D1a Ppd-D1a 213 BW45578 Rht-B1b Rht-D1a Ppd-D1a 214 BW45590 Rht-B1b Rht-D1a Ppd-D1a 215 BW45592 Rht-B1b Rht-D1a Ppd-D1b 216 BW45593 Rht-B1b Rht-D1a Ppd-D1a 217 BW45587 Rht-B1b Rht-D1a Ppd-D1a 218 BW45165 Rht-B1b Rht-D1a Ppd-D1a 219 BW45595 Rht-B1b Rht-D1a Ppd-D1a 220 BW48132 Rht-B1b Rht-D1a Ppd-D1a 222 BW48135 Rht-B1b Rht-D1a Ppd-D1b 223 BW48136 Rht-B1b Rht-D1a Ppd-D1a 224 BW48137 Rht-B1b Rht-D1a Ppd-D1a 225 BW48139 Rht-B1a Rht-D1b Ppd-D1a 226 BW48171 Rht-B1b Rht-D1a Ppd-D1a 227 BW48140 Rht-B1b Rht-D1a Ppd-D1a 228 BW48141 Rht-B1b Rht-D1a Ppd-D1a 229 BW48144 Rht-B1b Rht-D1a Ppd-D1a 230 BW48145 Rht-B1b Rht-D1a Ppd-D1a 231 BW48148 Rht-B1b Rht-D1a Ppd-D1a 232 BW48149 Rht-B1b Rht-D1a Ppd-D1a 234 BW48151 Rht-B1b Rht-D1a Ppd-D1a 235 BW48154 Rht-B1b Rht-D1a Ppd-D1a 236 BW48155 Rht-B1b Rht-D1a Ppd-D1a 237 BW48156 Rht-B1b Rht-D1a Ppd-D1a 238 BW48158 Rht-B1b Rht-D1a Ppd-D1a 239 BW48172 Rht-B1b Rht-D1a Ppd-D1b 240 BW48174 Rht-B1b Rht-D1b Ppd-D1a 241 BW48161 Rht-B1b Rht-D1a Ppd-D1a 242 BW48162 Rht-B1b Rht-D1a Ppd-D1a 243 BW48163 Rht-B1b Rht-D1a Ppd-D1a 244 BW48166 Rht-B1b Rht-D1a Ppd-D1a 245 BW48165 Rht-B1b Rht-D1a Ppd-D1a 246 BW48169 Rht-B1b Rht-D1a Ppd-D1a 247 BW48168 Rht-B1b Rht-D1a Ppd-D1a 248 BW49069 Rht-B1b Rht-D1a Ppd-D1a 249 BW49227 Rht-B1b Rht-D1a Ppd-D1b 250 BW49235 Rht-B1b Rht-D1a Ppd-D1a 251 BW49072 Rht-B1b Rht-D1a Ppd-D1a 252 BW49075 Rht-B1b Rht-D1a Ppd-D1a 253 BW49077 Rht-B1b Rht-D1a Ppd-D1a 335

254 BW49078 Rht-B1b Rht-D1a Ppd-D1b 255 BW49079 Rht-B1b Rht-D1a Ppd-D1a 257 BW49344 Rht-B1b Rht-D1a Ppd-D1a 258 BW49084 Rht-B1b Rht-D1a Ppd-D1b 259 BW49088 Rht-B1b Rht-D1a Ppd-D1a 260 BW49089 Rht-B1b Rht-D1a Ppd-D1a 261 BW49391 Rht-B1b Rht-D1a Ppd-D1a 262 BW49092 Rht-B1b Rht-D1a Ppd-D1a 263 BW49093 Rht-B1b Rht-D1a Ppd-D1a 264 BW49399 Rht-B1b Rht-D1a Ppd-D1a 265 BW49095 Rht-B1b Rht-D1a Ppd-D1a 266 BW49097 Rht-B1b Rht-D1a Ppd-D1a 268 BW49102 Rht-B1b Rht-D1a Ppd-D1a 269 BW49108 Rht-B1b Rht-D1a Ppd-D1a 271 BW49110 Rht-B1b Rht-D1a Ppd-D1a 272 BW49111 Rht-B1b Rht-D1a Ppd-D1a 273 BW49112 Rht-B1b Rht-D1a Ppd-D1a 274 BW49113 Rht-B1b Rht-D1a Ppd-D1a 275 BW49922 Rht-B1b Rht-D1a Ppd-D1a 276 BW49923 Rht-B1b Rht-D1a Ppd-D1a 277 BW49924 Rht-B1b Rht-D1b Ppd-D1a 278 BW49927 Rht-B1b Rht-D1a Ppd-D1a 279 BW49929 Rht-B1b Rht-D1a Ppd-D1a 280 BW49931 Rht-B1b Rht-D1a Ppd-D1a 281 BW49932 Rht-B1b Rht-D1a Ppd-D1a 282 BW49933 Rht-B1b Rht-D1a Ppd-D1b 283 BW49934 Rht-B1b Rht-D1a Ppd-D1a 286 BW49943 Rht-B1b Rht-D1a Ppd-D1a 287 BW49948 Rht-B1b Rht-D1a Ppd-D1a 288 BW49949 Rht-B1b Rht-D1a Ppd-D1a 289 BW49950 Rht-B1b Rht-D1a Ppd-D1a 290 BW49953 Rht-B1b Rht-D1a Ppd-D1a 291 BW49954 Rht-B1b Rht-D1a Ppd-D1a 292 BW49955 Rht-B1b Rht-D1a Ppd-D1a 293 BW49956 Rht-B1b Rht-D1a Ppd-D1a 294 BW49957 Rht-B1b Rht-D1a Ppd-D1b 295 BW49958 Rht-B1b Rht-D1a Ppd-D1a 296 BW48314 Rht-B1b Rht-D1a Ppd-D1a 297 BW49217 Rht-B1b Rht-D1a Ppd-D1a 298 BW49307 Rht-B1b Rht-D1a Ppd-D1a 299 BW49325 Rht-B1b Rht-D1a Ppd-D1b 336

300 BW49326 Rht-B1b Rht-D1b Ppd-D1b 301 BW49327 Rht-B1b Rht-D1a Ppd-D1b 302 BW49328 Rht-B1b Rht-D1a Ppd-D1b 303 BW49329 Rht-B1b Rht-D1a Ppd-D1b 304 BW49330 Rht-B1b Rht-D1a Ppd-D1b 305 BW49331 Rht-B1b Rht-D1a Ppd-D1b 306 BW49333 Rht-B1b Rht-D1a Ppd-D1a 307 BW49342 Rht-B1a Rht-D1b Ppd-D1a 308 BW49351 Rht-B1b Rht-D1a Ppd-D1a 309 BW49385 Rht-B1b Rht-D1a Ppd-D1a 310 BW49392 Rht-B1b Rht-D1a Ppd-D1a 311 BW49394 Rht-B1b Rht-D1a Ppd-D1a 312 BW49094 Rht-B1b Rht-D1a Ppd-D1a 313 BW49400 Rht-B1b Rht-D1a Ppd-D1a 315 BW49448 Rht-B1b Rht-D1a Ppd-D1a 316 BW49456 Rht-B1b Rht-D1a Ppd-D1a 317 BW49458 Rht-B1b Rht-D1a Ppd-D1a 318 Pasteur Rht-B1a Rht-D1a Ppd-D1b 319 AC Carberry Rht-B1b Rht-D1a Ppd-D1b 320 Norwell Rht-B1a Rht-D1a Ppd-D1b

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Appendix 16. Analysis of variance for 318 genotypes belonging to the NWDP for phenotypic traits evaluated at four different field experiments conducted in 2016 and 2017. AUNC NDVIdr AUSC Waxiness Grain yield F-value F-value F-value F-value F-value Fixed effect Entry 0.98* 4.57*** 2.68*** 6.38*** 2.42*** Estimate Estimate Estimate Estimate Estimate Random effects Location 8.68** - 52792* 0.61*** 945946*** Block (location) 0.53 0 62.39** 0.16 0 iBlock 0.53*** 46902 142.31*** 0.22*** 81618*** (location*block) Entry*location 0.55*** - 2602.87*** 1.19*** 235854*** Residual 0.76*** 15650 2048.46*** 0.96*** 619950*** Significance: *P≤0.05, **P≤0.001, ***P≤0.0001. Abbreviations: AUNC=Area under NDVI curve, AUSC=Area under SPAD curve, NDVIdr=NDVI derived from UAV/drone data.

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