Development and Characterization of Rice Genotypes for Water Use Efficiency and Oughtdr Resistance

University of Arkansas, Fayetteville ScholarWorks@UARK

Theses and Dissertations

12-2017

Development and Characterization of Rice Genotypes for Water Use Efficiency and oughtDr Resistance

Anuj Kumar University of Arkansas, Fayetteville

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Development and Characterization of Rice Genotypes for Water Use Efficiency and Drought Resistance

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Crop, Soil, and Environmental Sciences

Anuj Kumar CCS University, India Bachelor of Science in Agriculture, 2004 SVP University of Agriculture & Technology, India Master of Science in Plant Biotechnology, 2007

December 2017 University of Arkansas

This dissertation is approved for recommendation to the Graduate Council.

______Andy Pereira, PhD Dissertation Director

______Karen A. Moldenhauer, PhD Derrick M. Oosterhuis, PhD Committee Member Committee Member

______Edward E. Gbur, Jr., PhD Xueyan Sha, PhD Committee Member Committee Member

______Ainong Shi, PhD Committee Member

ABSTRACT Rice, the second largest staple food crop, uses 30% of global fresh water to complete its life cycle worldwide. Water deficits worldwide have become a serious problem affecting rice growth and ultimately grain yield. To solve the water problem globally, improvement of water use efficiency (WUE) and other drought resistance (DR) traits in rice genotypes would be a useful strategy using advanced genomics tools. In this study, our objectives were to 1) analyze phenotypes of the USDA rice mini-core collection (URMC) for WUE and DR related traits, 2) correlate drought response phenes for physiological traits and grain yield in the URMC, 3) utilize molecular genetic dissection of WUE and DR using genome-wide association (GWA) analysis in the URMC, and 4) conduct genome-wide meta analysis of QTLs for DR traits and grain yield components under drought stress. In the results, 35 rice genotypes showing <25% reduction, 14 rice genotypes exhibiting 25-40% reduction, and 8 rice genotypes showing >40% reduction, were drought resistant, moderate drought resistant, and drought sensitive for WUE, photosynthesis, biomass, and other DR traits under drought stress, respectively. The results suggest from the correlation analysis that strong correlation exits between major grain yield components (number of spikelets per panicle, number of filled and unfilled grains per panicle) and major morpho- physiological traits (plant biomass, photosynthesis and WUEi). In the GWA analysis, 24, 16, 26,

10, 19, 23, 7, 17, 11, 14, 17, 15, 29, 12, 18, and 19 significant SNPs were highly associated with

WUEi, TR, stomatal conductance, Ci & Ci/Ca , plant biomass, NOTs, RWC, LR, chlorophyll content, and chlorophyll fluorescence (Fv’, Fm’, Fv’/Fm’, PhiPSII, ETR, and qN) and their identified candidate genes for WUEi and DR traits. In the meta-analysis, 13 genome-wide

MQTLs were found useful containing higher number of QTLs, lower genetic distance with lower

CI. Therefore, this information would be useful for the geneticists to dissect the genetic architecture of WUE and DR traits for developing high yielding drought resistant rice genotypes.

ACKNOWLEDGEMENTS It is my great pleasure to express my thanks to Almighty God, through whose mercy and blessings, I have accomplished this journey through higher education to this PhD. I am honored to be able to complete this stage of my life, which has not always been easy but by the grace of God and help of many around me, has come to completion. I would therefore like to acknowledge my

“Mummy and Papa” for their sacrifices, faith, unconditional love, and every support that got me this far. Words are insufficient to give thanks for their untiring help, patience and affection and for being there by my side at all the times of ups & downs through life.

With a sense of gratitude and privilege, I would like to express my sincere thanks to my major advisor, Dr. Andy Pereira, Professor, Department of Crop, Soil, & Environmental Sciences, for his valuable guidance, continuous motivation, and unconditional support during my entire research. His patience and insights into the research subject have always made me realize and understand my research at a global level. His infinite capacity for hard work will always be an illuminating light to lead me in all my pursuits in the future too.

Distinctive words of thanks are due to Monsanto’s Beachell-Borlaug International

Scholars Program (MBBISP) especially Dr. Edward Runge and the entire panel, for awarding me the financial support, necessary facilities, international exposure, timely help whenever required, and for generating all kind of resources supporting the program.

I would like to express my fervent thanks to the members of my advisory committee Dr.

Karen A. Moldenhauer, Professor, Department of Crop, Soil, & Environmental Sciences, Dr.

Derrick M. Oosterhuis, Distinguished Professor, Department of Crop, Soil, & Environmental

Sciences, Dr. Edward E. Gbur, Jr., Professor, Agriculture Statistics Laboratory, Dr. Xueyan Sha,

Associate Professor, Department of Crop, Soil, & Environmental Sciences, and Dr. Ainong Shi,

Assistant Professor, Department of Horticulture, for their valuable advice, constructive suggestions and timely assistance during my research and courses.

With great sense of respect and deepest pleasure, I express my appreciation and immense gratitude to Dr. Arvind Kumar, Senior Scientist, International Rice Research Institute (IRRI),

Philippines for his guidance and training during my visit at IRRI.

I am extremely grateful to Dr. Amelia Henry, Scientist, CESD, IRRI, for her constructive suggestions, skillful help, meticulous supervision, cooperative & caring attitude that helped me at various stages of my experiments and stay at IRRI.

It is my great privilege to express my immense sense of gratitude towards my lovely wife

Ms. Sumedha Singh whose unconditional love, relentless support, patience & untiring day and night care helped me to accomplish this research in time.

My words utterly fail to be able to express my tribute to my brother Mr. Manuj Kumar, sister in law Ms. Priyanka, and my lovely sister Ms. Monika Singh for their continuous encouragement, unconditional love, silent prayers, understanding and patience during my entire life. Special affection for my little, cute nephew “Yug Choudhary” whose innocent smiling face gave me constant inspiration to complete my work and relax during burdened moments.

It gives me immense pleasure to express my deep and sincere gratitude towards present and past colleagues and members of my lab Dr. Julie Thomas, Dr. Supratim Basu and Dr.

Ramegowda Venkategowda from whom I learnt aspects of plant molecular genetics and physiology, fellow graduate students Ms. Ritu Mihani, Mr. Chirag Gupta, Ms. Miranti

Rahmaningsih, Ms. Yheni Dwiningsih, Ms. Jawaher Alkhatani and Ms. Ipeleng Randome with whom I shared lab and field work, and most importantly the untiring assistance in experimental

measurements in the greenhouse and field of Ms. Sara Yingling, as well as previous research support in the greenhouse by Ms. Alison Welch, and Ms. Jessica Perkins. To all my lab and field colleagues, I appreciate your intellectual enlightenment and thoughtful suggestions throughout the period of this study and for making this period of life an exciting and pleasant interaction for me in the lab and greenhouses at the University of Arkansas, Fayetteville, Arkansas.

I would like to present my bouquet of thanks to all field and office staff members of the department of Crop, Soil, & Environmental Sciences at the University of Arkansas, especially

Ms. Joda Fiore who helped me in the early days as a graduate student. I owe deep, profound and fervent thanks to all my friends, batch mates, seniors and juniors for their timely help and best wishes. Finally, I would like to thank all those people who knowingly or unknowingly helped me in my endeavor and I would like to wish everyone whose names are not written here or forgotten unintentionally, the best of luck for their future. As I submit my dissertation, it gives me a sense of immense contentment to express my gratitude to all those who have contributed in their special way to make the completion of my thesis possible, and help me take the step forward towards other goals in life.

DEDICATION This dissertation is dedicated to my parents Mr. JASBEER SINGH and Mrs. KANTI

DEVI, To Whom I Owe My Identity

TABLE OF CONTENTS CHAPTER-I INTRODUCTION AND REVIEW OF LITERATURE INTRODUCTION...... 2 REVIEW OF LITERATURE...... 6 REFERENCES...... 36 CHAPTER-II PHENOTYPIC ANALYSIS OF THE USDA RICE MINI-CORE COLLECTION FOR WATER USE EFFICIENCY AND DROUGHT RESISTANCE RELATED TRAITS ABSTRACT...... 63 INTRODUCTION...... 64 MATERIALS AND METHODS...... 68 RESULTS AND DISCUSSION...... 73 CONCLUSIONS...... 102 REFERENCES...... 103 CHAPTER-III CORRELATION OF RICE DROUGHT STRESS RESPONSE PHENES FOR PHYSIOLOGICAL TRAITS AND GRAIN YIELD IN THE USDA RICE MINI-CORE COLLECTION ABSTRACT...... 149 INTRODUCTION...... 150 MATERIALS AND METHODS...... 154 RESULTS AND DISCUSSION...... 157 CONCLUSIONS...... 171 REFERENCES...... 173 CHAPTER-IV MOLECULAR GENETIC DISSECTION OF WATER USE EFFICIENCY AND DROUGHT RESISTANCE USING GENOME-WIDE ASSOCIATION ANALYSIS IN THE USDA RICE MINI-CORE COLLECTION ABSTRACT...... 195 INTRODUCTION...... 196 MATERIALS AND METHODS...... 200 RESULTS AND DISCUSSION...... 204

CONCLUSIONS...... 220 REFERENCES...... 224 CHAPTER-V GENOME-WIDE META ANALYSIS (GWMA) OF QTLS FOR DROUGHT RESISTANCE TRAITS AND GRAIN YIELD COMPONENTS UNDER DROUGHT STRESS ABSTRACT...... 253 INTRODUCTION...... 254 MATERIALS AND METHODS...... 258 RESULTS AND DISCUSSION...... 261 CONCLUSIONS...... 269 REFERENCES...... 271 CHAPTER-VI OVERALL CONCLUSIONS ………………………………………………………………..303

LIST OF TABLES CHAPTER-II 2-1. The USDA rice mini-core collection of 214 and 7 popular rice genotypes used in this study…………………………………………………………………………………...... 109 2-2. Effect of drought stress on plant biomass exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes ……………………..116 2-3. Effect of drought stress on number of tillers in the URMC of 221 genotypes…………...117 2-4. Effect of drought stress on relative water content (RWC) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…………………………………………………………………………………118 2-5. Effect of drought stress on leaf rolling for expressing drought stress in the URMC of 221 genotypes…………………………………………………………………………………119 2-6. Drought stress response to photosynthesis exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes………………………..120 2-7. Drought stress response to transpiration rate (TR) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes………………121 2-8. Drought stress response to instantaneous water use efficiency (WUEi) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…………………………………………………………………………………122 2-9. Drought stress response to stomatal conductance exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes……………….123 2-10. Drought stress response to internal cellular CO2 concentration (Ci) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes……………………………………………………………………………...... 124 2-11. Effect of drought stress on intercellular CO2 concentration/ambient CO2 ratio (Ci/Ca) showing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes………………………………………………………………….125 2-12. Effect of Drought stress on variable chlorophyll fluorescence (Fv’) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes……………………………………………………………………………...... 126 2-13. Effect of Drought stress on maximum chlorophyll fluorescence (Fm’) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…………………………………………………………………………………127 v’ 2-14. Effect of Drought stress on F /Fm’ for identification of drought resistant, moderately resistant and drought sensitive genotypes in the URMC of 221 genotypes………………………...128 2-15. Effect of Drought stress on the efficiency of photosystem II (ΦPSII) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…………………………………………………………………………………129 2-16. Effect of Drought stress on electron transport rate (ETR) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes….130 2-17. Effect of Drought stress on non-photochemical quenching (qN) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…………………………………………………………………………………131 2-18. Effect of drought stress on chlorophyll content (SPAD) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes…132

2-19. Summary of drought resistant, moderately drought resistant, & drought sensitive genotypes expressed biomass, photosynthesis & WUEi in the URMC of 221 genotypes……………133 CHAPTER-III 3-1. Effects of drought stress on plant panicle length exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC…………………………………………….178 3-2. Effects of drought stress on number of spikelets per panicle exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC…………………………………………….179 3-3. Effects of drought stress on number of filled grains per panicle exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC…………………………………..180 3-4. Effects of drought stress on percent change in number of un-filled grains per panicle exhibiting drought experience in 81 rice genotypes of the URMC…………………...... 181 3-5. A. Effects of drought stress on plant productivity trait plant biomass, exhibiting DR, MDR and drought sensitivity at vegetative stage in 81 rice genotypes of the URMC in green house conditions…………………………………………………………………………………182 B. Effects of drought stress on photosynthesis exhibiting DR, MDR and drought sensitivity at vegetative stage in 81 rice genotypes of the URMC in green house conditions………..182 3-6. A. Effects of drought stress on WUEi exhibiting DR, MDR and drought sensitivity at vegetative stage in 81 rice genotypes of the URMC in green house conditions………….183 B. Summary of rice genotypes of the URMC showing Drought resistance (DR) and sensitivity (DS) for major grain yield components (spikelets/panicle, Number of filled grains & un-filled grains/panicle) at reproductive stage in the field conditions correlated with plant productivity(plant biomass) and physiological traits(photosynthesis and WUEi) at vegetative stage in green house conditions…………………………………………………………...183 CHAPTER-IV 4-1. Identification of 24 SNPs associated with instantaneous water use efficiency (WUEi) under drought stress by GWA analysis. Based on highly significant HS-SNPs (–log10 (p) ≥ 7.0) and probable P-SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to WUEi under drought stress in the URMC are listed. …………………. ……………………...... 228 4-2. Identification of 16 SNPs associated with photosynthesis under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to photosynthesis under drought stress in the URMC are listed……………………………229 4-3. Identification of 26 SNPs associated with transpiration rate (TR) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to TR under drought stress the URMC are listed……………………………………….…………...... 230 4-4. Identification of 10 SNPs associated with stomatal conductance under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to stomatal conductance under drought stress in the URMC are listed……...... 231 4-5. Identification of 19 SNPs associated with intercellular CO2 concentration (Ci) under drought stress by GWA analysis. Based on highly significant SNPs (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Ci under drought stress in the URMC are listed….……………………………………………...... 232 4-6. Identification of 19 SNPs associated with the ratio of intercellular CO2 (Ci) to the ambient CO2 concentration (Ci/Ca) under drought stress by GWA analysis. Based on probable SNPs

(–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Ci/Ca under drought stress in the URMC are listed…………………………………………………………………………..233 4-7. A. Identification of 23 SNPs associated with plant biomass under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to plant biomass under drought stress in the URMC are listed………………………………………………………………………………………234 B. Identification of 6 SNPs associated with number of tillers (NOT) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to NOT under drought stress in the URMC are listed..234 4-8. Identification of 17 SNPs associated with relative water content (RWC) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to RWC under drought stress in the URMC are listed…..…………………………………………………………………...... 235 4-9. A. Identification of 11 SNPs associated with leaf rolling (LR) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0- 6.9), the candidate genes associated to LR under drought stress in the URMC are listed….236 B. Identification of 14 SNPs associated with chlorophyll content (SPAD) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (– log10 (p) ≥ 5.0-6.9), the candidate genes associated to SPAD under drought stress in the URMC are listed…………………………………………………………………………..236 4-10. Identification of 17 SNPs associated with variable chlorophyll fluorescence (Fv’) under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fv’ under drought stress in the URMC are listed………….237 4-11. Identification of 15 SNPs associated with maximum chlorophyll fluorescence (Fm’) under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fm’ under drought stress in the URMC are listed………….238 4-12. Identification of 29 SNPs associated with the ratio of variable chlorophyll fluorescence to the maximum chlorophyll fluorescence (Fv’/Fm’) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fv’/Fm’ under drought stress in the URMC are listed……….239 4-13. Identification of 12 SNPs associated with the efficiency of photosystem II (PhiPS II) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to PhiPS II under drought stress in the URMC are listed...... …………………….…………………………………………..240 4-14. Identification of 18 SNPs associated with electron transport rate (ETR) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to ETR under drought stress in the URMC are included…………………………..……………………………………………………….241 4-15. Identification of 19 SNPs associated with non-photochemical quenching (qN) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to qN under drought stress in the URMC are included..……………………………..……………...... 242

CHAPTER-V 5-1. Drought stress related studies in rice included in the study ………………………………279 5-2. Drought stress related traits for which QTLs were identified included in the meta-analysis study.………………………………………………………………………………...... 281 5-3. List of Meta-QTLs (MQTLs) in which drought resistance related QTLs from different populations were identified……………………………………………………………….285

LIST OF FIGRUES CHAPTER-I 1.1. Morpho-physiological based adaptive mechanisms in response to drought stress in rice plants…………………………………………………………………………...... 60 1.2. Schematic representation of the strategies based on GWA analysis and genome-wide meta- analysis showing all the steps used for identifying the candidate genes and their molecular mechanisms in rice..…………..……………………………………………...... 61 CHAPTER-II 2-1. Growth conditions and phenotype of rice plants. A) Each pot was planted with one seedling and plants were grown in the controlled greenhouse conditions of 800-1000μmol PAR m-2 s- 1 light intensity, photoperiod of 13 h light and 11 h dark and RH of ~65%. B) Drought tolerant genotype with well-watered (Left) and drought stress (Right) plant. C) Drought sensitive genotype with well-watered (Left) and drought stress (Right) plant.…………..134 2-2. Environmental variation for biomass in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) Biomass under WW, shows non- significant environmental variation among all batches where batch 1, 2, 3 & 4 showing minimum (min) range 9.145, 9.123, 9.145 & 9.147, Q1 9.28925, 9.3695, 9.36125 & 9.2945, median 9.597, 9.604,9.582 & 9.578,Q3 9.979,9.990,9.96 & 9.905 and maximum (max) range 10.598,10.625,10.598 & 10.365, respectively and B) Biomass under DS, showing non- significant environmental variation among all the batches, where batch 1,2,3 & 4 showing min range 5.098, 5.156, 5.147, 5.098, Q1 5.918, 6.050, 6.004 & 5.978, median 6.643, 6.7, 6.650 & 6.574, Q3 7.601, 7.674, 7.610 &7.573 and max range 9.298, 9.364, 9.298 & 9.23, respectively...... 135 2-3. Environmental variation for photosynthesis in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) Photosynthesis under WW, it shows non-significant environmental variation among all batches where batch 1, 2, 3 & 4 showing min range 10.235, 10.554, 10.325 & 10.236 Q1 10.584, 10.661, 10.903 & 10.3410, median 11.121, 11.119, 11.184 & 11.241, Q3 12.498, 12.502, 12.581,12.606 and max range 15.365, 15.618, 15.988 & 16.022, respectively and B) Photosynthesis under DS, it shows non-significant environmental variation among all the batches where batch 1,2,3 & 4 showing min range 1.632, 1.550, 1.498 & 1.52 Q1 2.649, 2.711, 2.640 & 2.684, median 3.016, 3.186, 3.016 & 3.125 Q3 3.407, 3.619, 3.364 & 3.369 and max range 3.66, 3.747, 3.862 & 3.756, respectively………...... …………………………………………………..136 2-4. Environmental variation in Instantaneous water use efficiency (WUEi) in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) WUEi under WW, shows non-significant environmental variation among all batches where batch 1, 2, 3 & 4 showing min range 1.515, 1.553, 1.553 & 1.510 Q1 1.948, 1.989, 1.989 & 1.558 median 2.238, 2.160, 2.160 & 2.162 Q3 2.495, 2.401, 2.401 & 3.067 and max range 3.156, 3.109, 3.109 & 3.371, respectively and B) WUEi under DS, it shows non-significant environmental variation among all the batches where batch 1, 2, 3 & 4 showing min range 0.491, 0.456, 0.456 & 0.444 Q1 0.806, 0.795, 0.795 & 0.800 median 0.925, 0.9422, 0.942 & 0.965 Q3 1.893, 1.921, 1.921 & 1.722 and max range 2.349, 2.419, 2.419 & 2.128, respectively…………………………..137 2-5. Environmental variation in stomatal conductance in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) Stomatal conductance under WW, it shows non-significant environmental variation among all batches where batch

1, 2, 3 & 4 showing min range 0.082, 0.081, 0.081 & 0.081 Q1 0.087, 0.086, 0.088 & 0.086 median 0.132, 0.130, 0.131 & 0.131 Q3 0.166, 0.165, 0.166 & 0.165 and max range 0.234,0.224, 0.225 & 0.224, respectively and B) Stomatal conductance under DS, shows non-significant environmental variation among all the batches where batch 1, 2 ,3 & 4 showing min range 0.0324, 0.0336, 0.0324 & 0.0325 Q1 0.0435, 0.0457, 0.0450 & 0.0462, median 0.0785, 0.0793, 0.0760 & 0.0755 Q3 0.116, 0.115, 0.111 & 0.112 and max range 0.221, 0.220, 0.199 & 0.214, respectively………………………………………...... 138 2-6. Drought response of plant productivity traits (Plant biomass and No of tillers) in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of plant biomass (g/plant) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in plant biomass. B) Intrinsic value of no of tillers and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in no. of tillers under WW and DS. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).………………………………………………………………………………….139 2-7. Drought response of leaf morphological traits, Relative Water Content and Chlorophyll Content, in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of relative water content (%) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in relative water content. B) Intrinsic value of chlorophyll (SPAD) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in chlorophyll (SPAD). Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..………………………..140 2-8. Drought response of physiological traits Photosynthesis and Transpiration rate in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of photosynthesis (µmol CO2 m-2 s-1) under WW and DS and lowest (≤ 25%), moderate (26-40%), and highest reduction (≥ 40%) -2 -1 in photosynthesis. B) Intrinsic value of transpiration rate (mMol H2O m s ) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in transpiration rate. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..…………………………………………141 2-9. Drought response of physiological traits Instantaneous water use efficiency and Stomatal conductance in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of WUEi (µmol CO2/ mMol H2O) under WW and DS and increase (%) and reduction in WUEi. B) Intrinsic -2 -1 value of stomatal conductance (Mol H2O m s ) under WW and DS and increase (%) and reduction in stomatal conductance. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..…………..142 2-10. Drought response of physiological traits internal cellular CO2 concentration and the ratio of internal cellular CO2 and ambient CO2 in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of Ci (µmol CO2 mol-1) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Ci. B) Intrinsic value of the ratio of internal cellular CO2 and ambient CO2 (Ci/Ca) under WW and DS and lowest (≤ 25%), moderate (25- 40%), and highest reduction (≥ 40%) in Ci/Ca. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)....143 2-11. Drought response of physiological traits Fv’ and Fm’ in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value Fv’ under WW and DS and lowest (≤ 25%), moderate (25- 40%), and highest reduction (≥ 40%) in Fv’. B) Intrinsic value of Fm’ under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Fm’. Data are

expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..……………………………...... 144 2-12. Drought response to physiological traits (Fv’/Fm’ and the efficiency of Photosystem II) in 30 out of the URMC of 221 rice genotypes. A) It shows intrinsic value Fv’/Fm’ under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Fv’/Fm’. B) It displays intrinsic value of the efficiency of photosystem II under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in the efficiency of photosystem II. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)……………..……………………….…...145 2-13. Drought response to physiological traits electron transport rate and non-photochemical quenching in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value electron transport rate under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in electron transport rate. B) Intrinsic value of non-photochemical quenching under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in non- photochemical quenching. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)…..……………...146 2-14. Heat map showing Pearson’s correlation coefficients for 17 plant productivity and morpho- physiological traits measured in the URMC of 221 rice genotypes screened under WW & DS. The correlations (-1.0≤ r ≥1.0) are colored either in blue (positive correlation) or pink (negative correlation)……………………………………………...... 147 CHAPTER-III 3-1. Rice genotypes growing in field conditions. A) Nursery was grown in the plots with field soil for 25-days in greenhouse at controlled conditions. B) 10 equal size seedlings per genotype were transplanted individually. C) The plot flooded at all times served as well watered (WW) control. D) The plot stressed at reproductive stage served as drought stress (DS) treatment. E) The plot maintaining -70kPa soil water potential using tensiometer at reproductive stage served as a drought stress (DS) treatment.…………………………………………………184 3-2. Effects of drought stress on grain yield components (Panicle length and Spikelets per panicle) in 30 representative genotypes out of 81 rice genotypes of the URMC in field experiments. A) The length of panicle (cm) under WW and DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in panicle length as compare to WW plants. B) The number of spikelets per panicle under WW & DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in number of spikelets per panicle as compare to WW plants. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..……………………………………….……………….185 3-3. Effects of drought stress on grain yield components (Number of filled grains and un-filled grains per panicle) in 30 representative genotypes out of 81 rice genotypes of the URMC in field conditions. A) Number of filled grains per panicle under WW and DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in number of filled grains per panicle as compare to WW plants. B) The number of un-filled grains per panicle under WW & DS with lowest (≤ 25%) and highest (≥25%) increase in number of un-filled grains per panicle as compare to WW plants. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..…………..186 3-4. Drought responses of major plant productivity and physiological traits (Plant biomass and Photosynthesis) in 30 representative genotypes out of 81 rice genotypes of the URMC. A)

Amount of plant biomass (g/ plant) under WW and DS with lowest (≤ 25%), moderate (25- 40%), and highest (≥40%) reduction in plant biomass. B) Intrinsic value of photosynthesis with lowest (≤25%), moderate (25-40%), and highest reduction (≥40%) in photosynthesis as compare to WW plants. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..………………………..187 3-5. Drought responses to physiological trait (Instantaneous water use efficiency) in 30 representative genotypes out 81 rice genotypes of the URMC. A) It shows intrinsic value of WUEi (µmol CO2/ mMol H2O) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in plant biomass. B) It displays intrinsic value of no of tillers and increase (0.57-58%) and decrease (19.87-495%) in WUEi as compare to WW plants Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05)..………………………………………………………..188 3-6. Pearson’s correlation coefficients shown in correlation matrix heat map between 4 grain yield components and 17 plant productivity and morpho-physiological traits of 80 rice genotypes of the URMC screened in greenhouse and field conditions under WW & DS treatments. The correlations (-1 to +1) are colored either in green (positive correlation) or in yellow (negative correlation). ……………………………………………………………………………….189 3-7. Grain yield components and physiological traits strongly correlated with each other in the correlation matrix, were selected for the scatter plot shown. The scatter plot shows correlation between panicle length in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) in greenhouse under WW and DS contributing to higher yield. A) Panicle length is negatively correlated with photosynthesis and WUEi showing R-squared (R2) value for plant biomass having 0.223 and 0.087, respectively; while positively correlated with biomass having R2 value 0.105 under WW. B) It displays the panicle length is negatively correlated with photosynthesis and WUEi showing R2 value -0.168 and 0.0229, respectively while positively correlated with biomass having R2 value 0.270 under DS.………………190 3-8. Grain yield components and physiological traits strongly correlated with each other in the correlation matrix were selected for scatter plot. The scatter plot shows correlation between spikelets per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) condtributing higher yield in green house under WW and DS. A) It shows that spikelets per panicle is positively correlated with photosynthesis and WUEi showing R2 value 0.634 and 0.657, respectively while negatively correlated with biomass having R2 value 0.150 under WW. B) Display of spikelets per panicle positively correlated with photosynthesis and WUEi showing R2 value 0.665 and 0.738, respectively while postively correlated with biomass having R2 value of 0.0129 under DS.………………….191 3-9. Grain yield components and physiological traits strongly correlated with each other based on heat map correlation matrix were selected for scatter plot. The scatter plot shows correlation between the number of filled grains per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) contributing higher yield under WW and DS. A) Number of filled grains per panicle is positive correlated with photosynthesis WUEi, biomass showing R2 value 0.643, 0.684, and 0.121 respectively under WW. B) The number of filled grains per panicle is positively correlated with photosynthesis, WUEi, and biomass showing R2 value 0.588, 0.658, and 0.297, respectively under DS.…………………………………192

3-10. Grain yield components and physiological traits strongly correlated with each other based on heat map correlation matrix were selected for scatter plot. The scatter plot shows correlation between measured number of un-filled grains per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) condtributing to higher yield in the greenhouse under WW and DS. A) Number of un-filled grains per panicle is positive correlated with photosynthesis and WUEi showing R2 value 0.134 and 0.071, respectively while negatively correlated with biomass showing R2 value -0.090 under WW. B) Number of un-filled grains per panicle is positively correlated with photosynthesis and WUEi showing R2 value 0.402 and 0.478, respectively while negatviely correlated with 0.367 under DS.…………………………………………………………………………………………193 CHAPTER-IV 4-1. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A.) For instantaneous water use efficiency (WUEi), based on highest – log10 (P-values), 15 most significant SNPs associated with candidate genes were shown. B.) For photosynthesis, based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with WUEi and photosynthesis..…………...... 243 4-2. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(- log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) for transpiration rate (TR), based on highest –log10 (P-values), 19 most significant SNPs associated with candidate genes were shown. B) For stomatal conductance, based on highest –log10 (P-values), 9 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with TR and stomatal conductance………………………………………………………...244 4-3. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For intercellular CO2 concentration (Ci), based on highest –log10 (P- values), 18 most significant SNPs associated with candidate genes were shown. B) For the ratio of intercellular CO2 to the ambient CO2 concentration (Ci/Ca), based on highest –log10 (P-values), 14 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Ci and Ci/Ca.……………………..245 4-4. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0)

and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For biomass, based on highest –log10 (P- values), 18 most significant SNPs associated with candidate genes were shown. B) For number of tillers (NOT), based on highest –log10 (P-values), 6 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with biomass and NOT……………...………………………………………………………………………..246 4-5. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(- log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For relative water content (RWC), based on highest –log10 (P-values), 17 most significant SNPs associated with candidate genes were shown. B) For leaf rolling (LR), based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with RWC and LR………..………………...... 247 4-6. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. For chlorophyll content (SPAD), based on highest –log10 (P-values), 13 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with chlorophyll content.……………………………….248 4-7. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(- log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For variable fluorescence (Fv’), based on highest –log10 (P- values), 12 most significant SNPs associated with candidate genes were shown. B) For maximum fluorescence (Fm’), based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Fv’ and Fm’…………………………………………………………………249 4-8. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For the ratio of variable fluorescence to the maximum fluorescence (Fv’/Fm’), based on highest –log10 (P-values), 18 most significant SNPs associated with candidate genes were shown. B) For the efficiency of photosystem II (PhiPSII), based on

highest –log10 (P-values), 12 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Fv’/Fm’ and PhiPS II………..………………………………………………………………………………….250 4-9. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For electron transport rate (ETR), based on highest –log10 (P-values), 18 most significant SNPs associated with candidate genes were shown. B) For non- photochemical quenching (qN), based on highest –log10 (P-values), 19 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with ETR and qN………………….……………………………………………251 CHAPTER-V 5-1. Distribution of 911 QTLs related to drought response traits on rice chromosomes in the genome. The bar graphs show the frequency of drought related QTLs on individual rice chromosomes. The number on the top of bar graphs represents the total number of drought related QTLs each chromosome contains…...…………………………………………….288 5-2. Graphic presentation of integration and projection of 911 QTLs associated to 109 different drought related traits on the 12 rice chromosomes, from 58 different drought stress studies conducted in different populations published between 2000 to 2014 from different countries…………………………………………………………………………………...289 5-3. Projection of 156 drought related QTLs and identification of Meta-QTLs (MQTLs) on chromosome 1 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on the right of the chromosome. The five best MQTLs named MQTL1.1, MQTL1.2, MQTL1.3, MQTL1.4 & MQTL1.5 highlighted in brown, gray, orange, blue and coral colors were identified using meta-analysis………………..…………………………………………………………….290 5-4. Projection of 128 drought related QTLs and identification of MQTLs on chromosome 2 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 2. The five best MQTLs named MQTL2.1, MQTL2.2, MQTL2.3, MQTL2.4 & MQTL2.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.………….291 5-5. Projection of 105 drought related QTLs and identification of MQTLs on chromosome 3 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 3. Five best MQTLs named MQTL3.1, MQTL3.2, MQTL3.3, MQTL3.4 & MQTL3.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis…………..292 5-6. Projection of 90 drought related QTLs and identification of MQTLs on chromosome 4 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 4. Five best MQTLs named MQTL4.1, MQTL4.2, MQTL4.3, MQTL4.4 & MQTL4.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis…………..293

5-7. Projection of 49 drought related QTLs and identification of MQTLs on chromosome 5 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 5. Five best MQTLs named MQTL5.1, MQTL5.2, MQTL5.3, MQTL5.4 & MQTL5.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.………….294 5-8. Projection of 70 drought related QTLs and identification of MQTLs on chromosome 6 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 6. Five best MQTLs named MQTL6.1, MQTL6.2, MQTL6.3, MQTL6.4 & MQTL6.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis…………..295 5-9. Projection of 50 drought related QTLs and identification of MQTLs on chromosome 7 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 7. Five best MQTLs named MQTL7.1, MQTL7.2, MQTL7.3, MQTL7.4 & MQTL7.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.………….296 5-10. Projection of 82 drought related QTLs and identification of MQTLs on chromosome 8 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 8. Five best MQTLs named MQTL8.1, MQTL8.2, MQTL8.3, MQTL8.4 &MQTL8.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis…………..297 5-11. Projection of 69 drought related QTLs and identification of MQTLs on chromosome 9 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 9. Five best MQTLs named MQTL9.1, MQTL9.2, MQTL9.3, MQTL9.4 & MQTL9.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis…………..298 5-12. Projection of 35 drought related QTLs and identification of MQTLs on chromosome 10 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 10. Five best MQTLs named MQTL10.1, MQTL10.2, MQTL10.3, MQTL10.4 & MQTL10.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis……….299 5-13. Projection of 44 drought related QTLs and identification of MQTLs on chromosome 11 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 11. Five best MQTLs named MQTL11.1, MQTL11.2, MQTL11.3, MQTL11.4 & MQTL11.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis………300 5-14. Projection of 33 drought related QTLs and identification of MQTLs on chromosome 12 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 12. Five best MQTLs named MQTL12.1, MQTL12.2, MQTL12.3, MQTL12.4 & MQTL12.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.……….301

LIST OF PUBLISHED BOOK CHAPTER/REVIEW ARTICLE CHAPTER-I & V: Anuj Kumar, Supratim Basu, Ramegowda Venkategowda, Andy Pereira (2017). Mechanisms of drought tolerance in rice. In Takuji Saaski Editor-in-Chief (Edi.), Achieving sustainable cultivation of rice, Volume 1: Breeding for higher yield and quality (Pp 131-156). Burleigh Dodds Science Publishing Limited 82 High Street Sawston, Cambridge CB22 3HJ UK.

CHAPTER-I: Basu S, Ramegowda V, Kumar, A and Pereira. A (2016). Plant adaptation to drought stress. F1000Research, 5 (F1000 Faculty Rev): 1554. doi: 10.12688/f1000research.7678.1

ABRREVIATIONS 1000-GW : 1000-Grain Weight ABA : Abscisic Acid Content AFLP : Amplified Fragment Length Polymorphism ALP : Amplicon Length Polymorphism ANOVA : Analysis Of Variance ATP : Adenosine Tri-Phosphate BIO : Biomass BSA : Bulk Segregant Analysis BY : Biological Yield Ci : Intercellular CO2 CI : Confidence Interval Ci/Ca : Ratio of intercellular CO2 and ambient CO2 CID : Carbon Isotope Discrimination cM : centiMorgan COLL : Coleorhiza Length CRD : Completely Randomized Design CT : Canopy Temperature DA : Drought Avoidance DE : Drought Escape DFT : Delay in Flowering Time DLR : Days of Leaf Rolling DPE : Difference in Panicle Excretion DR : Drought resistance DRI : Drought Respond Index DRRV : Deep Root Rate in Volume DRW : Dry Root Weight DSE : Drought Sensitive DSF : Drought Sensitivity under Flowering DS : Drought Stress DSW : Dry Shoot Weight DT : Drought Tolerance DTF : Days To Flowering DTH : Days To Heading DTM : Days To Maturity EI : Economic Index EIPD : Economic Index Per Day ETR : Electron Transport Rate FarmCPU : Fixed and random model Circulating Probability Unification FC : Field Capacity FD : Flowering Date FGP : Filled Grain Per Plant FLL : Flag Leaf Length FLW : Flag Leaf Width Fm’ : Maximum Chlorophyll Fluorescence

FRW : Fresh Root Weight FSP : Fraction Sterile Panicle FSW : Fresh Shoot Weight Fv’ : Variable Chlorophyll Fluorescence Fv’/ Fm’ : Ratio of Variable Chlorophyll Fluorescence and Maximum Chlorophyll Fluorescence GAPIT : Genome Association and Prediction Integrated Tool GP : Germination Percentage GPP : Grain number Per Plant GR : Germination Rate GSOR : Genetic Stocks Oryza GWAS : Genome-wide Association Study GWP : Grain Weight Per Plant GY : Grain Yield HD : Heading Days HI : Harvest Index Kb : Kilo Basepair KW : Kernel Weight LAREA : Leaf Area LBF : Leaf Blade Flatness LBR : Length/Breadth Ratio LeafD : Leaf Drying LD : Linkage Disequilibrium LDW : Leaf Dry Weight LL : Leaf Length LN2 : Leaf N2 LOD : Logarithm Of the Odds LR : Leaf Rolling LRECT : Leaf Erectness LW : Leaf Width MAF : Minor Allele Frequency MAS : Marker-assisted Selection Mb : Million Basepair MDR : Moderate Drought Sensitive MLM : Mixed Linear Model MLMM : Multiple Loci Mixed Model MQTL : Meta-QTL MRD : Maximum Root Depth NOT : Number of Tiller O. sativa : Oryza sativa OA : Osmatic Adjustment PBN : Primary Branch Number PE : Panicle Exertion PF : Panicle Fertility PGY : Plot Grain Yield PH : Plant Height

PhiPSII : Efficiency of Photosystem II PHIPSII : The Yield of PSII under flowering PHOTO : Net Photosynthesis PL : Panicle Length PLuL : Plumule Length PN : Panicle Number PND : Panicle Neck Diameter PNOT : Productive Tillers PRDW : Penetrated Root Dry Weight PRT : Penetrated Root Thickness PSF : Percent Spikelet Fertility PSS : Percent Seed Set PW : Panicle Weight qN : Non-Photochemical Quenching QTL : Quantitative Trait Locus R2 : R-squared RaL : Radical Length RCBD : Randomized Complete Block Design RDW : Root Dry Weight REVS : Recovery Score RFLL : Relative Flag Leaf Length RFLP : Restriction Fragment Length Polymorphism RFLW : Relative Flag Leaf Width RFP : Relative Fertile Panicles RGR : Relative Growth Rate RGRD : Root Growth Rate in Depth RGRV : Root Growth Rate in Volume RGW : Relative Grain Weight RGY : Relative Grain Yield RILs : Recombinant Inbred Lines RL : Root Length ROS : Reactive Oxygen Species RPF : Root Pulling Force RPH : Relative Plant Height RSF : Relative Spike Fertility RSN : Relative Spikelet Number RSR : Root Shoot Ratio RTH : Root Thickness RV : Root Volume RW : Root Weight RWC : Relative Water Content SBIO : Shoot Biomass SBN : Secondary Branch Number SCAR : Sequence Characterized Amplified region SD : Spikelet Density

SDW : Shoot Dry Weight SEN : Senescence SF : Spikelet Fertility SLAREA : Specific Leaf Area SN : Spikelet Number SNP : Single Nucleotide Polymorphism SPAD : Chlorophyll Content SPN : Seeds Panicle SPP : Spikelets Per Panicle SSR : Simple Sequence Repeat STCON : Stomatal Conductance STOF : Stomata Frequency TASSEL : Trait Analysis by Association, Evolution and Linkage TGW : Total Grain Weight TR : Transpiration Rate TRDW : Total Root Dry Weight TSN : Total Spikelet Number URMC : USDA Rice Mini-Core Collection USDA : United States Department of Agriculture VB : Visual Basic WATERLOSS: Proportional Water Loss at time to reach leaf rolling score 4.5-DS WGAS : Whole Genome Association Study WUE : Water Use Efficiency WUEi : Instantaneous Water Use Efficiency WW : Well-Watered

CHAPTER-I INTRODUCTION AND REVIEW OF LITERATURE

INTRODUCTION Rice (Oryza sativa L.) is one of the most important cereal crops belonging to the grass family (Poaceae), the staple food for a large part of the world’s population, and has the second largest area under production following maize worldwide (FAO, 1996). It is considered as the world’s most diverse and versatile crop grown from 530 North in North-eastern China to 350

South in New South Wales, Australia (Mae, 1997; Santos et al, 2003). The genus Oryza, comprises two cultivated species (O. sativa grown in Asian countries and O. glaberrima grown in Africa) and approximately 21 wild species. Rice is widely distributed in tropical, subtropical and temperate regions (Vaughan, 1989) worldwide and can be classified into two major classes or subspecies (indica and japonica) and two subclasses (japonica classified into tropical japonica, temperate japonica & aromatic and indica classified into indica & aus) grown worldwide (Matsuo, 1952; Glaszmann, 1987; Ni et al, 2002; Garris et al, 2005; Ebana et al,

2010). Tropical japonica (Mae, 1997) is the type of rice commonly grown in the U.S. The first trial conducted on rice in the U.S. was established in Virginia in 1609 and commercial cultivation was started in South Carolina around the same time. Presently, rice is grown in seven states of the United States such as Arkansas, California, Louisiana, Mississippi, Missouri, Texas, and Florida (Kulp et al, 2003).

Due to its wide distribution and adaptability, rice serves as an excellent model system for studying plant evolutionary genomics with the broad range of morphological, physiological and developmental diversity found in both O. sativa and its widely distributed wild ancestors, O. rufipogon/O. nivara (Zhao et al, 2010). Globally, rice is grown over an area of about 149 million ha with an annual production of 600 million tones (Bernier et al, 2007). However, worldwide the annual improvement in rice yield has decreased from 2.4% (after the green revolution in the late

1980s) to 0.9% (Hossain, 2007) owing to multiple factors. Consequently, to meet the growing 2

food demands of the world’s population by 2050 (Godfray et al, 2010; Tester and Langridge,

2010), genetic improvements in rice genotypes that increase rice production will be necessary.

The scarcity of water in parts of the world causes dicreases in rice production. Rice production uses 30% of the global freshwater, which is a dwindling and unpredictable natural resource. Availability of water is projected to worsen with anticipated climate change. Inter- disciplinary scientists have been trying to understand and dissect the mechanisms of plant tolerance to drought stress using a variety of approaches; with limited success. Modern genomics and genetic approaches coupled with advances in precise phenotyping and breeding methodologies are expected to more effectively unravel the genes and metabolic pathways that confer drought tolerance in crops (Mir et al, 2012). Scarcity of water internationally (Gleick,

1993) caused by a rapidly increasing world population and accompanying rapid increase in water use for social and economic development, threatens the sustainability of the highly productive irrigated rice ecosystem. Hence, increasing WUE of both irrigated and rainfed crop production is a sustainable solution to growing water shortage. In addition, unforeseen drought at critical stages of rice growth can cause tremendous reduction in grain yield.

Water use efficiency (WUE), the ratio of biomass produced per unit water used, is an essential crop trait for sustainable water management in agriculture. Although WUE is an important component of yield (Passioura, 1986), existing genetic variability for WUE has not been extensively exploited through breeding, since improvements in WUE were often associated with reduction in dry matter accumulation and grain yield (Matus et al, 1995). However, work with transgenic plant models has shown that WUE can be increased in rice, primarily through increase in biomass, supported by increase in photosynthesis and sugars (Karaba et al, 2007;

Ambavaram et al, 2014). These observations suggested genetic improvement in rice with high

WUE could enhance water use efficiency through water-saving irrigation, which has vital significance for food safety as well as climate change (Impa et al, 2005; Zhao et al, 2008).

Many genotypes have evolved naturally that are highly water efficient and drought resistant, and these rice genotypes can be used to improve adapted rice cultivars with low water use efficincy and drought sensitivity under drought stress (Impa et al, 2005). Consequently, screening for natural variation for WUE and drought resistance related traits will provide the genetic resources for improving WUE of rice.

There are approximately 4,500,000 accessions in the plant germplasm collections worldwide (FAO, 1996), of which 9% or 400,000 accessions are rice (Hamilton and Raymond,

2005). The United States Department of Agriculture (USDA) started collecting rice germplasm from all over the world in the 1800s (Bockelman et al, 2002). The USDA rice mini-core

(URMC) collection was developed from 1,794 accessions in the main core collection representing over 18,000 accessions in the USDA global gene bank of rice (Yan et al, 2007).

This URMC has 217 accessions that have originated from 76 countries covering 15 geographic regions, a similar global distribution to the core collection. The phenotypic variation was assessed for 26 agronomic traits using 70 molecular markers to estimate genotypic diversity was accomplished in this mini-core, indicating a rich gene pool harboring valuable genes. In this collection, 203 accessions belong to O. sativa, eight to O. glaberrima, two each to O. nivara and

O. rufipogon, and one each to O. glumaepatula and O. latifolia. Two accessions, NSGC 5944 (PI

590413) and WC 10253 (PI 469300), are from unknown countries of origin. Each of these accessions is listed in the Genetic Stock Oryza (GSOR) collection at www.ars.usda.gov/spa/dbnrrc/gsor. The URMC composed of 217 accessions of Oryza sativa and related species has been selected to represent the diversity of rice based on phenotypic traits and

molecular marker variation (Agrama et al, 2009). Genetic variation among all these accessions indicates the possibility that any specific gene of interest can be isolated from the gene pool

(Garris et al, 2005). Moreover, population structure plays a significant role in accurately mapping genes associated with phenotypic traits of interest (Zhu and Yu, 2009). Genetic distance increases along with an increase in variation and heterosis or hybrid vigor, but it also increases genetic incompatibility between certain populations or parents (Yan et al, 2009). Striking a balance between the heterosis level and genetic compatibility is a challenge to plant breeders.

Therefore, after screening for drought resistance parameters, this collection can become an excellent resource of natural variation for phenotypic and physiological traits involved in WUE, other drought resistance related traits, and higher grain yield. The evaluation of this collection can help plant biotechnologists and breeders to effectively find phenotypic and physiological traits for WUE and drought resistance. The physiological and molecular information will also be useful in determining strategies for developing new drought resistant and high yielding commercial cultivars. Drought resistance in rice plants refers to multiple mechanisms including drought escape via a short life cycle or developmental plasticity, drought avoidance via enhanced water uptake and reduced water loss, drought tolerance via osmotic adjustment, antioxidant capacity, and desiccation tolerance, represented by diverse morpho-physiological traits (Yue et al, 2005).

Supporting molecular diversity analyses of the URMC in associated projects and the ability to construct high-density molecular genetic maps have provided the tools for dissecting the complex traits of WUE and drought resistance. Quantitative trait locus (QTL) mapping has been carried out in an attempt to determine the genetic basis of several traits that may be related to WUE and drought resistance (Lilley et al, 1996; Zhang et al, 1999, 2001; Robin et al, 2003).

In many crops such as soybean and rice, genome-wide association (GWA) analysis has potentially been more efficient for identifying a significant association between crop phenotype and genotype and the association has been used for finding novel genes related to crop productivity, biotic and abiotic stress tolerance (Wang et al, 2016). In this study, the information obtained from the GWA analysis will provide a platform for association analysis of diverse traits. Mapping of the genes responsible for diverse phenotypic traits will help breeders to use molecular markers for tagging these traits during selection, which will improve breeding efficiency especially for those quantitative traits that are difficult to characterize and are easily affected by environment. With the availability of these QTLs, several SNP based marker-assisted breeding (MAB) programs can be helpful in pyramiding the QTLs into high-yielding adapted varieties, which will increase rice production at global level. Moreover, the significance of the

QTL regions will not only improve the drought resistance in the adapted varieties using MAB but also develop new drought-resistant varieties using marker-assisted selection (MAS), and decode the physiological and molecular mechanisms behind the drought resistance advantage conferred by these QTLs.

REVIEW OF LITERATURE

Rice (Oryza sativa L.) is the second cereal crop after wheat not only in the term of harvested area but also in the term of importance as a staple food, it supplies more energy per hectare than other cereal crops and feeds more than half of the world’s population (Todaka et al,

2012). It has been grown for more than 7,000 years (Yunfei et al, 2007; Zong et al, 2007) in more than a hundred countries, with a total cultivated area of approximately 158 million hectares in 2009, producing more than 700 million tons annually (IRRI, 2011). The tremendous growth in human population worldwide is boosting demand for a corresponding growth in rice production

(Liang et al, 2010). By 2025, rice production will need to increase by more than 50 percent

(Khush, 2005 & Zhu et al, 2010). Rice production plays an important role in the economy of the

United States. In the United States, rice farms are the most capital-intensive farms, having the highest average rental rate of farm land and all rice acreage requires irrigation. In 2000-09, approximately 3.1 million acres in the US were under rice production, while an increase is expected in the next decade to approximately 3.3 million acres (Baldwin, 2011). For achieving this target, multidisciplinary scientists should come together and improve well-adapted varieties for drought resistance, insect pest resistance and higher grain yield.

Water deficit (Drought) in rice

Water plays a vital role in agriculture and food production and it is highly limited source

(Wang et al, 2006). Water deficit causes extreme reduction in food production worldwide, thus being a severe threat to food sustainability and security. For feeding the increasing world’s population with limited water source, high yielding varieties adapted to water deficit environmental conditions need to be developed (Foley et al, 2011).

Drought is the most devastating major stress among abiotic stresses. It depresses yield by

15-50 per cent depending on the vigor of the rice and duration of drought stress (Srividya et al,

2011). Reduction in rice production worldwide due to drought stress that affects approximately

23 million hectares of rainfed rice (Serraj et al, 2011) averages 18 million tons annually

(O’Toole, 2004; Lakshmi et al, 2012).

In rainfed areas, high yielding semi-dwarf rice varieties are not widely grown because of their poor adaptability in drought years (Pandey et al, 2000). Drought risk reduces productivity even in non-drought years, because farmers avoid applying fertilizer and other inputs for fear of crop lodging, and they become mired in a cycle of low productivity, poverty and food insecurity

(Venuprasad et al, 2008). Drought mitigation, through development of drought resistant varieties suitable for water-limiting environments with higher yields can be a key in improving rice production and ensuring food security to 9 billion people in the world. The development of these varieties requires a good knowledge of the morpho-physiological based adaptive mechanisms and the genetic control of the traits contributing to drought resistance and ultimately higher yield.

Drought adaptive mechanisms in rice

There are several mechanisms, which allow plants to adapt to drought stress. In field conditions, there are four common mechanisms but drought resistance is the major one and other three mechanisms are classified under drought resistance mechanism viz, drought escape, drought avoidance, and drought tolerance (Figure 1-1). The plants develop these mechanisms based on their molecular responses and morpho-physiological changes (Fukai and Cooper,

1995).

Drought resistance

Drought resistance is defined as the ability of a plant to produce its maximum economic product in a water-deficit environment as well as in a water rich environment (Kumar et al,

2017). Drought resistance is a complex trait whose effect depends on the action and interaction of different morphological, biochemical, and physiological characteristics (Mitra, 2001). The rice crop mainly responds to drought stress conditions by leaf rolling, stomatal closure and increased

ABA synthesis to minimize water deficit (Price et al, 2002). Physiological research suggests that drought resistance in rice mainly relies on WUE that allows minimum water usage for maximum production, osmotic adjustment that enables plants to maintain turgor and protect the meristem, and a reduction in water loss (Nguyen et al, 2004).

Alongside drought resistance, there are other subsidiary factors necessary for crop survival and increased production. Drought resistance in rice includes drought escape (DE) via a short life cycle or developmental plasticity, drought avoidance (DA) via enhanced water uptake and reduced water loss, drought tolerance (DT) via osmotic adjustment, antioxidant capacity and desiccation tolerance (Yue et al, 2006). Besides these, there is a complex network of stress, signaling and regulation of gene expression, which occurs in rice plants responding and adapting to the stresses. The stress signals are perceived through diverse known and unknown sensors and transduced by various signaling components to various physiological and metabolic responses which allow plants to adapt to the stresses (Zhu, 2002; Matsukura et al, 2010; Lata and Prasad,

2011).

Drought escape

Drought escape is defined as the ability of a plant to complete its life cycle before development of serious soil and plant water deficits. It involves two different mechanisms such as rapid phenological development and developmental plasticity. In rapid phenological development, plants are able to produce flowers with a minimum of vegetative structure enabling them to produce seeds on a limited water supply. On other hand, in developmental plasticity, plants able to produce an abundance of vegetative growth, flowers and seeds in seasons of abundant rain, enabling them to both escape drought and survive long periods without rain such as desert ephemerals. In drought-prone areas of southern USA, drought escape is an important mechanism that allows specially bred rice to produce grain despite limited water availability

(Bernier et al, 2008).

Drought avoidance

Drought avoidance is defined as the ability of plants to maintain relatively high tissue water potential despite a shortage of soil moisture. It is also called drought tolerance with high tissue water potential. The plant tissue has two options to maintain a high water level during periods of high evaporative demand and increasing soil water deficit, and these are to either reduce water loss or maintain the supply of water. Rice genotypes that can maintain water status through abscisic acid (ABA) biosynthesis, other biochemical mechanisms or adapted root systems come under the drought avoidance mechanism category. These genotypes are able to minimize the yield losses caused by drought (Singh et al, 2012). Mechanisms for improving water use efficiency and reducing water loss also confer drought avoidance. Rice genotypes, which avoid drought usually, have deep, coarse roots with a high ability for branching and soil penetration, higher root to shoot ratio, elasticity in leaf rolling, early stomatal closure and high cuticular resistance (Blum et al, 1989; Samson et al, 2002; Wang et al, 2006).

Drought tolerance

Drought tolerance is defined as an ability of a plant to survive, grow and produce seed satisfactorily, with a limited amount of water supply or under water deficit conditions (Turner,

1979; Delphine et al, 2010). Rice genotypes have evolved to be drought tolerant and these can be used for developing drought tolerant germplasm for breeding purposes. Drought is a quantitative trait, which has a complex phenotype and is under genetic control (McWilliam, 1989; Delphine et al, 2010). Genetically, drought tolerance in rice is controlled by polygenic effects, involving complex morpho-physiological mechanisms (Li and Xu, 2007), such as maintenance of turgor pressure through osmotic adjustment, increased elasticity of the cellwall in the cell, decreased cell size and desiccation tolerance by protoplasmic resistance (Sullivan and Ross, 1979). The

responses of plants to tissue water potential determine their level of drought tolerance (Mitra,

2001) and the traits associated with such phenomena are considered as secondary traits.

Secondary traits such as osmatic adjustment, relative water content, leaf rolling and stomatal conductivity have been used for selection (Nguyen et al, 1997; Babu et al, 2001; Kato et al,

2006).

Morpho-physiological based screening of naturally evolved genotypes from all over the world and an understanding of the drought-resistance mechanisms will help in identifying genes for drought adaptive germplasms.

Morphological characteristics affected by drought stress in rice

Plant succumb water deficit conditions either when the transpiration rate is very high or water availability to plants is reduced. It affects plant growth, development and finally rice production. When drought stress occurs, plants can have a reduction in elongation and expansion of growth as a survival technique (Zhu, 2002). Plant growth and development reduces leaf- surface traits (form, shape, composition of cuticular wax, leaf pubescence and leaf color), which affects the radiation load on the leaf canopy, delays in or reduces the rate of normal plant senescence as it approaches maturity, and inhibits stem reserves (Blum, 2009).

Several studies have shown that early morphological changes occur in rice upon exposure to drought stress conditions. Drought stress induces reduction in plant growth and development of rice (Tripathy et al, 2000). Due to the reduction in turgor pressure under drought stress, cell growth is severely impaired (Taiz and Zeiger, 2006). Drought affects both elongation as well as expansion growth (Shao et al, 2008), and inhibits cell enlargement more than cell division (Jaleel et al, 2009). It impairs the germination of rice seedlings (Jiang and Lafitte, 2007; Swain et al,

2014), reduces number of tillers, (Mostajeran and Rahimi-Eichi, 2009; Bunnag and Pongthai,

2013) and plant height (Sarvestani et al, 2008; Bunnag and Pongthai, 2013; Sokoto and

Muhammad, 2014). A common adverse effect is the reduction in biomass production (Farooq et al, 2009). However, there are few major morphological characteristics that are severely affected by drought stress and these traits may result in plant growth and productivity in rice.

Leaf rolling

Leaf rolling is one of the acclimation responses in rice and is used as a criterion for scoring drought stress resulting drought tolerance in a rice crop. Leaves of the crop plant frequently roll when plants experience water-limited condition. When leaf temperature increases, the stomata close and transpiration rate decreases sharply with leaf rolling (Sobarado, 1987).

Leaf rolling is a hydronasty that reduces light interception, transpiration and leaf dehydration

(Kadioglu and Terzi, 2007). It may help in maintaining internal plant water status (Turner et al,

1986; Abd Allah, 2009; Gana, 2011; Ha, 2014). If cell turgor is maintained under drought stress, it will result in delayed leaf rolling. However, increased leaf rolling under severe stress has the advantage of preventing water loss and radiation damage.

Subashri et al. (2009) and Salunkhe et al. (2011) found genetic variation in leaf rolling among rice genotypes and mapped QTLs associated with leaf rolling in rice. Thus, leaf rolling is an adaptive response to a water deficit in rice, and leaf angle is a character usually associated with plasticity in leaf rolling when an internal water deficit occurs (Chutia and Borah, 2012).

Various other leaf traits are also affected by a water deficit, which include reduction in the number of leaves per plant (Farooq et al, 2010; Cerqueira et al, 2013; Singh et al, 2013; Sokoto and Muhammad, 2014) leaf area and leaf area index (Kumar et al, 2014a). The reduction might be due to rapid decline in cell division and leaf elongation under drought. So, leaf characterstics

comprised of the number of leaves, leaf area, leaf angle and plasticity in leaf rolling and unrolling can be used as selection criteria in selecting drought resistant rice varieties.

Delayed leaf rolling is an important trait in rice, which could be improved by incorporating the gene(s) into those lines/varieties that perform better under irrigated conditions but not well under water stress conditions. Having the characteristics of delayed leaf rolling under water stress and faster recovery rate after removing the water stress in rice (Singh and

Mackill, 1991) was considered as a good trait because the flag leaf in rice crops plays an important role in grain filling and development (Evans et al, 1975). Therefore, the selection based on delayed leaf rolling proceeds to identify genotypes having more photosynthesis, which had an almost erect flag leaf enabling photosynthesis for longer duration (Yoshida et al, 1976;

Khus, 1995). These leaf attributes can be incorporated for improvement of drought tolerance in crops.

Plant biomass

Plant biomass is the most important source for renewable energy (Kirubakaran et al,

2009). Rice produces high amount of biomass, which is an important agronomic trait for higher grain production. High biomass rice is important to meet the growing demands of food, fodder, and bio-fuel. After coal and oil, it is the widely recognized source of biomass-derived energy, which can meet the demands of biofuel in the future (Werther et al, 2000).

A few studies have been reported to determine its response under drought conditions.

Currently, the U.S. fuel-ethanol industry uses corn grain for bioethanol production but this is not considered a sustainable source of energy, as an increase in demand for corn-based ethanol will have significant land requirements, compete with food and fodder industries, and reduce exports of animal products (Sun and Cheng, 2002; Elobeid et al, 2007). Because of these problems,

liquid fuel production from plant lignocellulose such as rice from rice-growing areas is considered a better alternative from both an agronomic and molecular plant breeding perspective.

Rice response to drought stress at different stages (Price and Courtois, 1999) and the response of rice genotypes to drought stress vary and depend on the characteristics of the drought stress environment. Under drought stress, rice plants reduce production of new tillers and leaves, leaf elongation, rolling of existing leaves and promotion of leaf death (O‟Toole and Cruz, 1980;

Turner et al, 1986).

Yuan et al. (2008) and Salas-Fernandez et al. (2009) suggested that there are many traits related to biomass but plant height, erect leaves, and tiller number play a significant role in plant architectural traits for increasing biomass. The contribution of dry matter partitioning from stem and leaf increases significantly under water stressed conditions compared to well-watered condition, thereby affecting grain yield (Kumar et al, 2006).

Atlin et al. (2002) reported that higher biomass production and harvest index are the key functions to improve grain yield at the vegetative and reproductive stages in rice, respectively.

Several studies have been reported to improve the biomass in rice under drought and increase the grain yields in drought sensitive areas. However, few successes have been reported because genetics and physiology of this trait are very complex (Li and Xu, 2007), and there is lack of effective screening criteria, and low heritability (Ouk et al, 2006).

Physiological characteristics affected by drought stress in rice

Drought resistance is a complex trait for the survival of plants against drought and is the result of the expression of several traits under a specific environment. Adaptive traits to different environments are effective for tolerance at specific drought stress levels, but there is no single

trait that has been identified to be involved in improving water status of plants under severe drought stress conditions. Several scientists from different disciplines such as Plant Physiology,

Agronomy, Molecular Biology, and Plant Breeding analyze drought stress mechanisms in plants and use this knowledge for applications in breeding for the improvement of traditional cultivars which are high yielding but drought sensitive (Kamoshita et al, 2008). A number of physiological traits, which play a significant role in drought resistance and crop productivity in rice, are described below.

Photosynthesis

Photosynthesis is a major metabolic process determining food production and it is severely affected by drought stress. The drought stress causes reduction in photosynthetic rate in rice (Ji et al, 2012; Lauteri et al, 2014; Yang et al, 2014a). There are many components limiting photosynthesis but CO2 diffusion is the major one due to early stomatal closure, reduced activity of photosynthesis related enzymes, the components related to biochemical process triose– phosphate formation and decreased photochemical efficiency of PSII. Alteration in these components reduces photosynthetic rate in plants. Moreover, drought stress decreases stomatal conductance (gs) and mesophyll conductance (gm) to CO2 diffusion for photosynthesis (Centritto et al, 2009). Thus, the ability to maintain stomatal (gs and mesophyll conductance (gm) under drought stress conditions determines the drought tolerance in rice verities (Lauteri et al, 2014).

PSII plays a vital role in supplying reducing energy and ATP. Electron transport chain initiates if

PSII activity surpasses the demand over reduction of the photosynthesis and PS II stimulates the formations of reactive oxygen species (ROS). Therefore, the balance between PSII activity and photosynthetic products must be normal. Drought stress severely affects PSII activity in the flag leaf of rice plants (Pieters and Souki, 2005) and it is due to drought inducing degradation of D1

polypeptide, increasing the activation of the PSII reaction center. Severe drought conditions limiting photosynthesis cause a decline in an enzyme known as Rubisco, responsible in the

Calvin Cycle (Bota et al, 2004 & Zhou et al, 2007). However, the quantity of the enzyme produced rescues Rubisco sites from dead end inhibition by promoting ATP-dependent conformational alterations and enhances protective mechanisms under drought stress (Ji et al,

2012). In recent studies, it has been proven that the introduction of the enzymes involved in photosynthesis of C4 plants enhance the photosynthesis and crop productivity under drought stress. Transgenic rice plants, which overexpress the production of enzyme, can greatly increase the crop productivity (Gu et al, 2012 & Zhou et al, 2009). This approach is open for developing drought tolerant and high yielding rice varieties.

Transpiration rate

Transpiration rate (T) is the process by which water is carried out from the leaves through small leaf pores called stomata. It maintains leaf temperature through evaporating water from the plant leaves and shows water status in the plants. Transpiration rate varies widely depending on weather conditions such high temperature, sunlight, wind, humidity, and water deficit. Drought stress is one of the serious conditions that affects plant growth and food production. During drought stress, plants maintain low leaf water potential and relative water content that reduce transpiration rate resulting in a high leaf temperature (Siddique et al, 2003).

Nerd and Nobel (1991) reported that during drought stress, total water content of Opuntia ficus-indica cladode was decreased by 57%. The water-storage parenchyma of the cladodes lost a greater fraction of water than the chlorenchyma, and thus showed a lower turgor potential. In another study on Hibiscus rosa-sinensis, relative water content, turgor potential, transpiration,

stomatal conductance and water-use efficiency were decreased under drought stress (Egilla et al,

2005)

Karaba et al. (2007) investigated a significant improvement in WUE and biomass production that is due to improved photosynthetic assimilation and decreased transpiration rate in transgenic rice plants under salt- and drought stress conditions by overexpressing Arabidopsis

HARDY (HRD) gene. Therefore, screening of natural variation for low transpiration rate in rice genotypes could be a solution to find high water use efficient and higher yielding rice varieties.

Water use efficiency (WUE)

Water use efficiency (WUE) can be defined as the ratio of carbon gained in photosynthesis (A) to water used in transpiration (T) (This et al, 2010). It can be expressed by following formula:

WUE=A/T

It is also measured as the biomass produced per unit transpiration. WUE describes the relationship between water use and crop production (Karaba et al, 2007). WUE in plants is an especially important consideration where available water resources are limited or diminishing, and a few studies have been conducted to measure WUE directly (Zhou et al, 2011). However,

WUE could be enhanced under restricted water by decreasing transpiration and evaporation rate and improving harvest index. Vasant (2012) reported that WUE has long been known to increase with increasing drought stress and reduced water supply. WUE has been proposed as a major trait for improvement of rice crop for drought tolerance under drought conditions (Condon and

Richards, 1992; Condon et al, 2002; Rebetzke et al, 2002; Richards et al, 2002). However, the application of WUE in breeding programs has been largely limited by the time-consuming and expensive screening process under field conditions for large populations (Chen et al, 2012).

Condon et al. (2004) suggested that there are three ways to mitigate water use by crop plants: (1) allow more available water to pass directly through the crop rather than allowing it to evaporate from an irrigated soil surface, (2) acquire more biomass in exchange for a given amount of water transpired by the crop, (3) increase the harvest index by partitioning a greater proportion of biomass into the harvested product.

Blum (2009) analyzed the role of WUE for crop improvement under drought stress and concluded that effective use of water during photosynthesis is a main target for the improvement of rice in water-limited conditions. Genotypic variation in WUE under limited water regimes is affected more by variation in water use (WU) rather than by variation in biomass (Blum, 2005).

Dingkuhn et al. (1991) studied that WUE is higher in tropical japonica, medium in indica, and low in aus rice in all cases with considerable variation.

Relative Water Content (RWC)

Relative water content (RWC) is an important trait that supports the evaluation of water- status in plants. It provides information on the accurate level of water in plants at a specific time point (Teulat et al, 2003). As RWC is related to cell volume, when it is measured in the flag leaf, it may closely reflect the balance between water supply to the leaf and transpiration rate (Sinclair and Ludlow, 1985). Drought resistant varieties show consistently higher leaf water potential in their tissues than susceptible types under drought stress conditions. Higher RWC has been reported in drought tolerant cultivars of wheat (Martin et al, 1997). Drought is reported to cause decrease in leaf water potential and relative water content (Flores-Nimedez et al, 1990; Munns and Cramer, 1996). The significant role of RWC for breeding under drought stress conditions has been shown in winter bread wheat by Schonfeld et al. (1988). Genetic analysis of RWC showed that it is a quantitative trait governed by many genes with additive effects (Teulat et al, 2003). 18

Stomatal conductance (gs)

Stomatal closure and leaf rolling participate together increase water use efficiency under drought stress in rice, and improve water use efficiency at moderate stress while non-stomatal inhibition of photosynthesis reduced water use efficiency (Dingkuhn et al, 1991). They also showed that partial stomatal closure and non-stomatal inhibition reduce assimilation rates in the afternoon in rice. Water deficiency in in the leaves inhibits gas exchange through three apparently independent mechanisms leaf rolling, reduced stomatal conductance, and non- stomata1 inhibition, which become evident only under drought stress in rice. Several studies have proven that stomata related traits such as accumulation of ABA, density, low conductance, and higher sensitivity to water in the leaves are the essential characteristics for the improvement of the rice crop under limiting water conditions.

Price et al. (1997) have mapped stomatal conductance and leaf rolling related mechanisms along with heading days. Ludlow and Muchow, 1990 suggested that the level of stomatal conductance to water status in a leaf is a highly heritable characteristic and several researchers have shown that stomatal conductance has significant genetic variation in rice

(Dingkuhn et al, 1991).

Osmotic Adjustment (OA)

Osmotic adjustment (OA) is described as a change in solute content of a plant cell. It maintains the ability to absorb water from the environment, thus maintaining water volume and turgor in reference to drought tolerance of crops (Turner and Jones, 1980; Yang et al, 1983;

Morgan, 1984) OA is a major adaptive component of drought resistance during drought stress conditions. Under drought stress, solutes accumulate in the plant cell during OA and decrease osmotic potential (Steponkus et al, 1982; Turner et al, 1986; Fukai and Cooper, 1995). Several

studies have shown that there are two main functions of OA for crop production under drought stress conditions: (I) OA maintains turgor pressure in a leaf, for the same leaf water potential supporting stomatal conductance under lower leaf water status (Ali et al, 1999), and (II) it improves root hair capacity for water uptake (Tangpremsri et al, 1991; Chimenti et al, 2006;

Blum, 2009).

Hsiao et al. (1984) demonstrated that leaf rolling, tissue death, and leaf senescence are delayed by OA under drought stress conditions in rice and OA has been shown to improve crop production under water-limited conditions in several other crops.

Chlorophyll content

Photosynthesis is an important process in which plants produce their food with the help of chlorophyll content to maintain crop growth and development. The photosynthetic ability of plants during the reproductive stage is positively associated with higher crop production

(Rawson et al, 1980). Many traits affect the photosynthetic capacity of plants under drought stress conditions.

Guo and Li (1996) reported that chlorophyll content is one of the major components for photosynthesis and its content has a positive relationship with photosynthetic rate. Several studies have shown that chlorophyll content is associated with improved crop production and transpiration efficiency under drought stress in many crops such as maize, sorghum, and wheat

(Benbella and Paulsen, 1998; Baenziger et al, 1999; Borrell et al, 2000, Haussmann et al, 2002;

Verma et al, 2004). Thus, maintaining higher chlorophyll content for a `longer period in plants during drought stress conditions could be a vital mechanism for enhancing crop production.

Abiotic stresses such as drought and heat decrease membrane stability, chlorophyll content and chlorophyll stability index in wheat (Sairam et al, 1996). The stability of higher chlorophyll

content contributes to help the plant control stress through better availability of chlorophyll. It leads to increase in photosynthetic rate and more dry matter production (Madhan et al, 2000).

Guo and Li (2000) and Fracheboud et al. (2004) have proven that chlorophyll content is positively correlated with the rate of carbon exchange in rice and maize crops under limiting environmental conditions. No QTL has been mapped for chlorophyll content in rice under drought stress conditions. Hence, understanding the role of chlorophyll components for photosynthesis under drought stress could be a criterion for the selection of drought tolerance and development of a superior crop.

Chlorophyll fluorescence and its components

Chlorophyll fluorescence is the light that has been re-emitted after being absorbed by chlorophyll molecules of plant leaves. Researchers usually study the plant eco-physiology by measuring the intensity and nature of light. A leaf absorbs light energy from sunlight, which stimulates electrons in chlorophyll molecules. The energy drives photosynthesis converting light energy in photosystem-II to chemical energy, which is used for food production by plants. If the photosynthetic rate is not sufficient, the excess energy can be dissapitted in the form of heat or re-emitted as chlorophyll fluorescence. Production of fluorescence is high when less energy is used in photosynthesis and thus photosynthetic efficiency can be evaluated by measuring the amount of chlorophyll fluorescence. Chlorophyll fluorimeter can be used for estimation the amount of fluorescence. There are the following fluorescence parameters, which are used in photosynthesis (Maxwell and Johnson, 2000) -

: Minimal fluorescence (arbitrary units). Fluorescence level when all antenna pigment complexes associated with the photosystem is assumed open (dark-adapted).

: Maximal fluorescence (arbitrary units). Fluorescence level when a high intensity flash has been applied. All antenna sites are assumed to be closed.

: Terminal fluorescence (arbitrary units). Fluorescence quenching value at the end of the test.

: Half rise time from to .

Fluorescence can show the ability of a plant to tolerate environmental stresses whether thoese stresses have damaged the photosynthetic apparatus (Maxwell and Johnson, 2000).

Liu et al. (2006) reported that the flowering period is one of the most critical periods to drought stress in rice. A reduction of spikelet fertility is caused by environmental stresses.

Tezara et al. (2002) showed the increasing of spikelet sterility under drought conditions due to a reduction of many key metabolic functions and physiological processes in plants.

Several studies have proven the reduction of chlorophyll fluorescence parameters of rice under drought stress. Chlorophyll fluorescence parameters indicate that the susceptibility in plants to drought stress is associated with a reduction of photosynthetic efficiency of PSII and enhanced non-photochemical quenching (Lichteuthaler and Miehe, 1997).

Sayed (2003) successfully used controlled chlorophyll fluorescence techniques along with measurements of net CO2 exchange and leaf water potential for the rapid screening of cereal crops for drought tolerance. Identifying photosynthesis related QTLs is an important strategy for improving crops with help of MAS because photosynthesis and its related parameters play a significant role in plant growth and development (Gu et al, 2012).

Jahn et al. (2011) reported that twenty different rice genotypes have detectable genetic variation in leaf photosynthetic rate but very few QTL mapping studies have been reported so far

(Teng et al, 2004; Zhao et al, 2008; Adachi et al, 2011).

Effect of drought stress on grain yield and its components in rice

Improvement of rice grain yield per unit land area is the only way to achieve improved rice production and to feed the world’s population. Rice is particularly sensitive to drought stress at the reproductive stage, which results in a drastic reduction in the growth of grain yield components (Hsiao, 1982; Venuprasad et al, 2008). When rice plants experience water-limited conditions, it affects morphological and physiological traits that ultimately result in a reduction of grain yield and its components. The major grain yield components: number of panicles per plant, panicle length, number of spikelet per panicle per plant, spikelet fertility, and number of filled and un-filled grain per panicle per plant and 100 seed weight are particularly important for developing and improving drought resistant, high yielding rice varieties. Therefore, screening of large number of diverse genotypes for these grain yield components could be a major approach to develop high yielding rice varieties under drought stress conditions.

Panicle length

Panicle length is one of the aspects of panicle architecture and determines the capacity of a rice panicle to hold grains. It is ultimately used as a grain yield-related trait. Panicle length, together with number of spikelet per plant, seed setting rate, and grain plumpness, determines the number of grains per panicle per plant. Therefore, grain yield increases in rice (Liu et al, 2016).

Panicles of rice plants are the sink organ that contribute in producing photoassimilates determining grain yield. Several research studies have shown that larger panicles play an important role in developing high yielding rice varieties. Drought stress severely reduces panicle length resulting low grain yield. During the reproductive stage, water deficit conditions are crucial to determine the response of several panicle traits including panicle length (Liu et al,

2009a). Hence, screening for natural variation in panicle length in different rice genotypes under

water limiting conditions could be a good selection criterion for plant biologists to find genes contributing to grain yield in a rice crop.

Spikelet fertility/number of spikelet per panicle

Spikelet fertility is a major grain yield component that is measured as the number of grains divided by the total number of spikelets on a plant and estimated under water deficit conditions. (Cruz and O’ Toole, 1983). Spikelet fertility can be visually estimated under greenhouse and field conditions and it is being used as an indirect selection for drought screening in rice (Garrity and O’Toole, 1994; Yue et al, 2005).

Ekanayake et al. (1989) investigated that spikelet sterility at low panicle water potential was significantly associated with degree of panicle emergence, spikelet opening and desiccation of floral parts.

Yue et al. (2005) showed that spikelet fertility under drought stress could be used as both an indicator and a target in the selection processes using molecular marker- assisted selection.

The drought resistant cultivar N22 had higher spikelet fertility than the drought-sensitive cultivar

N118 as a result of increased antioxidant enzyme activities in the panicles under water stress

(Selote and Khanna, 2004).

Kumar et al. (2014b) reported that drought stress at the reproductive stage caused reduction in number of spikelet (15.9%) and spikelet fertility (17.13%) which reduced grain yield (55.31%) in rice. They also found a significant negative correlation between grain yield with leaf rolling, leaf drying and spikelet sterility under drought stress conditions.

Number of grains per panicle

The number of grains is one of the crucial yield components that determine actual grain yield in rice. It is determined by rice variety and stand density. Most of the US rice cultivars/varieties produce approximately 90-110 grains per panicle (Espino, 2014). Therefore, the higher the plant density, the lower the numbers of grains per panicle reducing the production of number of grains per panicle.

The percentage of filled grains per panicle can be affected by multiple environmental factors such as heat, cold, and drought. However, drought stress is one of the factors that severely reduces the number of filled grains as well seed weight and increases the number of unfilled grains per panicle at the reproductive stage. Several studies have proven that drought stress imposed at tillering and flowering stages (reproductive stage) resulted a significant reduction in the number of panicles per plant, the number of spikelets per panicle, and percentage of filled grains per panicle per plant that ultimately reduced grain yield (Sadghi and

Danesh, 2011). Boonjung and Fukai, 1996 studied the drought stress during the grain filling period. Increasing the duration of the drought stress through grain filling severely affected the number of filled grains with the reduction of 40% and individual grain mass decreasing by 20%.

Grain yield

Grain yield of a crop is a function of the rate and duration of accumulation of dry weight in the economically valuable parts of the plant. Grain yield under drought stress is a complex quantitative trait whose repeatability is thought to be low relative to yield in non-stress environments, reducing selection efficiency (Fukai and Cooper, 1995; Venuprasad et al, 2007).

To date, many efforts have been taken to improve grain yield and its components under drought stress. Grain yield under drought stress conditions depends upon plant vigor and stress duration.

Yang et al. (1995) reported a 67 % reduction in grain yield due to water stress. Jeong et al, 2010 showed that rice plants significantly enhanced drought tolerance at the reproductive stage, with a grain yield increase of 25 per cent to 42 per cent over the controls under field drought conditions.

Kumar et al. (2009) reported that severe drought stress exhibited 65-85% yield reduction in rice compared with normal non-stress conditions and moderate drought stress reduced grain yield by 31-64% compared to non-stress conditions.

Jana and Ghildyal (1971) studied the effect of different levels of soil water at different growth stages on rice yield. They reported that different levels of water stress when compared to well-watered conditions reduced grain yield in rice at every growth stage. They found that the reproductive stage is most sensitive compared to the other growth stages. Water stress during the earlier growth stage (vegetative) affected the production of effective tillers resulting in a reduction of grain yield while water stress during the later growth stage (reproductive) appeared to affect the reproductive physiology by interfering with pollination, fertilization and grain filling.

Advanced genomics and molecular breeding approaches for drought resistance in rice

Molecular breeding has the potential to improve varieties with the desirable characteristics. In molecular plant breeding, breeders use molecular markers to improve varieties with desired traits. With the help of molecular markers, it would be possible to transfer desirable traits among the traditional varieties and novel genes from related wild species. Molecular markers might be helpful in studying quantitative traits, which are difficult to study using conventional breeding methods. In MAS, researchers can tag those quantitative traits using molecular markers. Drought is a complex trait which can’t be easily analyzed and used. 26

Molecular breeding techniques are used in assisting selection for drought related traits such as

WUE, photosynthesis, biomass, tiller number, chlorophyll content, chlorophyll fluorescence parameters, RWC, and stomatal conductance. Molecular markers such as simple sequence repeats (SSRs), sequence characterized amplified regions (SCARs), restriction fragment length polymorphism (RFLPs), random amplified polymorphic DNAs (RAPDs), inter-SSRs, amplified fragment length polymorphism (AFLPs), and amplicon length polymorphism (ALPs) can be used to check for drought resistance traits in the populations such as F2, backcross, double haploids, and recombinant inbred lines (RILs). Mapping and tagging of several drought resistance and agronomic traits with molecular markers have been reported.

The purpose of molecular breeding and genomics is to identify and enhance the resistance of the rice crop to drought stress. Genetic improvement of crops for drought resistance via traditional breeding is hampered by the multivariate evaluation of spatial and seasonal variations for drought timing and severity, and screening for a large number of component traits

(Courtois et al, 2003). Conventional breeding is often based on empirical selection for yield

(Atlin and Lafitte, 2002) which is far from optimal, since yield is a quantitative trait and characterized by low heritability and high genotype × environment (G × E) interactions (Babu et al, 2003). A major reason for the slow progress in developing drought resistant rice cultivars is the incidence of large genotype by environment interactions, which result from a combination of differences in genotypic adaptation and the heterogeneous environments within the target areas

(Fukai and Cooper, 1995).

Development of drought resistant rice varieties using molecular breeding and modern genomics approaches is of considerable economic value for increasing crop production in areas with low precipitation or without any proper irrigation system (Subbarao et al, 2005). There is

interest in evaluating the yield potential in rice by molecular breeding for improvement of drought tolerance that is a major component of drought resistance (Singh et al, 1996). High yield potential is the most important target trait for almost all breeding programs (Peng et al, 2008).

Considerable research has been undertaken to understand the genetic basis of putative drought- adaptive traits in rice (Price and Courtois, 1999; Courtois et al, 2000; Price et al, 2002; Babu et al, 2003; Robin et al, 2003). Plant physiology and modern genomics have led to new insights in drought tolerance providing breeders with new knowledge and tools for crop improvement

(Tuberosa and Salvi, 2006). Natural selection has favored mechanisms for adaptation and survival; breeding activity has directed selection towards increasing the economic yield of cultivated species (Cattivelli et al. 2008). Genetic improvement for drought resistance has been addressed using a conventional approach by selecting for yield and secondary traits (Farooq et al,

2009).

There are many investigations which are involved in the understanding of the physiological mechanisms that provide drought tolerance in rice (Fukai and Cooper, 1995), which include: newly developed molecular tools, screening, selection, and improvement of rice germplasm for drought tolerance (Jongdee et al, 2006; Lafitte et al, 2006; Bernier et al, 2007;

Venuprasad et al, 2007). In recent years, several research programs worldwide have been initiated in which many multidisciplinary scientists are working on screening, selecting and improving the rice germplasm collections for specific trait to solve drought stress problems using modern genomic- assisted and molecular breeding approaches.

QTL mapping and marker-assisted selection

Quantitative trait loci (QTLs) are chromosomal segments of DNA, which contain genes mapped to quantitative traits such as plant height, high protein content, and abiotic stress

tolerance etc. Mapping the regions of the genome containing genes for a quantitative trait is done using molecular markers such as SSRs, AFLP, and SNPs. This is an early step in identifying and sequencing the actual genes underlying trait variation. Quantitative traits refer to phenotypes (characteristics) that vary in degree and can be attributed to polygenic effects such as product of two or more genes, and the environmental variation effects. Subsequently, the identification and location of QTLs, which determine the variation for trait(s) of interest such as stresses and yield, called QTL analysis (Tanksley, 1993). Molecular marker and genome mapping techniques are powerful tools for genetic analysis of QTLs, which govern complex traits (Lander and Botstein, 1989; Tanksley, 1993) and have been extensively used for genetic dissection of agriculturally important traits in rice (Xiao et al, 1996; Yano et al, 1997; Yu et al,

1997; Li et al, 1997). Several studies have been reported on QTL analyses evaluating their locations, numbers, magnitude of phenotypic effects and pattern of gene of action (Vinh and

Paterson, 2005; Ashraf, 2010). The study of quantitative traits like drought tolerance and yield are very difficult as plant breeders lack sufficient information on the number of genetic factors, which control the traits, the location of the loci on the chromosome, and the contribution of single loci for trait expression (Stuber, 1992). Therefore, water stress tolerance needs an analytical approach for dissecting and studying the contribution of component traits using QTL mapping technique. Constructing QTL maps could help plant breeders utilize genetic information for traits of interests. Molecular markers would make selection for the quantitative traits easier. Therefore, several drought related traits such as osmotic adjustment (Lilley et al,

1996; Zhang et al, 1999; Zhang et al, 2001; Robin et al, 2003), cell-membrane stability (Tripathy et al, 2000), abscisic acid content (Quarrie et al, 1997), stomatal regulation (Price et al. 1997), leaf water status and root morphology (Yadav et al, 1997; Ali et al, 2000; Courtois et al, 2000;

Zhang et al, 2001; Kamoshita et al, 2002; Price et al, 2002; Yue et al, 2005) have been mapped for the use of their genetic information in advance plant breeding.

Courtois et al. (2000) identified QTLs for RWC and leaf rolling under drought stress in several types of populations such as double haploids of IR64/Azucena etc. Price et al, 1997 &

2002 identified a QTL for leaf rolling on chromosome 1 in an F2 population and this QTL also had a large effect for leaf rolling in field conditions under drought stress. QTLs for OA have been mapped in rice (Lilley et al, 1996) and barley (Teulat et al, 1998).

Kanbar et al. (2002) identified some QTLs linked to plant height, number of tillers, total root number, root dry weight, total plant length, and root to shoot length ratio in

CT9993/IR62266 DH lines under well-watered conditions.

Xu et al. (2009) identified seven QTLs for leaf WUE in a population of 98 Backcross

Inbred Lines (BILs) derived from a cross between temperate japonica and aus rice at the seedling stage (3–4 weeks from germination) with carbon isotope discrimination as the criterion and the largest additive effect of the QTL from aus rice was co-localized with QTLs for leaf length, tiller number, and nitrogen content.

Mian et al. (1996) carried out QTL mapping for WUE of beans with 120 F4 lines from the cross Young × PI416937 and detected four QTLs accounting for 38% of phenotypic variation. Gomez et al, 2010 mapped three minor QTLs for leaf drying by genotyping a subset of

250 RI lines derived from two locally adapted rice lines, and bulk segregant analysis (BSA) of the extreme phenotypes detected a common QTL on chromosome 1 for leaf rolling and leaf drying in these RILs. Transfer of drought related traits from a drought tolerant genotype to susceptible genotypes would enable the development of drought tolerant rice genotypes, which could be able to produce higher yield under adverse conditions.

Marker assisted selection (MAS) is a process in which molecular markers are used for indirect selection of a trait of interest such as grain yield, biotic & abiotic stress tolerance, and grain quality. This process is used in plant breeding as a molecular plant-breeding tool.

Xu and Crouch (2008) concluded that MAS is a potential tool for enhancing the breeding programs for the selection of target traits indirectly using molecular markers thar are closely linked to underlying genes or the actual gene sequences in the genome. MAS speeds up the recovery of the recipient genome, reduce the number of backcrosses required for gene introgression, and minimize the dependence on particular environmental conditions during the selection procedure (Tuberosa and Salvi, 2006). The recent identification of major QTLs governing grain yield under drought (Kumar et al, 2007; Venuprasad et al, 2009) has made possible the use of MAS for improving drought resistance (Serraj et al, 2011). The development of drought-resistant cultivars could be made more efficient by MAS introgressing of alleles of a

QTL conferring improved drought resistance into the genome of widely used genotypes through backcrossing (Bernier et al, 2007). MAS is the only technique that can easily improve complex traits like drought in comparison to conventional breeding strategies (Collins et al, 2008; Serraj et al, 2011).

Xu et al. (2005) reported the pyramiding of several QTLs for a single target trait with complex inheritance like drought tolerance. MAS can be a powerful tool to pyramid several drought related traits in the same rice genotypes, which can survive under severe drought conditions and be a source for stable rice production in the future.

Genome-wide association (GWA) analysis

A genome-wide association study (GWAS) is also known, as a whole genome association study (WGAS). It is a modern genetics approach that examines an association between a set of

genome-wide genetic variants and traits of different individuals. Generally, it is based on associations between single nucleotide polymorphisms (SNPs) and phenotype of individuals. In recent years, GWAS have also been used in several important crops such as rice, soybean, barley etc. and they have the potential to be even more efficient for identifying phenotype- genotype association because with just one-time genotyping of a population, the panel of inbred lines and a large set of accessions or mini-core collections can be kept immortal in seed banks and these lines can be phenotyped for different traits in different environments in both present and future studies (Wang et al, 2016). The schematic representation of GWA analysis including all the possible steps are given in Figure 1-2.

GWAS have been widely used to identify QTLs for quantitative traits in humans and animals, and have recently also become a popular method of mapping QTLs in plants.

Association mapping identifies the QTLs based on the historic recombination in a panel of diverse germplasm via the presence of linkage disequilibrium (LD) between SNPs and QTLs, i.e, the non-random association of alleles (Walash, 1998; Yu et al, 2008: Zhu et al, 2008).

GWAS are most commonly performed in diversity panels such as collections of unrelated diverse germplasm/mini- core collections developed from several geographical regions in order to maximize the diversity of alleles and haplotypes (Famoso et al, 2011; Huang et al, 2010, 2012,

Huang and Han, 2014; Zhao et al, 2011; Yang et al, 2014). It is not only very beneficial in the term of finding novel QTLs and candidate genes that regulate several agronomic traits but also it validates identified novel QTLs in a breeding population before they can be used for genomics- assisted selection. Therefore, these novel QTLs can be more directly utilized for marker assisted selection in applied breeding programs (Zhang et al, 2014).

Begum et al. (2015) performed GWAS for 19 agronomic traits including grain yield and its components in an elite breeding population of irrigated rice breeding lines from a diversity panel and identified 52 QTLs for 19 traits including large effect QTLs for flowering time and grain length/grain width/grain-length-width ratio. They also found haplotypes that can be used to select plants in the population for short stature and concluded how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

Zhang et al. (2016) analyzed a genome-wide association study using mixed linear model approach and detected a total of 23 significant (P < 0.05) trait–marker associations for

Aluminum (Al) tolerance in rice. Out of these associations, three associations (13%) were identical to the quantitative trait loci reported previously, and other 20 associations were reported for the first time in this study. They discussed the proportion of phenotypic variance (R2) in the

23 significant associations which ranged from 5.03 to 20.03% for Al tolerance. They also found several elite alleles for Al tolerance based on multiple comparisons of allelic effects, which could be used for Al tolerant rice cultivar development using marker-assisted breeding.

Yue et al. (2016) identified 47 SNPs with 27 significant loci for four grain shape traits using GWAS in rice. They also predicted 424 candidate genes from public databases.

Al-Shugeairy et al. (2015) used a diversity panel of rice for QTL mapping related to drought recovery traits using GWAS and found only one significant association on chromosome

2 for drought recovery with a physical position at 24559374 bp. They analyzed positional candidate genes underneath the QTL bioinformatically and through the literature revealing several interesting genes, which may offer potential for developing drought resistant rice cultivars

Ma et al. (2016) identified eighteen, five, and six associated loci for plant height, grain yield per plant, and drought resistant coefficient, respectively. They found nine known functional genes including five for plant height (OsGA2ox3, OsGH3-2, sd-1, OsGNA1,

OsSAP11/OsDOG), two for grain yield per plant (OsCYP51G3 and OsRRMh) and two for the drought resistant coefficient (OsPYL2 and OsGA2ox9), implying very reliable results. In the previous study, they reported OsGNA1 to regulate root development, but this study shows additional controlling of both plant height and root length. They claimed that OsRLK5 is a new drought resistant candidate gene discovered in this study. OsRLK5 mutants showed faster water loss rates in detached leaves. This gene plays an important role in the positive regulation of yield-related traits under drought conditions. They furthermore researched several new loci contributing to the three investigated traits (plant height, grain yield, and drought resistance).

Therefore, these associated loci and candidate genes will significantly improve our knowledge of the genetic control of these traits in rice.

Until now, few GWAS based studies have been reported for drought related traits in rice.

The schematic representation of the strategies used in this study are shown in figure 1-2.

Therefore, GWAS could be an approach to find significant phenotype-genotype association in a larger set of rice genotypes for water use efficacy and drought resistance related traits.

Genome-wide meta-analysis of drought related QTLs

Meta-analysis is an important statistical tool that integrates and analyzes multiple QTLs data from different studies on the same aspect. Pooling out the data of different studies allows greater statistical power for QTL identification and estimates their accurate genetic effects.

Therefore, meta-QTL analysis can give precise conclusions, which are stronger than individual

studies and insight into the genetic dissection of complex traits such as drought, salt and heat related traits. Meta-QTL analysis has been used for several agronomical traits in various crops.

In rice, Courtois et al, 2009 performed meta-analysis to study 675 QTLs controlling 29 root traits including root number, maximum root length, root thickness, root/shoot ratio, and root penetration index in 12 different rice populations from 24 published papers from 1995 to 2007 and developed a public database. They overlayed a number of root QTLs in 5-Mb segments covering the whole genome revealing the existence of “hot spots” that encompassed a few genes.

Swamy et al. (2011) reported a meta-analyses association with 53 grain yield QTLs from

15 different studies under drought stress conditions and found 14 meta-QTLs on 7 chromosomes in qDTY mapping populations developed by IRRI. Trijatmiko et al, 2014 showed QTL meta- analysis across diverse populations and the meta-QTL was conserved across genetic backgrounds and co-localized with QTLs for leaf rolling and osmotic adjustment. They identified a meta-QTL that has a QTL for percent seed set and grains per panicle under drought stress on chromosome 8 in the same region as a QTL for Osmatic adjustment that had been found previously in three different populations.

In other crops, meta-analysis has been applied to study Fusarium head blight resistance

(Liu et al, 2009b; Löffler et al, 2009), identification of quantitative traits such as grain protein content, pre-harvest sprouting tolerance, grain weight (Gupta et al, 2007), seed dormancy (Tyagi and Gupta, 2012), earliness traits (Hanocq et al, 2007), and QTL related to yield and yield components (Zhang et al, 2010) in wheat. Moreover, meta-analysis has been used to study plant height (Sun et al, 2012) and cyst nematode resistance QTL (Guo et al, 2006) in soybean, disease resistance in cacao (Lanaud et al, 2009), blight resistance and plant maturity traits in potato

(Danan et al, 2011), fiber quality in cotton (Lacape et al, 2010) and traits associated to drought, cold temperatures, waterlogging, salt content and mineral availability in barley (Li et al, 2013).

Keeping the above information in mind the present study on “Development and characterization of rice genotypes for water use efficiency and drought resistance” has been initiated with the following objectives.

Objectives:

1. Phenotypic analysis of the USDA rice mini-core collection for water use efficiency and

drought resistance related traits.

2. Correlation of rice drought response phenes for physiological traits and grain yield in the

USDA rice mini-core collection.

3. Molecular genetic dissection of water use efficiency and drought resistance using

genome-wide association analysis in the USDA rice mini-core collection.

4. Genome-wide meta-analysis (GWMA) of QTLs for drought resistance traits and grain

yield components under drought stress.

REFERENCES

Abd Allah, A. 2009. Genetic studies on leaf rolling and some root traits under drought conditions in rice (Oryza sativa L.). African Journal Biotechnology. 8 (22): 6241–6248.

Adachi, S., Y. Tsuru, N. Nito, K. Murata, T. Yamamoto, T. Ebitani, T. Ookawa, T. Hirasawa. 2011. Identification and characterization of genomic regions on chromosomes 4 and 8 that control the rate of photosynthesis in rice leaves. Journal of Experimental Botany. 62:1927–1938.

Agrama, H.A., W. Yan, F. Lee, R. Fjellstrom, M-H. Chen, M, Jia, A. McClung. 2009. Genetic assessment of a mini-core developed from the USDA rice gene bank. Crop Science. 49: 1336–1346.

Ali, M., C. R. Jensen, V. O. Mogensen, M. N. Andersen, and I. E. Henson (1999). Root signaling and osmotic adjustment during intermittent soil drying sustain grain yield of field- grown wheat. Field Crop Research. 62: 35-52.

Al-Shugeairy, Z., Adam H. Price, David Robinson. 2015. Genome Wide Association Mapping for Drought Recovery Trait in Rice (Oryza Sativa L.). International Journal of Applied Agricultural Sciences. 1(1): 11-18.

Ambavaram,, M., S. Basu, A. Krishnan, V. Ramegowda, U. Batlang, L. Rahman, N. Baisakh, A. Pereira. 2014. Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress". Nature Communications. 5: 5302-53016.

Ashraf, M. 2010. Inducing drought tolerance in plants: recent advances. Biotechnology Advances. 28: 169-83.

Atlin, G. N. and H. R. Lafitte. 2002. Marker-assisted breeding versus direct selection for drought tolerance in rice. In: Field Screening for Drought Tolerance in Crop Plants with Emphasis on Rice. 208. Saxena, N. P. and O’Toole, J. C., Eds., Proc. Int. Workshop on Field Screening for Drought Tolerance in Rice. 11–14 Dec 2000. ICRISAT, Patancheru, India, and The Rockefeller Foundation, New York.

Babu, R. C., B. D. Nguyen, V. Chamarerk, P. Shanmugasundaram, P. Chezhian, P. Jeyaprakash, S. K. Ganesh, A. Palchamy, S. Sadasivam, S. Sarkarung, L. J. Wade, and H. T. Nguyen. 2003. Genetic analysis of drought resistance in rice by molecular markers: Association between secondary traits and field performance. Crop Science. 43: 1457– 1469.

Babu, R. C., H. E. Shashidhar, J. M. Lilley, N. D. Thanh, J. D. Ray, S. Sadasivam, S. Sarkarang, J. C. O'Toole, and H. T. Nguyen. 2001. Variation in root penetration ability, osmotic adjustment and dehydration tolerance among accessions of rice adapted to rainfed lowland and upland ecosystems. Plant Breeding. 120: 233-238.

Baenziger, M., G.O. Edmeades, and H.R. Lafitte. 1999. Selection for drought tolerance increases maize yields across a range of nitrogen levels. Crop Science. 39:1035–1040.

Baldwin, K. April, 2011. Consolidation and Structural Change in the U.S. Rice Sector. DIANE Publishing. pp. front cover 11–. ISBN 978-1-4379-8478-1.

Begum H, Spindel JE, Lalusin A, Borromeo T, Gregorio G, Hernandez J, et al. 2015. Genome- wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLoS ONE 10(3): e0119873. doi:10.1371/journal.pone.0119873

Benbella, M. and G.M. Paulsen. 1998. Efficacy of treatments for delaying senescence of wheat leaves: II. Senescenece and grain yield under field conditions. Agronomy Journal. 90:332–338.

Bernier, J., A. Kumar, V. Ramaiah, D. Spaner, G. Atlin. 2007. A Large-Effect QTL for Grain Yield under Reproductive-Stage Drought Stress in Upland Rice. Crop Science. 47: 507.

Bernier, J., G. N. Atlin, R. Serraj, A. Kumar, D. Spaner. 2008. Breeding upland rice for drought resistance. Journal of Science & Food Agriculture. 88: 927–939.

Blum, A. 2005. Drought resistance, water-use efficiency, and yield potential—are they compatible, dissonant, or mutually exclusive? Australian Journal of Agricultural Research. 56: 1159.

Blum, A. 2009. Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. Field Crop Research. 112: 119-123.

Blum, A., L. Shpiler, G. Golan, J. Mayer. 1989. Yield stability and canopy temperature of wheat genotypes under drought stress. Field Crop Research. 22: 289–296.

Bockelman, H.E., R.H. Dilday, W. Yan, and D.M. Wesenberg. 2002. Germplasm collection, preservation, and utilization. p. 597–625. In C.W. Smith and R.H. Dilday (ed.) Rice: Origin, diversity in Oryza sativa L. Genetics. 169:1631–1638.

Boonjung, H and S. Fukai. 1996. Effects of soil water deficit at different growth stages on rice growth and yield under upland conditions. 2. Phenology, biomass production and yield. Field Crops Research. 48(1):47-55.

Borrell, A.K., G.L. Hammer, and R.G. Henzell. 2000. Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield. Crop Science. 40:1037–1048.

Bota, J., H. Medrano, J. Flexas. 2004. Is photosynthesis limited by decreased Rubisco activity and RuBP content under progressive water stress? New Phytology. 162(3): 671–681.

Bunnag, S and P Pongthai. 2013. Selection of rice (Oryza sativa L.) cultivars tolerant to drought stress at the vegetative stage under field conditions. American Journal of Plant Science. 4(9): 1701–1708.

Cattivelli, L., F. Rizza, F. W. Badeck, E. Mazzucotelli, A. M. Mastrangelo, E. Francia, C. Mare, A. Tondelli, and A. M. Stanca. 2008. Drought tolerance improvement in crop plants: An integrated view from breeding to genomics. Field Crop Research. 105: 1–14.

Centritto, M., M. Lauteri, M.C. Monteverdi, R. Serraj. 2009. Leaf gas exchange, carbon isotope discrimination, and grain yield in contrasting rice genotypes subjected to water deficits during the reproductive stage. Journal of Experimental Botany. 60(8): 2325– 2339.

Cerqueira, F. B., E. A. L. Erasmo, J. I. C. Silva, T. V. Nunes, G. P. Carvalho, A. A. Silva. 2013. Competition between drought-tolerant upland rice cultivars and weeds under water stress condition. Planta Daninha. 31(2): 291–302.

Chen, M., J. Luo, G. Shao, X. Wei, S. Tang, Z. Sheng, J. Song and P. Hu. 2012. Fine mapping of a major QTL for flag leaf width in rice, qFLW4, which might be caused by alternative splicing of NAL1. Plant Cell Reports. 31: 863-72.

Chimenti, C. A., M. Marcantonio, and A. J. Hall. 2006. Divergent selection for osmotic adjustment results in improved drought tolerance in maize (Zea mays L.) in both early growth and flowering phases. Field Crop Research. 95: 305–315.

Chutia, J. and S P Borah S P. 2012. Water stress effects on leaf growth and chlorophyll content but not the grain yield in traditional rice (Oryza sativa L.) genotypes of Assam, India: II. Protein and proline status in seedlings under PEG induced water stress. American Journal of Plant Science. 3(7): 971–980.

Collins, N. C., F. Tardieu, and R. Tuberosa. 2008. Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiology. 147: 469-86.

Condon, A. G. and R. A. Richards. 1992. Broad sense heritability and genotype 9 environment interaction for carbon isotope discrimination in field-grown wheat. Australian Journal of Agricultural Research. 43: 921–934.

Condon, A.G., R.A Richards, G.J. Rebetzke, G.D. Farquhar. 2002. Improving intrinsic water-use efficiency and crop yield. Crop Science. 42, 122–131.

Condon, A.G., R.A.Richards, G.J. Rebetzke, G.D. Farquhar. 2004. Breeding for high water-use efficiency. Journal of Experimental Botany. 55:2447–60.

Courtois, B., G. McLaren, P. K. Sinha, K. Prasad, R. Yadav, and L. Shen. 2000. Mapping QTL associated with drought avoidance in upland rice. Molecular Breeding. 6:55–66.

Courtois, B., L. Shen, W. Petalcorin, S. Carandang, R. Mauleon, and Z. Li.. 2003. Locating QTLs controlling constitutive root traits in the rice population IAC165/ Co39. Euphytica. 134: 335–345.

Courtois, B., Nourollah Ahmadi, Farkhanda Khowaja, Adam H. Price, Jean-François Rami, Julien Frouin, Chantal Hamelin, Manuel Ruiz. 2009. Rice Root Genetic Architecture: Meta-analysis from a Drought QTL Database. Rice. 2:115–128.

Cruz, R. and J. O'Toole. 1983. Genotypic variation in epicuticular wax of rice. Crop Science. 23:392-394.

Danan, S., J.B. Veyrieras, V. Lefebvre. 2011. Construction of a potato consensus map and QTL meta-analysis offer new insights into the genetic architecture of late blight resistance and plant maturity traits. BMC Plant Biology. 11-16.

Delphine, F., S. Jefferies, H. Kuchel, and P. Langridge. 2010. Genetic and genomic tools to improve drought tolerance in wheat. Journal of Experimental Botany. 61(12): 3211- 3222.

Dingkuhn, M., F. W. T. P. de Vries, S. De Datta, and H. Van Laar. 1991. Concepts for a new plant type for direct seeded flooded tropical rice. Direct seeded flooded rice in the tropics. 1: 17-18.

Ebana, K., J. Yonemaru, S. Fukuoka, H. Iwata, H. Kanamori, N. Namiki et al. 2010. Genetic structure revealed by a whole-genome single-nucleotide polymorphism survey of diverse accessions of cultivated Asian rice (Oryza sativa L.). Breeding Science. 60: 390–397.

Egilla, J.N., F.T. Davies Jr, T. W. Boutton. 2005. Drought stress influences leaf water content, photosynthesis, and water-use efficiency of Hibiscus rosa-sinensis at three potassium concentrations, Photosynthetica. 43: 135–140.

Ekanayake, I. J., S. K. De Datta and P. L. Steponkus. 1989. Spikelet sterility and flowering response of rice to water stress at Anthesis. Annals of Botany. 63: 257-264.

Elobeid, A., S. Tokgoz, and C. Hart. 2007. The ethanol outlook for Brazil and the United States and implications for livestock. International Sugar Journal. 109: 174–177.

Espino, L., 2014. Rice yield components. UC Rice Blog California Rice Production.

Evans, L.T., I. F.Wardlaw and R A Fisher. 1975. In: Crop Physiology some case stories. Evans L.T. (ed.), Cambridge University Press, London. pp 101-149.

Famoso, A.N., K. Zhao, R.T. Clark, C.W. Tung, M.H. Wright, C. Bustamante C, et al . 2011. Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS genetics. 7: e1002221 doi: 10.1371/journal.pgen.1002221

FAO (Food and Agriculture Organization of the United Nations). 1996. Global plan of action for the conservation and sustainable utilization of plant genetic resources for food and agriculture. FAO, Rome.

Farooq, M., A. Wahid, D. J. Lee, O. Ito, and K. H. M. Siddique. 2009. Advances in Drought Resistance of Rice. Critical Rev. Plant Science. 28: 199-217.

Farooq, M., N Kobayashi, O Ito, A Wahid, R Serraj. 2010. Broader leaves result in better performance of indica rice under drought stress. J Plant Physiol, 167(13): 1066–1075.

Fischer, R. A. 1981. Optimizing the use of water and nitrogen through breeding of crops. Plant and Soil. 58 (1–3): 249–278.

Flores-Nimedez, A., B. Vergara, and K. Doerffling. 1990. Effect of synthetic phytohormone analog on leaf water potential during chilling. International Rice Research Newsletter. 15:18-19.

Foley, J A, N Ramankutty, K A Braumann, E S Cassidy, J S Gerber, M Johnston, N D Mueller, C O’Connell, D K Ray, P C West, C Balzer, E M Bennett, S R Carpenter, J Hill, C Monfreda, S Polasky, J Rockström, J Sheehan, S Siebert, D Tilman, D P M Zaks . 2011. Solutions for a cultivated planet. Nature. 478: 337–342.

Fracheboud, Y., C. Jompuk, J.M. Ribaut, P. Stamp, J. Leipner. 2004. Genetic analysis of cold- tolerance of photosynthesis in maize. Plant Molecular Biology. 56:241–253.

Fukai, S. and M. Cooper. 1995. Development of drought resistant cultivars using physiomorphological traits in rice. Field Crop Research. 40: 67-86.

Gana, A. S. 2011. Screening and resistance of traditional and improved cultivars of rice to drought stress at Badeggi, Niger State, Nigeria. Agricultural & Biological Journal of North America. 2(6): 1027–1031.

Garris, A.J., T.H. Tai, J. Coburn, S. Kresovich, and S. McCouch. 2005. Genetic structure and history, technology, and production. Crop Production Ser #6149. John Wiley & Sons New York, NY.

Garrity, D.P., J.C. Toole. 1994. Screening rice for drought resistance at the reproductive phase. Field Crop Research. 39: 99-110.

Glaszmann, J.C. 1987. Isozymes and classification of Asian rice varieties. Theoretical Applied Genetics. 74: 21–30.

Gleick, P.H. 1993. “Water and conflict: Fresh water resources and international security.” International Security. 18(1):79-112.

Godfray, H.C. et al. 2010. Food security: the challenge of feeding 9 billion people. Science. 327: 812–818.

Gomez, S.M., N.M. Boopathi, S. Satheesh Kumar, T. Ramasubramanian, Z. Chengsong, P. Jeyaprakash, A. Senthil, and R.C. Babu. 2010. Molecular mapping and location of QTLs for drought-resistance traits in indica rice (Oryza sativa L.) lines adapted to target environments. Acta Physiology Plant. 32:355−364.

Gu, J. F., M. Qiu M, J. C. Yang. 2013. Enhanced tolerance to drought in transgenic rice plants overexpressing C4 photosynthesis enzymes. Crop Journal. 1(2): 105–114.

Gu, J., X.Yin, Paul C Struik, Tjeerd Jan Stomph, and Huaqi Wang. 2012. Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions. Journal of Experimental Botany. 63(1): 455–469.

Guo, B., D.A. Sleper, P. Lu, J.G. Shannon, H.T. Nguyen, P.R. Arelli. 2006. QTLs associated with resistance to soybean cyst nematode in soybean: meta-analysis of QTL locations. Crop Science 46:596-602.

Guo, P. and M. Li. 1996. Studies on photosynthetic characteristics in rice hybrid progenies and their parents I. chlorophyll content, chlorophyll-protein complex and chlorophyll fluorescence kinetics. Journal of Tropical & Subtropical Botany. 4(4):60–65.

Guo, P. and R. Li. 2000. Effects of high nocturnal temperature on photosynthetic organization in rice leaves. Acta Botanica Sinica. 42(7):13–18.

Gupta, P.K., H.S. Balyan, P.L. Kulwal. 2007. QTL analysis for some quantitative traits in bread wheat. Genetics. 8(11):807-814.

Ha, P T T. 2014. Physiological responses of rice seedlings under drought stress. Journal of Science & Development. 12(5): 635–640.

Hamilton, R.S. and R. Raymond. 2005. Toward a global strategy for the conservation of rice genetic resources. In: Toriyama K, Heong KL, Hardy B (eds.) Rice is life: scientific perspectives for the 21st century. Proceedings of the world rice research conference held in Tsukuba, Japan CD–ROM pp 47–49.

Hanocq, E., A. Laperche, O. Jaminon, A. L. Lainé, J. Le Gouis. 2007. Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis. Theoretical and Applied Genetics. 114(3):569-84.

Haussmann, B.I.G., V. Mahalakshmi, B.V.S. Reddy, N. Seetharama, C.T. Hash, and H.H. Geiger . 2002. QTL mapping of stay-green in two-sorghum recombinant inbred populations. Theoretical and Applied Genetics. 106:133–142.

Hossain, M. 2007. Rice facts: a balancing act. Rice Today. 6:37.

Hsiao, T. C. 1982. The soil plant atmosphere continuum in relation to drought and crop production. In: Drought Resistance in Crops with Emphasis on Rice, IRRI, Los Banos, Philippines pp39–52.

Hsiao, T. C., J. C. O'Toole, E. B. Yambao and N. C. Turner. 1984. Influence of osmotic adjustment on leaf rolling and tissue death in rice (Oryza sativa L.). Plant Physiology. 75: 338.

Huang, X. and B. Han. 2014. Natural Variations and Genome-Wide Association Studies in Crop Plants. Annual Review of Plant Biology. 65: 531–551 doi: 10.1146/annurev-arplant- 050213-035715

Huang, X., X. Wei, T. Sang, Q. Zhao, Q. Feng, Y. Zhao et al. 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics. 42: 961–967 doi: 10.1038/ng.695

Huang, X., Y. Zhao, X. Wei, C. Li, A. Wang, Q. Zhao et al. 2012. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nature Genetics. 44: 32–39 doi: 10.1093/jncimonographs/lgs007

Impa, S. M., S. Nadaradjan, P. Boominathan, et al. 2005. Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Science. 45: 2517–2522.

IRRI (International Rice Research Institute). 2011. Rough rice production by country and geographical region-USDA. Trend in the rice economy. In: World rice statistics. http://irri.org/index.php?option=com_k2&view=item&layout=item&id=9151&lang= en.

Jahn, C.E., J.K., Mckay, R. Mauleon, J. Stephens, K. McNally, D.R. Bush, H. Leung, and J.E. Leach. 2011. Genetic variation in biomass traits among 20 diverse rice varieties. Plant Physiology. 155: 157–168.

Jaleel, C A., P. Manivannan, A. Wahid, M. Farooq, H. J. Al-Juburi, R. Somasundaram, R. Panneerselvam. 2009. Drought stress in plants: A review on morphological characteristics and pigments composition. International Journal of Agricultural Biology. 11: 100–105.

Jana, R.K. and B.P. Ghildyal. 1971. Effect of varying soil water regimes during different growth phases on the yield of rice under different atmospheric evaporative demands. In : Il Riso Anno xx. 1: 31-37.

Jeong, J.S., Y.S. Kim, K.H. Baek, H. Jung, S.H. Ha, Y.D. Choi, M. Kim, C. Reuzeau and J- K.Kim, 2010. Root-specific expression of OsNAC10 improves drought tolerance and grain yield in rice under field drought conditions. Plant Physiology. 153, 185–197.

Ji, K. X., Y.Y. Wang, W.N. Sun, Q. J. Lou, H.W. Mei, S. H. Shen, H. Chen. 2012. Drought- responsive mechanisms in rice genotypes with contrasting drought tolerance during reproductive stage. Journal of Plant Physiology. 169(4): 336–344.

Jiang, W. and R Lafitte. 2007. Ascertain the effect of PEG and exogenous ABA on rice growth at germination stage and their contribution to selecting drought tolerant genotypes. Asian Journal of Plant Science. 6(4): 684–687.

Jongdee, B., G. Pantuwan, S. Fukai and K. Fischer. 2006. Improving drought tolerance in rainfed lowland rice: An example from Thailand. Agricultural Water Management. 80: 225- 240. Kadioglu, A., R. Terzi. 2007. A dehydration avoidance mechanism: Leaf rolling. The Botanical Review. 73(4): 290–302.

Kamoshita, A., L. J. Wade, M. L. Ali, M. S. Pathan, J. Zhang, S. Sarkarung, and H. T. Nguyen. 2002. Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions. Theoretical Applied Genetics.104: 880–893.

Kamoshita, A., R. C. Babu, N. M. Boopathi, and S. Fukai. 2008. Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crop Research. 109: 1–23.

Kanbar, A., H. E. Shashidhar, and S. Hittalmani. 2002. Mapping of QTL associated with root and related traits in DH population of rice (Oryza sativa L). Indian Journal of Genetics. 62(4): 287-290.

Karaba, A., S. Dixit, R. Greco, A. Aharoni, K.R. Trijatmiko, N. Marsch-Martinez, A. Krishnan, K.N. Nataraja M, Udayakumar, and A. Pereira. 2007. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene. Proceeding of the National Academy of Sciences. 104:15270-75.

Kato, Y., J. Abe, A. Kamoshita, and J. Yamagishi. 2006. Genotypic variation in root growth angle in rice (Oryza sativa L.) and its association with deep root development in upland fields with different water regimes. Plant and Soil. 287: 117–129.

Khus, G.S. 1995. Breaking the yield frontier of rice. Geology Journal. 35: 329- 332.

Khush, G. S. 2005. What it will take to feed 5.0 billion rice consumers by 2030. Plant Molecular. Biology. 59: 1–6.

Kirubakaran, V., V. Sivaramakrishnan, R. Nalini, T. Sekar, M. Premalatha, and P. Subramaniane 2009. A review on gasification of biomass. Renewable and Sustainable Energy Reviews. 13:179−186.

Kulp, K., Ponte, Jr, G. Joseph. 2000. Handbook of Cereal Science and Technology. Marcel Dekker. pp. 203–. ISBN 978-0-8247-8294-8.Khush, G. S. (2001). Green revolution: the way forward. Nature Review. 2: 815-822.

Kumar, A., S. Verulkar, S. Dixit, B. Chauhan, J. Bernier, R. Venuprasad, D. Zhao, M.N. Shrivastava. 2009. Yield and yield-attributing traits of rice (Oryza sativa L.) under lowland drought and suitability of early vigour as a selection criterion. Field Crop Research. 114: 99-107.

Kumar, A., S. Basu, R. Venkategowda, A. Pereira. 2017. Mechanisms of drought tolerance in rice. In Takuji Saaski Editor-in-Chief (Edi.), Achieving sustainable cultivation of rice Volume 1 Breeding for higher yield and quality (Pp 6-10). Burleigh Dodds Science Publishing Limited 82 High Street Sawston, Cambridge CB22 3HJ UK.

Kumar, R., A.K. Sarawgi, C. Ramos, S.T. Amarante, A.M. Ismail, and L.J. Wade. 2006. Partitioning of dry matter during drought stress in rainfed lowland rice. Field Crops Res. 98:1−11.Atlin, G.N., R. Venuprasad, J. Bernier, D. Zhao, P. Virk, and A. Kumar. 2008. Rice germplasm development for drought-prone environments: Progress made in breeding and genetic analysis at the International Rice Research Institute (IRRI). p. 35−59. In R. Serraj, J. Bennett and B. Hardy (ed.) Drought frontiers in rice: Crop improvement for increased rainfed production. IRRI/World Scientific Los Banos, Philippines/Singapore.

Kumar, R., R. Venuprasad, and G. N. Atlin. 2007. Genetic analysis of rainfed lowland rice drought tolerance under naturally occurring stress in eastern India: Heritability and QTL effects. Field Crop Research. 103: 42-52.

Kumar, S., S K Dwivedi, S S Singh, B P Bhatt, P Mehta, R Elanchezhian, V P Singh V P, O N Singh. 2014. Morpho-physiological traits associated with reproductive stage drought

tolerance of rice (Oryza sativa L.) genotypes under rain-fed condition of eastern Indo- Gangetic Plain. Indian Journal of Plant Physiology. 19(2): 87–93.

Kumar, S., S K Dwivedi, S S Singh, B P Bhatt, S. K Jha, S. Lekshmy, R Elanchezhian, O N Singh, and B. P. Bhatt. 2014. Identification of drought tolerant rice genotypes by analysing drought tolerance indices and morpho-physiological traits. SABRAO Journal of Breeding & Genetics. 46 (2): 217-230.

Lacape, J.M., D. Llewellyn, J. Jacobs, T. Arioli, D. Becker, S. Calhoun, Y. Al-Ghazi, S. Liu, O. Palaï, S. Georges, M. Giband H. de Assunção, P. A. Barroso, M. Claverie, G. Gawryziak, T. Jean, M. Vialle, C. Viot. 2010. Meta-analysis of cotton fiber quality QTLs across diverse environments in a Gossypium hirsutum x G. barbadense RIL population. BMC Plant Biology. 10:132.

Lafitte, H., Z. Li, C. Vijayakumar, Y. Gao, Y. Shi, J. Xu, B. Fu, S. Yu, A. Ali, and J. Domingo. 2006. Improvement of rice drought tolerance through backcross breeding: evaluation of donors and selection in drought nurseries. Field Crop Research. 97: 77-86.

Lakshmi, P. M., X. Chen, J. Clarke, J. Salmeron, and H. T. Nguyen. 2012. RNAi-mediated disruption of squalene synthase improves drought tolerance and yield in rice. Journal of Experimeal Botany. 63: 163-75.

Lanaud, C., O. Fouet, D. Clément, M. Boccara, A. M. Risterucci, S. Surujdeo-Maharaj, T. Legavre, and X. Argout. 2009. A meta–QTL analysis of disease resistance traits of Theobroma cacao L. Molecular Breeding. 24:361-374.

Lander, E. S. and D. Botstein. 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121: 185–199.

Lata, C. and M. Prasad. 2011. Role of DREBs in regulation of abiotic stress responses in plants. Journal of Experimeal Botany. 62: 4731-4748.

Lauteri, M., M. Haworth, R. Serraj, M. C. Monteverdi, M. Centritto. 2014. Photosynthetic diffusional constraints affect yield in drought stressed rice cultivars during flowering. PLoS One. 9(10): e109054.

Li, W.T., C. Liu, Y.X. Liu, Z.E. Pu, S.F. Dai, J.R. Wang, X.J. Lan, Y.L. Zheng, Y.M. Wei. 2013. Meta-analysis of QTL associated with tolerance to abiotic stresses in barley. Euphytica. 189(1):31-49.

Li, Z. K. and J. L. Xu. 2007. Breeding for drought and salt tolerant rice (Oryza sativa L.): progress and perspectives. Advances in Molecular Breeding toward Drought and Salt Tolerant Crops. 531-564.

Li, Z. K., S. R. M. Pinson, A. H. Paterson, W. D. Park, and J. W. Stansel. 1997. Epitasis for three grain yield components in rice (Oryza sativa L.). Genetics. 145: 453-465.

Liang, J., Y. Lu, P. Xiao, M. Sun, H. Corke and J. Bao. 2010. Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theoretical Applied Genetics. 121: 475-87.

Lichteuthaler, H.K. and J.A. Miehe. 1997. Fluorescence imaging as a diagnostic tool for plant stress. Trends in Plant Sciences. 2: 316-320.

Lilley, J. M., M. M. Ludlow, S. R. McCouch, and J. C. O’Toole. 1996. Locating QTLs for osmotic adjustment and dehydration tolerance in rice. Journal of Experimeal Botany. 47: 1427–1436.

Liu, E., Y. Liu, G. Wu1, S. Zeng, T.G.T.Thi, L. Liang, Y. Liang, Z. Dong, D. She, H. Wang, I.U. Zaid and D. Hong. 2016. Identification of a Candidate Gene for Panicle Length in Rice (Oryza sativa L.) Via Association and Linkage Analysis. Frontier in Plant Sciences. 7:00596.

Liu, G., H. Mei, H. Liu, X. Yu, G. Zou, L. Luo. 2009. Sensitivities of rice grain yield and other panicle characters to late-stage drought stress revealed by phenotypic correlation and QTL analysis. Molecular Breeding. 25: 603–613.

Liu, J. X., D. Q. Liao, R. Oane, L. Estenor, X. E. Yang, Z. C. Li, and J. Bennett. 2006. Genetic variation in the sensitivity of anther dehiscence to drought stress in rice. Field Crop Research. 97: 87–100.

Liu, S., M.D. Hall, C.A. Griff, A.L. Mckendry. 2009. Meta-analysis of QTL associated with fusarium head blight resistance in wheat. Crop Science. 49: 1955-1958.

Löffler, M., C.C. Schön, T. Miedaner. 2009. Revealing the genetic architecture of FHB resistance in hexaploid wheat (Triticum aestivum L.) by QTL meta-analysis. Molecular Breeding. 23(3):473-488.

Ludlow, M. M. and R. C. Muchow. 1990. A critical-evaluation of traits for improving crop yields in water-limited environments. Advance Agronomy. 43: 107–153.

Ma, X., F. Feng, H. Wei, H. Mei, K. Xu, S. Chen, T. Li, X. Liang, H. Liu, L. Luo. 2016. Genome-wide association study for plant height and grain yield in rice under contrasting moisture regimes. Frontier in Plant Sciences. 7:1801.

Madhan, M., M., S. Lakshmi Narayanan, and S.M. Ibrahim. 2000. Chlorophyll stability index: its impact on salt tolerance in rice. International Rice Research Notes. 25:38-39.

Mae. 1997. Physiological nitrogen efficiency in rice: Nirogen utilization, photosynthesis, and yield potential. Plant soil. 196: 201-210.

Manikavelu, A., N Nadarajan, S K Ganesh, R P Gnanamalar, R C Babu. 2006. Drought tolerance in rice: Morphological and molecular genetic consideration. Plant Growth Regulators. 50(2/3): 121–138.

Martin, M., J.A. Morgan, G. Zerbi, D.R. LeCain. 1997. Water stress rate effects on osmotic adjustment and cell-wall properties in winter wheat. Italian Journal of Agronomy. 1:11–20.

Matsukura, S., J. Mizoi, T. Yoshida, D. Todaka, Y. Ito, K. Maruyama, K. Shinozaki and K. Yamaguchi-Shinozaki. 2010. Comprehensive analysis of rice DREB2-type genes that encode transcription factors involved in the expression of abiotic stress-responsive genes. Molecular Genetics & Genomics. 283: 185–196. Matsuo, T. 1952. Gynecological studies on cultivated rice. Bulletin National Institute of Agriculture Science Series. D3: 1–111.

Matus, A., A.E. Slinkaed, and C.V. Kessel. 1995. Carbon-13 isotope discrimination at several growth stages in lentil, spring wheat and canola. Canadian Journal of Plant Sciences. 75: 577–581. Maxwell, K., N. Johnson. 2000. Chlorophyll fluorescence- a practical guide. Journal of Experimental Botany. 51(345): 659-668.

McWilliam, J. 1989. The dimensions of drought. In: Baker F, ed. Drought resistance in cereals. Wallingford, UK: CAB International. pp 1–11.

Mian, M. A. R., M. A. Bailey, D. A. Ashley. 1996. Molecular markers associated with water use efficiency and leaf ash in soybean. Crop Science. 36: 1252–1257.

Mir, Reyazul Rouf, Mainassara Zaman-Allah, Nese Sreenivasulu, Richard Trethowan, Rajeev K. Varshney. 2012. Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theoretical Applied Genetics. 125:625–645.

Mitra, J. 2001. Genetics and genetic improvement of drought resistance in crop plants. Current Science. 80: 758-762.

Morgan, J.M. 1984. Osmoregulation and water stress in higher plant. Ann. Rev. Plant Physiology. 35:299-319.

Mostajeran, A. V. Rahimi-Eichi. 2009. Effects of drought stress on growth and yield of rice (Oryza sativa L.) cultivars and accumulation of proline and soluble sugars in sheath and blades of their different ages leaves. American-European Journal of Agricultural & Environ Sciences. 5(2): 264–272.

Munns, R. and G. Cramer. 1996. Is coordination of leaf and root growth mediated by abscisic acid? Opinion. Plant and Soil. 185: 33-49.

Nerd, A. and P. M. Neumann. 2004. Phloem water transport maintains stem growth in a drought-stressed crop cactus (Hylocereus undatus) Journal of American Social & Horticultural Sciences. 129, 486–490.

Nerd, A. and P.S. Nobel. 1991. Effects of drought on water relations and non-structural carbohydrates in cladodes of Optunia ficus-indica. Plant Physiology. 81: 495-500.

Nguyen, H. T., R. C. Babu, and A. Blum. 1997. Breeding for drought tolerance in rice: physiology and molecular genetics considerations. Crop Science. 37: 1426–1434.

Nguyen, T. T., N. Klueva, V. Chamareck, A. Aarti, G. Magpantay, A. C. M. Millena, M. S. Pathan and H. T. Nguyen. 2004. Saturation mapping of QTL regions and identification of putative candidate genes for drought tolerance in rice. Molecular Genetics & Genomics. 272: 35–46.

Ni, J., P.M. Colowit, D. J. Mackill. 2002. Evaluation of genetic diversity in rice subspecies using microsatellite markers. Crop Science. 42: 601–607.

O’Toole, J. C. 2004. Rice and water: the final frontier. In: The first international conference on rice for the future. Bangkok Thailand: The Rockefeller Foundation.

O’Toole, J.C., and R.T. Cruz. 1980. Response of leaf water potential, stomatal resistance, and leaf rolling to water stress. Plant Physiology. 65:428−432.

Ouk, M., J. Basnayake, M. Tsubo, S. Fukai, K.S. Fischer, M. Cooper, and H. Nesbitt. 2006. Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice. Field Crops Research. 99:48−58.

Pandey, S., D. Behura, R. Villano and D. Naik. 2000. Economic costs of drought and farmers’ coping mechanisms: a study of rainfed rice systems in eastern India. IRRI discussion paper series number 39. IRRI Los Ban˜os, Philippines.

Passioura, J.B. 1986. Resistance to drought and salinity: Avenues for improvement. Australian Journal of Plant Physiology. 13: 191–201.

Peng, S., G. S. Khush, P. Virk, Q. Tang, and Y. Zou. 2008. Progress in ideotype breeding to increase rice yield potential. Field Crop Research. 108: 32-38.

Pieters, A. J., S. E. Souki. 2005. Effects of drought during grain filling on PSII activity in rice. J Plant Physiology. 162(8): 903–911.

Price, A. H. and B. Courtois. 1999. Mapping QTLs associated with drought resistance in rice: progress, problems and prospects. Plant Growth Regulators. 29: 123-133.

Price, A. H., E. M. Young, and A. D. Tomos. 1997. Quantitative trait loci associated with stomatal conductance, leaf rolling and heading date mapped in upland rice (Oryza sativa L.) New Phytology. 137: 83-91.

Price, A., K. Steele, B. Moore and R. Jones (2002). Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: II. Mapping quantitative trait loci for root morphology and distribution. Field Crop Research. 76: 25-43.

Quarrie, S. A., D. A. Laurie, J. Zhu, C. Lebreton, A. Semikhodskii, A. Steed, H. Witsenboer and C. Calestani. 1997. QTL analysis to study the association between leaf size and abscisic acid accumulation in droughted rice leaves and comparisons across cereals. Plant Molecular Biology. 35: 155-165.

Rawson, H.M., G.A. Constable, and G.N. Howe. 1980. Carbon production of sunflower cultivars in field and well-wateredled environments. II. Leaf growth. Australian Journal Plant Physiology. 7:575–586.

Rebetzke, G. J., A. G. Condon, R. A. Richards and G. D. Farquhar. 2002. Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rain-fed bread wheat. Crop Science. 42: 739–745.

Richards, R. A., G. J., Rebetzke, A. G. Condon, and van Herwaarden. 2002. Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Science. 42: 111–121.

Robin, S., M. S. Pathan, B. Courtois, R. Lafitte, S. Carandang, S. Lanceras, M. Amante, H. T. Nguyen and Z. Li. 2003. Mapping osmotic adjustment in an advanced back-cross inbred population of rice. Theoretical Applied Genetics. 107: 1288-96.

Sadghi, and SM. R. Danesh. 2011. Effects of water deficit roles at different stages of reproductive growth on yield components of rice. World Applied Science Journal. 13(9): 2021-2026.

Sairam, R.K., D.S. Shukla, and P.S. Deshmukh. 1996. Effect of homobrassinolide seed treatment on germination, β-amylase activity and yield of wheat under moisture stress conditions. Indian Journal of Plant Physiology. 1:141-144.

Salas-Fernandez, M.G., P.W. Becraft, Y. Yin, and T. Lubbersted. 2009. From dwarves to giants? Plant height manipulation for biomass yield. Trends in Plant Sciences. 14(8):454−461.

Salunkhe, A S., R Poornima, K S Prince, P Kanagaraj, J A Sheeba, K Amudha, K K Suji, A Senthil, R C Babu. 2011. Fine mapping QTL for drought resistance traits in rice (Oryza sativa L.) using bulk segregant analysis. Molecular Biotechnology. 49(1): 90– 95.

Samson, B., M. Hasan and L. Wade. 2002. Penetration of hardpans by rice lines in the rainfed lowlands. Field Crop Research. 76:175-188.

Santos, A.B., N.K Fageria, and A. S. Prabhu., 2003. Rice ratooning management practices for higher yields. Communication of Soil Sciences and Plant Analysis. 34:881-918.

Sarvestani, Z T., H Pirdashti, S A Sanavy, HB alouchi. 2008. Study of water stress effects in different growth stages on yield and yield components of different rice (Oryza sativa L.) cultivars. Pakistan Journal of Biological Sciences. 11(10): 1303–1309.

Sayed, O.H. 2003. Chlorophyll fluorescence as a tool in cereal crop research. Photosynthetica. 41: 321-330.

Schonfeld, M.P., J.C. Richard, B.F. Carver, N.W. Mornhi. 1988. Water relations in winter wheat as drought resistance indicators. Crop Science. 28:526–531.

Selote, D.S. and R. Khanna-Chopra. 2004. Drought-induced spikelet sterility is associated with an inefficient antioxidant defense in rice panicles. Plant Physiology. 121: 462–471.

Serraj, R., K. L. McNally, I. Slamet-Loedin, A. Kohli, S.M. Haefele, G. Atlin, and A. Kumar. . 2011. Drought resistance improvement in rice: an integrated genetic and resource management strategy. Plant Production Sciences. 14: 1-14.

Shao, H B., L Y Chu, M A Shao, C A Jaleel, H M Mi. 2008. Higher plant antioxidants and redox signaling under environmental stresses. Competent Redundant Biology. 331: 433– 441.

Siddique, K.H.M., S.P. Loss, B.D. Thomson. 2003. Cool season grain legumes in dryland Mediterranean environments of Western Australia: Significance of early flowering,

in: Saxena N.P. (Ed.), management of Agricultural Drought. Science Publishers, Enfield (NH), USA, pp 151–161.

Sinclair, T.R., and M.M. Ludlow. 1985. Who taught plants thermodynamics? The unfulfilled potential of plant water potential. Australian Journal of Plant Physiology. 12:213– 217.

Singh, A., K Sengar, R S Sengar. 2013. Gene regulation and biotechnology of drought tolerance in rice. International Journal of Biotechnology & Bioenergy Research. 4(6): 547–552.

Singh, H. and K T Mackill. 1991. Sensitivity of rice to water deficit at different growth stages. Philippines Crop Science. 16(1):11.

Singh, S., S. Pradhan, A. Singh, and O. Singh. 2012. Marker validation in recombinant inbred lines and random varieties of rice for drought tolerance. Australian Journal of Crop Science. 6: 606-612.

Singh, V. P., R. K. Singh, B. B. Singh, and R. S. Zeigler. 1996. Physiology of Stress Tolerance in Rice: Proceedings of the International Conference on Stress Physiology of Rice 28 February–5 March 1994 Lucknow U. P. India, 239.

Sobarado, M.A. 1987. Leaf rolling: A visual indication of water deficit in corn (Zea mays L.) Maydica. 32: 9-18.

Sokoto, M. B. and A. Muhammad. 2014. Response of rice varieties to water stress in Sokoto, Sudan Savannah, Nigeria. Journal of Biosciences & Medicines. 2(1): 68–74.

Srividhya, A., L. R. Vemireddy, S. Sridhar, M. Jayaprada, P. V. Ramanarao, A. S. Hariprasad, H. K. Reddy, G. Anuradha and E. Siddiq. 2011. Molecular Mapping of QTLs for Yield and its Components under Two Water Supply Conditions in Rice (Oryza sativa L.). Journal of Crop Science & Biotechnology. 14 (1): 45-56.

Steponkus, P. L., K. W. Shahan, and J. M. Cutler. 1982. Osmotic adjustment in rice. In: Drought Resistance in Crops with Emphasis on Rice. IRRI, Los Banes, Laguna, Philippines 18: 1-194.

Stuber, C. W. 1992. Biochemical and molecular markers in plant breeding. Plant Breeding Review. 9: 37–61.

Subashri, M., S Robin, K K Vinod, S Rajeswari, K Mohanasundaram, T S Raveendran. 2009. Trait identification and QTL validation for reproductive stage drought resistance in rice using selective genotyping of near flowering RILs. Euphytica. 166(2): 291–305.

Subbarao, G. V., O. Ito, R. Serraj, J. J. Crouch, S. Tobita, K. Okada, C. T. Hash, R. Oritz, and W. L. Berry. 2005. Physiological perspectives on improving crop adaptation to drought-justification for a systematic component based approach. In: Handbook of photosynthesis, (eds) Pessarkli, M, 2nd edition. Marcel and Dekker, New York 577- 594.

Sullivan, C. Y. and W. M. Ross. 1979. Stress Physiology in Crop Plants. Wiley, New York, 263– 281.

Sun, Y. and J.Y. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: a review. BioSource of Technology. 83: 1–11.

Sun, Y., H. Luan, Z. Qi, D. Shan, C. Liu, G. Hu, Q. Chen. 2012. Mapping and meta-analysis of height QTLs in soybean. Legume Genomics and Genetics. 3(1)1-7.

Swain, P., M. Anumalla, S. Prusty, B.C. Marndi, G.J.N. Rao. 2014. Characterization of some Indian native land race rice accessions for drought tolerance at seedling stage. Australian Journal of Crop Science. 8(3): 324–331

Swamy, M.B.P., Prashant Vikram, Shalabh Dixit, HU Ahmed, and Arvind Kumar. 2011. Meta- analysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genomics. 12:319.

Taiz, L. and E. Zeiger. 2006. Plant Physiology. 4th edn. Sunderland, MA: Sinauer Associates: 7– 64.

Tangpremsri, T., S. Fukai, K. S. Fischer and R. G. Henzell. 1991. Genotypic variation in osmotic adjustment in grain sorghum. 2. Relation with some growth attributes. Australian of Journal of Agricultural Research. 42: 759–767.

Tanksley, S. D. 1993. Mapping polygenes. Annals Review of Genetics. 27: 205-233.

Tanner, C. B. and T. R. Sinclair. 1983. Efficient water use in crop production: research or research? in Limitations to Efficient Water Use in Crop Production, H. M. Taylor, J. R. Wayne, and S. T. Thomas, Eds pp. 1–27, American Society of Agronomy Madison Wis USA.

Teng, S, Q. Qian, D. Zeng, Y. Kunihiro, K. Fujimoto, D. Huang, Zhu L. 2004. QTL analysis of leaf photosynthetic rate and related physiological traits in rice (Oryza sativa L.). Euphytica. 135:1–7.

Tester, M and P. Langridge. 2010 Breeding technologies to increase crop production in a changing world. Science. 327: 818–822.

Teulat, B., D. This, M. Khairallah, C. Borries, C. Ragot, P. Sourdille, P. Leroy, P. Monneveux, and A. Charrier. 1998. Several QTLs involved in osmotic-adjustment trait variation in barley (Hordeum vulgare L.). Theoretical Applied Genetics. 96: 688-698.

Teulat, B., N. Zoumarou-Wallis, B. Rotter, M. Ben Salem, H. Bahri, and D. This. 2003. QTL for relative water content in field-grown barley and their stability across Mediterranean environments. Theoretical Applied Genetics.108:181–188.

Tezera, W., V. Mitchell, S.P. Driscoll, and D.W. Lawlor. 2002. Effects of water deficit and its interaction with CO2 supply on the biochemistry and physiology of photosynthesis in sunflower. Journal of Experimental Botany. 53: 1781-1791.

This, D., J. Comstock, B. Courtois, Y. Xu, N. Ahmadi, W. M. Vonhof, C. Fleet, T. Setter, and S. McCouch. 2010. Genetic Analysis of Water Use Efficiency in Rice (Oryza sativa L.) at the Leaf Level. Rice. 3:72–86.

Todaka, D., K. Nakashima, K. Shinozaki and K. Yamaguchi-Shinozaki. 2012. Toward understanding transcriptional regulatory networks in abiotic stress responses and tolerance in rice. Rice. 5: 6.

Trijatmiko, K. R., Supriyanta, J. Prasetiyono, M. J. Thomson, C. M. Vera Cruz, S. Moeljopawiro, A. Pereira. 2014. Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population. Molecular Breeding. 34: 283–295.

Tripathy, J., J. Zhang, S. Robin, T. T. Nguyen, and H. Nguyen. 2000. QTLs for cell-membrane stability mapped in rice (Oryza sativa L.) under drought stress. Theoretical Applied Genetics. 100: 1197-1202.

Tuberosa, R. and S. Salvi. 2006. Genomics-based approaches to improve drought tolerance of crops. Trends in Plant Sciences. 11(8): 404-412.

Turner, N. C. 1979. Drought resistance and adaptation to water deficits in crop plants. In: Mussell H, Staples CR, eds. Stress physiol. in crop plants. New York: John Wiley and Sons. 343–372.

Turner, N. C., J. C. O’Toole, R. T. Cruz, E. B. Yambao, S. Ahmad, O. S. Namuco, and M. Dingkuhn. 1986. Responses of seven diverse rice cultivars to water deficits. 2. Osmotic adjustment, leaf elasticity, leaf extension, leaf death, stomatal conductance and photosynthesis. Field Crop Research. 13: 273-286.

Turner, N.C. and M.M. Jones. 1980. Turgor maintenance by osmotic adjustment: a review and evaluation. In: Turner, N.C., Kramer, P.J. (Eds), adaptation of plant wate and high temperature stress, Wiley NY pp. 87-103.

Tyagi, S.P., and P.K. Gupta. 2012. Meta-analysis of QTLs involved in pre-harvest sprouting tolerance and dormancy in bread wheat. Triticeae Genomics and Genetics. 3(2):9-24.

Vasant, D. 2012. Genome wide association mapping of drought resistance in rice (O.Sativa) Thesis.

Vaughan, D.A. 1989. Vaughan DA (1989). The genus Oryza L. Current status of taxonomy. IRRI Research Paper. Serial Number 138.

Venuprasad, R., C. O. Dalid, M. D. Valle, D. Zhao, M. Espiritu, M. T. S. Cruz, M. Amante, A. Kumar, and G. N. Atlin. 2009. Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk- segregant analysis. Theoretical Applied Genetics. 120: 177–190.

Venuprasad, R., H. R. Lafitte, and G. N. Atlin. 2007. Response to Direct Selection for Grain Yield under Drought Stress in Rice. Crop Science. 47: 285.

Venuprasad, R., M. T. Cruz, M. Amante, R. Magbanua, A. Kumar, and G. N. Atlin. 2008. Response to two cycles of divergent selection for grain yield under drought stress in four rice breeding populations. Field Crop Research. 107(3): 232-244.

Verma, V., M.J.Foulkes, A.J. Worland, R. Sylvester-Bradley, P.D.S. Caligari, and J.W. Snape. 2004. Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments. Euphytica. 135:255– 263.

Vinh, N. T. and A. H. Paterson. 2005. Genome mapping and its implication for stress resistance in plants. In: Ashraf M., Harris P. J. C., editors. Abiotic stresses: plant resistance through breeding and molecular approaches.

Walsh, M.L. 1998. Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates Inc.

Wang, H., Xun Xu, Filipe Garrett Vieira, Yunhua Xiao, Zhikang Li, Jun Wang, Rasmus Nielsen, and Chengcai Chu. 2016. The Power of Inbreeding: NGS-Based GWAS of Rice Reveals Convergent Evolution during Rice Domestication. Molecular Plant 9: 975– 985.

Wang, H., Y. Inukai, and A. Yamauchi. 2006. Root development and nutrient uptake. Critical Review of Plant Sciences 25: 279–301.

Werther, J., M. Saenger, E.U. Hartage, T. Ogada, and Z. Siagi. 2000. Combustion of agricultural residues. Programing Energy Combust Sciences. 26(1):1–27.

Xiao, J. S., S. Grandillo, S. R. Nahn, S.R. McCouch and S. D. Tanksley. 1996. Genes from wild rice improve yield. Nature. 384: 223.

Xu, Y and J.H. Crouch. 2008. Marker-assisted selection in plant breeding: from publication to practice. Crop Science 48: 391-407.

Xu, Y., D. This, R. Pausch, W. Vonhof, J. Coburn, J. Comstock. 2009. Leaf-level water use efficiency determined by carbon isotope discrimination in rice seedlings: genetic variation associated with population structure and QTL mapping. Theoretical Applied Genetics. 118:1065–81.

Xu, Y., S. R. McCouch, and Q. Zhang. 2005. How can we use genomics to improve cereals with rice as a reference genome? Plant Molecular Biology. 59: 7-26.

Yadav, R., B. Courtois, N. Huang, and G. McLaren. 1997. Mapping genes controlling root morphology and root distribution in a doubled-haploid population of rice. Theoretical Applied Genetics. 94: 619–632.

Yan, W.G., J.N. Rutger, R.J. Bryant, H.E. Bockelman, R.G. Fjellstrom, M.H. Chen, T.H. Tai, and A.M. McClung. 2007. Development and evaluation of a core subset of the USDA rice (Oryza sativa L.) germplasm collection. Crop Sci 47:869–878.

Yan, W.G., Y. Li, H.A. Agrama, D. Luo, F. Gao, X. Lu, and G. Ren. 2009. Association mapping of stigma and spikelet characteristics. Molecular Breeding. 24:277–292.

Yang, C.M., C.L. Chen, and F.J.M Sung. 1983. Water realtion in rice. I. Leaf diffusive tolerance and osmotic adjustment of field grown rice genotypes. Proceedings of the National Sciences. 7(3):288-292.

Yang, J.C., Z.Q. Zhu, Z.Q. Wang. 1995. Effect of soil moisture levels on the yield and physiological characteristics of rice. Acta Agronomy Sinica. 21: 110-114.

Yang, P. M., Q. C. Huang, G. Y. Qin, S. P. Zhao, J. G. Zhou. 2014a. Different drought-stress responses in photosynthesis and reactive oxygen metabolism between autotetraploid and diploid rice. Photosynthetica. 52(2): 193–202.

Yang, W., Z. Guo, C. Huang, L. Duan, G. Chen, N. Jiang N et al. 2014b. Combining high- throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature Communications. 5: 5301.

Yano, M., Y. Harushima, Y. Nagamura, N. Kurata, Y. Minobe and T. Sasaki. 1997. Identification of quantitative trait loci controlling heading date of rice using a high- density linkage map. Theoretical Applied Genetics. 95: 1025-1032.

Yoshida, S., D. A. Forno, J. H. Cock, K. H. Gomez. 1976. Routine procedure for growing rice plants in culture solution. In: Laboratory Manual for Physiological Studies of Rice. Los Banos Philippines IRRI pp. 61-66.

Yu, J., JB Holland, MD McMullen, ES Buckler. 2008. Genetic Design and Statistical Power of Nested Association Mapping in Maize. Genetics .178: 539–551.

Yu, S. B., J. X. Li, C. G. Xu, Y. F. Tan, Y. J. Gao, X. H. Li, Q. Zhang, and M. A. S. Maroof. 1997. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proceedings of the National Academy of Sciences. 94: 9226-9231.

Yuan, J.S., K.A. Tiller, H. Al-Hamad, N.R. Stewart, and C. Stewart. 2008. Plants to power: Bioenergy to fuel the future. Trends in Plant Sciences. 13(8):421−429.

Yue, B., L. Xiong, W. Xue, Y. Xing, L. Luo, and C. Xu. 2005. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil Theoretical Applied Genetics. 111: 1127-36. Yue, B., W. Xue, L. Xiong, X. Yu, L. Luo, K. Cui, D. Laun, Y. Xing and Q. Zhang. 2006. Genetic Basis of Drought Resistance at Reproductive Stage in Rice: Separation of Drought Tolerance From Drought Avoidance. Genetics. 172: 1213–1228.

Yue, F., Q. Lu, R. Zhai, M. Zhang, Q. Xu, Y. Yang, S. Wang, X. Yuan, H. Yu, Y. Wang, X. Wei. 2016. Genome wide association mapping for grain shape traits in indica rice. Planta 244:819–830.

Yunfei, Z., S. GuoPing and C. XuGao. 2007. Characteristics of the short rachillae of rice from archaeological sites dating to 7000 years ago. Chinese Sciences of Bulletin. 52: 1654-1660.

Zhang, J., H. Zheng, A. Aarti, G. Pantuwan, T. Nguyen, J. Tripathy, A. Sarial, S. Robin, R. Babu and B. D. Nguyen. 2001. Locating genomic regions associated with components of drought resistance in rice: comparative mapping within and across species. Theoretical Applied Genetics. 103: 19-29.

Zhang, J., R. C. Babu, G. Pantuwan, A. Kamoshita, A. Blum, S. Sarkarung, J. C. O’Toole and H. T. Nguyen. 1999. Molecular dissection of drought tolerance in rice: from physio morphological traits to field performance. In: Genetic improvement of rice for water- limited environments (Eds) O. Ito, J. C. O’Toole and B. Hardy. International Rice Research Institute Manila Philippines 331– 343.

Zhang, L.Y., D.C. Liu, X. L. Guo, W.L. Yang, J.Z. Sun, D.W. Wang, A. Zhang. 2010. Genomic distribution of quantitative trait loci for yield and yield related traits in common wheat. Journal of Integrative Plant Biology. 52(11):996-1007.

Zhang, P., K. Zhong, Q. M Qasim, H Tong. 2016. Association Analysis in Rice: From Application to Utilization. Frontier in Plant Sciences. 7:1202.

Zhang, Z., U. Ober, M. Erbe, H. Zhang, N. Gao, J.L. He, et al. 2014. Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies PloS one. 9.

Zhao, K., C-W. Tung, G.C. Eizenga, M.H. Wright, M.L. Ali, A.H. Price et al. 2011. Genome- wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Communications. 2: 467 doi: 10.1038/ncomms1467

Zhao, K., M. Wright, J. Kimball, G. Eizenga, A. McClung, M. Kovach,W. Tyagi, M. L. Ali, C.W. Tung, A. Reynolds, C. D. Bustamante, S. R. McCouch. 2010. Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS ONE. 5(5): e10780.

Zhao, X., J. Xu, M. Zhao, R. Lafitte, L. Zhu, B. Fu, Y. Gao, Z. Li. 2008. QTLs affecting morph physiological traits related to drought tolerance detected in overlapping introgression lines of rice (Oryza sativa L.). Plant Science. 174: 618–625.

Zhou G.S., LIU Fang, CAO JinHua, YUE Bing, and XIONG LiZhong. 2011. Detecting quantitative trait loci for water use efficiency in rice using a recombinant inbred line population. Chinese Sciences of Bulletin. 56(14): 481–1487.

Zhou, W., Y. Li, B.C. Zhao, R. C. Ge, Y. Z. Shen, G. Wang , Z. J. Huang. 2009. Overexpression of TaSTRG gene improves salt and drought tolerance in rice. Journal of Plant Physiology. 166(15): 1660–1671.

Zhou, Y., H.M. Lam, J. Zhang. 2007. Inhibition of photosynthesis and energy dissipation induced by water and high light stresses in rice. Journal of Experimental Botany. 58 (5): 1207–1217.

Zhu, C, and J. Yu. 2009. Nonmetric multidimensional scaling corrects for population structure in whole genome association studies. Genetics. 182:875–888.

Zhu, C., M Gore, ES Buckler, J Yu. 2008. Status and Prospects of Association Mapping in Plants. The Plant Genome Journal. 1: 5.

Zhu, J. K. 2002. Salt and drought stress signal transduction in plants. Annals Review of Plant Biology. 53: 247–273.

Zhu, J., Y. Zhou, Y. Liu, Z. Wang, Z. Tang, C. Yi, S. Tang, M. Gu, and G. Liang. 2010. Fine mapping of a major QTL controlling panicle number in rice. Molecular Breeding. 27: 171-180.

Zong, Y., Z. Chen, J. Innes, C. Chen, Z. Wang, and H. Wang. 2007. Fire and flood management of coastal swamp enabled first rice paddy cultivation in east china. Nature. 449: 459- 462.

Figure 1-1. Morpho-physiological based adaptive mechanisms in response to drought stress in rice plants.

Figure 1-2: Schematic representation of the strategies based on GWA analysis and genome-wide meta-analysis showing all the steps used for identifying the candidate genes and their molecular mechanisms in rice.

CHAPTER-II PHENOTYPIC ANALYSIS OF THE USDA RICE MINI-CORE COLLECTION FOR WATER USE EFFICIENCY AND DROUGHT RESISTANCE RELATED TRAITS

ABSTRACT Rice (Oryza sativa L.) is an important world cereal crop that uses 30% of the global fresh water resources during its life cycle (Bouman, 2009). Water deficit is the most prominent constraint affecting rice growth and development at all the growth stages. The vegetative stage is one of the most vulnerable periods, at which drought stress causes a great impact on morpho- physiological processes involved in crop productivity, thereby reducing grain yield. Therefore, efforts on screening diverse rice germplasm under drought stress could provide the basic understanding of the natural variation in plant productivity, leaf morphological and physiological traits contributing to high yield production in rice. In this study, we screened the USDA rice mini-core collection (URMC) composed of 221 rice genotypes for instantaneous water use efficiency (WUEi), plant biomass and number of tillers, and various morpho-physiological traits such as relative water content (RWC), leaf rolling, leaf chlorophyll content, photosynthesis, transpiration rate, stomatal conductance and other gas-exchange parameters such as Ci, Ci/Ca, Fv’,

Fm’, Fv’/Fv’, ETR, PhiPSII and qN at the vegetative stage using a controlled 10-days gravimetric drought stress under greenhouse conditions. Based on the results, we foucused on identifying rice genotypes that maintained higher WUEi, photosynthesis and biomass under drought stress.

Out of the URMC of 221 rice genotypes, 35 genotypes showed ‘high’ levels of drought resistance traits having higher water use efficiency, photosynthesis, and biomass along with

≤25% reduction under drought stress. Likewise, 14 genotypes exhibited ‘moderate’ drought resistance with medium levels of WUE, photosynthesis and biomass under drought stress showing 25-40% reduction, and 8 genotypes with the lowest WUE, photosynthesis and biomass under drought stress and ≥40% reduction in all these three traits as compare to well-watered plants, respectively. Our analysis showed that the genotypes N22, Vandana, GSOR301418 and

GSOR301408 showed consistently higher WUE, photosynthesis and biomass, and GSOR310100

with overall lower WUE, photosynthesis and biomass would be useful material for geneticists and molecular plant breeders to dissect the genetic architecture of drought resistance.

INTRODUCTION

Rice (Oryza sativa L.) is the staple food for the majority of the world’s population and accounts for 20 percent of the world's total calorie intake. It belongs to the biggest grass family

“Poaceae” and has the second largest area under production following maize worldwide. It is considered as the world’s most diverse and versatile crop grown from 530 North in Northeastern

China to 350 South in New South Wales, Australia (Mae, 1997; Santos et al, 2003). The genus

Oryza, comprising two cultivated species O. sativa grown in Asian countries, and O. glaberrima grown in Africa, along with about 21 wild species. Rice is widely distributed in tropical, subtropical and temperate regions (Vaughan, 1989) worldwide. Rice can be classified into two classes or sub-species named indica and japonica where further classified indica is made of indica and aus and japonica is made of tropical japonica, temprate japonica and aromatic

(Matsuo, 1952; Glaszmann, 1987; Ni et al, 2002; Garris et al, 2005; Ebana et al, 2010). Tropical japonica (Mae, 1997) is the type of rice commonly grown in the U.S. Rice is grown in six states of the United States Arkansas, California, Louisiana, Mississippi, Missouri, Texas, and Florida

(Kulp et al, 2003).

Arkansas is the nation’s largest rice-growing state, producing half the nation’s rice with nearly nine billion pounds annually. Rice is grown on more than 1.3 million acres each year, mainly in the eastern Arkansas counties, from Louisiana to Missouri. All of Arkansas rice is grown under irrigated conditions, with approximately 70 to 90 ha-cm of water required for rice during the growing season (Arkansas Cooperative Extension Service, 1982).

The rice crop is very important to the economy of Arakansas and the most capital- intensive with the highest national land rental rate average. In 2000-09, approximately 3.1 million acres in the US was under rice production while an increase is expected in the next decade to approximately 3.3 million acres (Baldwin, 2011). The U.S. rice crop is flood irrigated and contributes about 19% of the world rice trade (USDA, 1989).

Rice is a semi-aquatic plant and its production is water intensive (Wassmann et al, 2009;

Bouman et al, 2007). Around 50% of the land used world-wide for rice production is irrigated,

34% of total rice cropped area is rainfed lowlands, 9% is rainfed uplands, and 7% flooded systems. Irrigated rice alone contributes 75% of the global rice production (IRRI, 2007). A broad spectrum of cultivars and land races with varying degrees of adaptation to water stress, are present within currently available rice germplasm (Price et al, 1990). Due to its wide distribution and adaptability, rice serves as an excellent model system for studying plant evolutionary genomics with the broad range of morphological, physiological and developmental diversity found in both O. sativa and it’s widely distributed wild ancestor, O. rufipogon/O. nivara (Zhao et al, 2010).

Globally, rice is cultivated over an area of about 149 million ha with an annual production of 600 million tones (Bernier et al, 2007). However, the annual improvement in rice yield has decreased from 2.4% (after the green revolution in the late 1980s) to 0.9% (Hossain,

2007), owing to multiple factors.

Water deficit is one of the major abiotic stresses limiting crop growth and yield. Water shortage has become a global crisis and it is a serious threat to agriculture worldwide (Abdelbagi et al, 1993). With a huge demand of water for crop production, water scarcity will be increasingly severe considering the increase in human population and the development of

urbanization along with the fast-growing economies of many countries, which might further restrict the increase of food production (Howell, 2001). Improvement of rice genotypes for drought resistance is required, and a major challenge for worldwide agriculture remains crop production under forthcoming drought stress conditions. Drought stress causes alteration in morphological, physiological, and biochemical processes, but morpho-physiological processes such as photosynthesis, transpiration rate, water use efficiency, stomatal conductance, chlorophyll content, electron transport chain, tillering and biomass, all ultimately determine grain yield (Qing et al, 2001). The amount of water used per unit biomass produced by a crop, known as water use efficiency (WUE), is closely associated with photosynthetic activity, dry matter production and grain yield in many species (Tollenaar and Aguilera, 1992; Qing et al, 2001).

Although WUE is an important component of yield (Passioura, 1986), the existing genetic variability for WUE has not been extensively exploited through breeding, since improvements in

WUE were often associated with reduction in dry matter accumulation and grain yield (Matus et al, 1995). However, work with transgenic plant models has shown that WUE can be increased in rice, primarily through increase in biomass, supported by increase in photosynthesis and sugars

(Karaba et al, 2007; Ambavaram et al, 2014). These observations suggested genetic improvement in rice with high WUE could reduce water shortage through water-saving irrigation, which has very vital significance for food safety as well as climate change (Impa et al,

2005; Zhao et al, 2008).

Many genotypes have naturally evolved and been selected for high WUE and drought tolerance, and these rice genotypes can be used to improve other rice cultivars for producing superior biomass and/or economic yield under field drought stress, which have higher yield but lower WUE and drought tolerance (Impa et al, 2005). Consequently, screening of natural

variation for WUE and drought resistance related traits will provide the genetic resources for improving WUE of rice.

The USDA rice mini-core (URMC) collection was developed from 1,794 accessions in its main core collection representing over 18,000 accessions in the USDA global gene bank of rice (Yan et al, 2007). This URMC has 217 accessions that have originated from 76 countries covering 15 geographic regions, a similar global distribution to the core collection. Full coverage of phenotypic variation assessed for 26 traits and genotypic diversity estimated by 70 molecular markers was achieved in this mini-core, indicating a rich gene pool harboring valuable genes. In this collection, 203 accessions belong to O. sativa, eight to O. glaberrima, two each to O. nivara and O. rufipogon and one each to O. glumaepatula and O. latifolia. Two accessions, NSGC 5944

(PI 590413) and WC 10253 (PI 469300), are from unknown countries of origin. Each of these accessions is listed in the Genetic Stock Oryza (GSOR) collection at www.ars.usda.gov/spa/dbnrrc/gsor. The 217 URMC accessions of O. sativa and related species have been shown to broadly represent the diversity of rice based on phenotypic traits and molecular marker variation (Agrama et al, 2009). Genetic variation among all these accessions indicates the possibility that any specific gene of interest can be isolated from the gene pool

(Garris et al, 2005). Moreover, population structure plays a significant role in accurately mapping genes associated with phenotypic traits of interest (Zhu and Yu, 2009). Therefore, after screening for drought resistance parameters, this collection can become an excellent resource of natural variation for phenotypic and physiological traits involved in WUE, other drought resistance related traits and higher grain yield under drought. The evaluation of this collection can help plant biotechnologists and breeders to effectively find phenotypic and physiological traits for WUE and drought resistance. The physiological and molecular information will also be

useful in determining strategies for developing new drought resistant and high yielding commercial cultivars. Drought resistance in rice plants refers to multiple mechanisms including a) drought escape via a short life cycle or developmental plasticity, b) drought avoidance via enhanced water uptake and reduced water loss, and c) drought tolerance via osmotic adjustment, antioxidant capacity, and desiccation tolerance, all of which are represented by diverse morpho- physiological traits (Yue et al, 2005).

Based on survey of literature, no drought stress studies have been reported on this USDA rice mini-core collection for water use efficiency and drought resistance related traits. Careful selection of suitable physiological traits and rapid/non-destructive methods of quantifying them would be very valuable for improving drought resistance. In this study, we screened and evaluated the USDA rice mini-core collection for water use efficiency and drought related morpho-physiological parameters, which identify drought resistant genotypes with the potential to improve high yielding rice cultivars for productivity under drought stress.

MATERIALS AND METHODS

Plant material

The United States Department of Agriculture (USDA) rice mini-core collection (URMC) of 214 accessions of rice accessions (out 217 URMC accessions, three accessions were not delivered) and 7 adapted cultivars to Arkansas, totaling 221 rice genotypes (Table 2-1), were collected from USDA ARS Dale Bumpers National Rice Research Center, Stuttgart, Arkansas,

USA (Agrama et al, 2009) in summer 2013.

Drought stress treatment at vegetative stage

Due to a lack of greenhouse space and the capacity to handle a large set of plants (~4500 plants) in a single experiment, the whole URMC of 221 rice genotypes was split and screened in four batches during summer 2013. The genotype “Nipponbare” (NB) which is the sequenced reference rice genome, was used in each batch as a common genotype to determine environmental variation between all four batches.

Rice seeds were germinated by imbibing the seeds with deionized water in an incubator in the dark at 37oC for two days. Each seedling was placed in each PVC pot sizing 12.7 cm x

12.7 cm, single PVC pot filled with a known weight of a Redi-earth potting mix (Sun Gro

Horticulture Distribution) (Figure 2-1). Drought stress was imposed on 45-days old plants by withholding the water until the moisture level dropped to 50% of field capacity (FC) deteimined by the standard formula, and maintained for continuous 10-days by weighing them daily and replenishing water lost through evapo-transpiration (Batlang et al, 2013). A set of plants of each genotype maintained at 100% FC served as control (Figure 2-2). In this stress period, each pot was weighed daily at a fixed time of the day, and water lost was replenished to maintain the required 50% FC (Ramegowda et al, 2014) (Figure 2-2). All the rice genotypes were grown in the greenhouse at Altheimer Laboratory, University of Arkansas, Fayetteville. For greenhouse conditions, the temperature was maintained between 28 to 30oC (Ghadirnezhad and Fallah, 2014) during the day and 22 to 23o C at night, and the light was set at a light/dark 13/11hours cycle with maximum light intensity (800-1000µmol PAR m-2 s-1) with 65% relative humidity required for rice plants. The experimental design was a compeletely randomized design (CRD) with 10 replications (each replication is 1 plant in greenhouse) with two treatments.

Measurement of morpho-physiological parameters under drought stress at vegetative stage

In order to determine growth response to drought stress, the following morph- physiological parameters measured after 10-days drought stress are described.

Number of tillers

The total number of tillers per plant, per replication, was recorded after 10-days of drought stress. The mean number of tillers per plant of the all the plants was calculated and expressed as number of tillers per plant.

Leaf rolling (LR) score

After 10 days of drought stress, the leaf rolling score on first five leaves on each plant was recorded using a standard evaluation system for rice. A 1 to 5 scale, standardized for rice

(IRRI, 1996) determined at midday. The range is from 1- indicating fully turgid and unrolled leaves, to 5- indicating all leaves are completely rolled.

Plant biomass

All the rice plants sampled from each genotype were dried in a temperature controlled dryer for 4-5 days and cleaned. The fresh weight and dry weight after drying of the plants was determined and recorded. To determine the plant biomass, shoots were harvested, oven-dried at

80oC for 72h, and weighed. (Sarvestani et al, 2008; Ramegowda et al, 2014).

Relative water content (RWC)

RWC was determined in second leaf of each plant of each treatment (WW & DS) per genotype and the leaf fragments of the same length were excised and fresh weight was measured immediately. Fresh weight of all the genotypes were done between 8:30AM to 12:30PM of the day. Subsequently, the leaf fragments were hudrated to full turgidity by floating them on 70

dionized water for 6 hours. After 6 hours, the leaf fragments were wiped with paper towels, and the turgid weight was measured. The leaf samples were oven-dried at 800C for 72 hours to determine the dry weight of the leaf fragments. The RWC was calculated using the formula proposed by Turner (1986):

RWC%= (Fresh weight-Dry weight /Turgid weightw-Dry weight) x100

After 10 days of continuous drought stress, physiological parameters such as photosynthesis rate, transpiration rate, stomatal conductance, intercellular CO2 concentration

(Ci), the ratio of intercellular CO2 concentration (Ci) and the ambient CO2 (Ca/Ci), chlorophyll fluorescence components (Fv’, Fm’, Fv’/Fm’, electron transport rate ETR, the efficiency of photosystem II PhiPS II, and non-photochemical quenching-qN) were measured using a portable

-1 photosynthesis meter LICOR 6400XT at a CO2 concentration of 370 μmolmol , light intensity of 1,000 μmol m-2 s-1, and 55% to 60% relative humidity at the tenth day of drought stress treatment (Ramegowda et al, 2014; De Freitas et al, 2016). The medium portion of the 2nd fully expanded leaf of ten plants per treatment (WW & DS) per genotype was used to measure all the physiological parameters. Each leaf sample was clamped into the LICOR in such a way by avoiding the midrib area. All the physiological measurements were captured twice in the LICOR

6400XT. All the physiological parameters were measured between 8:30AM to 12:30PM of the day.

Instantaneous water use efficiency (WUEi)

WUEi, a drived parameter, was calculated as the ratio of photosynthesis (A) to transpiration rate (T). For WUEi, photosynthesis and transpiration rate were measured on the medium portion of the 2nd fully expended leaf of ten plants per treatment (WW & DS0 per

genotype using LICOR 6400XT between 8:30AM to 12:30PM of the day. The measuremnts were captured twice in each leaf of the plant by LICOR 6400XT. WUEi is expressed as µmol

CO2/ mMol H2O. The formula of WUEi is given:

Estimation of chlorophyll content (SPAD)

After 10-days of drought stress period, chlorophyll content was measured in all the rice genotypes using soil and plant analyzer development (SPAD)-502 Plus Chlorophyll Meter

(Spectrum Technologies, USA). The fully expended leaves on ten plants per treatment (WW &

DS) per genotype were used to estimate cholorophyll content in all the rice genotypes. Each leaf sample was inserted into the clean sample slot of the SPAD in such a way by avoiding the midrib area of the leaves and captured three readings of each leaf.

Statistical analysis

In this study, the experiment was conducted in a compeletely randomized design (CRD) with 10 replications (each replication is 1 plant in greenhouse) with two treatments (WW and

DS). Data for water use effiency and other morpho-physiological traits were analyzed by analysis of variance (ANOVA) using JMP version 12.0. The statistical model included the effects of genotype, treatment (WW & DS), and interaction of genotype and treatment. The

Tukey’s HSD was used to compare the means of treatments (WW & DS) among all the genotypes for significant effects (Tukey’s HSD, P < 0.05) using JMP version 12 as Tukey’s

HSD can determine slight difference between the means. The correlation analysis was also performed using JMP version 12.0 to correlate the ability of water use efficiency and other morpho-physiological traits in the URMC of 221 rice genotypes.

RESULTS AND DISCUSSION

Screening and identification of rice genotypes for plant productivity and morphological traits under DS The plant productivity related traits were analyzed for plant biomass and number of tillers; morphological traits such as relative water content (RWC), leaf rolling (LR) and chlorophyll content, which showed response under drought stress at vegetative stage in rice plants.

Drought response of rice genotypes for plant biomass and number of tillers

Plant biomass is measured as a total dry matter of plants after drying, and is the green plants’ weight that is produced by photosynthesis using solar energy (McKendry et al, 2002).

Plant biomass is considered one of the most important sources for renewable energy

(Kirubakaran et al, 2009). Rice produces more biomass, which is an important plant productivity trait contributing to grain production. Use of rice genotypes containing higher biomass is important to meet the growing demands of food, fodder, and bio-fuel. Based on the analysis of variance, there are significant differences between WW and DS plants and reduction in plant biomass among all 221 of rice genotypes of the URMC. There is also an interaction between treatment (WW and DS) and rice genotypes, which shows the ‘difference in percentage reduction’ of plant biomass. Based on this criterion, rice genotypes were classified into three categories for each of the traits studied- 1) Drought resistant (≤ 25% of reduction), 2) moderate drought resistant (25-40% of reduction), and 3) drought sensitive (≥40% of reduction). De

Freitas et al. (2016) derived this categorization was used for all the traits as was adapted from

The environmental variation between all four batches was analyzed, since all 221 rice genotypes of the URMC were screened in four batches with a planting interval of 15 days between each.

The analysis showed that there was no significant difference in plant biomass between all the

batches, based on a common check genotype NB (Figure 2-2). Under WW condition, the check genotype NB shows non-significant variation for biomass among all four batches. Batch 1, 2, 3

& 4 show a minimum range 9.145, 9.123,9.145 & 9.147, first quartile (Q1) 9.28925, 9.3695,

9.36125 & 9.2945, median 9.597, 9.604, 9.582 & 9.578, third quartile (Q3) 9.979, 9.990, 9.96 &

9.905 and maximum range 10.598, 10.625, 10.598 & 10.365, respectively. Under drought stress condition, the biomass also shows non-significant variation among all the batches where batch 1,

2, 3 & 4 show a minimum range of 5.098, 5.156, 5.147, 5.098; the first quartile (Q1) 5.918,

6.050, 6.004 & 5.978; median 6.643, 6.700, 6.650 & 6.574; third quartile (Q3) 7.601, 7.674,

7.610 & 7.573; and maximum range 9.298, 9.364, 9.298 & 9.23, respectively. After the analysis of data on screening of the URMC of 221 genotypes for plant biomass under WW and DS at vegetative stage, there were 83 categorized as drought resistant, 79 moderately drought resistant, and 59 drought sensitive genotypes, which showed less than 25 %, 25-40%, with more than 40% reduction in plant biomass as compare to WW plants, respectively (Table 2-2). The URMC of

221 genotypes exhibited a reduction in plant biomass compare to WW plants, a set of 30 representative genotypes are shown in Figure 2-6A. In these 30 representative genotypes, 10 genotypes such as GSOR enteries 301418, 311695, N22, 311078, 311667, 311792, 310757,

311185, 311694 and Vandana were categorized drought resistant having higher plant biomass under DS with less than 25% reduction, the next 10 genotypes including GSOR enteries 310849,

311684, 310481, 310317, 311795, 310345, 311793, 310932, 311794 and 310801 exhibited moderate drought resistance having medium amount of plant biomass under DS between 25-40% reduction, and the next 10 genotypes GSOR enteries 311278, 310998, 310440, 310219, 310510,

301379, 311689, 311669, 311787 and 311692 showed drought sensitivity with the lowest plant biomass under DS with more than 40% reduction in plant biomass compare to the WW plants.

Under the drought resistant category, GSOR301418, N22, and Vandana genotypes maintained highest plant biomass under DS with 20.01%, 21.67% and 24% reduction in plant biomass as compare to WW plants, respectively while moderate drought resistant genotypes GSOR enteries

310481, 310317, 311795, 310345, 311793, 310932, 311794 and 310801 expressed medium amount of plant biomass under DS with 27.82%, 28.25%, 28.30%, 28.45%, 28.66%, 28.71%,

28.72%, 28.91%, 28.92%, and 28.98% reduction in plant biomass, respectively. In the drought sensitive category, GSOR enteries 311689, 311669, 311787 and 311692 showed lowest plant biomass under DS with 59.81%, 61.66%, 66.13%, and 69.58% reduction in plant biomass compare to WW, respectively (Figure 2-6A).

Plant biomass is an important agronomic trait for plant productivity, and shows positive correlation (Figure 2-14) with electron transport rate (ETR), the efficiency of photosystem II

(PhiPSII), intercellular CO2 concentration (Ci), the ratio of internal cellular CO2 and ambient

CO2 concentration (Ci/Ca), transpiration rate (TR), leaf rolling (LR), and number of tillers (NOT) under WW & DS conditions. It is only positively correlated with chlorophyll content (SPAD) under DS and negatively correlated with photosynthesis, instantaneous water use efficiency

(WUEi), stomatal conductance, variable chlorophyll fluorescence (Fv’), maximum chlorophyll fluorescence (Fm’), the ratio of chlorophyll fluorescence to the maximum chlorophyll fluorescence (Fv’/Fm’), non-photochemical quenching (qN) and relative water content (RWC) under both WW and DS conditions. However, under WW conditions, plant biomass is only negatively correlated with chlorophyll content. For increasing food production and improvement of new bioenergy feedstock, the screening and identification of high plant biomass producers among the diverse rice genotypes could be a useful way to use natural genetic variation for developing high yielding and biofuel producing varieties. In this study, we identified natural

variation in plant biomass in diverse rice genotypes for achieving these future targets for genetic dissection of plant productivity traits.

Along with plant biomass, the number of tillers (NOTs) in cereal crops is an important plant productivity and agronomic trait, which plays a crucial role in grain production, bearing grain in branches and is a model system for investigating the amount of branching in the rice crop (Li et al, 2003). Higher NOTs in rice genotypes are considered to contribute in higher grain production, and NOTs are severely affected by drought stress at the early vegetative stage in rice.

For the screening and identification of rice genotypes with higher NOTs under DS, the URMC of

221 genotypes were screened for NOTs at vegetative stage under 10-days gravimetric drought stress. The NOTs were recorded under both WW and DS conditions in all 221 diverse rice genotypes after 10-days continuous drought stress. Based on the analysis of variance, there are significant differences between WW and DS conditions and reduction in NOTs among the

URMC of 221 genotypes. There is also an interaction between treatment (WW and DS) and rice genotypes, which shows the difference in reduction of NOTs. The analysis of data from screens of the URMC of 221 genotypes, identified 114 drought resistant, 83 moderately drought resistant, and 24 drought sensitive genotypes, which showed respectively less than 25%, 25-40% with more than 40% reduction in NOTs compare to WW plants (Table 2-3). The URMC of 221 genotypes showed reduction in NOTs, a set of 30 representative genotypes are presented in

Figure 2-6B. In these 30 representative genotypes, 10 genotypes such as N22, Vandana, GSOR enteries 301066, 301419, 301418, 301408, 311725, 311635, 311140, and 311327 showed drought resistance having higher NOTs under WW & DS with less than 25% reduction. The next

10 genotypes GSOR enteries 311123, 311669, 310080, 310950, 310020, 310510, 311255,

310045, 311491 and 310007 exhibited moderate drought resistance with medium NOTs under

DS with 25-40% reduction, and the last 10 genotypes GSOR enteries 310861, 310777, 310932,

310715, 310714, 311693, 311668, 310703, 311667 and 310723 showed drought sensitivity with lower NOTs under DS with more than 40% reduction in NOTs compare to WW plants, respectively. Under the drought resistant category, N22, Vandana, GSOR enteries 301066,

301419, 301418, 301408 genotypes showed highest NOTs under WW and DS, and 5.31%,

8.04%, 9.54%, 10.20%, 12.03% and 14.09% reduction while in moderate drought resistant category, GSOR enteries 310080, 310020, 310045 and 310007 expressing overall medium NOTs under WW and DS with 28.39%, 32.12%, 37.17% and 39.70% reduction in NOTs as compare to

WW plants, respectively. In the drought sensitive category, GSOR enteries 310703, 311667 and

310723 showed lowest NOTs and 53.94%, 57.57% and 58.42% reduction in NOTs as compare to WW plants, respectively (Figure 2-6B). The number of tillers trait is positively correlated with

TR, Ci, Ci/Ca, PhiPSII, ETR, plant biomass, and chlorophyll content under WW & DS conditions and negatively correlated with photosynthesis, WUEi, stomatal conductance, Fv’, Fm’, FV’/Fm’, qN, LR, and RWC (Figure 2-14). To target more grain production in rice, the identification of diverse rice germplasm having higher number of tillers under drought stress conditions would be an important step to improve adapted Arkansas rice genotypes for higher grain production.

Drought response of rice genotypes for relative water content, leaf rolling, and leaf chlorophyll content Relative water content (RWC) of a leaf is the water content of the sampled leaf tissue relative to the maximal water content it can hold at full turgidity or fully hydrated state. It is regarded as the most important measure of plant water status in terms of the physiological consequence of cellular water deficit, and reflects the balance between water supply to the leaf tissue and the transpiration rate (Lugojan and Ciulca, 2011). RWC is one of the major morphological based measurements that defines the drought resistance of a plant based on water

status of the leaves. Therefore, drought tolerant plants show higher RWC in comparison to drought sensitive plants. Screening of the URMC of 221 genotypes under 10-days gravimetric drought stress for identified significant differences were found between WW & DS for reduction in RWC among the 221 genotypes for percentage reduction in RWC (Figure 2-7A). Based on this criterion, rice genotypes were classified into three categories - 1) Drought resistant (DR, ≤

25% of reduction), 2) moderately-drought resistant (MR, 25-40% of reduction), and 3) drought sensitive (DS, ≥40% of reduction). The screen identified 22 drought resistant, 79 moderately drought resistant, and 120 drought sensitive genotypes; which showed less than 25%, 25-40% with more than 40% reduction in, respectively (Table 2-4). In the analysis, the 221 URMC genotypes showed significant reductions, a set of 30 representative genotypes are shown in

Figure 2-7A, with significant reduction in RWC compare to WW plants. Among these 30 representative genotypes, 10 genotypes GSOR enteries 311572, 311667, 311669, 311788,

310503, 310226, 311613, Vandana, 311684, & 301418 showed drought resistance with an overall higher RWC under DS with less than 25% reduction; the next class of 10 genotypes

GSOR enteries 301408, N22, 310481, 311435, 310052, 311654, 310932, 311781, 311140 and

310715 exhibited moderate drought resistance with medium RWC under DS with 25-40% reduction; and the last set of 10 genotypes GSOR enteries 310045, 310007, 310747, 310200,

311794, 311775, 310345, 311643, 310204 and 310338 showed drought sensitivity with overall lowest RWC under DS with more than 40% reduction in RWC compare to WW plants (Figure

2-7A). In the drought resistant category, GSOR enteries 311572, 311667, 311788, 310503,

310226, Vandana and 301418 showed 57.17%, 61.415, 70.825, 69.31%, 67.53%, 77.06%, and

72.09% RWC under DS with 10.54%, 13.38%, 18.81%, 18.91%, 20.96%, 21.50% and 24.35% reduction in RWC as compare to WW plants. In the moderate drought resistant category, GSOR

enteries 310052, 311654, 311781 and 310715 showed 57.58%, 40.18%, 55.72%, and 55.16%

RWC under DS, with 32.43%, 33.14%, 34.47% and 38.05% reduction in RWC, respectively.

Under the drought sensitive category GSOR enteries 311643, 310204, and 310338 showed

12.69%, 11.78% and 10.14% RWC under DS and 83.54%, 86.11% and 88.38% reduction in

RWC, respectively (Figure 2-7A). In this study, we studied several leaf morphological, physiological and plant productivity traits; among which RWC comprises a leaf morphology based physiological trait, which determines drought tolerance ability based on leaf water status under DS in plants. Therefore, under both WW & DS, RWC is positively correlated with photosynthesis, WUEi, Ci; while under WW conditions only RWC is positively correlated with

ETR, TR, stomatal conductance and biomass. Under WW & DS conditions, RWC is negatively correlated with Fv’, Fm’, Fv’/Fm’ PhiPSII, qN, Ci/Ca, NOTs and chlorophyll content, while under

DS only, it is negatively correlated with TR, stomatal conductance, ETR, and biomass (Figure

2-14). For the development of drought tolerant rice genotypes, the rice genotypes with high

RWC under DS can be used in crosses to improve popular and high yielding rice genotypes for drought tolerance.

Leaf rolling is an important leaf characteristic response of plant acclimation under adverse environmental conditions in plants. It is an indicator of drought stress, which indicates the leaf response to drought stress, maintaining metabolic activities in plants under DS, and exhibits the drought escape mechanism in plants. The leaf of the plant frequently rolls when plants experience water-limiting conditions. Under increasing leaf temperature and water deficit conditions, leaves start rolling and initiate the closing of the stomata to reduce transpiration rate

(Sobarado, 1987). The specific time points also matter for leaf rolling, as some genotypes show early leaf rolling that is an important indication determining early response to drought stress, and

reduces transpiration under DS. These genotypes show a drought escape mechanism of drought resistance. To characterize the drought resistance mechanisms, we screened the URMC of 221 genotypes for leaf rolling under 10-days gravimetric drought stress treatment. After 10-days of stress period, leaf-rolling score based on IRRI’s standard leaf rolling scale (1-5) was recorded, described in materials and methods. Statistical analysis shows significant differences among the

URMC of 221 genotypes under DS in the terms of reduction in LR. Analysis of the leaf rolling scores in the 221 URMC genotypes showed 59 genotypes with 1-1.9, 36 genotypes with 2.0-2.9,

59 genotypes with 3.0-3.9, 65 genotypes with 4.0-4.9, and 2 genotypes with a 5.0 leaf rolling score under drought stress (Table 2-5). As leaf rolling is the first visual indicator of DS, its severity increases with duration of drought stress, and resulting in leaf drying. After 10-days of

DS, the rice genotypes N22, Vandana, 301066, 301379, 301418 and 301408 showed the lowest leaf rolling score (1.0-2.9) exhibiting drought resistance while 310226 and 311710 exhibited highest leaf rolling score (5.0) showing drought sensitivity. In correlation analysis under DS, the leaf-rolling score is negatively correlated with photosynthesis, TR, WUEi, stomatal conductance,

Ci, Ci/Ca, Fv’, Fm’, Fv’/Fm’, qN, ETR, PhiPS-II, RWC and NOTs; not with biomass and chlorophyll content (Figure 2-14).

Chlorophyll is a pigment that determines the plants’ leaf green color and absorbs the long wavelengths of light contributing to photosynthesizing of carbohydrates in plants. It plays a significant role in plant growth and productivity. The chlorophyll content of the leaf is an indicator of the photosynthetic capacity in plants. The amount of chlorophyll in the leaf is affected by several abiotic stresses, but drought stress is one of the major abiotic stresses, which reduces chlorophyll content in rice (Sairam et al, 1996). Thus, maintaining the amount of chlorophyll in leaf tissue under DS can be a vital approach to increase photosynthesis and

contribute towards more food production. Therefore, identification of high chlorophyll content genotypes has been a special interest for multidisciplinary scientists. In this study, the URMC of

221 genotypes was screened for leaf chlorophyll content under 10-days of gravimetric drought stress at vegetative stage. After the stress period, chlorophyll content was measured using SPAD chlorophyll meter in both WW and DS rice genotypes. Based on the analysis of variance, the significant differences between WW and DS among the URMC of 221 genotypes for chlorophyll content were found and there was an interaction between treatment (WW and DS) and rice genotypes, which shows differences between genotypes in reduction of chlorophyll content.

Analysis of data of the URMC of 221 genotypes on chlorophyll content reduction according to our general criteria, 219 genotypes indicated a drought resistant response with less than 25% reduction and 2 genotypes a drought sensitive response with more than 40% reduction in chlorophyll content compare to WW plants, respectively with no genotype as moderate drought resistant (Table 2-18). Since this distribution is skewed within the intervals of 25% and 45% reduction, we adjusted the intervals for resistant at <25% reduction, moderate resistant between

25-40% and sensitive as >40% reduction. The whole URMC of 221 genotypes showed reduction change in chlorophyll content as compare to WW plants, a set of 30 representative genotypes are shown in Figure 2-7B. In these 30 representative genotypes, 28 genotypes such as 311735,

311603, 311744, 310111, 311693, 311781, N22, 310475, 310809, 310965, 310134, 311685,

Vandana, 301419, 301418, 311188, 311180, 311123, 311185, 311117, 311765, 311286, 311385,

301379, 310007, 310102, 310702 and 310354 showed higher chlorophyll content under WW &

DS with less than 25% reduction, and only 2 genotypes such as 301408 and 311779 exhibited drought sensitivity showing lowest chlorophyll content with more than 40% reduction in chlorophyll content as compare to WW plants. Under the drought resistant category, 311715,

317144, N22, Vandana and 301418 genotypes showed 46.72%, 43.08%, 35.73%, 36.4% and

40.83% chlorophyll content under DS and 2.05%, 3.08%, 8.06%, 11.58%, and 15.65% reduction in chlorophyll content as compare to WW plants, respectively. Under drought sensitive category,

301408 and 311779 showed 37.66% and 37.34 % chlorophyll content under DS and 7.20% and

74.02% reduction in chlorophyll content, respectively (Figure 2-7B).

In this study, several plant productivity, leaf morphological and physiological traits were analyzed, and chlorophyll content is one of the leaf morphology based physiological traits, which determines high photosynthetic capacity of plants under DS. Based on the correlation matrix, chlorophyll content is positively correlated with Fv’, Fm’, Fv’/Fm’ PhiPS-II, qN, Ci/Ca, and stomatal conductance under WW & DS, while under WW conditions, chlorophyll content is only positively correlated with Ci and NOTs. RWC is negatively correlated with photosynthesis, TR,

WUEi, RWC, biomass under WW & DS conditions while under DS, it is only negatively correlated with LR (Figure 2-14). For identification of higher chlorophyll containing rice genotypes, the diverse rice genotypes should be screened for chlorophyll content under WW and

DS, and used for developing and improving popular and high yielding rice genotypes for drought resistant genotypes.

Screening of rice genotypes for physiological traits photosynthesis, WUEi, transpiration rate, stomatal conductance and their components under DS Besides plant productivity and leaf morphological traits, various physiological processes and their components are affected and several physiological responses induced under drought stress at the vegetative stage in plants. These responses help the plants to adapt adverse environmental conditions and maintain plant growth and productivity contributing to grain production.

Photosynthesis is one of the major metabolic processes determining plant growth and productivity, subsequently contributing to higher grain yield. It is affected by drought stress and reduction in photosynthetic rate has been well documented in rice (Ji et al, 2012). To analyze the reduction in photosynthetic rate, we screened the URMC of 221 genotypes under 10-days drought stress in the vegetative stage. To analyze the environmental variation between all four batches, as the URMC of 221 genotypes were screened in four batches with an interval of 15 days in planting, there was not significant difference in photosynthesis based on a common check genotype (NB) among all the batches (Figure 2-3). Under WW condition, the check genotype NB, shows non-significant variation for photosynthesis among all four batches with batch 1, 2, 3 & 4 showing minimum range of 10.235, 10.554, 10.325 & 10.236; the first quartile

(Q1) 10.584, 10.661, 10.903 & 10.3410, median 11.121, 11.119, 11.184 & 11.241, third quartile

(Q3) 12.498, 12.502, 12.581, 12.606 and maximum range 15.365, 15.618, 15.988 & 16.022, respectively while under drought stress condition, it shows non-significant variation among all the batches where batch 1, 2, 3 & 4 showing minimum range 1.632, 1.550, 1.498 & 1.52, first quartile (Q1) 2.649, 2.711, 2.640 & 2.684, median 3.016, 3.186, 3.016 & 3.125, third quartile

(Q3) 3.407, 3.619, 3.364 & 3.369 and maximum range 3.66, 3.747, 3.862 & 3.756, respectively.

Based on the analysis of variance, there were significant differences between WW and DS plants in the rate of photosynthesis and reduction in photosynthesis among the URMC of 221 genotypes. There is an interaction between treatment (WW and DS) and rice genotypes, which indicates a difference in the reduction of photosynthesis. After analysis of screening data of the

URMC of 221 genotypes, 103 genotypes were designated as drought resistant, 49 genotypes moderate drought resistant, and 69 were genotypes drought sensitive genotypes, showing less than 25%, 25-40% with more than 40% reduction in photosynthesis as compare to WW plants,

respectively (Table 2-6). The whole URMC of 221 genotypes showed reduction in change of photosynthesis as compare to WW plants, a set of 30 representative genotypes are shown in

Figure 2-8A. In these 30 representative genotypes, 10 genotypes such as 301408, 310861, N22,

310415, 311788, 310958, Vandana, 310489, 301418 and 311123 showed drought resistance having higher rate of photosynthesis with less than 25% reduction in photosynthesis; the next 10 genotypes 310630, 311278, 310131, 311592, 311751, 311269, 311484, 311644, 311642 and

311188 exhibited moderate drought resistance having medium rate of photosynthesis and 25-

40% reduction in photosynthesis and last 10 genotypes 310354, 311739, 310351, 310381,

311710, 311795, 311185, 311794, 311741 and 310566 showed drought sensitivity having lower rate of photosynthesis with more than 40% reduction in photosynthesis as compare to WW plants, respectively. Under drought resistant category, 301408, 310861, N22, 310415, 311788,

310958, Vandana and 301418 genotypes showed 16.96, 19.21, 13.41, 17.18, 19.14, 22.52, 22.25,

-2 -1 and 11.12 µmol CO2 m s photosynthesis under DS and 3.99%, 6.97%, 7.02%, 7.13%, 7.14%,

7.96%, 8.93 and 24.15% reduction photosynthesis while moderate drought resistant category,

311278, 310131, 311592, 311751 and 311188 showed 10.61, 6.85, 4.65, 5.04, and 3.15 µmol

-2 -1 CO2 m s photosynthesis and 26.72%, 27.14%, 27.14 %, 27.48% and 30.34% reduction in photosynthesis as compare to WW plants, respectively. Under drought sensitive category,

-2 -1 311794, 311741 and 310566 showed 1.42, 1.43 and 0.78 µmol CO2 m s photosynthesis under

DS and 77.61%, 86.32% and 90.72% reduction photosynthesis, respectively (Figure 2-8A). In the correlation analysis, photosynthesis is positively correlated with TR, WUEi, stomata conductance, Fv’, Fm’, Fv’/Fm’, qN, ETR, and RWC under WW & DS, while under WW photosynthesis is only positively correlated with Ci and LR. However, photosynthesis is negatively correlated with PhiPSII, Ci/Ca, NOTs, biomass and chlorophyll content under WW

and DS but under DS, it is only negatively correlated with Ci (Figure 2-14). For identification of higher photosynthetic rice genotypes, the diverse rice genotypes should be screened for photosynthesis under WW and DS and used for developing drought resistant and high yielding rice genotypes.

Transpiration rate is a process for water movement through plants’ parts such as leaf, stem and flowers, although the leaf is the major source of water lost, which is 97-99% by leaf transpiration or guttation via stomata in plants. Under drought stress, it becomes very high, inhibits the plant growth & development, and finally reduces rice production (Pandey et al,

2015). For identification of rice genotypes showing lower transpiration rate ability, we screened the URMC of 221 genotypes for transpiration rate at vegetative stage under 10-days gravimetric drought stress. After 10-days of drought stress period, the transpiration rate was measured using portable photosynthetic system LICOR 6400XT. Based on the analysis of variance, there were significant differences between WW and DS plants for transpiration rate among the URMC of

221 genotypes and there was an interaction between treatment (WW and DS) and rice genotypes, which shows the difference in reduction in transpiration rate. After analysis of data of the URMC of 221 genotypes, 186 genotypes were defined drought resistant, 24 genotypes moderate drought resistant, and 11 genotypes drought sensitive showing more than 40% , 25-40 % and less than

25% reduction in transpiration rate as compare to WW plants, respectively (Table 2-7). The

URMC of 221 genotypes showed significant reduction in transpiration rate as compare to WW plants, a set of 30 representative genotypes are presented in Figure 2-8B. Among these 30 representative genotypes, the last 10 genotypes 301408, 311255, 311779, 311688, 311393,

311600, 310958, 311181, 310489, 311725 and 311734 showed drought resistance with lower transpiration under WW as well as DS conditions, and more than 40% reduction in transpiration

rate as compare to WW plants, the second last 10 genotypes N22, 310415, 311699, 310998,

301418, 311327, 311284, 310630, 311278 and 301408 exhibited moderate drought resistance having medium transpiration rate under WW as well as DS conditions and 25-40% reduction in transpiration rate compare to WW plants, and the first 10 genotypes 301066, 310428, 311643,

310317, 311532, 301379, 310397, 311281, 310389 and 310779 showed drought sensitivity with higher transpiration rate under WW as well as DS conditions, and less than 25% reduction in transpiration rate as compare to WW plants respectively (Figure 2-8B). Under the drought resistant category 311255, 311688, 311181, 310489 and 311725 genotypes showed 1.35, 1.34,

-2 -1 0.97, 1.20 and 1.19 mMol H2O m s transpiration rate under DS and 60.28%, 61.73%, 63.45%,

64.15% and 65.86% reduction in transpiration rate while moderate drought resistant category,

- N22, 301418, 311284, 311278, and 301408 showed 2.0, 1.76, 1.86, 1.59,and 1.73 mMol H2O m

2s-1 transpiration rate and 28.32%, 31.09%, 33.17%, 33.38% and 33.43% reduction in transpiration rate as compare to WW plants, respectively (Figure 2.8B). Under the drought sensitive category 301066, 310317, 311532, and 311281 showed 2.27, 4.2, 3.4, and 2.7 73 mMol

-2 -1 H2O m s transpiration rate under DS and 9.4%, 18.69%, 1.89% and 21.08% reduction in transpiration rate as compare to WW plants, respectively (Figure 2-8B). In the correlation analysis, transpiration rate is positively correlated with photosynthesis, stomatal conductance,

ETR, PhiPSII, biomass and number of tillers under WW & DS, and under WW condition, transpiration rate is only positively correlated with RWC. However, transpiration rate is negatively correlated with WUEi, Ci, Ci/Ca, Fv’, Fm’, Fv’/Fm’, qN and chlorophyll content under

WW and DS but under DS, it is only negatively correlated with RWC (Figure 2-14).

Instantaneous water use efficiency (WUEi) is known as photosynthetic water use efficiency, which refers to the ratio of the rate of photosynthesis (rate of carbon assimilation) to

the transpiration rate in the plant system. It is more dependent on high photosynthesis and low transpiration rate when plants experience water deficit condition. It is considered an important process that determines plant growth and grain yield under drought stress, and a vital component of drought resistance mechanisms in crops. In recent years, several studies have been conducted to improve WUE in several crops but limited success. Therefore, pursuing the goal of improving

WUE in rice, the URMC of 221 genotypes was screened for water use efficiency at vegetative stage using 10-days gravimetric drought stress approach. After 10-days of DS, photosynthesis and transpiration rate was measured in WW and DS plants and WUEi calculated as the ratio of photosynthesis (A) and transpiration rate (T). To analyze the environmental variation, as the

URMC of 221 genotypes were screened in four batches, with an interval of 15 days, there was no a significant variation in WUEi based on a common check genotype (NB) among all the batches

(Figure 2-4). Under WW conditions, the check genotype NB shows non-significant variation for

WUEi among all four batches with batch 1, 2, 3 & 4 showing minimum range of 1.515, 1.553,

1.553 & 1.510, first quartile (Q1) 1.948, 1.989, 1.989 & 1.558, median 2.238, 2.160, 2.160 &

2.162, third quartile (Q3) 2.495, 2.401, 2.401 & 3.067 and maximum range of 3.156, 3.109,

3.109 & 3.371, respectively while under DS, it shows non-significant environmental variation among all the batches where batch 1, 2, 3 & 4 showing minimum range 0.491, 0.456, 0.456 &

0.444, first quartile (Q1) 0.806, 0.795, 0.795 & 0.800, median 0.925, 0.9422, 0.942 & 0.965, third quartile (Q3) 1.893, 1.921, 1.921 & 1.722 and maximum range 2.349, 2.419, 2.419 &

2.128, respectively (Figure 2-4). Based on statistical analysis, there were significant differences between WW and DS among the URMC of 221 genotypes in WUEi and there was an interaction between treatment (WW and DS) and rice genotypes, which shows the difference in reduction in

WUEi. In the findings of the URMC of 221 genotypes, 163 genotypes were drought resistant that

showed 0.57 to 63% increase in WUEi and 58 genotypes were drought sensitive that exhibited

0.59 to 495% decrease in WUEi as compare to WW plants (Table 2-8). The whole URMC of

221 genotypes showed increase and decrease in WUEi as compare to WW plants, with 30 representative genotypes shown in Figure 2-9A. In these 30 representative genotypes, 15 genotypes 311435, 310200, 311173, 310494, 311781, 311423, 310990, 311769, 311141,

310849, 311537, 310879, Vandana, 301408, and N22 showed drought resistance having higher

WUEi as compare to WW plants and increase in WUEi while 15 genotypes 310381, 311596,

310007, 311710, 311385, 311327, 310338, 311794, 311795, 311792, 311739, 311185, 311544,

311741 and 310566 exhibited drought sensitivity having lower WUEi as compare to WW plants and reduction in WUEi (Figure 2-9A). Under drought resistant category 311435, 310200,

311173, 310494, 311781, 311423, 310990, 311769, 311141, 310849, 311537, 310879, Vandana,

301408, and N22 genotypes showed 6.58, 10.21, 18.17, 10.85, 12.82, 5.95, 10.92, 6.68, 14.65,

15.25, 7.14, 8.51, 15.41, 9.77 and 6.49 µmol CO2/mMol H2O WUEi under DS and 3.99%,

6.97%, 7.02%, 7.13%, 7.14%, 7.96%, 8.93 and 24.15% increase WUEi as compare to WW plants, respectively while drought sensitive category, 311185, 311544, 311741 and 310566 showed 1.76, 1.27, 1.50, and 0.70 µmol CO2/mMol H2O WUEi under DS and 183.24%,

194.36%, -200.94 and 495.01% reduction in WUEi as compare to WW plants, respectively

(Figure 2-9A). In the correlation analysis, WUEi is positively correlated with photosynthesis,

TR, Ci, Fm’, RWC and leaf rolling under WW & DS where as under WW, WUEi is only positively correlated with Fv’, Fv’/Fm’ and qN (Figure 2-14). However, under WW and DS, WUEi is negatively correlated with TR, stomatal conductance, PhiPSII, ETR, Ci/Ca, biomass, number of tillers, and chlorophyll content; but under DS it is only negatively correlated with Fv’, Fv’/Fm’ and qN (Figure 2-14).

Stomatal conductance is the measurement of the rate of gas exchange (CO2 uptake) and transpiration (water loss) through stomata in the leaf. It is determined by the physical resistance to the movement of gasses between the air and leaf interior in plants. Therefore, more stomatal conductance allows more gas exchange, resulting in higher photosynthesis but more water loss, indicating the higher transpiration rate in the plant. Several studies have proven that drought stress induces reduction in stomatal conductance resulting in limited CO2 supply for photosynthesis, which declines rice production (Pandey et al, 2015). For identification of rice genotypes having more stomatal conductance under DS, the URMC of 221 genotypes were screened for stomatal conductance at the vegetative stage under 10-days of continuous gravimetric drought stress. To analyze the environmental variation, as the URMC of 221 genotypes were screened in four batches planted at intervals of 15 days and there was no significant variation in stomatal conductance based on comparing the common check genotype

(NB) included in all the batches (Figure 2-5). Under WW conditions, the check drought sensitive genotype NB shows non-significant variation for stomatal conductance among all four batches where batch 1, 2, 3 & 4 showing minimum range of 0.082, 0.081, 0.081 & 0.081, first quartile

(Q1) 0.087, 0.086, 0.088 & 0.086, median 0.132, 0.130, 0.131 & 0.131, third quartile (Q3) 0.166,

0.165, 0.166 & 0.165 and maximum range 0.234, 0.224, 0.225 & 0.224, respectively. Under DS it also shows non-significant environmental variation for stomatal conductance among all the batches where batch 1, 2 ,3 & 4 showing minimum range of 0.0324, 0.0336, 0.0324 & 0.0325, first quartile (Q1) 0.0435, 0.0457, 0.0450 & 0.0462, median 0.0785, 0.0793, 0.0760 & 0.0755, third quartile (Q3) 0.116, 0.115, 0.111 & 0.112 and maximum range 0.221, 0.220, 0.199 &

0.214, respectively (Figure 2-5). Based on the analysis of variance, there are significant differences between WW and DS plants among the URMC of 221 genotypes, and there is an

interaction between treatment (WW and DS) and rice genotypes, which shows the differences in reduction of stomatal conductance. In the results of the URMC of 221 genotypes, 61 genotypes were drought resistant, 45 genotypes were moderate drought resistant, and 115 genotypes were drought sensitive showing less than 25%, 25-40% with more than 40% reduction in stomatal conductance as compare to WW plants, respectively (Table 2-9). The whole URMC of 221 genotypes showed reduction in change of stomatal conductance as compare to WW plants, with

30 representative genotypes shown in Figure 2-9B. In these 30 representative genotypes, 10 genotypes N22, 311383, 311151, 310767, 301066,301418, 301408,311699, 311236 and 311123 showed drought resistance having more stomatal conductance with less than 25% reduction in stomatal conductance, the next 10 genotypes 311188, 310630, 310238, 310809, 311613, 310846,

311725, 310442, 310906 and 311606 exhibit moderate drought resistance having medium rate of stomatal conductance, and 25-40% reduction in stomatal conductance in the last 10 genotypes

311078, 310351, 310515, 310773, 310354, 310446, 310087, 311423, 311417 and 310200 showed drought sensitivity with lower stomatal conductance with more than 40% reduction in stomatal conductance as compare to WW plants, respectively. In the drought resistant category,

N22, 311383, 310767, 301066, 301418, 301408 and 311699 genotypes showed 0.096, 0.119,

2 -1 0.104, 0.085, 0.064, 0.063 and 0.145 Mol H2O m- s stomatal conductance under DS with

1.39%, 2.7%, 4.15%, 7.3%, 8.9%, 11.44%, and 17.88% reduction in stomatal conductance, while the moderate drought resistant category of 311188, 310846 and 310442 showed 0.041, 0.066 and

2 -1 0.058 Mol H2O m- s stomatal conductance and 25.47%, 34.90% and 37.80% reduction in stomatal conductance as compare to WW plants, respectively. Under the drought sensitive

2 -1 category, 310351, 310515 and 310200 showed 0.032, 0.029 and 0.029 Mol H2O m- s stomatal conductance under DS and 73.75%, 78.85% and 86.40% reduction in stomatal conductance,

respectively (Figure 2-9B). In the correlation analysis, under both WW & DS, stomatal conductance is positively correlated with photosynthesis, TR, Fv’, Fm’, Fv’/Fm’, qN and Ci/Ca, and under WW conditions stomatal conductance is only positively correlated with PhiPSII. However, under both WW and DS, stomatal conductance is negatively correlated with WUEi, Ci, ETR,

RWC, biomass, LR, number of tillers and chlorophyll content, but under DS it is only negatively correlated with PhiPSII (Figure 2-14).

Intercellular CO2 concentration (Ci) is the amount of carbon dioxide that is available and enters into the plant leaf through stomata. It determines the rate of photosynthesis contributing to food production in plants. Under drought stress, plants experience water deficit conditions reducing stomatal conductance that results in limited CO2 supply for photosynthesis, and photosynthesis is ultimately reduced by intercellular CO2 depletion (Farquhar and Sharkey,

1982). Ci is one of the elemental components of photosynthesis, which is affected by drought stress at the vegetative stage in rice (Pandey et al, 2015) and plays a critical role in reducing rice production. The URMC of 221 rice genotypes were screened for Ci at the vegetative stage, using the portable photosynthetic system LICOR 6400XT for both WW and DS rice plants. Based on statistical analysis, we found significant differences between WW and DS among the URMC of

221 rice genotypes and an interaction between treatment (WW & DS) and rice genotypes, which shows significant difference in reduction in Ci. After screening and data analysis, 131 genotypes indicated drought resistance having more Ci under WW & DS with less than 25% reduction in Ci,

44 genotypes indicated moderate drought resistance showing medium amount of Ci under WW &

DS and 25- 40% reduction in Ci and 46 genotypes were drought sensitive showing lowest Ci with more than 40% reduction in Ci as compare to WW plants, respectively (Table 2-10). Based on analysis of variance, the whole URMC of 221 of genotypes showed reduction in Ci with the

subset of 30 rice genotypes shown in figure 2-10A. In these 30 rice genotypes, 10 genotypes

301066, 311691, N22, Vandana, 310389, 310471, 311697, 311576, 301408 and 301418 showed lower amount of Ci under DS with less than 25% reduction in Ci as compare to WW plants, the next 10 genotypes 311787, 310446, 311141, 310984, 310566, 311544, 310503, 310475, 311286 and 310588 exhibit medium amount of Ci under DS and 25-40% reduction in Ci as compare to

WW plants, and the last 10 genotypes 310809, 310849, 311173, 311153, 310997, 310990,

310861, 310510, 311105 and 310210 showed lowest Ci under DS with more than 40% reduction in Ci compared to WW plants, respectively (Figure 2-10A). The drought resistant genotypes

301066, N22, Vandana, 310389, 301408 and 301418 showed 553.70, 554.29, 668.38, 657.93,

-1 798.63 and 544.64 µmol CO2 mol under DS and 6.37%, 12.43%, 15.49%, 15.85%, 17.47% and

21.54% reduction in Ci while moderate drought resistant genotypes such as 311787, 310446,

311141, 310984, 310475 and 311205 exhibited 120.45, 171.29, 96.14, 117.24, 120.65 and

-1 144.13 µmol CO2 mol under DS and 26.24%, 29.73%, 33.81%, 37.73%, 38.29% and 39.01% reduction in Ci as compared to WW plants, respectively (Figure 2-10A). Drought sensitive genotypes 310809, 310997, 310990, 310861, 310510, 311105 and 310210 showed 41.86, 55.14,

-1 50.47, 40.0, 53.32, 34.33 and 16.59 CO2 mol under DS and 60.95%, 71.79%, 75.53%, 79.21%,

80.37%, 85.75% and 86.47% reduction in Ci as compare to WW plants, respectively (Figure 2-

10A). Correlation analysis, under both WW & DS, showed that Ci is positively correlated with photosynthesis, WUEi, Fv’, Fm’, Fv’/Fm’, qN, Ci/Ca and biomass, and under WW, Ci is only positively correlated with PhiPSII (Figure 2-14). Ci is negatively correlated with TR, stomatal conductance, ETR, RWC, LR, number of tillers and chlorophyll content, and under DS Ci is only negatively correlated with PhiPS II (Figure 2-14).

Along with intercellular CO2 concentration, the ratio of intercellular CO2 to the ambient

CO2 concentration (Ci/Ca) is also an important component of plant metabolism that contributes to photosynthetic processes in plants. It is the ratio of intercellular CO2 concentration (Ci) in the leaf to the ambient CO2 concentration (Ci/Ca) in the external environment, which maintains a balance of gas exchange in the plants’ system for enhancing photosynthesis. In rice, it is affected by early drought stress at any plant growth stage, which is positively correlated with carbon isotope discrimination (CID), enhancing WUE and reducing transpiration rate (Pandey et al,

2015). For identification of higher valued (Ci/Ca) rice genotypes, we screened the URMC of 221 rice genotypes for (Ci/Ca) under drought stress at vegetative growth stage. Along with photosynthesis, transpiration rate, stomatal conductance and Ci, Ci/Ca was measured using portable photosynthetic system LICOR 6400XT for both WW and DS plants at the end of 10- days continuous drought stress. Based on the analysis of variance, there are significant differences between WW and DS treatments among the URMC of 221 rice genotypes, and an interaction between treatment and genotype, which shows significant reduction in Ci/Ca. After analysis of the data, 82 genotypes were identified as drought resistant on basis of higher value of

Ci/Ca under WW & DS, and less than 25% reduction in Ci/Ca, 27 genotypes were identified as moderate drought resistant exhibiting medium values of Ci/Ca under WW & DS and 25-40% reduction in Ci/Ca and 112 genotypes were termed drought sensitive showing lower values of

Ci/Ca under WW & DS with more than 40% reduction in (Ci/Ca) as compare to WW plants, respectively (Table 2-11). Analysis of the data of the whole URMC of 221 rice genotypes showed significant reduction in Ci/Ca of DS compared to WW plants, an example of 30 representative rice genotypes are shown in Figure 2-10B. In these 30 representative rice genotypes, 10 genotypes 311249, 310039, 311188, 311643, 311572, 310703, 311667, 311046,

311689 and 310399 showed higher value of Ci/Ca under WW as well as DS with less than 25% reduction in Ci/Ca, the next 10 genotypes named 311794, 311111, 311547, 310219, 311790,

N22, 310428, Vandana, 311695 and 310471 exhibited medium values of Ci/Ca under WW as well as DS and 25-40% reduction in Ci/Ca, and the last 10 genotypes showed lower values of

Ci/Ca under WW as well as DS, with more than 40% reduction in Ci/Ca compared to WW plants, respectively (Figure 2-10B). Under the drought resistant category, the genotypes 310039,

311188, 311572, 310703 and 311046 showed 0.676, 0.567, 0.646, 0.677 and 0.630 Ci/Ca under

DS and 12.23%, 14.63%, 20.06%, 20.88% and 22.21% reduction in Ci/Ca while moderate drought resistant category genotypes 311111, 311547, N22, 310428, Vandana, 311695 and

310471 showed 0.393, 0.449, 0.475, 0.500, 0.469, 0.340 and 0.318 Ci/Ca under DS and 35.83%,

35.92%, 36.46%, 36.575, 36.85%, 37.44% and 37.52% reduction in Ci/Ca as compare to WW plants, respectively (Figure 2-10B). Under the drought sensitive category, genotypes such as

311236, 311113 and 310220 showed 0.084, 0.056 and 0.083 Ci/Ca under DS and 81.90%,

83.78& and 86.47% reduction in Ci/Ca as compare to WW plants, respectively (Figure 2-10B).

In correlation analysis, Ci/Ca is positively correlated with stomatal conductance, Ci), Fv’, Fm’,

Fv’/Fm’, qN, biomass, number of tillers and chlorophyll content and under WW, it is only positively with PhiPSII (Figure 2-14). However, Ci/Ca is negatively correlated with photosynthesis, TR, WUEi, ETR and RWC and under DS, it is only negatively correlated with

PhiPSII (Figure 2-14).

Drought response to chlorophyll fluorescence and its components

The concept of chlorophyll fluorescence measurements is useful for application in assessing stress on plants. Chlorophyll in the leaf absorbs light energy that can be utilized through three mechanisms: it can be used to initiate photosynthesis, excess energy can be

dissipated as heat, and it can be re-emitted as light called chlorophyll fluorescence (Maxwell and

Johnson, 200). To use chlorophyll fluorescence measurements to analyze photosynthesis, the difference between photochemical quenching (the efficiency of photosystem II symbolized by

PhiPSII) and non-photochemical quenching (qN-heat dissipation) must be made. There are several chlorophyll fluorescence parameters such as variable fluorescence (Fv’), maximum fluorescence (Fm’), the ratio of variable fluorescence to maximum fluorescence (Fv’/Fm’), the efficiency of photosystem-II (PhiPSII), non-photochemical quenching (qN), and plant electron transport rate (ETR) that can be used to analyze chlorophyll fluorescence in plants. Drought stress induces reduction in chlorophyll content, limiting photosynthesis and ultimately reducing rice production (Pandey et al, 2015). As chlorophyll content is reduced under drought stress, other chlorophyll related parameters are affected by drought stress. The effect of drought stress on quantum yield of photosystem (PhiPSII) has been reported (Pandey et al, 2015), but nothing more has been reported on other chlorophyll fluorescence parameters in rice under stress.

Therefore, to study the effect of drought stress on chlorophyll fluorescence parameters, we screened the URMC of 221 rice genotypes for Fv’, Fm’, Fv’/Fm’, PhiPSII and ETR under 10-days of continuous DS. Based on analysis of variance, there were significant differences between

WW and DS plants for chlorophyll fluorescence among the URMC of 221 rice genotypes and an interaction between treatment (WW & DS) and genotype, which shows reduction in all chlorophyll fluorescence parameters (above mentioned) as compare to WW plants.

For variable fluorescence (Fv’), after analysis of the population screen data, 99 genotypes showed drought resistance based on higher amount of Fv’ under WW & DS, and less than 25% reduction in Fv’, 91 genotypes exhibited moderate drought resistance having medium amount of

Fv’ under WW & DS and 25-40% reduction in Fv’ and 31 genotypes showed drought sensitivity

having lower amount of Fv’ under WW & DS with more than 40% reduction in Fv’ as compare to

WW plants, respectively (Table 2-12). The whole URMC of 221 rice genotypes showed significant reduction in Fv’ compared to WW plants, a set of 30 representative genotypes are shown in Figure 2-11A. In these 30 rice genotypes, 10 genotypes 311620, 311561, 311074,

311111, N22, 311788, 311545, 311180, 311576 and 311105 showed higher amount of Fv’ under

DS with less than 25% reduction in Fv’, the next 10 genotypes 301418, 311188, 310204, 311286,

311693, 311181, 311140, 310846, 311544 and Vandana exhibited medium amount of Fv’ under

DS and 25-40% reduction in Fv’, and the last 10 genotypes 310200, 310788, 311206, 310415,

311695, 310510, 311100, 310566, 310779 and 310546 showed lower amount of Fv’ under DS with more than 40% reduction in Fv’ as compare to WW, respectively (Figure 2-11A). Under drought resistant category genotypes 311620, 311561, 311074, 311111, 311545 and 311576 showed 1391.91, 1243.41, 1304.49, 1012.87, 1028.53 and 1282.60 Fv’ under DS and 5.07%,

10.16%, 13.84%, 15.49%, 20.25% and 21.99% reduction in Fv’ while 301418, 311188, 310204 and Vandana exhibited 598.55, 582.49, 694.35 and 502.71 Fv’ under DS and 27.60%, 29.51%,

32.20% and 39.07% reduction in Fv’ as compare to WW plants, respectively (Figure 2-11A).

Under drought sensitive category, 310415, 310510, 310779 and 310546 showed 377.68, 348.60,

399.49 and 369.11 Fv’ under DS and 49.90%, 53.37%, 60.14% and 61.38% reduction in Fv’ as compare to WW plants, respectively (Figure 2-11A). In correlation analysis, under both WW &

DS, Fv’ is positively correlated with photosynthesis, stomatal conductance, Fm’, Fv’/Fm’, qN, Ci/

Ca, biomass and chlorophyll content, and under WW it is only positively correlated with WUEi

(Figure 2-14). However, Fv’ is negatively correlated with transpiration rate, Ci, PhiPSII, ETR,

RWC, LR and number of tillers and under DS, it is only negative correlated with WUEi (Figure

2-14).

For maximum chlorophyll fluorescence (Fm’), after data analysis, 201 genotypes showed drought resistance having higher amount of Fm’ under WW & DS with less than 25% reduction in

Fm’, 17 genotypes exhibited moderate drought resistance having medium amount of Fm’ under

WW & DS and 25-40% reduction in Fm’ and 3 genotypes showed drought sensitivity having lower amount of Fm’ under WW & DS with more than 40% reduction in Fm’ as compare to WW plants, respectively (Table 2-13). The URMC of 221 rice genotypes showed a reduction in Fm’ compared to WW plants, a set of 30 representative genotypes are shown in Figure 2-11B. In these 30 rice genotypes, 10 genotypes 301418, N22, 301066, 311249, 310715, 310220, 311691,

310997, 311697 and Vandana showed higher amount of Fm’ under DS with less than 25% reduction in Fm’, next 10 genotypes such as 311173, 310446, 311688, 311385, 310519, 311255,

311491, 310354, 311644 and 310007 exhibited medium amount of Fm’ under DS and 25-40% reduction in Fm’ and last 10 genotypes named 310724, 311206, 311016, 310779, 310566,

310200, 310489, 310510, 310546 and 311100 showed lower amount of Fm’ under DS with more than 40% reduction in Fm’ as compare to WW, respectively ( Figure 2-11B). Under drought resistant category, 301418, N22, 311249, 310715, 311691, 310997, 311697 and Vandana showed 1331.18, 969.80, 1236.75, 1212.71, 1617.81, 1454.40, 1366.22 and 1148.73 Fm’ under

DS, and 1.90%, 5.57%, 11.71%, 12.91%, 15.26%, 17.73%, 20.65% and 22.46% reduction in Fm’ while 311173, and 31007 exhibited 958.51 and 805.75 Fm’ under DS and 25.91% and 29.43% reduction in Fm’ as compare to WW plants, respectively (Figure 2-11B). Under Drought sensitive category, 310510 and 310546 showed 881.42 and 853.75 Fv’ under DS and 40.26% and

44.38% reduction in Fm’ as compare to WW plants, respectively (Figure 2-11B). In correlation analysis, under both WW & DS, Fm’ is positively correlated with photosynthesis, stomatal conductance, Fv’, Fv’/Fm’, non-photochemical quenching (qN), Ci/ Ca, biomass and chlorophyll

content while only under WW, it is positively correlated with WUEi (Figure 2-14). However, Fm’ is negatively correlated with transpiration rate, Ci, PhiPSII, ETR, RWC, leaf rolling and number of tillers while only under DS, it is negative correlated with WUEi ( Figure 2-14).

For the ratio of variable fluorescence to maximum fluorescence (Fv’/Fm’), after analysis of screening data, 175 genotypes showed drought resistance having higher amount of Fv’/Fm’ under

WW & DS with less than 25% reduction in , Fv’/Fm’, 44 genotypes exhibited moderate drought resistance having medium amount of Fv’/Fm’ under WW & DS and 25-40% reduction in Fv’/Fm’ and 2 genotypes showed drought sensitivity having lower amount of Fv’/Fm’ under WW & DS with more than 40% reduction in Fv’/Fm’ as compare to WW plants, respectively(Table 2-14).

The whole URMC of 221 rice genotypes showed reduction in Fv’/Fm’ as compare to WW plants, a set of 30 representative genotypes are shown in Figure 2-12A. In these 30 rice genotypes, 10 genotypes 311576, 311547, N22, 311779, 311689, 310317, 310007, 310200, Vandana and

311105 showed higher amount of Fv’/Fm’ under DS with less than 25% reduction in Fv’/Fm’, next

10 genotypes such as 301418, 311685, 310039, 311596, 310849, 310389, 311695, 310415,

310809 and 310515 exhibited medium amount of Fv’/Fm’ under DS and 25-40% reduction in

Fv’/Fm’ and last 10genotypes named 310588, 310788, 310566, 311181, 310687, 311787, 310144,

310481, 310779 and 310211 showed lower amount of Fv’/Fm’ under DS with more than 40% reduction in Fv’/Fm’ as compare to WW, respectively (Figure 2-12A). Under drought resistant category, 311576, 311547, N22, 311689 and 311105 showed 0.612, 0.531, 0.470, 0.540 and

0.520 Fv’/Fm’ under DS and 6.03%, 10.17%, 11.46%, 18.50% and 23.25% reduction in Fv’/Fm’ while 301418, 310039, 310849, and 310515 exhibited 0.449, 0.360, 0.365 and 0.365 Fv’/Fm’ under DS and 26.18%, 32.73%, 33.17% and 36.29% reduction in Fv’/Fm’ as compare to WW plants, respectively (Figure 2-12A). Under Drought sensitive category, 310687, 310779 and

310211 showed 0.360, 0.347 and 0.379 Fv’/Fm’ under DS and 37.55%, 42.45% and 43.23% reduction in Fv’/Fm’ as compare to WW plants, respectively (Figure 2-12A). In correlation analysis, under both WW & DS, Fv’/Fm’ is positively correlated with photosynthesis, stomatal conductance, Fv’, Fm’, non-photochemical quenching (qN), Ci/ Ca, biomass and chlorophyll content while only under WW, it is positively correlated with WUEi (Figure 2-14). However,

Fv’/Fm’ is negatively correlated with transpiration rate, Ci, PhiPSII , ETR, RWC, leaf rolling and number of tillers while only under DS, it is negative correlated with WUEi ( Figure 2-14).

For efficiency of photosystem II (PhiPS II), after data analysis, 52 genotypes showed drought resistance having higher PhiPS II under WW & DS with less than 25% reduction in

PhiPS II, 111 genotypes exhibited moderate drought resistance having medium PhiPSII under

WW & DS and 25-40% reduction in PhiPS II and 58 genotypes showed drought sensitivity having lower PhiPS II under WW & DS with more than 40% reduction in PhiPS II as compare to

WW plants, respectively (Table 2-15). The whole URMC of 221 rice genotypes showed reduction in PhiPS II as compare to WW plants, a set of 30 representative genotypes are shown in Figure 2-12B. In these 30 rice genotypes, 10 genotypes 311787, 311258, 301066, 311699,

310020, 311545, 311151, 311466, 310723 and Vandana showed higher PhiPS II under DS with less than 25% reduction in PhiPS II, next 10 genotypes such as 310802, 311117, 310052,

310471, 311167, 311180, 311689, N22, 311613 and 311181 exhibited medium PhiPS II under

DS and 25-40% reduction in PhiPS II and last 10 genotypes named 311100, 311249, 301419,

311113, 311544, 310984, 310515, 310546, 311173 and 310510 showed lower PhiPS II under DS with more than 40% reduction in PhiPS II as compare to WW, respectively (Figure 2-12B).

Under drought resistant category, 311787, 311699, 310020, 311466, 310723 and Vandana showed 0.190, 0.130, 0.132, 0.148, 0.138 and 0.113 PhiPS II under DS and 10.10%, 18.63%,

20.01%, 22.01%, 23.85% and 24.62% reduction in PhiPS II while 311117, 310052, 311167,

311180, N22 and 311613 exhibited 0.082, 0.110, 0.0737, 0.088, 0.067 and 0.085 PhiPS II under DS and 28.80%, 29.52%, 30.67%, 32.60%, 34.20%, 38.72% and 39.62% reduction in

PhiPS II as compare to WW plants, respectively (Figure 2-12B). Under Drought sensitive category, 310984, 310515, 310546, 311173 and 310510 showed 0.017, 0.057, 0.044, 0.047 and

0.031 PhiPS II under DS and 62.90%, 65.54%, 65.83%, 69.83% and 74.45% reduction in PhiPS

II as compare to WW plants, respectively (Figure 2-12B). In correlation analysis, under both

WW & DS, PhiPS II is positively correlated with transpiration rate, ETR, biomass, number of tillers and chlorophyll content while only under WW, it is positively correlated with stomatal conductance and Ci/ Ca (Figure 2-14). However, PhiPS II is negatively correlated with photosynthesis, WUEi, Ci, Fv’, Fv’/Fm’, qN, RWC and leaf rolling while only under DS, it is negatively correlated with stomatal conductance and Ci/Ca (Figure 2-14).

From the analysis of screening data for electron transport rate (ETR), 53 genotypes showed drought resistance having higher ETR under WW & DS with less than 25% reduction in

ETR, 108 genotypes exhibited moderate drought resistance having medium ETR under WW &

DS and 25-40% reduction in ETR and 60 genotypes showed drought sensitivity having lower

ETR under WW & DS with more than 40% reduction in ETR as compare to WW plants, respectively (Table 2-16). The whole URMC of 221 rice genotypes showed reduction in ETR as compare to WW plants, a set of 30 representative genotypes are shown in Figure 2-13A. In these

30 rice genotypes, 10 genotypes 310779, 311684, 310791, 310219, 310715, 310023, 311545,

Vandana, N22 and 311603 showed higher ETR under DS with less than 25% reduction in ETR, next 10 genotypes such as 310052, 311794, 311491, 311790, 310211, 311688, 311111, 311532,

311600 and 311181 exhibited medium ETR under DS and 25-40% reduction in ETR and last 10

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genotypes named 311249, 311113, 311544, 310546, 301418, 310645, 311173, 301419, 310670 and 310510 showed lower ETR under DS with more than 40% reduction in ETR as compare to

WW, respectively (Figure 2-13A). Under drought resistant category, 310219, 310023 and

Vandana showed 108.84, 114.48 and 73.89 ETR under DS and 18.27%, 20.50% and 23.98% reduction in ETR while 310052, 311794, 311491, 311790 and 310211 exhibited 71.36, 88.04,

71.64, 97.41 and 89.81 ETR under DS and 25.97%, 27.88%, 28.78%, 30.19% and 31.51% reduction in ETR as compare to WW plants, respectively (Figure 2-13A). Under drought sensitive category, 311113, 310546, 301418, 310670 and 310510 showed 26.34, 28.77, 29.11,

26.42 and 21.10 ETR under DS and 60.79%, 65.70%, 66.62%, 70.36% and 71.98% reduction in

ETR as compare to WW plants, respectively (Figure 2-13A). In correlation analysis, under both

WW & DS, ETR is positively correlated with transpiration rate, the efficiency of PS II, biomass and number of tillers while only under WW, it is positively correlated with photosynthesis and chlorophyll content (Figure 2-14). However, ETR is negatively correlated with WUEi, stomatal conductance, Ci, Fv’, Fv’/Fm’, qN, Ci/Ca, RWC and leaf rolling while only under DS, it is negatively correlated with photosynthesis and chlorophyll content (Figure 2-14).

After data analysis for non-photochemical quenching (qN) in the URMC of 221 rice genotypes, 210 genotypes showed drought resistance having higher qN under WW & DS with less than 25% reduction in qN, 10 genotypes exhibited moderate drought resistance having medium qN under WW & DS and 25-40% reduction in qN and 1 genotype showed drought sensitivity having lower qN under WW & DS with more than 40% reduction in qN as compare to WW plants, respectively (Table 2-17). The whole URMC of 221 rice genotypes showed reduction in ETR as compare to WW plants, a set of 30 representative genotypes are shown in

Figure 2-13B. In these 30 rice genotypes, 19 genotypes 311046, 310348, 311765, 310156,

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310345, 311181, 311180, 311173, 301419, 301418, 311078, 311698, N22, 310777, Vandana,

311286, 311544, 311140 and 310801 showed higher qN under DS with less than 25% reduction in qN, next 10 genotypes such as 310809, 301408, 310510, 310849, 310945, 311188, 310566,

310546, 310779 and 311206 exhibited medium qN under DS and 25-40% reduction in qN and last 1 genotype named 310984 showed lowest qN under DS with more than 40% reduction in qN as compare to WW, respectively (Figure 2-13B). Under drought resistant category, 311046,

310156, 310345, 311181, 311180, 311173, 311078, 311698, 310777, 311544 and 311140 showed 2.89, 2.00, 1.85, 1.81, 2.23, 1.91, 2.34, 2.20, 1.92, 2.10 and 1.95 qN under DS and

3.95%. 8.08%, 8.98%, 10.28%, 12.64%, 14.81%, 20.18%, 21.07%, 21.43%, 24.02% and 24.47% reduction in qN while under moderate drought resistant category, 310510, 310849, 310546 and

310779 exhibited 1.53, 1.66, 1.55 and 1.67 qN under DS and 26.42%, 27.70%, 30.16% and

30.24% reduction in qN as compare to WW plants, respectively (Figure 2-13B). Under Drought sensitive category, 310984 showed 1.04 qN under DS and 65.95% reduction in qN as compare to

WW plants, respectively (Figure 2-13B). In correlation analysis, under both WW & DS, qN is positively correlated with photosynthesis, stomatal conductance, Fv’, Fm’ Fv’/Fm’, Ci/Ca, leaf rolling and chlorophyll content while only under WW, it is positively correlated with WUEi

(Figure 2-14). However, qN is negatively correlated with transpiration rate, Ci, PhiPS II, ETR,

RWC, biomass and number of tillers while only under DS, it is negatively correlated with WUEi

(Figure 2-14).

CONCLUSIONS

From the review of literature, there has not yet been a study reported in which the URMC of 221 rice genotypes was screened and used for the identification of natural variation for plant productivity, leaf morphological and physiological traits contributing to high yield production in

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rice. We screened the URMC of 221 rice genotypes for water use efficiency, biomass and number of tillers, and multiple morpho-physiological traits such as relative water content, leaf rolling, leaf chlorophyll content, photosynthesis, transpiration rate, stomatal conductance, intercellular CO2, the ratio of intercellular CO2 and ambient CO2 concentration, Fv’, Fm’, Fv’/Fv’, electron transport rate, the efficiency of photosystem II and non-photochemical quenching at vegetative stage under drought stress conditions in greenhouse. Based on the findings of this study, our focus was to identify the rice genotypes having more water use efficiency, photosynthesis and biomass that can directly enhance plant productivity under drought stress.

Out of the URMC of 221 rice genotypes, 35 rice genotypes showed drought resistance based of our criteria of maintaining water use efficiency, photosynthesis and biomass under drought stress with less than 25% reduction in these three traits compared to WW plants, 14 rice genotypes exhibited moderate drought resistance having medium rate of water use efficiency, photosynthesis and biomass under drought stress and a limited 25-40% reduction in all these three traits compared to WW plants and 8 rice genotypes showed the levels of water use efficiency, photosynthesis and biomass under drought stress with more than 40% reduction in all these three traits compared to WW plants (Table 2-19). Drought resistant genotypes such as N22,

Vandana, 301418 and 301408 and drought sensitive genotype named 310100 are well adapted for flowering and grain yield to rice growing state Arkansas. The results and plant materials that we have developed for drought resistance with good plant productivity traits can be useful for plant breeders for developing high yielding and drought resistant rice genotypes for adverse environmental conditions in the future.

REFERENCES

Abdelbagi, M.I. and E.H. Anthony.1993. Inheritance of carbon isotope discrimination and water- use efficiency in cowpea. Crop Science. 33: 498–503.

103

Agrama, H.A., W. Yan, F. Lee, R. Fjellstrom, M-H. Chen, M, Jia, A. McClung. 2009. Genetic assessment of a mini-core developed from the USDA rice gene bank. Crop Science. 49: 1336–1346.

Ambavaram, M., S. Basu, A. Krishnan, V. Ramegowda, U. Batlang, L. Rahman, N. Baisakh, A. Pereira. 2014. Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress". Nature Communications. 5: 5302-53016.

Arkansas Cooperative Extension Service. 1982. Rice production handbook. MP 19207. 5M-4- 88RV. Cooperative Extension Service, University of Arkansas, Fayetteville. Pp 60.

Batlang, U., N. Baisakh, M.M. Ambavaram, and A. Pereira. 2013. Phenotypic and physiological evaluation for drought and salinity stress responses in rice. Methods in Molecular Biology. 956:209-25. DOI: 10.1007/978-1-62703-194-3_15.

Baldwin, K. April, 2011. Consolidation and Structural Change in the U.S. Rice Sector. DIANE Publishing. pp. front cover 11–. ISBN 978-1-4379-8478-1.

Bouman, B.A.M. 2009. How much water does rice use. Rice Today. 8: 28-29.

Bouman, B.A.M., E. Humphreys, T.P. Tuong and R. Barker. 2007. Rice and water. Advance Agronomy. 92: 187–237.

De Freitas, G.P.M., S. Basu, V. Ramegowda, E.B. Braga, and A. Pereira. 2016. Comparative analysis of gene expression in response to cold stress in diverse rice genotypes. Biochemical and Biophysical Research Communications. 471(1):253-259. DOI:10.1016/j.bbrc.2016.02.004.

104

Garris, A.J., T.H. Tai, J. Coburn, S. Kresovich, and S. McCouch. 2005. Genetic structure and history, technology, and production. Crop Production Ser #6149. John Wiley & Sons New York, NY.

Glaszmann, J.C. 1987. Isozymes and classification of Asian rice varieties. Theoretical Applied Genetics. 74: 21–30.

Hossain, M. 2007. Rice facts: a balancing act. Rice Today. 6:37.

Howell, T. A. 2001. Enhancing water use efficiency in irrigated agriculture. Agronomy Journal.93: 281–289.

Impa, S. M., S. Nadaradjan, P. Boominathan, et al. 2005. Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Science. 45: 2517–2522.

IRRI (International rice research institute). 1996. Standard Evaluation System for rice. 4th ed. Int. Network for Genetic Evaluation of Rice, Genetic Resource Center, Los Baños, The Philippines.

IRRI. 2007. The rice environments or ecosystems. Available at http://www.knowledgebank.irri.org/ericeproduction/0.3._Rice_environments.htm.

Karaba, A., S. Dixit, R. Greco, A. Aarons, K.R. Trijatmiko, N. Marsch-Martinez, A. Krishnan, K.N. Nataraja, M. Udayakumar, and A. Pereira. 2007. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene. Proceeding of the National Academy of Sciences, USA. 104:15270- 75.

Kirubakaran, V., V. Sivaramakrishnan, R. Nalini, T. Sekar, M. Premalatha, and P. Subramaniane 2009. A review on gasification of biomass. Renewable and Sustainable Energy Reviews. 13:179−186.

Kulp, K., Ponte. Jr, G. Joseph. 2000. Handbook of Cereal Science and Technology. Marcel Dekker. pp. 203–. ISBN 978-0-8247-8294-8.Khush, G. S. (2001). Green revolution: the way forward. Nature Review. 2: 815-822.

105

Li, X., Q. Qian, Z. Fu, Y. Wan, G. Xiong, D. Zeng, X. Wang, X. Liu, S. Teng, F. Hiroshi, M. Yuan, D. Luok, B. Han and J. Li. 2003. Control of tillering in rice. Nature. 42: 619- 621.

Lugojan, C. and S. Ciulca. 2011. Evaluation of relative water content in winter wheat. Journal of Horticulture & Forest Biotechnology 15: 173–177.

Mae. 1997. Physiological nitrogen efficiency in rice: Nirogen utilization, photosynthesis, and yield potential. Plant soil. 196: 201-210.

Matsuo, T. 1952. Gynecological studies on cultivated rice. Bulletin National Institute of Agriculture Science Series. D3: 1–111.

Matus, A., A.E. Slinkaed, and C.V. Kessel. 1995. Carbon-13 isotope discrimination at several growth stages in lentil, spring wheat and canola. Canadian Journal of Plant Sciences. 75: 577–581.

Maxwell, K. and Johnson, N. 2000. Chlorophyll fluorescence- a practical guide. Journal of Experimental Botany. 51(345): 659-668.

McKendry, P. 2002. Energy production from biomass (part 1): overview of biomass. Bioresource Technology. 83: 37–46.

Ni, J., P.M. Colowit, D. J. Mackill. 2002. Evaluation of genetic diversity in rice subspecies using microsatellite markers. Crop Science. 42: 601–607.

Pandey, V. and A. Shukla. 2015. Acclimation and tolerance strategies of rice under drought stress. Rice Science. 22: 147-161.

Passioura, J.B. 1986. Resistance to drought and salinity: Avenues for improvement. Australian Journal of Plant Physiology. 13: 191–201.

Price, M., JALALUDDIN, M.D. and R.H. DILDAY. 1990. Screening of rice genotypes (Oryza sativa L.) for drought tolerance under field conditions. Proceedings Arkansas Academy of Science. 90: 91-93.

Qing, Z.M., L.G. Jing and C.R. Kai. 2001. Photosynthesis characteristics in eleven cultivars of sugarcane and their responses to water stress during the elongation stage. Proceedings of International Society of Sugarcane Technologist. 24: 642-643.

Ramegowda, V., S. Basu, A. Krishnan, and A. Pereira. 2014. Rice growth under drought kinase is required for drought tolerance and grain yield under normal and drought stress conditions. Plant Physiology. 166(3):1634-1645. DOI:10.1104/pp.114.248203.

106

Santos, A.B., N.K Fageria, and A. S. Prabhu. 2003. Rice ratooning management practices for higher yields. Communication of Soil Sciences and Plant Analysis. 34:881-918.

Sarvestani, Z.T., H. Pirdashti, S.A.M.M. Sanavy, and H. Balouchi. 2008. Study of water stress effects in different growth stages on yield and yield components of different rice (Oryza sativa L.) cultivars. Pakistan Journal of Biological Sciences.11 (10):1303- 1309. DOI:10.3923/pjbs.2008.1303.1309.

Sobarado, M.A. 1987. Leaf rolling: A visual indication of water deficit in corn (Zea mays L.) Maydica. 32: 9-18.

Tollenaar, M. and A. Aguilera. 1992. Radiation use efficiency of an old and new maize hybrid. Agronomy Journal. 84: 536-541.

Turner, N C. 1986. Adaptation to water deficits: A changing perspective. Australian Journal Plant Physiology. 13(1): 175–189.

USDA. 1989. Rice situation and outlook yearbook. United States Department of Agriculture, Economic Research Service. Pp 45.

Vaughan, D.A. 1989. The genus Oryza L. Current status of taxonomy. IRRI Research Paper. Serial Number 138.

Wassmann, R., S.V.K.S. Jagadish, S. Heuer, A. Ismail, E. Redona, R. Serraj, R.K. Singh, G. Howell, H. Pathak and K. Sumfleth. 2009. Climate change affecting rice production: the physiological and agronomic basis for possible adaptation strategies. Advance Agronomy. 101: 59-121.

Werther, J., M. Saenger, E.U. Hartage, T. Ogada, and Z. Siagi. 2000. Combustion of agricultural residues. Programing Energy Combust Sciences. 26(1):1–27.

Yan, W.G., Y. Li, H.A. Agrama, D. Luo, F. Gao, X. Lu, and G. Ren. 2009. Association mapping of stigma and spikelet characteristics. Molecular Breeding. 24: 277–292.

107

Zhu, C, and J. Yu. 2009. Nonmetric multidimensional scaling corrects for population structure in whole genome association studies. Genetics. 182: 875–888.

108

Table 2-1. The URMC of 214 accessions and 7 adapted rice cultivars to Araksnas used in the study S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 1 310007 O. sativa Karang Serang Indonesia TRJ 2 310015 O. sativa Mayang Khang Indonesia IND 3 310020 O. sativa E B Gopher United States TRJ-TEJ 4 310023 O. sativa RD 218 Dominican Republic TRJ-TEJ-ARO 5 310039 O. sativa C 5560 Thailand TRJ 6 310045 O. sativa LEAH United States TRJ 7 310052 O. sativa Quinimpol Philippines TRJ 8 310080 O. sativa TAICHU MOCHI 59 Taiwan TRJ 9 310087 O. sativa WC 2811 Micronesia TRJ 10 310100 O. sativa Ao Chiu 2 Hao China IND 11 310102 O. sativa Criollo Chivacoa 2 Venezuela TRJ 12 310111 O. sativa Bombilla Spain TEJ 13 310131 O. sativa Secano do Brazil El Salvador TRJ 14 310134 O. sativa BERLIN Costa Rica IND 15 310144 O. sativa British Honduras Belize TRJ 16 310156 O. sativa Sel. No. 388 Uruguay TEJ-TRJ

109 17 310161 O. sativa SHIMIZU MOCHI Japan TEJ 18 310196 O. sativa 81B/25 Suriname IND 19 310200 O. sativa Chin Chin Panama IND 20 310204 O. sativa Italica Carolina Poland TEJ 21 310210 O. sativa KRASNODARSKIJ Russian Federation TEJ 22 310211 O. sativa Pergonil 15 Portugal TEJ 23 310219 O. sativa Red Khosha Cerma Afghanistan ARO 24 310220 O. sativa Safut Khosha Afghanistan AUS 25 310226 O. sativa NORIN 11 Japan TEJ 26 310238 O. sativa R 75 Senegal TRJ 27 310241 O. sativa UZ ROSZ M38 Uzbekistan TEJ 28 310301 O. sativa H57-3-1 Argentina TEJ 29 310317 O. sativa IARI 6626 India AUS 30 310337 O. sativa Khao Phoi Laos TRJ-TEJ-ARO 31 310338 O. sativa Khao Luang Laos TRJ-TEJ-ARO 32 310345 O. sativa J.P. 5 Australia TEJ 33 310348 O. sativa C 8429 Papua New Guinea TRJ

Table 2-1. Continued. S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 34 310351 O. sativa Warrangal Culture India IND 35 310354 O. sativa Padi Pohon Batu Malaysia TRJ 36 310381 O. sativa NC 1/536 Pakistan AUS 37 310389 O. sativa Won Son Zo No. 11 Korea IND 38 310397 O. sativa Chacareiro Uruguay Uruguay TEJ 39 310399 O. sativa Doble Carolina Uruguay AUS 40 310415 O. sativa Ai Chueh Ta Pai Ku Taiwan IND 41 310420 O. sativa Thang 10 Vietnam IND 42 310428 O. sativa Sipirasikkam Indonesia TRJ 43 310440 O. sativa TJ Guyana IND 44 310442 O. sativa PD 46 Sri Lanka IND 45 310446 O. sativa Acheh Malaysia IND 46 310471 O. sativa PATNAI 6 Myanmar AUS 47 310475 O. sativa K8C-263-3 Suriname IND 48 310480 O. sativa Djimoron Guinea IND

110 49 310481 O. sativa Anandi India IND 50 310489 O. sativa Chun 118-33 China IND 51 310494 O. sativa WW 8/2290 Netherlands TRJ-IND-AUS 52 310503 O. sativa Manga Kely 694 Madagascar IND-AUS 53 310510 O. sativa BLUE STICK Fiji TEJ 54 310515 O. sativa Nam Dawk Mai Thailand IND 55 310519 O. sativa GUYANE 1 Chad IND 56 310546 O. sativa MAHSURI Malaysia IND 57 310566 O. sativa INIAP 7 Ecuador IND 58 310588 O. sativa Onu B Zaire TRJ 59 310598 O. sativa Red Pakistan AUS-IND-TEJ 60 310615 O. sativa Dichroa Alef Uslkij Kazakhstan TRJ 61 310630 O. sativa BKN 6987-68-14 Thailand IND 62 310632 O. sativa IR 4482-5-3-9-5 Philippines IND 63 310645 O. sativa MOROBEREKAN Guinea TRJ 64 310670 O. sativa KUBANETS 508 Russian Federation TEJ 65 310687 O. sativa IR 9660-48-1-1-2 Philippines IND 66 310693 O. sativa Bakiella 1 Sri Lanka IND

Table 2-1. Continued. S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 67 310702 O. sativa Jumli dhan Nepal TEJ-TRJ-ARO 68 310703 O. sativa N-2703 Nepal AUS 69 310714 O. sativa TCHAMPA Iran AUS 70 310715 O. sativa PHUDUGEY Bhutan AUS 71 310723 O. sativa WIR 3039 Tajikistan AUS 72 310724 O. sativa Ak Tokhum Azerbaijan ARO 73 310747 O. sativa BHIM DHAN Nepal TEJ-TRJ-ARO 74 310757 O. sativa RP2151-173-1-8 India IND 75 310767 O. sativa HB-6-2 Hungary TEJ 76 310773 O. sativa ECIA76-S89-1 Cuba IND 77 310777 O. sativa WC 3532 Peru TRJ 78 310779 O. sativa GPNO 1106 Guatemala TRJ 79 310788 O. sativa Toga India IND 80 310791 O. sativa Kin Shan Zim China IND 81 310799 O. sativa Ragasu Taiwan TRJ-TEJ

111 82 310801 O. sativa Tobura Taiwan TEJ—TEJ 83 310802 O. sativa Tamanishiki Japan TEJ 84 310809 O. sativa Yong Chal Byo Korea, South TEJ 85 310814 O. sativa Grassy Haiti TRJ 86 310836 O. sativa GPNO 5055 United States TRJ 87 310846 O. sativa Kao Chio Lin Chou Taiwan IND 88 310849 O. sativa Pan Ju China IND 89 310861 O. sativa Niwahutaw Mochi Japan TEJ 90 310879 O. sativa 6360 Turkey TEJ 91 310883 O. sativa Somewake Japan TEJ 92 310887 O. sativa Buphopa Myanmar ARO-TEJ-TRJ 93 310901 O. sativa Juppa Nepal IND 94 310906 O. sativa Ardito Italy TEJ 95 310910 O. sativa TAINO 38 Taiwan AUS-IND-TEJ 96 310932 O. sativa 17-9-4 Mexico IND 97 310945 O. sativa Dular India AUS 98 310950 O. sativa NANTON NO. 131 Taiwan TRJ-TEJ-ARO 99 310958 O. sativa 2 Afghanistan TRJ-TEJ-IND

Table 2-1. Continued S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 100 310965 O. sativa Vary Tarva Osla Portugal TEJ 101 310984 O. sativa CSORNUJ Hungary TEJ 102 310990 O. sativa R 67 Senegal TRJ 103 310997 O. sativa LUSITANO Portugal TEJ 104 310998 O. sativa WC 4443 Bolivia TEJ 105 311016 O. sativa IR 238 Philippines IND 106 311046 O. sativa IARI 6621 India AUS 107 311074 O. sativa Mitak Indonesia TRJ 108 311078 O. sativa Gazan Afghanistan TEJ 109 311100 O. sativa ARC 6578 India AUS 110 311105 O. sativa Hsin Hsing Pai Ku Taiwan IND 111 311111 O. sativa 99216 India AUS 112 311113 O. sativa Shui Ya Jien Hong Kong IND 113 311117 O. sativa Tranoeup Beykher Cambodia IND 114 311123 O. sativa 10340 Italy IND

112 115 311140 O. sativa AKP 4 India IND 116 311141 O. sativa SORNAVARI Mali AUS 117 311151 O. sativa TD 70 Thailand TEJ 118 311153 O. sativa IR 2061-214-2-3 Philippines IND 119 311167 O. sativa TAINUNG 45 Taiwan IND 120 311173 O. sativa Manga 629 Madagascar IND-AUS 121 311180 O. sativa Sapundali Local India IND-AUS 122 311181 O. sativa Tauli Nepal AUS 123 311185 O. sativa Bombon Spain TEJ 124 311188 O. sativa Dara Indonesia AUS 125 311206 O. sativa 79 Guyana ARO 126 311236 O. sativa B805D-MR-16-8-3 Indonesia IND 127 311249 O. sativa TONO BREA 439 Dominican Republic IND 128 311255 O. sativa Saraya Fiji AUS 129 311258 O. sativa Botika S/R Zaire TRJ 130 311266 O. sativa CO 13 India IND 131 311269 O. sativa Shimla Early Iraq IND-AUS 132 311278 O. sativa Montakcl Egypt AUS

Table 2-1. Continued. S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 133 311281 O. sativa A 152 Bangladesh IND-TRJ 134 311284 O. sativa LA PLATA GENA Argentina AUS 135 311286 O. sativa UZ ROS 59 Uzbekistan IND 136 311327 O. sativa Gasym Hany Azerbaijan ARO 137 311383 O. sativa DARMALI Nepal TEJ-ARO-TRJ 138 311385 O. sativa KAUKKYI ANI Myanmar TRJ 139 311393 O. sativa Celiaj Azerbaijan TEJ 140 311417 O. sativa CNTLR80076-44-1 Thailand IND 141 311423 O. sativa IR 58614-B-B-8-2 Philippines IND 142 311435 O. sativa CM1, HAIPONG Vietnam IND 143 311466 O. sativa KECHENGNUO China IND 144 311483 O. sativa 4484 China IND 145 311484 O. sativa 4595 China IND 146 311491 O. sativa YOU-I B China IND 147 311497 O. sativa CHUNJIANGZAO N China IND

113 148 311532 O. sativa Egyptian Wild Type Turkey TEJ 149 311537 O. sativa A 5 Japan TEJ 150 311539 O. sativa C.B. II Japan AUS 151 311544 O. sativa Gallawa Sri Lanka AUS 152 311545 O. sativa Ittikulama Sri Lanka AUS 153 311547 O. sativa Karayal Sri Lanka AUS 154 311554 O. sativa Srav Prapay Cambodia IND 155 311561 O. sativa Nang Bang Bentre Vietnam AUS 156 311563 O. sativa DNJ 179 Bangladesh AUS 157 311572 O. sativa DJ 24 Bangladesh AUS 158 311573 O. sativa DJ 102 Bangladesh AUS 159 311576 O. sativa DNJ 121 Bangladesh AUS 160 311586 O. sativa Santhi 990 Pakistan ARO-IND-AUS 161 311592 O. sativa UZ ROS 7-13 Uzbekistan AUS 162 311596 O. sativa SL 22-620 Sierra Leone AUS 163 311600 O. sativa Jyanak Bhutan TEJ-ARO-TRJ 164 311603 O.glaberrima TOg 7025 Sierra Leone Glaberrima 165 311606 O. sativa Dhan Nepal IND

Table 2-1. Continued. S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 166 311613 O. sativa Spin Mere Afghanistan AUS 167 311620 O. sativa Romeno Portugal TEJ 168 311635 O. sativa AMANE Sri Lanka IND 169 311642 O. sativa Tia Bura Indonesia TRJ 170 311643 O. sativa Padi Tarab Arab Malaysia TRJ 171 311644 O. sativa P 35 India AUS 172 311654 O. sativa CAROLINO 164 Chad AUS 173 311656 O. sativa ASWINA 330 Bangladesh AUS 174 311667 O. sativa HKG 98 Mali AUS 175 311668 O. sativa Daudzai Field Mix Pakistan AUS 176 311669 O. sativa JP 5 Pakistan AUS-IND 177 311677 O. sativa Karabaschak Bulgaria TEJ 178 311684 O. sativa Hi Muke Kazakhstan AUS 179 311685 O. sativa WIR 911 Russian Federation TEJ 180 311688 O.glaberrima GPNO 25912 El Salvador Glaberrima

114 181 311689 O.glaberrima TOG 7102 Mali Glaberrima 182 311690 O.glaberrima TOg 7135 Senegal Glaberrima 183 311691 O.glaberrima TOg 7161a Senegal Glaberrima 184 311692 O.glaberrima TOg 7257 Chad Glaberrima 185 311693 O.glaberrima TOg 7267 Cameroon Glaberrima 186 311694 O.glaberrima HG 24 Burkina Faso Glaberrima 187 311695 O.glumaepatula NSGC 5944 United States Glumapatula 188 311697 O.latifolia - Honduras Latifolia 189 311698 O.nivara NSGC 5935 India Nivara 190 311699 O. nivara A100943-R Myanmar Nivara 191 311710 O.sativa Lua Chua Chan Vietnam TRJ 192 311713 O.sativa Sereno Jamaica IND 193 311725 O.sativa A 36-3 Myanmar IND 194 311727 O.sativa Nahng Sawn Thailand IND 195 311734 O.sativa ARC 10633 India IND 196 311735 O.sativa Simpor Brunei TRJ 197 311736 O.sativa Coppocina Bulgaria TRJ

Table 2-1. Continued. S.No. GSOR# Taxonomy Genotype Origin country Ancestry* 198 311739 O.sativa FUJISAKA 5 Japan IND 199 311741 O.sativa SOC NAU Vietnam IND 200 311744 O.sativa Heo Trang Vietnam IND 201 311745 O.sativa WONG CHIM Hong Kong IND 202 311748 O.sativa Bogarigbeli Burkina Faso IND 203 311751 O.sativa Magoti Burundi IND 204 311765 O.sativa Pa Boup Sierra Leone AUS 205 311769 O.sativa Pakkali Philippines ARO 206 311775 O.sativa THAVALU Sri Lanka AUS 207 311779 O.sativa WC 10253 Uncertain - 208 311781 O.sativa Krachek Chap Indochina IND 209 311787 O.sativa KRASNODARSKIJ Russian Federation TEJ 210 311788 O.sativa EMBRAPA 1200 Brazil TRJ 211 311790 O.sativa WAB462-10-3-1 Cote D'Ivoire TRJ

115 212 311792 O.sativa Cypress United States TRJ

213 311793 O.sativa IR64 Philippines IND 214 311794 O.sativa M202 United States TEJ 215 311795 O.sativa Nipponbare Japan TEJ 216 301418 O.sativa Bengal USA TEJ 216 301408 O.sativa Kaybonnet USA TEJ 217 301419 O.sativa Katy USA TEJ 218 301066 O.sativa IRAT 117 Philippines IND 219 301379 O.sativa Cocodrie USA TEJ 220 - O.sativa Vandana India AUS 221 - O.sativa N 22 India IND (*) Ancestry: aus=AUS; indica=IND; temperate japonica=TEJ; tropical japonica=TRJ; aromatic=ARO.

Table 2-2. Effect of drought stress on plant biomass exhibiting drought resistance, moderately drought resistance and drought sensitivity in the USDA rice mini-core collection (URMC) of 221 genotypes. Plant Biomass Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301408, 301418, 310007, 310102 301066, 310015, 310020, 310039 301379, 301419, 310023, 310045 310131, 310156 ,310196, 310204 310052, 310080, 310087, 310100 310134, 310219, 310226, 310238 310210, 310301, 310338, 310397 310111, 310144, 310161, 310200 310351, 310440, 310480, 310510 310399, 310415, 310420, 310428 310211, 310220, 310241, 310317 310515, 310519, 310546, 310687 310446, 310471, 310489, 310494 310337, 310345, 310348, 310354 310693, 310773, 310777, 310779 310503, 310566, 310588, 310598 310381, 310389, 310442, 310475 310788, 310791, 310814, 310836 310615, 310630, 310632, 310670 310481, 310645, 310702, 310724 310861, 310883, 310910, 310945 310703, 310714, 310715, 310723 310767, 310799, 310801, 310802 310990, 310997, 310998, 311074 310747, 310757, 310901, 310906 310809, 310846, 310849, 310879 311100, 311140, 311173, 311181 310965, 310984, 311046, 311078 310887, 310932, 310950, 310958 311188, 311206, 311249, 311258 311113, 311117, 311151, 311153 311016, 311105, 311111, 311123 311266, 311269, 311278, 311281

116 311185, 311236, 311423, 311483 311141, 311167, 311180, 311255 311327, 311383, 311385, 311393 311491, 311537, 311554, 311563 311284, 311286, 311417, 311435 311484, 311532, 311539, 311669

311572, 311573, 311592, 311600 311466, 311497, 311544, 311545 311689, 311692, 311699, 311736 311606, 311620, 311635, 311642 311547, 311561, 311576, 311586 311745, 311787, 311790 311643, 311644, 311654, 311667 311596, 311603, 311613, 311656 311668, 311685, 311688, 311693 311677, 311684, 311690, 311691 311694, 311695, 311697, 311698 311710, 311727, 311739, 311751 311713, 311725, 311734, 311735 311765, 311775, 311779, 311781 311741, 311744, 311769, 311788 311793, 311794, 311795 311792, N22, Vandana

Table 2-3. Effect of drought stress on number of tillers in the URMC of 221 genotypes. No of tillers Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301379, 301408, 301418 310007, 310015, 310020, 310039 310588, 310670, 310693, 310703 301419, 310023, 310087, 310100 310045, 310052, 310080, 310131 310714, 310715, 310723, 310777 310102, 310111, 310134, 310144 310161, 310196, 310200, 310210 310779, 310861, 310910, 310932 310156, 310204, 310226, 310238 310211, 310219, 310220, 310348 310945, 310997, 311153, 311249 310241, 310301, 310317, 310337 310389, 310397, 310446, 310471 311483, 311484, 311547, 311667 310338, 310345, 310351, 310354 310510, 310598, 310687, 310702 311668, 311693, 311695, 311734 310381, 310399, 310415, 310420 310791, 310849, 310879, 310883 310428, 310440, 310442, 310475 310950, 310998, 311016, 311105 310480, 310481, 310489, 310494 311111, 311113, 311117, 311123 310503, 310515, 310519, 310546 311141, 311167, 311173, 311180 310566, 310615, 310630, 310632 311185, 311188, 311206, 311255 310645, 310724, 310747, 310757 311258, 311266, 311269, 311278 310767, 310773, 310788, 310799 311281, 311284, 311286, 311385

117 310801, 310802, 310809, 310814 311393, 311417, 311423, 311466 310836, 310846, 310887, 310901 311491, 311497, 311532, 311537 310906, 310958, 310965, 310984 311539, 311544, 311576, 311592 310990, 311046, 311074, 311078 311596, 311606, 311613, 311642 311100, 311140, 311151, 311181 311654, 311656, 311669, 311677 311236, 311327, 311383, 311435 311684, 311690, 311710, 311735 311545, 311554, 311561, 311563 311744, 311745, 311765, 311769 311572, 311573, 311586, 311600 311775, 311779, 311792 311603, 311620, 311635, 311643 311644, 311685, 311688, 311689 311691, 311692, 311694, 311697 311698, 311699, 311713, 311725 311727, 311736, 311739, 311741 311751, 311781, 311787, 311788 311790, 311793, 311794, 311795 N22, Vandana

Table 2-4. Effect of drought stress on relative water content (RWC) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Relative Water Content (RWC) Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction ) 301418, 310226, 310399, 310503 301066, 301379, 301408, 301419 311016, 311592, 310489, 311690, 311735 311561, 311563, 311572, 311573 310015, 310020, 310052, 310087 311694, 310802, 311258, 310861, 310906 311613, 311667, 311669, 311684 310134, 310220, 310301, 310337 311188, 311668, 311111, 311151, 310415 311685, 311692, 311725, 311727 310348, 310351, 310354, 310389 310645, 310879, 310632, 311423, 310901 311734, 311736, 311741, 311744 310420, 310428, 310440, 310442 311689, 310023, 310381, 310615, 311327 311788, Vandana 310446, 310471, 310475, 310481 310990, 311793, 311792, 311600, 311779 310494, 310515, 310546, 310566 311787, 311255, 310161, 310630, 310238 310598, 310670, 310687, 310714 310836, 311173, 310809, 310998, 311100 310715, 310723, 310887, 310932 311113, 310210, 310801, 310910, 311635 310945, 311105, 311117, 311123 311078, 311699, 310702, 310757, 310997 311140, 311141, 311181, 311185 310950, 310156, 311167, 311697, 310317

118 311206, 311236, 311249, 311269 311698, 311739, 310241, 311586, 310849 311278, 311281, 311284, 311383 310480, 311745, 310814, 311074, 311266 311385, 311393, 311417, 311435 311180, 310039, 311286, 310219, 310588 311466, 311483, 311484, 311491 311765, 310080, 311677, 311554, 310767 311497, 311532, 311537, 311539 311153, 310984, 310965, 310846, 311547 311544, 311576, 311596, 311606 311695, 310510, 310958, 311545, 310799 311620, 311654, 311656, 311688 310111, 311769, 310883, 310100 310773 311691, 311693, 311710, 311713 311751, 310703, 310693, 310519, 310788 311781, 311790, N22 310724, 310791, 311046, 310777, 311603 310211, 310144, 310397, 310131, 311795 310196, 310102, 311644, 311642, 310779 310045, 310007, 310747, 310200, 311794 311775, 310345, 311643, 310204, 310338

Table 2-5. Effect of drought stress on leaf rolling expressing drought stress in the URMC of 221 genotypes Leaf rolling score in genotypes Score 1-1.9 Score 2.0-2.9 Score 3.0-3.9 Score 4.0-4.9 Score 5.0 301066, 301379 301418, 301419 301408, 310015 310020, 310023, 310045 310226, 311710 310007, 310156 310200, 310219 310039, 310087 310052, 310080, 310102 310161, 310196 310220, 310241 310100, 310131 310111, 310134, 310144 310204, 310238 310767, 310799 310210, 310301 310211, 310348, 310354 310317, 310337 310849, 310887 310351, 310381 310415, 310420, 310440 310338, 310345 310910, 310945 310399, 310428 310442, 310446, 310480 310389, 310397 310984, 311100 310471, 310475 310481, 310510, 310515 310489, 310494 311111, 311153 310503, 310519 310546, 310566, 310588 310773, 310791 311249, 311255 310598, 310615 310630, 310632, 310645 310836, 310861 311266, 311269 310670, 310687 310693, 310702, 310703 311016, 311105 311278, 311281 310714, 310715 310724, 310809, 310901 311236, 311258 311385, 311484 310723, 310747 310932, 310950, 310965 311383, 311417 311545, 311547 310757, 310777 310997, 311046, 311074

119 311423, 311435 311554, 311603 310779, 310788 311123, 311140, 311167 311466, 311483 311692, 311697 310801, 310802 311173, 311180, 311185

311491, 311497 311698, 311727 310814, 310846 311188, 311206, 311284 311544, 311561 311765, 311769 310879, 310883 311286, 311532, 311537 311563, 311572 311794, Vandana 310906, 310958 311596, 311635, 311642 311573, 311586 310990, 310998 311644, 311654, 311656 311592, 311606 311078, 311113 311669, 311677, 311685 311613, 311689 311117, 311141 311688, 311695, 311699 311690, 311691 311151, 311181 311744, 311790 311693, 311713 311327, 311393 311734, 311735 311539, 311600 311736, 311739 311620, 311643 311775, 311779 311667, 311668 311781, 311787 311684, 311694 311792, 311795 311725, 311741 N22, 311576 311745, 311751 311788 311793

Table 2-6. Drought stress response to photosynthesis exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Photosynthesis Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301379, 301408, 301418 301419, 310111, 310131, 310156 311603, 310007, 310015, 310020 310023, 310102, 310134, 310144 310204, 310211, 310519, 310546 310039, 310045, 310052, 310080 310161, 310196, 310210, 310219 310615, 310630, 310693, 310715 310087, 310100, 310200, 310241 310220, 310226, 310238, 310301 310723, 310747, 310757, 310777 310337, 310338, 310345, 310348 310317, 310389, 310397, 310399 310791, 311105, 311111, 311117 310351, 310354, 310381, 310440 310415, 310420, 310428, 310471 311123, 311140, 311188, 311249 310442, 310446, 310494, 310510 310475, 310480, 310481, 310489 311269, 311278, 311383, 311435 310515, 310566, 310588, 310645 310503, 310598, 310632, 310767 311484, 311539, 311545, 311554 310670, 310687, 310702, 310703 310779, 310788, 310799, 310801 311561, 311573, 311576, 311586 310714, 310724, 310773, 310836 310802, 310809, 310814, 310846 311592, 311620, 311642, 311644 311016, 311046, 311074, 311078 310849, 310861, 310879, 310883 311656, 311668, 311684, 311685 311100, 311185, 311327, 311385 310887, 310901, 310906, 310910 311697, 311698, 311751, 311775 311417, 311423, 311544, 311563

120 310932, 310945, 310950, 310958 311781 311596, 311600, 311654, 311689 310965, 310984, 310990, 310997 311690, 311691, 311693, 311695 310998, 311113, 311141, 311151 311710, 311713, 311725, 311735 311153, 311167, 311173, 311180 311736, 311739, 311741, 311744 311181, 311206, 311236, 311255 311745, 311792, 311793, 311794 311258, 311266, 311281, 311284 311795 311286, 311393, 311466, 311483 311491, 311497, 311532, 311537 311547, 311572, 311606, 311613 311635, 311643, 311667, 311669 311677, 311688, 311692, 311694 311699, 311727, 311734, 311765 311769, 311779, 311787, 311788 311790, N22, Vandana

Table 2-7. Drought stress response on transpiration rate (TR) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Transpiration rate (TR) Drought resistant genotypes Moderately drought resistant Drought sensitive (≥40% reduction) genotypes genotypes (25-40 % reduction) (≤25% reduction) 301419, 310007, 310015, 310020, 310023, 310039, 310045 301408, 301418, 310226, 310238 301066, 301379, 310317 310052, 310080, 310087, 310100, 310102, 310111, 310131 310399, 310415, 310420, 310630 310389, 310397, 310428 310134, 310144, 310156, 310161, 310196, 310200, 310204 310799, 310809, 310945, 310950 310779, 311281, 311532 310210, 310211, 310219, 310220, 310241, 310301, 310337 310965, 310998, 311113, 311151 311544, 311643 310338, 310345, 310348, 310351, 310354, 310381, 310440 311278, 311284, 311327, 311667 310442, 310446, 310471, 310475, 310480, 310481, 310489 311669, 311685, 311699, N22 310494, 310503, 310510, 310515, 310519, 310546, 310566 310588, 310598, 310615, 310632, 310645, 310670, 310687 310693, 310702, 310703, 310714, 310715, 310723, 310724 310747, 310757, 310767, 310773, 310777, 310788, 310791 310801, 310802, 310814, 310836, 310846, 310849, 310861

121 310879, 310883, 310887, 310901, 310906, 310910, 310932 310958, 310984, 310990, 310997, 311016, 311046, 311074 311078, 311100, 311105, 311111, 311117, 311123, 311140 311141, 311153, 311167, 311173, 311180, 311181, 311185 311188, 311206, 311236, 311249, 311255, 311258, 311266 311269, 311286, 311383, 311385, 311393, 311417, 311423 311435, 311466, 311483, 311484, 311491, 311497, 311537 311539, 311545, 311547, 311554, 311561, 311563, 311572 311573, 311576, 311586, 311592, 311596, 311600, 311603 311606, 311613, 311620, 311635, 311642, 311644, 311654 311656, 311668, 311677, 311684, 311688, 311689, 311690 311691, 311692, 311693, 311694, 311695, 311697, 311698 311710, 311713, 311725, 311727, 311734, 311735, 311736 311739, 311741, 311744, 311745, 311751, 311765, 311769 311775, 311779, 311781, 311787, 311788, 311790, 311792 311793, 311794, 311795, Vandana

Table 2-8. Drought stress response in instantaneous water use efficiency (WUEi) exhibiting drought resistance and drought sensitivity in the URMC of 221 genotypes. Instantaneous Water Use Efficiency (WUEi) Drought resistant (0.57-63% increase in WUEi) Drought sensitive (0.59-495% reduction in WUEi) 301066, 301379, 301408, 301418, 310023, 310080, 310087, 310102 301419, 310007, 310015, 310020, 310039, 310045 310111, 310131, 310134, 310144, 310156, 310161, 310196, 310200 310052, 310100, 310241, 310337, 310338, 310345 310204, 310210, 310211, 310219, 310220, 310226, 310238, 310301 310348, 310351, 310354, 310381, 310397, 310428 310317, 310389, 310399, 310415, 310420, 310442, 310446, 310471 310440, 310566, 310588, 310615, 310630, 310670 310475, 310480, 310481, 310489, 310494, 310503, 310510, 310515 310693, 310702, 310703, 310724, 310836, 311185 310519, 310546, 310598, 310632, 310645, 310687, 310714, 310715 311249, 311278, 311281, 311327, 311385, 311417 310723, 310747, 310757, 310767, 310773, 310777, 310779, 310788 311544, 311596, 311600, 311642,, 311654, 311668 310791, 310799, 310801, 310802, 310809, 310814, 310846, 310849 311684, 311685, 311690, 311693, 311710, 311713 310861, 310879, 310883, 310887, 310901, 310906, 310910, 310932 311725, 311735, 311739, 311741, 311744, 311745 310945, 310950, 310958, 310965, 310984, 310990, 310997, 310998 311792, 311793, 311794, 311795 311016, 311046, 311074, 311078, 311100, 311105, 311111, 311113 311117, 311123, 311140, 311141, 311151, 311153, 311167, 311173

122 311180, 311181, 311188, 311206, 311236, 311255, 311258, 311266 311269, 311284, 311286, 311383, 311393, 311423, 311435, 311466

311483, 311484, 311491, 311497, 311532, 311537, 311539, 311545 311547, 311554, 311561, 311563, 311572, 311573, 311576, 311586 311592, 311603, 311606, 311613, 311620, 311635, 311643, 311644 311656, 311667, 311669, 311677, 311688, 311689, 311691, 311692 311694, 311695, 311697, 311698, 311699, 311727, 311734, 311736 311751, 311765, 311769, 311775, 311779, 311781, 311787, 311788 311790, N22, Vandana

Table 2-9. Drought stress response to stomatal conductance exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Stomatal conductance Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301408, 301418, 310015 301379, 310134, 310238, 310338, 310345 301419, 310007, 310020, 310039, 310045 310023, 310102, 310226, 310317 310442, 310475, 310503, 310630, 310714 310052, 310080, 310087, 310100, 310111 310389, 310399, 310415, 310420 310777, 310788, 310801, 310809, 310836 310131, 310144, 310156, 310161, 310196 310428, 310440, 310471, 310480 310846, 310883, 310906, 311105, 311113 310200, 310204, 310210, 310211, 310219 310489, 310598, 310693, 310703 311140, 311181, 311185, 311188, 311249 310220, 310241, 310301, 310337, 310348 310723, 310724, 310747, 310767 311258, 311466, 311491, 311586, 311592 310351, 310354, 310381, 310397, 310446 310779, 310799, 310814, 310861 311596, 311606, 311613, 311635, 311642 310481, 310494, 310510, 310515, 310519 310887, 310932, 310945, 310950 311654, 311667, 311668, 311685, 311695 310546, 310566, 310588, 310615, 310632 310958, 310965, 310997, 310998 311725, 311735, 311739, 311779, 311795 310645, 310670, 310687, 310702, 310715 311123, 311151, 311167, 311236 310757, 310773, 310791, 310802, 310849 311269, 311278, 311281, 311286 310879, 310901, 310910, 310984, 310990

123 311383, 311497, 311539, 311643 311016, 311046, 311074, 311078, 311100 311669, 311677, 311692, 311694 311111, 311117, 311141, 311153, 311173 311699, 311727, 311734, 311736 311180, 311206, 311255, 311266, 311284 311765, 311769, 311787, 311788 311327, 311385, 311393, 311417, 311423 N22 311435, 311483, 311484, 311532, 311537 311544, 311545, 311547, 311554, 311561 311563, 311572, 311573, 311576, 311600 311603, 311620, 311644, 311656, 311684 311688, 311689, 311690, 311691, 311693 311697, 311698, 311710, 311713, 311741 311744, 311745, 311751, 311775, 311781 311790, 311792, 311793, 311794 Vandana

Table 2-10. Drought stress response to internal cellular CO2 concentration (Ci) exhibiting drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes.

Intercellular CO2 Concentration (Ci) Drought resistant genotypes Moderately drought resistant Drought sensitive genotypes (≤25 % reduction) genotypes (≥40% reduction) (25-40 % reduction) 301066, 301408, 301418, 310007, 310015 310020 301379, 301419, 310087, 310144 310196, 310200, 310210, 310226 310023, 310039, 310045, 310052 310080, 310100 310156, 310161, 310211, 310220 310238, 310480, 310510, 310515 310102, 310111, 310131 310134, 310204 310219 310301, 310397, 310440, 310446 310773, 310777, 310809, 310814 310241, 310317, 310337, 310338, 310345, 310348 310475, 310481, 310494, 310503 310849, 310861, 310879, 310910 310351 310354, 310381, 310389, 310399, 310415 310519, 310546, 310566, 310588 310932, 310945, 310958, 310990 310420, 310428, 310442, 310471, 310489, 310598 310715, 310779, 310799, 310802 310997, 311105, 311117, 311123 310615, 310630, 310632, 310645, 310670, 310687 310846, 310883, 310887, 310901 311140, 311151, 311153, 311173 310693, 310702, 310703, 310714, 310723, 310724 310906, 310984, 311141, 311167 311180, 311181, 311206, 311266 310747, 310757, 310767, 310788, 310791, 310801 311236, 311255, 311258, 311281 311269, 311284, 311417, 311423 310836, 310950, 310965, 310998, 311016, 311046 311286, 311466, 311544, 311547 311435, 311483, 311491, 311497 311074, 311078, 311100, 311111, 311113, 311185 311603, 311644, 311741, 311787 311532, 311537, 311698, 311779

124 311188, 311249, 311278, 311327, 311383, 311385 311781, 311790

311393, 311484, 311539, 311545, 311554, 311561 311563, 311572, 311573, 311576, 311586, 311592 311596, 311600, 311606, 311613, 311620 311635 311642, 311643, 311654, 311656, 311667, 311668 311669, 311677, 311684, 311685, 311688, 311689 311690, 311691, 311692, 311693, 311694, 311695 311697, 311699, 311710, 311713, 311725, 311727 311734, 311735, 311736, 311739, 311744, 311745 311751, 311765, 311769, 311775, 311788, 311792 311793, 311794, 311795, N22, Vandana

Table 2-11. Effect of drought stress on intercellular CO2 concentration/ambient CO2 ratio (Ci/Ca) showing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes.

Internal cellular CO2 concentration/ambient CO2Ci/Ca ratio Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 310007, 310015, 310020 310381, 310399, 310415, 310420 301379, 301408, 301418, 301419 310023, 310039, 310045, 310052 310440, 310598, 310615, 310703 310087, 310144, 310156, 310161 310080, 310100, 310102, 310111 310757, 310802, 310883, 311046 310196, 310200, 310204, 310210 310131, 310134, 310241, 310317 311074, 311078, 311278, 311383 310211, 310219, 310220, 310226 310337, 310338, 310345, 310348 311554, 311572, 311603, 311606 310238, 310301, 310389, 310397 310351, 310354, 310442, 310702 311635, 311667, 311689, 311725 310428, 310446, 310471, 310475 310714, 310723, 310724, 310747 311741, 311751, 311765 310480, 310481, 310489, 310494 310836, 310945, 310950, 310965 310503, 310510, 310515, 310519 310998, 311016, 311100, 311188 310546, 310566, 310588, 310630 311249, 311255, 311327, 311385 310632, 310645, 310670, 310687 125 311393, 311539, 311545, 311561 310693, 310715, 310767, 310773

311563, 311573, 311576, 311586 310777, 310779, 310788, 310791 311592, 311596, 311600, 311613 310799, 310801, 310809, 310814 311620, 311642, 311643, 311654 310846, 310849, 310861, 310879 311656, 311668, 311669, 311677 310887, 310901, 310906, 310910 311684, 311685, 311688, 311690 310932, 310958, 310984, 310990 311691, 311692, 311693, 311694 310997, 311105, 311111, 311113 311698, 311699, 311710, 311713 311117, 311123, 311140, 311141 311727, 311734, 311735, 311736 311151, 311153, 311167, 311173 311739, 311744, 311745, 311792 311180, 311181, 311185, 311206 311793, 311795 311236, 311258, 311266, 311269 311281, 311284, 311286, 311417 311423, 311435, 311466, 311483 311484, 311491, 311497, 311532 311537, 311544, 311547, 311644 311695, 311697, 311769, 311775 311779, 311781, 311787, 311788 311790, 311794, N22, Vandana

Table 2-12. Effect of Drought stress on variable chlorophyll fluorescence (Fv’) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes.

Variable chlorophyll fluorescence (Fv’) Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 310045, 310087, 310100 301408, 301418, 301419, 310015 301379, 310007, 310039, 310200 310111, 310131, 310134, 310156 310020, 310023, 310052, 310080 310211, 310415, 310446, 310481 310161, 310219, 310241, 310301 310102, 310144, 310196, 310204 310489, 310494, 310510, 310515 310317, 310337, 310351, 310399 310210, 310220, 310226, 310238 310546, 310566, 310687, 310723 310442, 310471, 310480, 310503 310338, 310345, 310348, 310354 310724, 310779, 310788, 310809 310598, 310615, 310645, 310670 310381, 310389, 310397, 310420 310849, 310997, 311100, 311151 310693, 310702, 310703, 310715 310428, 310440, 310475, 310519 311173, 311185, 311206, 311255 310757, 310836, 310883, 310887 310588, 310630, 310632, 310714 311695, 311787, 311795 310901, 310906, 310910, 310932 310747, 310767, 310773, 310777 310945, 310950, 310965, 310998 310791, 310799, 310801, 310802

126 311046, 311074, 311105, 311111 310814, 310846, 310861, 310879 311153, 311180, 311236, 311249 310958, 310984, 310990, 311016 311266, 311269, 311281, 311284 311078, 311113, 311117, 311123 311393, 311417, 311435, 311466 311140, 311141, 311167, 311181 311484, 311537, 311539, 311545 311188, 311258, 311278, 311286 311547, 311561, 311563, 311572 311327, 311383, 311385, 311423 311573, 311576, 311586, 311592 311483, 311491, 311497, 311532 311600, 311603, 311606, 311613 311544, 311554, 311596, 311644 311620, 311635, 311642, 311643 311677, 311685, 311688, 311689 311654, 311656, 311667, 311668 311691, 311693, 311694, 311697 311669, 311684, 311690, 311692 311698, 311710, 311725, 311727 311699, 311713, 311734, 311736 311735, 311744, 311769, 311775 311739, 311741, 311745, 311751 311790, 311793, Vandana 311765, 311779, 311781, 311788 311792, 311794, N22

Table 2-13. Effect of Drought stress on maximum chlorophyll fluorescence (Fm’) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes.

Maximum chlorophyll fluorescence (Fm’) Drought resistant genotypes Moderately drought resistant Drought sensitive (≤25 % reduction) genotypes genotypes (25-40 % reduction) (≥40% reduction) 301066, 301379, 301408, 301418, 301419, 310015, 310020 310007, 310200, 310354, 310446 310510, 310546, 311100 310023, 310039, 310045, 310052, 310080, 310087, 310100 310489, 310519 310566, 310724 310102, 310111, 310131, 310134, 310144, 310156, 310161 310779 311016, 311173, 311206 310196, 310204, 310210, 310211, 310219, 310220, 310226 311255, 311385, 311491 311644 310238, 310241, 310301, 310317, 310337, 310338, 310345 311688 310348, 310351, 310381, 310389, 310397, 310399, 310415 310420, 310428, 310440, 310442, 310471, 310475, 310480 310481, 310494, 310503, 310515, 310588, 310598, 310615 310630, 310632, 310645, 310670, 310687, 310693, 310702 310703, 310714, 310715, 310723, 310747, 310757, 310767 310773, 310777, 310788, 310791, 310799, 310801, 310802

127 310809, 310814, 310836, 310846, 310849, 310861, 310879 310883, 310887, 310901, 310906, 310910, 310932, 310945 310950, 310958, 310965, 310984, 310990, 310997, 310998 311046, 311074, 311078, 311105, 311111, 311113, 311117 311123, 311140, 311141, 311151, 311153, 311167, 311180 311181, 311185, 311188, 311236, 311249, 311258, 311266 311269, 311278, 311281, 311284, 311286, 311327, 311383 311393, 311417, 311423, 311435, 311466, 311483, 311484 311497, 311532, 311537, 311539, 311544, 311545, 311547 311554, 311561, 311563, 311572, 311573, 311576, 311586 311592, 311596, 311600, 311603, 311606, 311613, 311620 311635, 311642, 311643, 311654, 311656, 311667, 311668 311669, 311677, 311684, 311685, 311689, 311690, 311691 311692, 311693, 311694, 311695, 311697, 311698, 311699 311710, 311713, 311725, 311727, 311734, 311735, 311736 311739, 311741, 311744, 311745, 311751, 311765 311769, 311775, 311779, 311781, 311787, 311788, 311790, 311792 311793, 311794, 311795, N22, Vandana

Table 2-14. Effect of Drought stress on Fv’/Fm’ for identification of drought resistant, moderately resistant and drought sensitive genotypes in the URMC of 221 genotypes. Fluorescence component (Fv’/Fm’) Drought resistant genotypes Moderately drought resistant Drought sensitive (≤25 % reduction) genotypes genotypes (25-40 % reduction) (≥40% reduction) 301066, 301379, 301408, 310007, 310015, 310020, 310023 301418, 301419, 310039, 310102 310211, 310779 310045, 310052, 310080, 310087, 310100, 310111, 310131 310144, 310210, 310389, 310415 310134, 310156, 310161, 310196, 310200, 310204, 310219 310420, 310428, 310481, 310494 310220, 310226, 310238, 310241, 310301, 310317, 310337 310515, 310546, 310566, 310588 310338, 310345, 310348, 310351, 310354, 310381, 310397 310632, 310687, 310723, 310767 310399, 310440, 310442, 310446, 310471, 310475, 310480 310773, 310777, 310788, 310799 310489, 310503, 310510, 310519, 310598, 310615, 310630 310809, 310849, 310997, 311141 310645, 310670, 310693, 310702, 310703, 310714, 310715 311167, 311173, 311181, 311185 310724, 310747, 310757, 310791, 310801, 310802, 310814 311188, 311206, 311286, 311596 310836, 310846, 310861, 310879, 310883, 310887, 310901 311685, 311693, 311695, 311698

128 310906, 310910, 310932, 310945, 310950, 310958, 310965 311744, 311775, 311787, 311795 310984, 310990, 310998, 311016, 311046, 311074, 311078

311100, 311105, 311111, 311113, 311117, 311123, 311140 311151, 311153, 311180, 311236, 311249, 311255, 311258 311266, 311269, 311278, 311281, 311284, 311327, 311383 311385, 311393, 311417, 311423, 311435, 311466, 311483 311484, 311491, 311497, 311532, 311537, 311539, 311544 311545, 311547, 311554, 311561, 311563, 311572, 311573 311576, 311586, 311592, 311600, 311603, 311606, 311613 311620, 311635, 311642, 311643, 311644, 311654, 311656 311667, 311668, 311669, 311677, 311684, 311688, 311689 311690, 311691, 311692, 311694, 311697, 311699, 311710 311713, 311725, 311727, 311734, 311735, 311736, 311739 311741, 311745, 311751, 311765, 311769, 311779, 311781 311788, 311790, 311792, 311793, 311794, N22, Vandana

Table 2-15. Effect of Drought stress on the efficiency of photosystem II (ΦPSII) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Efficiency of Photosystem II Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301379, 310007, 310020 310015, 310023, 310039, 310045 301408, 301418, 301419, 310087 310080, 310100, 310102, 310111 310052, 310134, 310196, 310200 310204, 310241, 310338, 310354 310131, 310144, 310156, 310161 310211, 310219, 310220, 310301 310480, 310489, 310510, 310515 310210, 310226, 310238, 310317 310345, 310348, 310351, 310381 310546, 310588, 310598, 310687 310337, 310389, 310399, 310475 310397, 310415, 310420, 310428 310703, 310724, 310747, 310801 310615, 310630, 310693, 310715 310440, 310442, 310446, 310471 310814, 310849, 310883, 310887 310723, 310779, 310791, 310809 310481, 310494, 310503, 310519 310901, 310906, 310984, 310990 310910, 310932, 310965, 310998 310566, 310632, 310645, 310670 310997, 311074, 311078, 311100 311151, 311258, 311266, 311393 310702, 310714, 310757, 310767 311113, 311123, 311140, 311173 311466, 311483, 311484, 311497 310773, 310777, 310788, 310799 311188, 311206, 311249, 311255 311545, 311563, 311572, 311603 310802, 310836, 310846, 310861 311269, 311278, 311281, 311284 311667, 311684, 311692, 311699 310879, 310945, 310950, 310958 311286, 311385, 311423, 311435

129 311727, 311787, 311788, 311016, 311046, 311105, 311111 311539, 311544, 311600, 311677 Vandana 311117, 311141, 311153, 311167 311694, 311698, 311725, 311741 311180, 311181, 311185, 311236 311745, 311790 311327, 311383, 311417, 311491 311532, 311537, 311547, 311554 311561, 311573, 311576, 311586 311592, 311596, 311606, 311613 311620, 311635, 311642, 311643 311644, 311654, 311656, 311668 311669, 311685, 311688, 311689 311690, 311691, 311693, 311695 311697, 311710, 311713, 311734 311735, 311736, 311739, 311744 311751, 311765, 311769, 311775 311779, 311781, 311792, 311793 311794, 311795, N22

Table 2-16. Effect of Drought stress on electron transport rate (ETR) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Electron Transport Tate (ETR) Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301379, 310007, 310020 301408, 310015, 310039, 310045 301418, 301419, 310087, 310204 310023, 310080, 310100, 310102 310052, 310111, 310131, 310134 310241, 310301, 310338, 310354 310144, 310156, 310161, 310210 310196, 310200, 310211, 310345 310480, 310489, 310494, 310503 310219, 310220, 310226, 310238 310348, 310351, 310381, 310389 310510, 310515, 310519, 310546 310317, 310337, 310475, 310598 310397, 310399, 310415, 310420 310588, 310645, 310670, 310723 310615, 310687, 310715, 310767 310428, 310440, 310442, 310446 310801, 310814, 310849, 310883 310779, 310791, 310809, 310846 310471, 310481, 310566, 310630 310887, 310901, 310990, 310997 310910, 310932, 310998, 311141 310632, 310693, 310702, 310703 311074, 311078, 311100, 311113 311258, 311266, 311393, 311466 310714, 310724, 310747, 310757 311123, 311140, 311167, 311173 311483, 311484, 311497, 311545 310773, 310777, 310788, 310799 311185, 311188, 311206, 311249 311547, 311603, 311667, 311684 310802, 310836, 310861, 310879 311255, 311269, 311278, 311281

130 311692, 311699, 311727, 311787 310906, 310945, 310950, 310958 311284, 311286, 311385, 311423 311788, 311792, 311795, N22 310965, 310984, 311016, 311046 311435, 311539, 311544, 311596 Vandana 311105, 311111, 311117, 311151 311656, 311668, 311677, 311694 311153, 311180, 311181, 311236 311725, 311741, 311745, 311781 311327, 311383, 311417, 311491 311532, 311537, 311554, 311561 311563, 311572, 311573, 311576 311586, 311592, 311600, 311606 311613, 311620, 311635, 311642 311643, 311644, 311654, 311669 311685, 311688, 311689, 311690 311691, 311693, 311695, 311697 311698, 311710, 311713, 311734 311735, 311736, 311739, 311744 311751, 311765, 311769, 311775 311779, 311790, 311793, 311794

Table 2-17. Effect of Drought stress on non-photochemical quenching (qN) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Non-Photochemical Quenching (qN) Drought resistant genotypes Moderately drought Drought sensitive (≤25 % reduction) resistant genotypes genotypes (25-40 % reduction) (≥40% reduction) 301066, 301379, 301418, 301419, 310007, 310015, 310020, 310023 301408, 310510, 310546 310984 310039, 310045, 310052, 310080, 310087, 310100, 310102, 310111 310566, 310779, 310809 310131, 310134, 310144, 310156, 310161, 310196, 310200 310204 310849, 310945, 311188 310210, 310211, 310219, 310220, 310226, 310238, 310241 310301 311206 310317, 310337, 310338, 310345, 310348, 310351, 310354 310381 310389, 310397, 310399, 310415, 310420, 310428, 310440 310442 310446, 310471, 310475, 310480, 310481, 310489, 310494, 310503 310515, 310519, 310588, 310598, 310615, 310630, 310632, 310645 310670, 310687, 310693, 310702, 310703, 310714, 310715, 310723 310724, 310747, 310757, 310767, 310773, 310777, 310788, 310791 310799, 310801, 310802, 310814, 310836, 310846, 310861, 310879

131 310883, 310887, 310901, 310906, 310910, 310932, 310950, 310958 310965, 310990, 310997, 310998, 311016, 311046, 311074, 311078 311100, 311105, 311111, 311113, 311117, 311123, 311140, 311141 311151, 311153, 311167, 311173, 311180, 311181, 311185, 311236 311249, 311255, 311258, 311266, 311269, 311278, 311281, 311284 311286, 311327, 311383, 311385, 311393, 311417, 311423, 311435 311466, 311483, 311484, 311491, 311497, 311532, 311537 311539 311544, 311545, 311547, 311554, 311561, 311563, 311572 311573 311576, 311586, 311592, 311596, 311600, 311603, 311606 311613 311620, 311635, 311642, 311643, 311644, 311654, 311656, 311667 311668, 311669, 311677, 311684, 311685, 311688, 311689, 311690 311691, 311692, 311693, 311694, 311695, 311697, 311698, 311699 311710, 311713, 311725, 311727, 311734, 311735, 311736, 311739 311741, 311744, 311745, 311751, 311765, 311769, 311775, 311779 311781, 311787, 311788, 311790, 311792, 311793, 311794, 311795 N22, Vandana

Table 2-18. Effect of drought stress on chlorophyll content (SPAD) expressing drought resistance, moderately drought resistance and drought sensitivity in the URMC of 221 genotypes. Chlorophyll (SPAD) Drought resistant genotypes (≤25 % reduction) Drought sensitive genotypes(≥40% reduction) 301066, 301379, 301418, 301419, 310007, 310015, 310020, 310023 301408, 311779 310039, 310045, 310052, 310080, 310087, 310100, 310102, 310111 310131, 310134, 310144, 310156, 310161, 310196, 310200, 310204 310210, 310211, 310219, 310220, 310226, 310238, 310241, 310301 310317, 310337, 310338, 310345, 310348, 310351, 310354, 310381 310389, 310397, 310399, 310415, 310420, 310428, 310440, 310442 310446, 310471, 310475, 310480, 310481, 310489, 310494, 310503 310510, 310515, 310519, 310546, 310566, 310588, 310598, 310615 310630, 310632, 310645, 310670, 310687, 310693, 310702, 310703 310714, 310715, 310723, 310724, 310747, 310757, 310767, 310773 310777, 310779, 310788, 310791, 310799, 310801, 310802, 310809 310814, 310836, 310846, 310849, 310861, 310879, 310883, 310887

132 310901, 310906, 310910, 310932, 310945, 310950, 310958, 310965 310984, 310990, 310997, 310998, 311016, 311046, 311074, 311078 311100, 311105, 311111, 311113, 311117, 311123, 311140, 311141 311151, 311153, 311167, 311173, 311180, 311181, 311185, 311188 311206, 311236, 311249, 311255, 311258, 311266, 311269, 311278 311281, 311284, 311286, 311327, 311383, 311385, 311393, 311417 311423, 311435, 311466, 311483, 311484, 311491, 311497, 311532 311537, 311539, 311544, 311545, 311547, 311554, 311561, 311563 311572, 311573, 311576, 311586, 311592, 311596, 311600, 311603 311606, 311613, 311620, 311635, 311642, 311643, 311644, 311654 311656, 311667, 311668, 311669, 311677, 311684, 311685, 311688 311689, 311690, 311691, 311692, 311693, 311694, 311695, 311697 311698, 311699, 311710, 311713, 311725, 311727, 311734, 311735 311736, 311739, 311741, 311744, 311745, 311751, 311765, 311769 311775, 311781, 311787, 311788, 311790, 311792, 311793, 311794 311795, N22, Vandana

Table 2-19. Summary of drought resistant, moderately drought resistant, & drought sensitive genotypes expressing a combination of biomass, photosynthesis & WUEi in the URMC of 221 genotypes. Biomass, Photosynthesis & WUEi Drought resistant genotypes Moderately drought resistant genotypes Drought sensitive genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301408, 301418, 310102, 310210 310111, 310211, 311105, 311111 310045, 310100, 310351, 310440 310301, 310399, 310415, 310420 311123, 311435, 311545, 311561 310836, 311327, 311385, 311745 310471, 310489, 310503, 310598 311576, 311586, 311603, 311751 310632, 310901, 310906, 310965 311775, 311781 310984, 311113, 311151, 311153 311236, 311483, 311491, 311537 311572, 311606, 311635, 311643 311688, 311694, 311734, 311769 311788, N22, Vandana

133

134 B C

Figure 2-1. Growth conditions and phenotype of rice plants. A) Each pot was planted with one seedling and plants were grown in the controlled greenhouse conditions of 800-1000μmol PAR m-2 s-1 light intensity, photoperiod of 13 h light and 11 h dark and RH of ~65%. B) Drought tolerant genotype with well-watered (Left) and drought stress (Right) plant. C) Drought sensitive genotype with well-watered (Left) and drought stress (Right) plant.

A B

135 Figure 2-2. Environmental variation of biomass in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) Biomass under WW, shows non-significant

environmental variation among all batches where batch 1, 2, 3 & 4 showing minimum (min) range 9.145,9.123,9.145 & 9.147, Q1 9.28925, 9.3695,9.36125 & 9.2945, median 9.597,9.604,9.582 & 9.578,Q3 9.979,9.990,9.96 & 9.905 and maximum (max) range 10.598,10.625,10.598 & 10.365, respectively and B) Biomass under DS, showing non-significant environmental variation among all the batches, where batch 1,2, 3 & 4 showing min range 5.098, 5.156, 5.147, 5.098, Q1 5.918, 6.050, 6.004 & 5.978, median 6.643, 6.7, 6.650 & 6.574, Q3 7.601, 7.674, 7.610 &7.573 and max range 9.298, 9.364, 9.298 & 9.23, respectively.

A B

Figure 2-3. Environmental variation for photosynthesis in four batches based on common check genotype (NB) shown in boxplots with non-significant differences. A) Photosynthesis under WW, it shows non-

136 significant environmental variation among all batches where batch 1, 2, 3 & 4 showing min range 10.235, 10.554, 10.325 & 10.236 Q1 10.584, 10.661, 10.903 & 10.3410, median 11.121, 11.119, 11.184 & 11.241, Q3 12.498, 12.502, 12.581,12.606 and max range 15.365, 15.618, 15.988 & 16.022, respectively and B) Photosynthesis under DS, it shows non-significant environmental variation among all the batches where batch 1,2, 3 & 4 showing min range 1.632, 1.550, 1.498 & 1.52 Q1 2.649, 2.711, 2.640 & 2.684, median 3.016, 3.186, 3.016 & 3.125 Q3 3.407, 3.619, 3.364 & 3.369 and max range 3.66, 3.747, 3.862 & 3.756, respectively.

A B

137 Figure 2-4. Environmental variation in Instantaneous water use efficiency (WUEi) in four batches based

on common check genotype (NB) shown in boxplots with non-significant differences. A) WUEi under WW, shows non-significant environmental variation among all batches where batch 1, 2, 3 & 4 showing min range 1.515, 1.553, 1.553 & 1.510 Q1 1.948, 1.989, 1.989 & 1.558 median 2.238, 2.160, 2.160 & 2.162 Q3 2.495, 2.401, 2.401 & 3.067 and max range 3.156, 3.109, 3.109 & 3.371, respectively and B) WUEi under DS, it shows non-significant environmental variation among all the batches where batch 1, 2, 3 & 4 showing min range 0.491, 0.456, 0.456 & 0.444 Q1 0.806, 0.795, 0.795 & 0.800 median 0.925, 0.9422, 0.942 & 0.965 Q3 1.893, 1.921, 1.921 & 1.722 and max range 2.349, 2.419, 2.419 & 2.128, respectively.

A B

Figure 2-5. Environmental variation in stomatal conductance in four batches based on common check

138 genotype (NB) shown in boxplots with non-significant differences. A) Stomatal conductance under WW,

it shows non-significant environmental variation among all batches where batch 1, 2, 3 & 4 showing min range 0.082, 0.081, 0.081 & 0.081 Q1 0.087, 0.086, 0.088 & 0.086 median 0.132, 0.130, 0.131 & 0.131 Q3 0.166, 0.165, 0.166 & 0.165 and max range 0.234,0.224, 0.225 & 0.224, respectively and B) Stomatal conductance under DS, shows non-significant environmental variation among all the batches where batch 1, 2 , 3 & 4 showing min range 0.0324, 0.0336, 0.0324 & 0.0325 Q1 0.0435, 0.0457, 0.0450 & 0.0462, median 0.0785, 0.0793, 0.0760 & 0.0755 Q3 0.116, 0.115, 0.111 & 0.112 and max range 0.221, 0.220, 0.199 & 0.214, respectively.

A 18 Biomass_WW Biomass_DS % Reduction change 80 16 70 14 * 60 * 12 * 50 10 * * 40 8 * * * * * * * * 30 6 * * * * * * * * * * * 20 Biomass Biomass (g/plant) 4 * * * * * 10 2 Reduction % Biomass in

0 0

N22

311793 301418 311695 311078 311667 311792 310757 311185 311694 310849 311684 310481 310317 311795 310345 310932 311794 310801 311278 310998 310440 310219 310510 301379 311689 311669 311787 311692 Vandana

B 25 No of tillers_WW No of tillers_DS % Reduction change 70

139 60 20 * * 50 15 * 40 * * 10 30

No tillers of * * * * 20 5 * * * * * * * * 10

0 0 Reduction % No in tillers of

N22

310950 310714 301066 301419 301418 301408 311725 311635 311140 311327 311123 311669 310080 310020 310510 311255 310045 311491 310007 310861 310777 310932 310715 311693 311668 310703 311667 310723 Vandana Figure 2-6. Drought response of plant productivity traits (Plant biomass and No of tillers) in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of plant biomass (g/plant) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in plant biomass. B) Intrinsic value of no of tillers and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in number of of tillers under WW and DS. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

120 A RWC_WW RWC_DS % Reduction change 100 100 80 80 * * * * * * * * * 60 * * * * * 60 * * * * 40 40 * * * 20 20 * * * * * * *

0 0 Reduction % RWC in

Relative Relative WaterContent (%)

N22

311572 311667 311669 311788 310503 310226 311613 311684 301418 301408 310481 311435 310052 311654 310932 311781 311140 310715 310045 310007 310747 310200 311794 311775 310345 311643 310204 310338 Vandana

B 70 SPAD_WW SPAD_DS % Reduction change 90 80 60 70

140 50 60 * * * 50 40 * * * * * * * * * * * * 40 * * * * * * * * * * * 30 30 20 20 10 10 Chlorophyll Chlorophyll Content(SPAD) 0

0 -10

% Reduction Reduction % Chlorophyll in Content

N22

301379 311735 311603 311744 310111 311693 311781 310475 310809 310965 310134 311685 301419 301418 311188 311180 311123 311185 311117 311765 311286 311385 310007 310102 310702 310354 301408 311779 Vandana Figure 2-7. Drought response of leaf morphological traits, Relative Water Content and Chlorophyll Content, in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of relative water content (%) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in relative water content. B) Intrinsic value of chlorophyll (SPAD) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in chlorophyll (SPAD). Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

30 Photo_WW Photo_DS % Reduction change 120

)

1 -

s 25 100 2

- * m

2 20 * 80 * 15 * 60 * * * 10 40 * * * * * 5 * * 20 * * * * * * *

0 0 Reduction % Photosynthesis in Photosynthesis(µmol Photosynthesis(µmol CO

-5 N22 -20

310861 311278 301408 310415 311788 310958 310489 301418 311123 310630 310131 311592 311751 311269 311484 311644 311642 311188 310354 311739 310351 310381 311710 311795 311185 311794 311741 310566 Vandana

141 B

7 Trans_rate_WW Trans_rate_DS % Reduction change 80 O O

2 6 70 5 60 * * 50 4

* 40 )

1 3 - * *

s * * * * 30 2 * * * * Rate - 2 * * * * * * * * 20 m * * * 1 * * 10

0 0

N22

% Reduction Reduction % Transpirationin

Transpiration Rate (mMol H

310428 311643 310317 311532 301379 310397 311281 310389 310779 310415 311699 310998 301418 301066 311327 311284 310630 311278 301408 311255 311779 311688 311393 311600 310958 311181 310489 311725 311734

Figure 2-8. Drought response of physiological traits Photosynthesis and Transpiration rate in 30 out of -2 -1 the URMC of 221 rice genotypes. A) Intrinsic value of photosynthesis (µmol CO2 m s ) under WW and DS and lowest (≤ 25%), moderate (26-40%), and highest reduction (≥ 40%) in photosynthesis. -2 -1 B) Intrinsic value of transpiration rate (mMol H2O m s ) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in transpiration rate. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

A 25 WUEi_WW WUEi_DS % Increase change 200

O) 100 2 20 0 15 -100 *

/ / mMol H 10 -200 2 * * * * * * -300 Efficiency * 5 * * * * * * * * * * * * -400

0 -500 Increase % WUEiin

Instantanous Instantanous Water Use (µmol (µmol CO

-5 N22 -600

311795 311185 311435 310200 311173 310494 311781 311423 310990 311769 311141 310849 311537 310879 301408 310381 311596 310007 311710 311385 311327 310338 311794 311792 311739 311544 311741 310566 Vandana

0.5 Cond_WW Cond_DS % reduction change 100

)

1 -

S 0.45 2 - 80 0.4

142 O O M

2 0.35 60 0.3 0.25 * 40 0.2 20 0.15 * 0.1 * * * * * * * * * * * 0 0.05 * * * * *

0 -20

% % Reduction in Stomatal Conductance

Stomatal Stomatal Conductance (MolH

N22

311151 310630 311383 310767 301066 301418 301408 311699 311236 311123 311188 310238 310809 311613 310846 311725 310442 310906 311606 311078 310351 310515 310773 310354 310446 310087 311423 311417 310200 Figure 2-9. Drought response of physiological traits Instantaneous water use efficiency and Stomatal conductance in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value of WUEi (µmol CO2/ mMol H2O) under WW and DS and increase (%) and reduction in WUEi. B) Intrinsic value of stomatal -2 -1 conductance (Mol H2O m s ) under WW and DS and increase (%) and reduction in stomatal conductance. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

1200 100 Ci_WW Ci_DS % Reduction change 2 90 1000 80

) * 1 - 800 70 * *

mol 60 concentration concentration

2 600 * * 2 50 400 40 * * * concentration 200 * * * * 30 (µmol (µmol CO * * * * * * * * * 20 0

Intercellular Intercellular CO % Reduction Reduction % Intercellular in CO

-200 N22 0

310997 301066 311691 310389 310471 311697 311576 301408 301418 311787 310446 311141 310984 310566 311544 310503 310475 311286 310588 310809 310849 311173 311153 310990 310861 310510 311105 310210 Vandana

1 Ci/Ca_WW Ci/Ca_DS % Reduction change 100 2 2

143 0.9 90 0.8 80

* * 0.7 * * 70 (Ci/Ca) * * 0.6 * * 60 0.5 * * * * * * 50 /Ambinet /Ambinet CO * * 2 0.4 * 40 0.3 * 30 (Ci/Ca) (Ci/Ca) * * 0.2 * * * * * 20 0.1 * * * 10 * Reduction % in

0 0

N22

Intercellular Intercellular CO

310428 311249 310039 311188 311643 311572 310703 311667 311046 311689 310399 311794 311111 311547 310219 311790 311695 310471 310480 310801 310196 310238 310301 311180 311236 311117 311113 310220 Vandana

Figure 2-10. Drought response of physiological traits internal cellular CO2 concentration and the ratio of

internal cellular CO2 and ambient CO2 in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value -1 of Ci (µmol CO2 mol ) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Ci. B) Intrinsic value of the ratio of internal cellular CO2 and ambient CO2 (Ci/Ca) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Ci/Ca.

Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

A 2000 Fv'_WW Fv'_DS % Reduction change 70 1800 60 1600 50 1400 * * * 1200 40 1000 * * * * Fv' * 800 * * 30 * * * * * * * * 600 * * * * * * 20 400 * * * * 200 10 Reduction % FV' in

0 0

N22

311620 311561 311074 311111 311788 311545 311180 311576 311105 301418 311188 310204 311286 311693 311181 311140 310846 311544 310200 310788 311206 310415 311695 310510 311100 310566 310779 310546 Vandana

B 3000 Fm'_WW Fm'_DS % Reduction change 60 2500 50 144 40 2000 * * * * 30 1500 * * Fm' * * * * * * * * * * * * 20 1000 * * * * * * * * * 10

500 0 Reduction % Fm' in

0 -10

N22

301418 311644 310779 301066 311249 310715 310220 311691 310997 311697 311173 310446 311688 311385 310519 311255 311491 310354 310007 310724 311206 311016 310566 310200 310489 310510 310546 311100 Vandana

Figure 2-11. Drought response of physiological traits Fv’ and Fm’ in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value Fv’ under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Fv’. B) Intrinsic value of Fm’ under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Fm’. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

A 0.8 Fv'/Fm'_WW Fv'/Fm'_DS % Reduction change 50 0.7 0.6 * 40 * * * * 0.5 * * * 30 * * * * * * * 0.4 * * * * * * * * * * * * * * * 20 Fv'/Fm' 0.3 0.2 10 0.1

0 0 Reduction % Fv'/Fm' in

N22

311576 311547 311779 311689 310317 310007 310200 311105 301418 311685 310039 311596 310849 310389 311695 310415 310809 310515 310588 310788 310566 311181 310687 311787 310144 310481 310779 310211 Vandana

B 0.25 PhiPS2_WW PhiPS2_DS 90 80 0.2 * 70

145 60 0.15 * * * * 50 * * * * * * 40 0.1 * * * * * * * 30 * * * * * * * * 20 Photosystem 0.05 * * * * 10

0 0

Efficiency Efficiency Photosystem of II

% Reduction in Efficiency Reduction % Efficiency in of

N22

311699 311167 310984 311787 311258 301066 310020 311545 311151 311466 310723 310802 311117 310052 310471 311180 311689 311613 311181 311100 311249 301419 311113 311544 310515 310546 311173 310510 Vandana

Figure 2-12. Drought response to physiological traits (Fv’/Fm’ and the efficiency of Photosystem II) in 30 out of the URMC of 221 rice genotypes. A) It shows intrinsic value Fv’/Fm’ under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in Fv’/Fm’. B) It displays intrinsic value of the efficiency of photosystem II under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in the efficiency of photosystem II. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

A 160 ETR_WW ETR_DS % Reduction change 80

140 70

) 1 - 120 * 60

S * 2 - 100 * * * 50 M * - 80 * * * * 40 * 60 * * * * 30 40 * * * * * * * 20 (µmol (µmol e * * * * * * 20 10 Reduction % ETRin

Electron TransportRate 0 0

N22

310779 311684 310791 310219 310715 310023 311545 311603 310052 311794 311491 311790 310211 311688 311111 311532 311600 311181 311249 311113 311544 310546 301418 310645 311173 301419 310670 310510 Vandana

3.5 B qN_WW qN_DS % Reduction change 80 3 70 146 2.5 * * 60 * * * * 50 2 * * * * * * * * * * * * * * * * * * * 40 1.5 * * * 30 (qN) 1 20

0.5 10 % Reduction qNin

0 0

Photochemical Photochemical Quenching

N22

Non

311180 311046 310348 311765 310156 310345 311181 311173 301419 301418 311078 311698 310777 311286 311544 311140 310801 310809 301408 310510 310849 310945 311188 310566 310546 310779 311206 310984 Vandana

Figure 2-13. Drought response to physiological traits Electron transport rate and Non-photochemical quenching in 30 out of the URMC of 221 rice genotypes. A) Intrinsic value electron transport rate under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in electron transport rate. B) Intrinsic value of non-photochemical quenching under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in non-photochemical quenching. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

147

Figure 2-14. Heat map showing Pearson’s correlation coefficients for 17 plant productivity and morpho- physiological traits measured in the URMC of 221 rice genotypes screened under WW & DS. The correlations (-1 to +1) are colored either in blue (positive correlation) or pink (negative correlation).

CHAPTER-III CORRELATION OF RICE DROUGHT STRESS RESPONSE PHENES FOR PHYSIOLOGICAL TRAITS AND GRAIN YIELD IN THE USDA RICE MINI-CORE COLLECTION

148

ABSTRACT Rice (Oryza sativa L.) is an important cereal crop feeding half of the world’s population, and ranks second after wheat for the larger harvested area worldwide. It is a good food source supplying more than 50% of the calories consumed by world population. Based on various survey and statistics, rice yield will need to be increased ~30% over the next 20 years to meet food demands due to increased in human population and economic development. However, water deficit (drought stress) has become a serious problem in past few years and will be a problem for meeting food demands worldwide. Drought stress causes significant reduction in grain yield affecting several grain yield components. In this study, a comparison was made of rice productivity traits in the greenhouse and the field, under well-watered and drought stress conditions, which could be distinguished into a number of overlapping phenes. For this experiment, 81 rice genotypes of the USDA rice mini-core collection (URMC) were screened for the grain components such as panicle length, number of spikelets per panicle, number of filled grains and unfilled grains per panicle at the reproductive stage in the field conditions, and the physiological traits plant biomass, photosynthesis and instantaneous water use efficiency

(WUEi) at the vegetative stage in greenhouse conditions. The screens identified 5 rice genotypes out of the 81 URMC, including GSOR301066, GSOR301408, GSOR301418, N22 and Vandana, as consistently drought resistant with less than 25% reduction of grain yield components in the field under drought stress compared to WW plants, as well as less than 25% reduction in physiological traits at the vegetative stage under controlled drought compared to WW plants in the greenhouse. The drought resistant genotypes exhibited grain yield component phenes for longer panicle length, higher number of spikelets per panicle, higher number of filled grains per panicle, and lower number of unfilled grains per panicle under drought stress in the field, as well as higher amount of plant biomass and photosynthesis rate under greenhouse conditions at

149

vegetative stage. In contrast, the 22 rice genotypes GSOR entries 310007, 310020, 310039,

310100, 310144, 310338, 310345, 310348, 310351, 310381, 310389, 31044, 310515, 310566,

310588, 310645, 310687, 310724, 310773, 311793, 311794 and 311795 were categorized as drought sensitive exhibiting smaller panicle length, lower numbers of spikelets per panicle, lower number of filled grains per panicle and higher number of unfilled grains per panicle, under drought stress with more than 40 % reduction in all these grain yield traits under drought stress compared to WW plants in field conditions, as well as exhibiting lower amount of plant biomass, lower rate of photosynthesis and WUEi with more than 40% reduction in all these physiological traits in greenhouse conditions at vegetative stage, respectively. In the correlation analysis of yield components and vegetative traits, the major grain yield components of number of spikelets per panicle, number of filled and unfilled grains per panicle showed strong positive correlation with the major physiological traits of plant biomass, photosynthesis and WUEi. This information is useful to dissect the genetic architecture of different components of drought resistance in diverse rice genotypes, and use the genotypes within breeding programs for developing drought resistant and high yielding rice cultivars.

INTRODUCTION Rice (Oryza sativa L.) is one of the important cereal crops that feeds about half of the world’s population. It ranks second after wheat in harvested area worldwide, but it’s importance as a food crop is greater than other cereal crops as it is a good source of food energy per hectare, supplying more than 50% of the calories consumed by the world population (Khush, 2005;

Sindhu, 2013). The major rice producing countries are China, India, Indonesia, Bangladesh,

Vietnam, Thailand, Myanmar, Philippines, Brazil, Japan, USA, Pakistan, and the Republic of

Korea, and they export ~3.5% rice of total rice production to the U.S. Annually about 154 million hectares, which accounts for ~11% of world’s cultivated land, are cultivated for rice 150

production (Khush, 2000). Based on several surveys and statistical estimates, rice production needs to be increased ~30% over the next 20 years to meet food demands from the human population increase, and economic development (Sindhu, 2013). However, limited water availability for rice during the growing season can be a big problem to meet our food demands.

The rice crop requires a large of amount of water for completing its life cycle and it takes

~3,000 -5,000 liters to produce 1 kg (2.20462 pounds) of rice, which is ~ 2-3 times more than other cereals crops such as wheat and maize (Bouman, 2002). Water is an important factor for rice production, although limited water is available for rice growing regions (Bindraban, 2001).

Presently, water scarcity is an ever-increasing problem and has become the most serious constraint to rice production and yield stability in many rice-growing areas, particularly in rainfed ecosystems (Nguyen et al, 1997). Diverse and location-specific decreases in quality, reduced resourcing, and increase in competition between urban and industrial users are the major reasons for the depleting water availability (Postel, 1997).

The increasing water scarcity causes severe drought stress, threatening conventional rice cultivation all over world (Tuong and Bouman, 2003). Drought stress is one of the most threatening factors that affects all of the rice growth stages seedling, vegetative, and reproductive with the reproductive stage being most affected (Garrity and O’Toole, 1994; Ito et al, 2006;

Farooq et al, 2009; Bunnag and Pongthai, 2013), and reduces the yield component phenes which ultimately affect grain yield (Dey and Uppadhaya, 1996). Drought stress affects various physiological processes (photosynthesis, water use efficiency, stomatal conductance), crucial processes for plant growth and production of assimilates, that all cumulatively reduce rice grain yield. At the vegetative stage, water deficit reduces biomass and delays number of tillers and panicle initiation (Lilley and Fukai, 1994). Drought stress causes an increase in number of un-

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filled grain and reduction in overall grain production at the flowering stage (Sadeghi and Danesh,

2011). According to various studies, there is also a significant reduction in the number of tillers and panicles when drought is imposed at the tillering stage, and more significantly water stress causes a reduction in panicle length, number of panicles per plant, percentage of filled grains, percentage of un-filled grains, and 1000-seed weight affecting grain yield at the late vegetative and reproductive stages in rice (Amiri et al, 2009; Kupp et al, 1971). Studies by Ito et al. (2000) showed that drought stress might cause up to 77% reduction in grain yield, and longer periods of drought can reduce 90% of grain yield during the reproductive stage. Drought stress mainly affects various physiological processes such as photosynthesis and WUEi, reducing assimilate production and causes a slower rate of delivery of sugar to the reproductive organs resulting in failure of male gametophyte development that cause spikelet sterility in rice (Takeoka et al,

1992; Garrity and O’Toole, 1994; Brancher et al, 1996; Ito et al, 2000).

Due to the significant effects of drought stress on physiological traits at the vegetative stage and grain yield components at the reproductive stage, a deeper understanding of plant productivity traits, or physiological and grain yield component phenes, will help to dissect the genetic architecture and develop high yielding drought-resistant rice cultivars. In order to analyze the genetic variation in morpho-physiological traits at the vegetative stage, and grain yield components at reproductive stage, various studies have been conducted, but none have comprehensively studied the correlations between morpho-physiological traits at the vegetative stage under controlled drought conditions in the greenhouse and grain yield components at the reproductive stage under drought stress in field conditions in rice. Several previous studies have shown the correlation between total grain yield and its components such as under well- watered and drought stress conditions in the field (Blum, 1988; Garrity and O’Toole, 1994; Yue et al,

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2006; Sellamuthu et al, 2015). Spikelet fertility has been shown to be strongly correlated with total grain yield under drought at reproductive stress (Garrity and O’Toole, 1994).

There are many rice genotypes naturally evolved that have higher water use efficiency, photosynthesis, and biomass which correlating with grain yield components. These rice genotypes can be used to improve other rice cultivars, which have higher yield but lower WUE and drought tolerance, for improving their grain yield under field drought stress (Impa et al,

2005). Consequently, investigating the diverse core collection of rice genotypes for morpho- physiological and grain yield under drought stress, and an analysis of the correlation between the traits will provide information for developing drought resistant and high yielding cultivars.

In order to study the potential diverse rice genotypes for high grain yield under drought stress, The USDA rice mini-core collection (URMC) of 217 rice genotypes is a good diverse pool harboring valuable genes, which was developed from 1,794 accessions of the main core collection representing over 18,000 accessions in the USDA global gene bank of rice (Yan et al,

2007), originating from 76 different countries covering 15 geographic regions. The URMC of

217 accessions of Oryza sativa and related species has been used to study genetic diversity based on phenotypic traits and molecular marker variation (Agrama et al, 2009), and the genetic diversity among all these accessions indicates a high probability of selecting specific genes of interest from the gene pool (Garris et al, 2005). Therefore, after dissection of the drought resistance phenotypes, this collection can become an excellent resource of natural variation for morph-physiological and plant productivity traits involved in WUE, photosynthesis, biomass and grain yield components contributing total grain yield.

Until date, there is no study on the use of the URMC of 221 rice genotypes to screen for

WUE, photosynthesis and biomass in the greenhouse and grain yield under field conditions for

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drought stress. In this study, we screened and analyzed the URMC for morpho-physiological and plant productivity traits in the greenhouse, and grain yield component traits under field conditions for drought stress response, to understand the relation between morpho-physiological and grain yield components to improve elite rice genotypes for higher yield.

MATERIALS AND METHODS

Plant materials

The URMC of 214 accessions (Out of 217 accessions, 3 accessions were not delivered) and 7 adapted cultivars to Arkansas, totaling 221 rice genotypes (Table 2-1) were collected from the USDA ARS Dale Bumpers National Rice Research Center, Stuttgart, Arkansas, USA

(Agrama et al, 2009) in summer 2013.

Nursery preparation

The URMC of 221 rice genotypes is a very diverse collection and each genotype has a slightly different flowering time. Based on flowering time, the URMC of 221 rice genotypes were planted in a greenhouse nursery to synchronize flowering at the same time. According to the synchronization plan, the late flowering genotypes were germinated first while the early flowering genotypes were germinated late, synchronizing flowering for all the genotypes at the same time. The nursery was prepared with 50 seeds per genotype in 12.7 cm x 12.7 cm PVC pots filled with silt loam soil in the greenhouse at Altheimer Laboratory, Fayetteville in 2015

(Figure 3-1A).

Field preparation and nursery transplanting

The field experiment was conducted under well-watered (WW) and drought stress (DS) conditions at Araksnas Agricultural Research & Extenson Center, University of Arkansas,

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Fayetteville during summer 2015. For this study, the field plot was prepared with the size of 20 m x 18 m that was completely tilled before making levees and kept dry for 15 days. After 15 days of drying, the field was tilled again, levelled to zero slope and compacted for holding water for the experiment. The whole field was divided in two equal sized plots by one levee. The area of each plot was 17 m x 15 m. After the field was divided, a 25-cm high transplanting bed was prepared with a 30-cm of buffer created around each plot for adjusting the moisture error when drought was imposed. The experimental design used was a randomized complete block design

(RCBD) with two blocks and two treaments (WW & DS). Before 2-days of transplanting, both plots were flooded and after 25-days of germination, 10 equal sized seedlings of each genotype were transplanted on the bed in each plot (Figure 3-1B). The row-to-row and plant-to-plant spacing of 25 cm x 15 cm in both plots was maintained. Urea was applied at the rate of 135 pound per acre in three splits, the first after 10-15 days of transplanting, the second at maximum tillering, and the third at panicle initiation in both plots. To control weeds, the weeds were removed manually from both plots to control.

Drought stress screening at reproductive stage

For drought screening, the drought stress plot was drained after all synchronized genotypes started booting (R2 growth stage) and irrigation was withheld to impose drought stress at the reproductive stage (Figure 3-1D), while the other plots were kept flooded all the time and served as a WW plot (Figure 3-1C). Three tensiometers were installed at three spots in the drought-stressed plot just after draining, the first was in the beginning of the plot, the second in the middle, and the third at the end of the plot. Once the soil tension fell down to -70 kPa at 30- cm soil depth, life-saving irrigation was provided thereafter through flash flooding in the drought

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stressed plot (Figure 3-1E), and water was drained after 24h to impose the next cycle of drought stress (Kumar et al, 2014a).

Data recoded for grain yield and its components

In this study, only 81 genotypes out of the URMC flowered and experienced drought stress at the reproductive stage (R2), while 140 rice genotypes did not flower synchronously as they had larger difference in flowering time. After harvesting of the 81 rice genotypes grown under WW & DS conditions, data were recorded from randomly selected 5 panicles per genotype, for panicle length (cm), number of spikelets per panicle, number of filled grains per panicle, number of un-filled grains and their reduction calculated compared to WW plants using

IRRI’s standard methods (IRRI, 1994).

Drought stress screening at vegetative stage in greenhouse conditions

The URMC of 221 rice genotypes were screened for morpho-physiological and productivity traits at the vegetative stage (V7) using the 10-days gravimetric drought stress approach in the greenhouse at the Altheimer Laboratory, University of Arkansas, Fayetteville.

All methods and statistical analyses have been described in Chapter 2. For the morpho- physiological screening data at the vegetative stage, only 81 rice genotypes from the whole

URMC, which have grain yield components data from field stress, were used for investigating the relationship between grain yield components and morph-physiological traits.

Statistical analysis

The experiment was conducted in a randomized complete block design (RCBD) with two blocks and two treaments (WW & DS). Data for grain yield componets were analyzed by analysis of variance (ANOVA) using JMP version 12.0. The statistical model included the

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effects of genotype, treatment (WW & DS), and interaction of genotype and treatment. The

Tukey’s HSD was used to compare the means of treatments (WW & DS) among all the genotypes for significant effects (Tukey’s HSD, P < 0.05) using JMP version 12 as Tukey’s

HSD can determine slight difference between the means. The correlation analysis was also performed using JMP version 12.0 to study correlations between grain yield components at reproductive stage under field drought stress, and morpho-physiological trait analysis at vegetative stage under 10- days gravimetric drought stress.

RESULTS AND DISCUSSION

Effects of drought stress on grain yield component phenes in field conditions

The main purpose of this screening study in the field is to investigate the grain yield potential of rice genotypes grown under drought stress. The data could be potentally used to improve cultivars for grain yield under drought stress. Various studies have shown that grain yield is severely affected by drought stress at the reproductive stage (Kumar et al, 2014a). The grain yield potential in cereal crops depends on the performance of many grain yield components under drought stress. In rice genetics and breeding studies, high grain yield potential and yield stability of diverse rice germplasm is evaluated under water deficit conditions in different environments (Sleper and Poehlman, 2006). The yield stability and grain yield potential of rice genotypes are the most important traits to study for testing drought resistance under field drought stress conditions (Sellamethu et al, 2015). Identification of high grain yield potential of a genotype under normal conditions is important as a base to find the ability of high yield under drought stress conditions. Severity of drought stress is an important parameter, which determines the selection of effective methods based on yield loss under drought stress, as compare to well- watered conditions, which have been used for screening of rice genotypes for drought tolerance

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(Brukner and Frohberg, 1987). Three growth stages seedling, vegetative and reproductive, are strongly and differently affected by drought stress, which severely affects grain yield components reducing grain yield in rice (Dey and Uppadhaya, 1987). At the reproductive stage, drought stress causes flower abortion, as well as a reduction in panicle length, number of spikelets per panicle, number of filled grains and an increase in the number of un-filled grains

(Hsiao et al, 1976). In this study we screened 81 rice genotypes of the URMC for grain yield components such as panicle length, number of spikelets per panicle, number of filled grains and un-filled grains per panicle in field conditions under drought stress at the reproductive stage.

Based on the level of reduction in the grain yield components, rice genotypes were classified into three categories - 1) Drought resistant (≤ 25% of reduction), 2) moderately-drought resistant (25-

40% of reduction), and 3) drought sensitive (≥40% of reduction). This criterion was used for each of the grain yield components and follows that of De Freitas et al. (2016).

Panicles are the part of rice plants, which use assimilates produced by photosynthesis for their growth and development (Liu et al, 2010). Several research studies have shown that number of panicles and their length have a strong relationship with many grain yield components (IRRI,

1994). Panicle length (PL) is one of the important grain components, which contributes in increasing grain yield in cereal crops, and is affected by drought stress that reduces PL. It is therefore necessary to screen the diverse rice genotypes by measuring panicle length under drought stress. To determine drought stress effected PL, we screened 81 rice genotypes of the

URMC for PL under drought stress in field conditions. After harvesting all genotypes, the panicle length was measured and compared to WW plants. Based on the analysis of variance, there were significant differences between WW and DS treatments in all these grain yield components among all 81 rice genotypes of the URMC, and there was an interaction between

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treatment (WW & DS) and rice genotype, which shows reduction in PL in comparison to WW plants. After analysis of drought screening data, 67 rice genotypes showed differing levels of drought resistance with panicles that were less than 25% of the WW planicle length, 13 rice genotypes exhibited moderate drought resistance with medium panicles which were 25-40% less of the WW, while only 1 genotype showed drought sensitivity with a panicle shorter than 40% of the length of the WW panicles. (Table 3-1). All 81 rice genotypes showed % reduction in panicle length under DS, but only 30 representative rice genotypes are shown in Figure 3-2A. In these 30 representative rice genotypes, the first 11 rice genotypes namely 311173, 310489, 310351,

311677, 310102, N-22, 311654, 310773, 311690, 310317, 311016, 310338, Vandana, 311284,

311141 and 310345 showed longer panicle length under DS with less than 25% reduction, the next 13 rice genotypes 310220, 310779, 311592, 310039, 311668, 310211, 310442, 311793,

310144, 311255, 310747, 310814 and 310007 exhibited medium panicle length under WW &

DS with 25-40% reduction and the last rice genotypes named 311236 showed shorter panicle length under WW & DS with more than 40% reduction in panicle length as compare to WW plants, respectively (Figure 3-2A). Under the drought resistant category, 310489, 310351,

310102, N22, 311654, 311690, 311016, 310338, Vandana, and 311284 showed 24.94, 23.50,

25.4, 19.98, 20.04, 22.70, 21.6, 23.44, 23.88 and 24.76 cm long panicle under DS with 1.11%,

3.53%, 3.98%, 4.31%, 4.66%, 6.04%, 6.08, 7.13%, 7.72% and 7.88% reduction in panicle length as compare to WW plants, respectively. Under the moderate resistant category, 310779, 311592,

310271, 310442, 311793, 311255 and 310007 exhibited 17.3, 16.18, 15.96, 16.48, 14.0, 17.28 and 17.7 cm long panicle length under DS and 25.17%, 25.50%, 26.85%, 27.90%, 28.13%,

31.15% and 34.92% reduction in panicle length as compare to WW plants, respectively (Figure

3-2A). Under the drought sensitive category, 311236 showed 12.44 cm long panicle under DS

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and 40.81% reduction in panicle as compare to WW plants (Figure 3-2A). Based on the correlation matrix, panicle length under WW conditions is positively correlated with the number of un-filled grains per panicle under WW conditions while panicle length under DS conditions is negatively correlated with spikelets per panicle under both WW & DS conditions, number of filled grain per panicle under WW conditions and number of un-filled grains per panicle under

DS conditions (Figure 3-6). However, panicle length under DS conditions is positively correlated with panicle length under WW, spikelets and number of filled grains per panicle under both WW

& DS and number filled grains per panicle under DS conditions (Figure 3-6).

Along with panicle length, the number of spikelets per panicle is also an important component determining grain yield in rice. The number of spikelets per plant is the sum of spikelets on each panicle. Research studies on grain yield components have shown natural variation in the number of spikelets per panicle per plant in rice, where the number of spikelets per panicle is higher in the early emerging tillers (Counce et al, 1996), and reduces from main tiller towards the primary and secondary tillers (Kuroda et al, 1999). Drought stress affects panicle initiation, causing spikelet sterility that reduces number of spikelets per panicle, and slow grain filling resulting in lower grain yield in rice (Kamoshita et al, 2004; Botwright Acuna et al,

2008; Liu et al, 2010). To analyze the natural variation in diverse rice genotypes for the number of spikelets per panicle under DS, we screened the 81 diverse rice genotypes of the URMC for the number of spikelets per panicle in the field treatment conditions. After harvesting, the number of spikelets per panicle were counted in comparison to WW plants. Based on the analysis of variance, there were significant differences between WW & DS plants for number of spikelets per panicle among all the genotypes, and there was an interaction found between treatment and genotype, which shows a significant reduction in the number of spikelets per

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panicle as compare to WW plants. Analysis of the screening data showed 28 rice genotypes that are drought resistant, 13 rice genotypes categorized as moderately drought resistant having medium number of spikelets per panicle, and 40 rice genotypes that showed drought sensitivity; having respectively more numbers, medium numbers, and least numbers of spikelets per panicle under WW & DS, with less than 25%, 25-40% with more than 40% reduction in number of spikelets per panicle as compare to WW plants (Table 3-2). All 81 rice genotypes showed a reduction in number of spikelets per panicle under DS, with 30 representative rice genotypes shown in Figure 3-2B. In these 30 representative rice genotypes, the first 10 rice genotypes namely 301418, 311561, 301066, 311151, 311385, N-22, 301408, Vandana, 310317, and 310420 showed more numbers of spikelets per panicle under WW & DS with less than 25% reduction, the next 10 rice genotypes 310846, 311788, 311483, 310515, 311690, 311141, 311258, 311603,

311181, and 310724, exhibited medium numbers of spikelets per panicle under WW & DS with

25-40% reduction, and the last 10 rice genotypes 311787, 311592, 311532, 311677, 311668,

310007, 311417, 311284, 311286, and 310039, showed least numbers of spikelets per panicle under WW & DS, with more than 40% reduction in number of spikelets panicle as compare to

WW plants, respectively (Figure 3-2B). Under the drought resistant category, 301418, 311561,

301066, 311151, N22, 301408, Vandana, and 310420 showed 109.4, 121.4, 71.2, 221.4, 99.2,

123.9, 220.4, and 150.2 numbers of spikelets per panicle under DS and 1.45%, 1.94%, 3.52%,

3.99%, 5.34%, 7.12%, 13.56, and 24.29% reduction in number of spikelets per panicle as compare to WW plants respectively; while under moderate resistant category, 311788, 310515,

311690, 311258, 311603, and 310724 exhibited 86.4, 66.6, 73.2, 85.6, and 75.8 numbers of spikelets per panicle under DS and 29.98%, 31.65%, 32.18%, 34.72%, 35.84%, and 39.84% % reduction in number of spikelets per panicle as compare to WW plants, respectively (Figure 3-

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2B). Under the drought sensitive category, 311787, 311677, 310007, 311417, and 310039 showed 37.6, 24.8, 26.3. 28.75, and 5.6 numbers of spikelets per panicle under DS and 65.82%,

67.87%, 71.35%, 75.35%, and 88.70 reduction in number of spikelets per panicle as compare to

WW plants, respectively (Figure 3-2B). In correlation analysis shown in the matrix, the number of spikelets under both WW & DS conditions is positively correlated with number of filled, unfilled grains per panicle under both WW & DS conditions, panicle length under DS conditions, and number of spikelets per panicle under DS conditions while number of spikelets per panicle under DS conditions is negatively correlated with panicle length under WW conditions (Figure

3-6). However, the number of spikelets per panicle under WW conditions is positively correlated with panicle length under DS conditions (Figure 3-6).

Number of filled grains per panicle is the most important grain yield component determining total plant grain yield based on the sum of the filled grain numbers after anthesis in plants. Water deficit at the reproductive stage is the most serious and devastating to grain yield, which has a major effect on pollination causing flower abortion and abscissions ultimately reducing the number of filled grains in rice (Hsiao et al, 1976). To investigate the drought resistance in rice genotypes based on lower reduction in number of filled grains per panicle, the

81 diverse rice genotypes of the URMC were screened under drought stress in the field conditions. After harvesting all the genotypes at maturity, the number of filled grains per panicle was counted for the WW and DS treated plants. Based on statistical analysis of variance, significant differences were found between WW & DS plants for number of filled grains per panicle among the 81 rice genotypes of the URMC and a reduction in number of filled grains per panicle as compare to WW plants. There was also an interaction between treatment and genotype, which shows significant differences between WW & DS plants, and reduction in the

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number of filled grains per panicle. After analysis of the screening data, 5 rice genotypes were found to show drought resistance and 76 rice genotypes that showed drought sensitivity, having more numbers and less numbers respectively of filled grains per panicle under DS, with less than

25% with more than 40% reduction in number of filled grains per panicle, respectively (Table 3-

3). All 81 rice genotypes of the URMC showed significant reduction in number of filled grains per panicle under DS but only 30 representative rice genotypes are shown in Figure 3-3A. In these 30 representative rice genotypes, only 5 rice genotypes namely 301418, N 22, 301408,

Vandana, and 301066 showed higher numbers of filled grains per panicle under WW & DS with less than 25% reduction, and 25 rice genotypes 311151, 311180, 311181, 311561, 310428,

311656, 311620, 310687, 311236, 311677, 310144, 310442, 310102, 311393, 310348, 310345,

310338, 311547, 310007, 310381, 311385, 310566, 311417, 311284 and 311794 exhibited the lower range of numbers of filled grains per panicle under WW & DS, with 40% reduction in number of filled grains per panicle as compare to WW plants, respectively (Figure 3-2A). Under the drought resistant category, N22, 301418, Vandana, 301408 and 301066 showed 65.2, 100.0,

102.5, 195.4 and 48 numbers of filled grains per panicle under DS and 2.43%, 4.39, 2.29%.

7.41% and 2.98% reduction in number of filled grains per panicle as compare to WW plants, respectively while under moderate drought resistant category no genotype was found classified

(Figure 3-3A). Under drought sensitive genotypes, 311393, 310338, 310007, 310381, 311385 and 311417 exhibited 3.0, 2.8, 2.55, 2.5, 2.0, and 2.5 numbers of filled grains per panicle under

DS and 95.61%, 95.85%, 96.08%, 96.63%, 96.73%, 96.73% and 96.74% reduction in number of filled grains per panicle as compare to WW plants, respectively (Figure 3-3A). Based on the correlation matrix analysis, number of filled grains per panicle under both WW & DS conditions is positively correlated with panicle length under WW & DS, the number of spikelets per panicle

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under WW & DS, and the number of un-filled grains per panicle under both WW & DS conditions (Figure 3-6).

Along with number of filled grains, the number of un-filled grains per panicle is also one of the important grain yield components that is inversely proportional to number of filled grains affected by adverse environmental conditions in cereal crops. Drought stress causes an increase in numbers of un-filled grains per panicle, and a higher percentage of number of un-filled grains in the reproductive stage in rice (Kumar et al, 2006; Devatgar et al, 2009). Higher numbers of un-filled grains per panicle under different measures of drought stress, indicates drought sensitivity in rice. For the identification of drought resistant genotypes based on the lowest numbers of un-filled grains per panicle under drought stress, we screened 81 rice genotypes of the URMC for number of unfilled grains per panicle under field conditions. After harvesting of all the genotypes, the number of unfilled grains per panicle was counted per plant from drought stressed and WW plots. Based on the analysis of variance, significant differences were found between WW & DS plants for number of unfilled grains per panicle among all the rice genotypes, and also an interaction between treatment and genotype, which shows an increase in number of unfilled grains per panicle as compare to WW plants. After analysis of the stress screening data, 11 rice genotypes showed consistent drought resistance response with a lower number of unfilled grains per panicle under DS with less than 25% increase, and 62 rice genotypes showed drought sensitivity with more numbers of unfilled grains per panicle under

DS with more than 40% increase in numbers of unfilled grains per panicle while there were 8 rice genotypes in which number of unfilled grains per panicle showed reduction because these genotypes had more filled grains than unfilled grains per panicle (Table 3-3). All 81 rice genotypes of the URMC exhibited significant increase in numbers of unfilled grains per panicle

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under drought stress conditions in comparison to WW conditions, with 30 representative rice genotypes were shown in Figure 3-3B. In these 30 representative rice genotypes, 10 rice genotypes Vandana, 301418, 311668, N-22, 310020, 301066, 311793, 301408, 310998 and

310102 showed lowest numbers of unfilled grains per panicle with less than 25% increase under

DS, and 20 rice genotypes namely 311654, 311795, 310687, 311181, 310144, 311656, 310906,

311603, 311180, 311255, 310420, 311483, 310814, 311491, 311690, 311141, 310777, 311547,

310428 and 311236 exhibited highest numbers of unfilled grains per panicle with more than 25% increase in number of unfilled grains per panicle as compare to WW plants, respectively (Figure

3-3B). Under the drought resistant category, Vandana, 301418, 311668, N22, 301066, and

301408 showed 25.0, 11.0, 27.5, 34.0, 15.2 and 15.4 and 3.3%, 3.77%, 4.96%, 6.25%, 13.43% under DS conditions and 16.67% increase in number of unfilled grains per panicle as compare to

WW plants, respectively, while under the drought sensitive category 311236, 310428, 311547, and 310777 exhibited 66.8, 110.6, 51.2, 50.4 numbers of unfilled grains and 3240%, 2533.34%,

2460% and 846.15% increase in numbers of unfilled grains per panicle as compare to WW plants, respectively (Figure 3-3B). In correlation analysis among all the grain yield components, the number of unfilled grains per panicle under WW conditions is positively correlated with panicle length, the number of spikelets per panicle, and number of filled grains per panicle under

WW and DS conditions (Figure 3-6), while the number of unfilled grains per panicle under DS conditions is negatively correlated with panicle length under DS conditions (Figure 3-6).

Drought stress response phenes for plant productivity and physiological traits evaluated in greenhouse conditions Water deficit is an important environmental factor that affects several morpho- physiological processes, inducing various physiological responses helping plants to adapt to adverse environmental conditions (Pandey et al, 205). For improving water use in plants, these

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morpho-physiological processes should be optimized under drought stress (Serraj et al, 2009).

Understanding of individual physiological response phenes of rice plants to drought stress may play a critical role to dissect drought resistance mechanisms in rice (Pandey et al, 2015).

Response of plants to drought stress is can be measured by the effects of drought on plant growth and development, affecting elongation and expansion growth ultimately reducing rice yield

(Anjum, et al, 2003; Kusaka, et al, 2005; Shao et al, 2008). Grain yield affected by drought stress that shows responses to shortening the grain filling time (Shahryari et al, 2008), gas exchange components in the leaf, the size of the source and assimilate translocation (Farooq et al, 2009).

The reduction in grain yield under drought stress is caused by reduction in CO2 assimilation rate reducing plant biomass and ultimately a decrease in WUEi leading to a reduction in plant growth and productivity (Anjum et al, 2011). In this study, 81 rice genotypes of the URMC were screened for the major plant productivity trait biomass, and morpho-physiological traits photosynthesis and WUEi under drought stress, at the vegetative stage in greenhouse conditions.

At the end of the stress period, plant biomass was recorded, photosynthesis and WUEi measured using the portable photosynthetic system LICOR 6400XT. Based on statistical analysis of variance, there were significant differences between WW & DS stress plants for all these three- plant productivity and physiological traits and there was an interaction between treatment and genotype, which shows reduction in all these traits as compare to WW plants.

For plant biomass in the response to drought, there are 25 rice genotypes that showed drought resistance, 38 rice genotypes exhibited moderate drought resistance and 18 rice genotypes showed drought sensitivity having less than 25%, 25-40, with more than 40% reduction in plant biomass (Table 3-5A). All 81 rice genotypes of the URMC showed reduction in plant biomass out of which 30 representative rice genotypes are shown in Figure 3-4A. In

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these 30 rice genotypes, the first 10 rice genotypes 310747, 301408, 311668, 310428, 301418,

N22, 311620, 310757, 310906 and Vandana showed higher biomass under WW and DS conditions with less than 25% reduction, the next 10 rice genotypes 311466, 311794, 311111,

310039, 311690, 311596, 310846, 311016, 311284 and 310814 exhibited medium amount of biomass under WW & DS conditions and 25-40% reduction, and the last 10 rice genotypes

311266, 311181, 310351, 311393, 311385, 311258, 311140, 311532, 310998 and 311787 showed lowest biomass under WW & DS conditions with more than 40% reduction in biomass as compare to WW plants, respectively (Figure 3-4A). Under the drought resistant category,

301408, 301418, N22, and Vandana showed 13.51g, 12.68g, 11.02g and 11.78g biomass under

DS conditions with 8.45%, 20.01%, 21.67% and 24.17% reduction in biomass, respectively while under the moderate drought resistant category, 311794, 310039, 311690 and 310814 exhibited 7.04g, 5.07g, 4.83g and 4.69g biomass under DS conditions with 28.92%, 33.12%,

33.98% and 40.78% reduction in biomass as compare to WW plants, respectively (Figure 3-4A).

Under the drought sensitive category, 31181, 31140, and 311787 showed 2.68g, 2.77g and 2.52g biomass with 47.33%, 52.65% and 66.13% reduction in biomass as compare to WW plants, respectively (Figure 3-4A). Based on correlation analysis, plant biomass under WW & DS conditions is negative correlated with photosynthesis and WUE under WW & DS conditions

(Figure 3-6).

In the response of photosynthesis to drought, 41 rice genotypes showed drought resistance, 13 rice genotypes exhibited moderate drought resistance and 27 rice genotypes showed drought sensitivity having higher, medium, and lower rate of photosynthesis under DS conditions with less than 25%, 25-40, with more than 40% reduction in photosynthesis as compare to WW plants, respectively (Table 3-5B). All 81 rice genotypes of the URMC showed

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reduction in photosynthesis with 30 rice genotypes represented in Figure 3-4B. In these 30 rice genotypes, the first 10 rice genotypes 301408, N22, 301066, Vandana, 311181, 311173, 310906,

311677, 311258 and 301418 showed higher rate photosynthesis under WW and DS conditions with less than 25% reduction, the next 10 rice genotypes 311140, 311656, 311620, 310715,

311684, 311111, 311668, 310757, 310747 and 311561 exhibited medium rate of photosynthesis under WW & DS conditions with 25-40% reduction, and the last 10 rice genotypes 311690,

311596, 310588, 310007, 310100, 310351, 310381, 311795, 311794 and 310566 showed the lowest rate of photosynthesis under WW & DS conditions with more than 40% reduction in photosynthesis as compare to WW plants, respectively (Figure 3-4B). Under the drought resistant category, 301408, N22, 301066, Vandana, 311181, 311173, 310906 and 301418

-2 -1 showed 16.96, 13.41, 11.81, 22.25, 26.90, 24.39, 23.19, and 11.75 µmol CO2 m s photosynthesis under DS, and 3.99%, 7.02%, 7.73%, 8.93%, 9.92%, 13.86%, 15.90% and

20.83% reduction in photosynthesis, respectively; while under the moderate drought resistant

-2 -1 category, 311656, 311620 and 310757 exhibited 6.54, 6.98 and 8.63 µmol CO2 m s photosynthesis under DS conditions and 31.59%, 34.60%, 36.48% and 39.87% reduction in photosynthesis as compare to WW plants, respectively (Figure 3-4B). Under drought sensitive

-2 -1 category, 310588, 311794 and 310566 showed, 1.42, and 0.78 µmolCO2 m s photosynthesis under DS and 66.05%, 77.67% and 90.72% reduction in photosynthesis as compare to WW plants, respectively (Figure 3-4B). Based on correlation analysis, photosynthesis under WW &

DS conditions is positively correlated with WUEi while negatively correlated with biomass under WW & DS conditions (Figure 3-6).

For WUEi, there are 58 rice genotypes that show drought resistance, and 23 genotypes exhibiting drought sensitivity, having higher and lower WUEi under WW & DS conditions with

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0.57 to 58.26% increase and 0.64-495% reduction in photosynthesis as compare to WW plants, respectively (Table 3-6A). All 81 rice genotypes of the URMC showed changes in WUEi but only 30 rice genotypes are represented in Figure 3-5. In these 30 rice genotypes, the first 15 genotypes named 311173, 310990, 311141, 311466, 311491, 311180, 310846, 311236,

Vandana, 301408, N22, 311016, 310317, 301066 and 301418 showed higher WUEi under WW and DS conditions with 0.57-58% reduction, and 15 rice genotypes 310348, 310345, 310588,

311654, 311417, 310724, 310020, 310381, 311596, 310007, 311385, 310338, 311794, 311795 and 310566 exhibited lowest WUEi under WW & DS conditions with 0.63-495% reduction in

WUEi as compare to WW plants, respectively (Figure 3-5). Under the drought resistant category,

311173, 310990, 311141, 311466, 311180, 310846, Vandana, 301408, 301418, and 301066 showed 18.17, 10.92, 14.65, 17.32, 14.46, 12.47,15.41, 9.78, 6.35 and 5.24 µmol CO2/mMol

-2 -1 H2O m s WUEi under DS and 58.26%, 52.25%, 31.80%, 50.00%, 46.46%, 45.95%, 27.47%,

21.94 and 0.57% reduction in WUEi respectively, while under the drought sensitive category,

310338, 311794, 311795 and 310566, exhibited 1.06, 0.82 and 8, 1.09 and 0.705µmol

CO2/mMol H2O WUEi under DS conditions and 79.85%, 88.34%, 94.56%, and 495.01% reduction in WUEi as compare to WW plants, respectively (Figure 3-5). Based on correlation analysis, WUEi under WW & DS conditions is positively correlated with photosynthesis while negatively correlated with biomass under WW & DS conditions (Figure 3-6).

Relationship between grain yield components in field conditions and physiological traits in greenhouse conditions under drought stress The degree of relationship or correlation between any traits is important in plant breeding as well as in genetics for investigating rare alleles in the population. It helps plant breeders to do indirect selection in diverse germplasm and provides a basic understanding of the traits (Konate et al, 2016). The correlation matrix analysis is shown in Figure 3-6 with numerical

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values, indicating the relationship between grain yield components under WW & DS in the field conditions and physiological traits in the vegetative stage under WW & DS in greenhouse conditions. However, plant productivity traits such as plant biomass and physiological traits photosynthesis and WUEi, are the major traits showed strong correlation with grain yield components in the correlation matrix (Figure 3-6).

Based on correlation analysis, panicle length under WW conditions is correlated with plant biomass, photosynthesis and WUEi showing R2 = 0.105, 0.223, and 0.087 (Figure 3-7A).

Under DS conditions, panicle length showed imoroved correlation with plant biomass, photosynthesis and WUEi showing R2= 0.227 0.168, and 0.0229 (Figure 3-7B). The results suggest that based on R2 values under DS conditions, the panicle length performed slightly better than panicle length under WW in correlation with plant biomass, photosynthesis and WUEi.

Several studies have shown a positive correlation between panicle length and number of panicle per plant with plan biomass (Zhou et al, 2010).

In regression and correlation analysis, number of spikelets per panicle under WW conditions is correlated with photosynthesis and WUEi showing R2 = 0.634 (63.4%) and 0.657

(65.7%) respectively and it is also correlated with plant biomass exhibiting R2= 0.150 (Figure 3-

8A) but with poor correlation. Under Ds conditions, number of spikelets per panicle showed improved correlation with photosynthesis and WUEi showing R2 = 0.655 (65.5%) and 0.738

(73.8%) respectively, and it is also correlated with plant biomass exhibiting R2 = 0.0129 (Figure

3-8B). In this study, results showed that number of spikelets per panicle under WW & DS conditions exhibited a relationship with photosynthesis and WUEi. The correlation between number of spikelets and physiological traits (photosynthesis and WUEi) under DS conditions is better than under WW conditions. Therefore, overall, photosynthesis and WUEi contributes in

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increasing the number of spikletes per panicle enhancing grain yield in rice. Until date, no study was found that could show the similar results.

Number of filled grains per panicle under WW conditions is correlated with plant biomass, photosynthesis and WUEi showing R2 of 0.121, 0.643 (64.3%) and 0.684 (68.4%), respectively (Figure 3-9A). Under DS conditions, number of filled grains per panicle is also correlated with photosynthesis, WUEi and plant biomass showing R2 = 0.588 (58.8%), 0.658

(65.8%), and 0.279 (27.9%), respectively (Figure 3-8B). In this study, results showed that the number of filled grains per panicle under WW conditions exhibited better relationship with photosynthesis and WUEi than DS conditions while plant biomass showed good relationship under DS condtions than WW conditions. The correlation between number of filled grains per panicle and physiological traits (photosynthesis, WUEi and biomass) under DS conditions is better than under DS conditions. Therefore, overall, photosynthesis, WUEi and plant biomass under DS contributes in increasing the number of filled grains per panicle enhancing grain yield in rice. Until date, no study was found that could show these kind of similar results.

The number of unfilled grains per panicle under WW conditions is correlated with photosynthesis, WUEi, and plant biomass showing R2 = 0.134, 0.071, and 0.090 respectively

(Figure 3-10A). Under DS conditions, number of unfilled grains per panicle is correlated with photosynthesis, WUEi, and plant biomass showing R2= 0.402, 0.478, and 0.367, respectively

(Figure 3-10B). Until date, no study has been reported that shows the kind of results reported here.

CONCLUSIONS

The main target of any field study for grain yield in rice crop is to investigate the yield potential. Plant breeders can use the information on yield potential to develop high yielding

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genotypes. Various studies have shown that grain yield and its components are severely affected by drought stress at the reproductive stage in the field. Grain yield in cereal crops depends on the performance of several grain yield components under drought stress. We screened 81 URMC rice genotypes for grain yield components (panicle length, number of spikelets per panicle, number of filled grains & unfilled grains per panicle) at the reproductive stage in field conditions and plant biomass, photosynthesis and WUEi at the vegetative stage under drought stress in the greenhouse. Based on analysis of variance, there was significant variation between WW and DS tretments for grain yield components at the reproductive stage and physiological traits at the vegetative stage among all 81 rice genotypes. There was an interaction between treatment and rice genotype, which reflects the reduction in all the grain yield components under field conditions and physiological traits in the green house compared to WW plants. Compiling the results, 5 rice genotypes out of 81 URMC rice genotypes 301066, 301408, 301418, N22 and

Vandana, were drought resistant showing longer panicle length, higher numbers of spikelets per panicle, higher number of filled grains per panicle and lower number of unfilled grains per panicle with ≤25% reduction in all e grain yield components as comparted to WW plants in field conditions, as well exhibiting higher amount of plant biomass, higher rate of photosynthesis and

WUEi with ≤25% reduction in all these physiological traits as compare to WW plants in greenhouse conditions at the vegetative stage. Howevwr, 22 rice genotypes 310007, 310020,

310039, 310100, 310144, 310338, 310345, 310348, 310351, 310381, 310389, 31044, 310515,

310566, 310588, 310645, 310687, 310724, 310773, 311793, 311794 and 311795 were drought sensitive exhibiting smaller panicle length, lower numbers of spikelets per panicle and filled grains per panicle, and higher number of unfilled grains per panicle under drought stress, with

≥40 % reduction in all the grain components compared to WW plants in field conditions, as well

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as exhibiting higher amount of plant biomass, higher rate of photosynthesis and WUEi with

≥40% reduction in all these physiological traits in the green house conditions at the vegetative stage, respectively (Table 3-6B). In the correlation analysis, major grain yield components such as number of spikelets per panicle, number of filled and unfilled grains per panicle showed better correlation with major physiological traits such plant biomass, photosynthesis and WUEi. The results suggest that drought resistant genotypes having higher grain yield as well as higher photosynthesis and WUEi can be useful for plant breeders for improving Arkansas’s traditional varieties for drought resistance.

REFERENCES

Agrama, H.A., W. Yan, F. Lee, R. Fjellstrom, M-H. Chen, M, Jia, A. McClung. 2009. Genetic assessment of a mini-core developed from the USDA rice genebank. Crop Science. 49: 1336–1346.

Amiri, E., M. Khandan, H.R. Bozorgi, S.M. Sadeghi and M. Rezaei. 2009. Response of rice varieties to water limit conditions in North Iran. World Applied Science Journal. 6(9): 1190-1192.

Anjum, F., M. Yaseen, E. Rasul, A. Wahid and S. Anjum. 2003. Water stress in barley (Hordeum vulgare L.). I. Effect on morphological characters. Pakistan Journal of Agricultural Sciences. 40: 43–44.

Anjum, S. A., X. Xie, L. Wang, M. F. Saleem, C. Man, and W. Lei. 2011. Morphological, physiological and biochemical responses of plants to drought stress. African Journal of Agricultural Research. 6(9): 2026–2032.

Bindraban, P.S. 2001.Water for food: converting inundated rice into dry rice. In: Hengsdijk H, Bindraban PS, editors. Water-savings rice production systems. Proceedings of the international workshop on water-savings rice production system at Nanjing University, China, 2-4 April 2001. Plant Research International (PRI), Report 33.Wageningen, Netherlands. P 5-14.

Blum, A. 1988. Plant Breeding for Stress Environments. Boca Raton: CRC Press, Inc.

173

Botwright Acuña, T. L., H.R. Latte, L.J. Wade. 2008. Genotype and environment interactions for grain yield of upland rice backcross lines in diverse hydrological environments. Field Crops Research. 108(2): 117–125.

Bouman, B.A.M. 2002. Aerobic Rice (Han Dao): A new way of growing rice in water short areas. Molecular and General Genetics 4: 53-61.

Brancher, A., F. A. de Oliveira Camargo, and G. de Araújo Santos. 1996. Occurrence of physiological disease in flooded rice fields. Cienca Rural. 26(1):149-151. DOI: 10.1590/S0103-84781996000100028.

Brukner, P.L. and R.C. Frohberg. 1987. Stress tolerance and adaptation in spring wheat. Crop Science 27: 31-36.

Bunnag S, P. Pongthai. 2013. Selection of rice (Oryza sativa L.) cultivars tolerant to drought stress at the vegetative stage under field conditions. American Journal of Plant Science. 4(9): 1701–1708.

Counce, P.A., T.J. Siebenmorgen, M.A. Poag, G.E. Holloway, M.F. Kocher and R. Lu. 1996. Panicle emergence of tiller types and grain yield of tiller order for direct-seeded rice cultivars. Field Crops Research. 47: 235-242.

Davatgar, N., M.R. Neishabouri, A.R. Sepaskhah, and A. Soltani. 2009. Physiological and morphological responses of rice (Oryza sativaL.) to varying water stress management strategies. International Journal of Plant Production. 3(4): 19-32.

Dey, M.M. and H.K. Upadhyaya. 1996. Yield loss due to drought, cold and submergence in Asia. In: Rice Research in Asia: Progress and Priorities. (Eds. R.E. Evenson, R.W. Herdt and M. Hossain) CAB International. pp. 291-303.

Farooq, M., A. Wahid, N. Kobayashi, D. Fujita and S.M.A. Basra. 2009. Plant drought stress: effects, mechanisms and management. Agronomy Sustainable Development. 29: 185–212.

174

Garris, A.J., T.H. Tai, J. Coburn, S. Kresovich, and S. McCouch. 2005. Genetic structure and history, technology, and production. Crop Production, Ser. #6149. John Wiley & Sons New York, NY.

Garrity, D.P., L.R. Oldeman, and R.A. Morris. 1986. Rainfed lowland rice ecosystems: Characterization and distribution. In: Progress in Rainfed Lowland Rice. IRRI, Manila, Philippines. pp. 3–23.

Hsiao, T.C., E. Fereres, E. Acvedo, W. Henderson. 1976. Water stress and dynamics of growth and yield of crop plants. pp: 281-305. In: (eds. Lang, O.L., Kappan, L., and Schulz, E.D.). Water and plant life. Problems and modern approaches. Springer-verlog.

Impa, S. M., S. Nadaradjan, P. Boominathan. 2005. Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Science. 45: 2517–2522.

IRRI (International Rice Research Institute). 1994. Rice almanac (2nd Eds). pp 181. IRRI, Los Banos, Philippines.

Ito, O., G. Cabuslay, and E. Ella. 2000. Drought and subemergence in rice production. J. S. Nanda (Eds.) Rice Breeding and Genetics, Research Priorities, and Challenges (pp. 73–97). Enfield: Science Publishers.

Ito, Y., K. Katsura, K. Maruyama, T. Taji, M. Kobayashi, M. Seki, K, Shinozaki, K. Yamaguchi- Shinozaki. 2006. Functional analysis of rice DREB1/CBF-type transcription factors involved in cold-responsive gene expression in transgenic rice. Plant Cell Physiology. 47(1): 141–153.

Kamoshita A, R. Rofriguez, A. Yamauchi, L. J. Wade. 2004. Genotypic variation in response of rainfed lowland to prolonged drought and re-watering. Plant Production Science. 7(4): 406–420.

Khush, G. S. 2005. What it will take to feed 5.0 billion rice consumers by 2030. Plant Molecular Biology. 59: 1–6.

Khush, G.S. 2000.Taxonomy and origin of rice. In: Aromatic rices. R.K. Singh, U.S. Singh, G.S. Khush (Eds). Oxford and IBH Publishing Co. Pvt. Ltd. New Delhi. pp 47-69.

175

Konate, A.K., A. Zongo, H. Kam, A. Sanni, and A. Audebert. 2016. Genetic variability and correlation analysis of rice (Oryza sativa L.) inbred lines based on agro- morphological traits. African Journal of Agricultural Research. 11(35): 3340-3346.

Krupp, H.K., S.K. De Datta and S.N. Balaoing. 1971. The effect of water stress at different growth stages on growth and yield of lowland rice. In: Procedding of the Second Annual Scientific Meeting. Crop Science of the Plilippines. Pp 398-411.

Kumar, A., S. Dixit, T. Ram, R.B. Yadav, K.K. Mishra, N.P. Mandal. 2014. Breeding high- yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. Experimental Journal of Botany. 65 (21): 6265-6278.

Kumar, R., A.K. Sarawgi, C. Ramos, S.T. Amarante, A.M. Ismail, W.J. Wade. 2006. Partioning of dry matter during drought stress in rainfed lowland rice. Field Crops Research. 96: 455-465.

Kuroda, E., S. Abe, F. Ishibashi, M. Hirano, and T. Murata. 1999. Varietal difference in the relationship between the number of panicles per hill and the number of spikelets per panicle on the main stems and on primary and secondary tillers of rice. Japanese Journal of Crop Sciences. 68: 385-389.

Kusaka, M., M. Ohta, and T Fujimura. 2005. Contribution of inorganic components to osmotic adjustment and leaf folding for drought tolerance in pearl millet. Plant Physiology. 125: 474–489.

Lilly, J.M. and S. Fukai. 1994. Effect of timing and severity of water deficit on four diverse rice cultivars. 1. Phonological development, crop growth and grain yield. Field Crops Research. 37: 225-235.

Liu, G., H. Mei, G. Zou and L. Luo. 2010. Sensitivities of rice grain yield and other panicle characters to late-stage drought stress revealed by phenotypic correlation. Molecular Breeding. 25:603–613.

Nguyen, H.T., R.C Babu and A. Blum. 1997. Breeding for drought resistance in rice: physiology and molecular genetic considerations. Crop Science. 37: 1426–1434.

Pandey, V. and A. Shukla. 2015. Acclimation and Tolerance Strategies of Rice under Drought Stress. Rice Science. 22(4): 147-161.

Postel, S. 1997. Last Oasis. Facing water scarcity, New York: Norton and Company. pp 239.

176

Sadeghi, S.M. and R. K Danesh. 2011. Effects of water deficit role at different stages of reproduction growth on yield components of rice. World Applied Sciences Journal. 13(9): 2021-2026.

Sellamuthu, R., C. Ranganathan, and R. Serraj. 2015. Mapping QTLs for reproductive-stage drought resistance traits using an advanced backcross population in upland rice. Crop Science. 55:1–13.

Serraj, R., A. Kumar, K.L. McNally, I. Slamet-Loedin, R. Bruskiewich, R. Mauleon, J. Cairns, R.J. Hijmans. 2009. Improvement of drought resistance in rice. Advance Agronomy. 103: 41–98.

Shao, H. B., L.Y. Chu, M.A. Shao, C.A. Jaleel, H.M. Mi. 2008. Higher plant antioxidants and redox signaling under environmental stresses. Comptes Rendus Biologies. 331: 433– 441.

Singh, N. 2013. Molecular and physiological studies to identify traits enhancing rice cultivars adaptation to aerobic conditions for sustainable rice production in water short situations. Thesis published. pp 106.

Takeoka, Y., A. Al Mamun, T. Wada, and P.B. Kaufman. 1992. Reproductive Adaptation of Rice to Environmental Stress. Tokyo: Japan Scientific Societies Press.

Tuong, T.P. and B.A.M. Bouman. 2003. Rice production in water-scarce environments. In: J.W. Kijne, R. Barker, D. Molden, (Eds). Water productivity in agriculture: limits and opportunities for improvement. CABI Publishing, UK. pp 53-67.

Yue, B., W. Xue, L. Xiong, X. Yu, L. Luo, K. Cui, D. Jin, Y. Xing, and Q. Zhang. 2006. Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics. 172(2):1213-1228. DOI:10.1534/genetics.105.045062

Zhou, H.K., Y. Hayat, L. J. Fang, R.F. Guo, J.M. He, H.M. Xu. 2010. Analysis of genetic and genotype X environment interaction effects for agronomic traits of rice (Oryza sativa L.) in salt tolerance. Pakistan Journal of Botany. 42:3239- 3246.

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Table 3-1. Effects of drought stress on plant panicle length exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC. Percent reduction in Panicle length DR genotypes MDR genotypes DS genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301408, 301418 310007, 310039, 310144, 310211 311236 310020, 310100, 310102 310220, 310442, 310747, 310779 310317, 310338, 310345 310814, 311255, 311592, 311668 310348, 310351, 310381 311793 310389, 310420, 310428 310489, 310515, 310566 310588, 310645, 310687 310715, 310724, 310757 310773, 310777, 310802 310846, 310906, 310990 310998, 311016, 311111 311140, 311141, 311151 311173, 311180, 311181 311258, 311266, 311284 311286, 311385, 311393 311417, 311466, 311483 311491, 311497, 311532 311547, 311561, 311596 311603, 311620, 311654 311656, 311677, 311684 311690, 311787, 311788 311794, 311795, N22 Vandana Abbreviations: URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DSE, Drought Sensitive.

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Table 3-2. Effects of drought stress on number of spikelets per panicle exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC. Percent Reduction in Number of Spikelets per Panicle DR genotypes MDR genotypes DS genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301408, 301418 310515, 310687, 310724, 310846 310007, 310020, 310039 310211, 310317, 310389 311111, 311141, 311181, 311258 310100, 310102, 310144 310420, 310428, 310588 311483, 311603, 311656, 311690 310220, 310338, 310345 310645, 310779, 310814 311788 310348, 310351, 310381 310906, 311016, 311151 310442, 310489, 310566 311180, 311236, 311385 310715, 310747, 310757 311491, 311497, 311561 310773, 310777, 310802 311596, 311620, 311654 310990, 310998, 311140 311684, 311795, N22 311173, 311255, 311266 Vandana 311284, 311286, 311393 311417, 311466, 311532 311547, 311592, 311668 311677, 311787, 311793 311794

Abbreviations: URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DSE, Drought Sensitive.

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Table 3-3. Effects of drought stress on number of filled grains per panicle exhibiting DR, MDR and drought sensitivity in 81 rice genotypes of the URMC. Percent Reduction in Number of Filled Grains per Panicle DR genotypes MDR genotypes DS genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301408, 301418 NA 310007, 310020, 310039 N-22, Vandana 310100, 310102, 310144 310211, 310220, 310317 310338, 310345, 310348 310351, 310381, 310389 310420, 310428, 310442 310489, 310515, 310566 310588, 310645, 310687 310715, 310724, 310747 310757, 310773, 310777 310779, 310802, 310814 310846, 310906, 310990 310998, 311016, 311111 311140, 311141, 311151 311173, 311180, 311181 311236, 311255, 311258 311266, 311284, 311286 311385, 311393, 311417 311466, 311483, 311491 311497, 311532, 311547 311561, 311592, 311596 311603, 311620, 311654 311656, 311668, 311677 311684, 311690, 311787 311788, 311793, 311794 311795 Abbreviations: URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DSE, Drought Sensitive; NA, Not Available

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Table 3-4. Effects of drought stress on percent change in number of un-filled grains per panicle exhibiting drought resistance in 81 rice genotypes of the URMC. Percent change in Number of Un-filled Grains per Panicle % increase in genotypes % reduction in genotypes DR genotypes DS genotypes (≤25% increase) (≥25% increase) 301066, 301408, 301418 310100, 310144, 310211, 310220 310007, 310039, 310802 310020, 310102, 310747 310317, 310338, 310345, 310348 310990, 311266, 311284 310998311668, 311793 310351, 310381, 310389, 310420 311286, 311592 N 22, Vandana 310428, 310442, 310489, 310515 310566, 310588, 310645, 310687 310715, 310724, 310757, 310773 310777, 310779, 310814, 310846 310906, 311016, 311111, 311140 311141, 311151, 311173, 311180 311181, 311236, 311255, 311258 311385, 311393, 311417, 311466 311483, 311491, 311497, 311532 311547, 311561, 311596, 311603 311620, 311654, 311656, 311677 311684, 311690, 311787, 311788 311794, 311795

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Table 3-5A. Effects of drought stress on the plant productivity trait of plant biomass, exhibiting DR, MDR and drought sensitivity at vegetative stage in 81 rice genotypes of the URMC in green house conditions. Percent Reduction in Plant Biomass DR genotypes MDR genotypes DS genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301408, 301418, 310007 301066, 310020, 310039, 310100 310351, 310515, 310687 310102, 310338, 310420 310144, 310211, 310220, 310317 310773, 310777, 310779 310428, 310489, 310566 310345, 310348, 310381, 310389 310814, 310990, 310998 310588, 310715, 310747 310442, 310645, 310724, 310802 311140, 311173, 311181 310757, 310906, 311151 310846, 311016, 311111, 311141 311258, 311266, 311385 311236, 311483, 311491 311180, 311255, 311284, 311286 311393, 311532, 311787 311592, 311620, 311654 311417, 311466, 311497, 311547 311668, 311788, N22 311561, 311596, 311603, 311656 Vandana 311677, 311684, 311690, 311793 311794, 311795

Abbreviations: URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DS, Drought Sensitive.

Table 3-5B. Effects of drought stress on photosynthesis exhibiting DR, MDR and drought sensitivity at vegetative stage in 81 rice genotypes of the URMC in green house conditions. Percent Reduction in Photosynthesis DR genotypes MDR genotypes DS genotypes (≤25 % reduction) (25-40 % reduction) (≥40% reduction) 301066, 301408, 301418 310211, 310715, 310747, 310757 310007, 310020, 310039 310102, 310144, 310220 310777, 311111, 311140, 311561 310100, 310338, 310345 310317, 310389, 310420 311592, 311620, 311656, 311668 310348, 310351, 310381 310428, 310489, 310779 311684 310442, 310515, 310566 310802, 310814, 310846 310588, 310645, 310687 310906, 310990, 310998 310724, 310773, 311016 311141, 311151, 311173 311385, 311417, 311596 311180, 311181, 311236 311603, 311654, 311690 311255, 311258, 311266 311793, 311794, 311795 311284, 311286, 311393 311466, 311483, 311491 311497, 311532, 311547 311677, 311787, 311788 N22, Vandana

Abbreviations: URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DSE, Drought Sensitive.

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Table 3-6A. Effects of drought stress on WUEi exhibiting DR, MDR and drought sensitivity at the vegetative stage of 81 rice genotypes of the URMC in green house conditions. Percent Change in WUEi DR genotypes DS genotypes (0.57-58% increase) (0.64-495% reduction) 301066, 301408, 301418, 310102, 310144, 310211 310007, 310020, 310039, 310100 310220, 310317, 310389, 310420, 310442, 310489 310338, 310345, 310348, 310351 310515, 310645, 310687, 310715, 310747, 310757 310381, 310428, 310566, 310588 310773, 310777, 310779, 310802, 310814, 310846 310724, 311385, 311417, 311596 310906, 310990, 310998, 311016, 311111, 311140 311654, 311668, 311684, 311690 311141, 311151, 311173, 311180, 311181, 311236 311793, 311794, 311795 311255, 311258, 311266, 311284, 311286, 311393 311466, 311483, 311491, 311497, 311532, 311547 311561, 311592, 311603, 311620, 311656, 311677 311787, 311788, N22, Vandana

Abbreviations: WUEi, Instantaneous water use efficiency; URMC, USDA rice mini-core collection; DR, Drought Resistant; MDR, Moderately Drought Resistant; DSE, Drought Sensitive.

Table 3-6B. Summary of rice genotypes of the URMC showing Drought resistance (DR) and sensitivity (DS) for major grain yield components (spikelets/panicle, Number of filled grains & un-filled grains/panicle) at reproductive stage in the field conditions correlated with plant productivity (plant biomass) and physiological traits(photosynthesis and WUEi) at vegetative stage in green house conditions. Percent Reduction in Grain yield Components, Plant Productivity trait) and Physiological traits DR genotype (≤25 % reduction) DS genotypes (≥25% reduction) 301066, 301408, 301418, N22, 310007, 310020, 310039, 310100, 310144, 310338 Vandana 310345, 310348, 310351, 310381, 310389, 310442 310515, 310566, 310588, 310645, 310687, 310724 310773, 311793, 311794, 311795

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A C

B D

Figure 3-1. Rice genotypes growing in field conditions. A) Nursery was grown in the plots with field soil for 25-days in greenhouse at controlled conditions. B) 10 equal size seedlings per genotype were transplanted individually. C) The plot flooded at all times served as the well watered (WW) control. D) The plot stressed at the reproductive stage served as the drought stress (DS) treatment. E) The plot maintained at -70kPa soil water potential using tensiometer at the reproductive stage showed the severity of DS.

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A 35 PL_WW PL _DS % Reduction in PL 50

30 40

25 30 * 20 * * * * * * * * * * * 20 * * 15 * 10

Panicle length(cm) 10

5 0 Reduction % Panicle in length

0 -10

N-22

311016 310814 311173 310489 310351 311677 310102 311654 310773 311690 310317 310338 311284 311141 310345 310220 310779 311592 310039 311668 310211 310442 311793 310144 311255 310747 310007 311236 Vandana

B 350 S/P_WW S/ P_DS % Reduction in S/P 100

300 80 250 * 60 200 * * 40 150 * * * * * 20 100 * * Spikelets Spikelets per Panicle * * * * * 50 0 * * * * * * * *

0 -20

% Reduction Reduction % Spikelets in perPanicle

N-22

310007 301418 311561 301066 311151 311385 301408 310317 310420 310846 311788 311483 310515 311690 311141 311258 311603 311181 310724 311787 311592 311532 311677 311668 311417 311284 311286 310039 Vandana

Figure 3-2. Effects of drought stress on grain yield components (Panicle length and Spikelets per panicle) in 30 representative genotypes out of 81 rice genotypes of the URMC in field experiments. A) The length of panicle (cm) under WW and DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in panicle length as compare to WW plants. B) The number of spikelets per panicle under WW & DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in number of spikelets per panicle as compare to WW plants. Data are expressed as a mean of five replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

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A 350 NOFG/P_WW NOF G/P_DS % Reduction in NOFG/P 120

300 100 250 80 200 * 60 150 40

100 * * * * * Filled grains Filled grains perPanicle 50 * 20 Reduction % Number in of * * * * Number Number Filledof Grains per Panicle * * * *

0 * * * * * * * * * * * * * 0

N-22

311236 310348 301418 301408 301066 311151 311180 311181 311561 310428 311656 311620 310687 311677 310144 310442 310102 311393 310345 310338 311547 310007 310381 311385 310566 311417 311284 311794 Vandana

160 B NOUFG/P_WW NOUFG/P_DS % Increasse in NOUFG/ P 4000

140 3500

120 3000 2500 100 2000 80

1500 filled filled Grains per Panicle - 60

1000 filled filled Grains perPanicle

40 * -

500 Increase % Number in of

* Un * 20 * * * 0 * * * * * * * Number Number Un of * * *

0 * * * -500

N-22

301418 310420 311668 310020 301066 311793 301408 310998 310102 311654 311795 310687 311181 310144 311656 310906 311603 311180 311255 311483 310814 311491 311690 311141 310777 311547 310428 311236 Vandana

Figure 3-3. Effects of drought stress on grain yield components (Number of filled grains and unfilled grains per panicle) in 30 representative genotypes out of 81 rice genotypes of the URMC in field conditions. A) Number of filled grains per panicle under WW and DS with lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in number of filled grains per panicle as compare to WW plants. B) The number of un-filled grains per panicle under WW & DS with lowest (≤ 25%) and highest (≥25%) increase in number of un-filled grains per panicle as compare to WW plants. Data are expressed as a mean of five replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

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A 18 Biomass_WW Biomass_DS % Reduction in Biomass 80 16 70 14 * * 60 12 * * 50 10 40 8 * * 30 6 * Biomass Biomass (g/plant) * * * * * * * * 20 4 * * * * * * Reduction % Biomass in * * * * * 2 10

0 0

N22

311596 310747 301408 311668 310428 301418 311620 310757 310906 311466 311794 311111 310039 311690 310846 311016 311284 310814 311266 311181 310351 311393 311385 311258 311140 311532 310998 311787 Vandana

B 35 Photosynthesis_WW Photosynthesis_DS % Reduction in Photosynthesis 120

1 100

- *

S 2 - * 25 *

M 80 2 2 20 60

15 *

* * * 40 10 * * * * * * 20 5 * * * * * * * * * Reduction % Photosynthesis in Photosynthesis Photosynthesis (µmol CO 0 0

-5 N22 -20

311690 310100 301408 301066 311181 311173 310906 311677 311258 301418 311140 311656 311620 310715 311684 311111 311668 310757 310747 311561 311596 310588 310007 310351 310381 311795 311794 310566 Vandana

Figure 3-4. Drought responses of major plant productivity and physiological traits (Plant biomass and Photosynthesis) in 30 representative genotypes out of 81 rice genotypes of the URMC. A) Amount of plant biomass (g/ plant) under WW and DS with lowest (≤ 25%), moderate (25-40%), and highest (≥40%) reduction in plant biomass. B) Intrinsic value of photosynthesis with lowest (≤25%), moderate (25-40%), and highest reduction (≥40%) in photosynthesis as compare to WW plants. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

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25 WUEi_WW WUEi_DS % Increase in WUEi 200

O) 100 2 20

/ / mMol H 2 15 -100 * * 10 * -200 * * * * -300

* Increase % WUEiin 5 * *

* -400 0

-500

N22

301408 311654 311794 311173 310990 311141 311466 311491 311180 310846 311236 311016 310317 301066 301418 310348 310345 310588 311417 310724 310020 310381 311596 310007 311385 310338 311795 310566 Instantanous Instantanous water use efficiency (µmol CO -5 Vandana -600

Figure 3-5. Drought responses to physiological trait (Instantaneous water use efficiency) in 30 representative genotypes out 81 rice genotypes of the URMC. A) It shows intrinsic value of WUEi (µmol CO2/ mMol H2O) under WW and DS and lowest (≤ 25%), moderate (25-40%), and highest reduction (≥ 40%) in plant biomass. B) It displays intrinsic value of no of tillers and increase (0.57- 58%) and decrease (19.87-495%) in WUEi as compare to WW plants. Data are expressed as a mean of ten replicates ± SE. The means with asterisks (*) are significantly different (Tukey’s HSD, P < 0.05).

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Figure 3-6. Pearson’s correlation coefficients shown in correlation matrix heat map between 4 grain yield components and 17 plant productivity and morpho-physiological traits of 81 rice genotypes of the URMC screened in greenhouse and field conditions under WW & DS treatments. The correlations (-1 to + 1) are colored either in green (positive correlation) or in yellow (negative correlation). Abbreviations: PL_WW, Panicle length (cm) under WW; PL_DS, Panicle length under DS; S/P_WW, Spikelets per panicle under WW; S/P_DS, Spikelets per panicle under DS; NOFG_WW, Number of filled grains per panicle under WW; NOFG_DS, Number of filled grains per panicle under DS, NOUFG_WW, Number of un-filled grains per panicle under WW; NOUFG_DS, number of un-filled grains per panicle under DS; Photo_WW, Photosynthesis under WW; Photo_DS, Photosynthesis under DS, Trans_rate_WW, Transpiration rate under WW, Trans_rate-DS, Transpiration rate under DS; WUEi_WW, Instantanous water use efficiency under WW; WUEi_DS, Instantanous water use efficiency under WW; Cond_WW, Stomatal conductance under WW; Cond_DS, Stomatal conductance under DS; Ci_WW, Intercellular CO2 concentration under WW; Ci_DS, Intercellular CO2 concentration under DS; Fv’_WW, Variable fluorescence under WW; Fv’_DS, Variable fluorescence under DS; Fm’_WW, Maximum fluorescence under WW; Fm’_DS, Maximum fluorescence under DS; Fv’/Fm’_WW, Ratio of variable fluorescence (Fv’) to the maximum fluorescence (Fm’) under WW; Fv’/Fm’_DS, Ratio of variable fluorescence (Fv’) to the maximum fluorescence (Fm’) under DS; PhiPSII_WW, Efficiency of photosystem II under WW; PhiPSII_WW, Efficiency of photosystem II under DS; qN_WW, Non-photochemical quenching under WW; qN_DS, Non-photochemical quenching under DS; ETR_WW, Electron transport rate under WW; ETR_DS, Electron transport rate under DS; Ci/ Ca_WW, ratio of intercellular CO2 to the ambient CO2 under WW; Ci/ Ca_DS, ratio of intercellular CO2 to the ambient CO2 under DS; RWC_WW, Relative water content under WW; RWC_DS, Relative water content under DS; Bio_WW, Biomass under WW; Bio_DS, Biomass under DS; LR_WW, Leaf rolling under WW; LR_DS, Leaf rolling under DS; NOT_WW, Number of tillers under WW; NOT_DS, Number of tillers under DS; SPAD_WW, Chlorophyll content under WW and SPAD_DS, Chlorophyll content under DS.

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A 40 Bio_WW Photo_WW WUEi_WW

Linear (Bio_WW) Linear (Photo_WW) Linear (WUEi_WW) ) & ) &

1 35

2 -

m 30

2 O) under WW 2 25

/mMol /mMol H R² = 0.223 2 15

10 R² = 0.105 Bio (g/plant), Photo(µmolCO WUEi(µmolCO 5 R² = 0.087

0 0 5 10 15 20 25 30 35 Panicle length (cm) under WW B 35 Bio_DS Photo_DS WUEi_DS Linear (Bio_DS) Linear (Photo_DS) Linear (WUEi_DS)

) )

s 2

- 25

2 O) DS under

2 20

15 R² = 0.168

/mMol /mMol H 2 10 R² = 0.270 5 R² = 0.0229

0 & & (µmolCO WUEi Biomass Biomass (g/plant), Photo(µmolCO 0 5 10 15 20 25 30 Panicle length (cm) under DS Figure 3-7. Grain yield components and physiological traits correlated with each other in the correlation matrix, were selected for the scatter plot shown. The scatter plot shows correlation between panicle length in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) in greenhouse under WW and DS contributing to higher yield. A) Panicle length is correlated with photosynthesis, WUEi, plant biomass showing R-squared (R2) value 0.223 and 0.087, and 0.105 under WW. B) It displays the panicle length is correlated with photosynthesis, WUEi, and plant biomassshowing R2 value 0.168, 0.0229, and 0.270, respectively.

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A 40 Bio_WW Photos_WW WUEi_WW Linear (Bio_WW) Linear (Photos_WW) Linear (WUEi_WW)

) & 35

s 2

- 30

m 2

2 O) under WW 2 25 R = 0.634

/mMol /mMol H 2 15

10 R2 = 0.150 R2 = 0.657

Bio(g/plant), Photo(µmolCO WUEi(µmolCO 0 0 50 100 150 200 250 300 350 Spikelets per Panicle under WW 35 B Bio_DS Photo_DS WUEi_DS

Linear (Bio_DS) Linear (Photo_DS) Linear (WUEi_DS)

) &

1 -

s 30

- m

2 2

O) under DS 25 R = 0.655 2

/mMol /mMol H 2 15

10 R2 = 0.738

2 Bio(g/plant), Photo(µmolCO WUEi(µmolCO 5 R = 0.0129

0 0 50 100 150 200 250 300 Spikelets per Panicle under DS Figure 3-8. Grain yield components and physiological traits correlated with each other in the correlation matrix were selected for scatter plot. The scatter plot shows correlation between spikelets per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) condtributing higher yield in green house under WW and DS. A) It shows that spikelets per panicle is correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.634, 0.657, and 0.150, respectively under WW. B) Display of spikelets per panicle correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.665, 0.738, and 0.0129, respectively under DS.

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A 40 Bio_WW Photo_WW WUEi_WW Linear (Bio_WW) Linear (Photo_WW) Linear (WUEi_WW) 35

30 2

) WUEi& R = 0.643

s 2

- 25

2 O) under under O) WW

2 20

/mMol /mMol H 2 10 R2 = 0.121 5 2

(µmolCO R = 0.684

0 Bio(g/plant), Photo(µmolCO 0 50 100 150 200 250 300 350 Number of Filled Grains per Panicle under WW 35 B Bio_DS Photo_DS WUEi_DS Linear (Bio_DS) Linear (Photo_DS) Linear (WUEi_DS) R2 = 0.588

) & 30

2 -

m 25

O) under DS 2

20 /mMol /mMol H

2 15

2 10 R = 0.658 R2 = 0.297

Bio(g/plant), Photo(µmolCO WUEi(µmolCO 0 0 50 100 150 200 250 Number of Filled Grains per Panicle under DS Figure 3-9. Grain yield components and physiological traits correlated with each other based on heat map correlation matrix were selected for scatter plot. The scatter plot shows correlation between the number of filled grains per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) contributing higher yield under WW and DS. A) Number of filled grains per panicle is correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.643, 0.684, and 0.121 respectively under WW. B) The number of filled grains per panicle is y correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.588, 0.658, and 0.297, respectively under DS.

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40 A Bio_WW Photo_WW WUEi_WW Linear (Bio_WW) Linear (Photo_WW) Linear (WUEi_WW)

) & ) &

s 2

- 30

2 O) under WW

2 25

20 R = 0.134

/mMol /mMol H 2 15

10 R2 = 0.090

5 2 WUEi(µmolCO

Biom(g/plant), Biom(g/plant), Photo(µmolCO R = 0.071

0 0 20 40 60 80 100 120 140 Number of Un-filled Grains per panicle under WW

B Bio_DS Photo_DS WUEi_DS 35 Linear (Bio_DS) Linear (Photo_DS) Linear (WUEi_DS)

) & 30

2 -

m 25

O) under DS 2 20 R2 = 0.402

/mMol /mMol H 2 10 R2 = 0.478 5 R2 = 0.367

0 Bio(g/plant), Photo(µmolCO WUEi(µmolCO 0 20 40 60 80 100 120 140 160 Number of Un-filled Grains per Panicle under DS Figure 3-10. Grain yield components and physiological traits correlated with each other based on heat map correlation matrix were selected for scatter plot. The scatter plot shows correlation between measured number of un-filled grains per panicle in field conditions and major physiological traits (Biomass, Photosynthesis & WUEi) condtributing to higher yield in the greenhouse under WW and DS. A) Number of un-filled grains per panicle is correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.134, 0.071, and 0.090, respectively under WW. B) Number of un-filled grains per panicle is correlated with photosynthesis, WUEi, and plant biomass showing R2 value 0.402, 0.478, and 0.367, respectively under DS.

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CHAPTER-IV MOLECULAR GENETIC DISSECTION OF WATER USE EFFICIENCY AND DROUGHT RESISTANCE USING GENOME-WIDE ASOCIATION ANALYSIS IN THE USDA RICE MINI-CORE COLLECTION

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ABSTRACT Rice (Oryza sativa L.) is an important food crop and plays a vital role to ensure food sustainability and security worldwide. It is sensitive to water deficit and uses 30% of the global fresh water resources during its life cycle. Drought is one of the major abiotic stress constraints that negatively affects morpho-physiological processes at the vegetative stage in rice. Water-use efficiency (WUE) is an important physiological component determining drought resistance and yield under drought stress. Natural genetic variation for WUE and other physiological traits have not been widely used in breeding for improvement of water use in crop plants, nor in association studies of dry matter accumulation and grain yield. Genome wide association (GWA) analysis is a powerful molecular genetic tool to analyze natural variation for phenotypic traits associated with genome-wide SNPs, and identifies candidate genes related to the traits. In this study, a

GWA analysis was conducted for WUEi, photosynthesis, transpiration rate, stomatal conductance, Ci, Ci/Ca, plant biomass, number of tillers, RWC, leaf rolling, chlorophyll content,

Fv’, Fm’, Fv’/Fm’, ETR, PhiPS II and qN, in a population of 206 rice genotypes of the URMC with a panel of 204,262 genome-wide SNPs using the FarmCPU model. In the GWA analysis, a genomic window of ±10 kb in the genome was used in RAPDB and MSU databases. In this analysis, 24 SNPs were found linked with 24 candidate genes for WUEi, 16 SNPs linked with 29 candidate genes for photosynthesis, 26 SNPs linked with 32 candidate genes for TR, 10 SNPs linked with 11 candidate genes for stomatal conductance, 19 SNPs linked with 22 candidate genes for Ci and Ci/Ca, 23 SNPs linked with 25 candidate genes for plant biomass, 7 SNPs linked with 7 candidate genes for number of tillers, 17 SNPs linked with 24 candidate genes for

RWC, 11SNPs linked with 11 candidate genes for LR, 14 SNPs linked with 17 candidate genes for chlorophyll content, 17 SNPs linked with 25 candidate genes for Fv’, 15 SNPs linked with 19 candidate genes for Fm’, 29 SNPs linked with 34 candidate genes for Fv’/Fm’, 12 SNPs linked

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with 21 candidate genes for PhiPSII, 18 SNPs linked with 22 candidate genes for ETR, and 19

SNPs linked with 24 candidate genes for qN. The candidate genes found associated to the traits were significantly enriched for genes related to biosynthesis of polysaccharides and glycoproteins, response to drought stress tolerance, response to stomatal opening/closure, development of chloroplast, photosystem I and II and others. The candidate genes associated with WUEi and drought resistance related traits will be validated in future research.

INTRODUCTION Rice (Oryza sativa L.) is an important food crop and plays a vital role to ensure food sustainability and security worldwide. The three major cereal crops rice, wheat and maize together account for 87% of all grain production in the world. However, rice ranks second after wheat growing in larger harvested area and supplies more than 50% calories consumed by world population (Khush, 2005). Presently, rice is grown in six states of the United States Arkansas,

California, Louisiana, Mississippi, Missouri, and Texas (Kulp et al, 2003).

The rice crop requires a large quantity of water for completing its life cycle, using ~3,000

-5,000 liters to produce 1 kg (2.20462 pounds) of rice, which is ~ 2-3 times more than other cereals crops such as wheat and maize (Bouman, 2002). Water is an important factor for rice production but limited water is available in rice growing regions (Bindraban, 2001). Currently, water scarcity is an ever-increasing problem and has become the most serious constraint to rice production and yield stability in many rice-growing areas, particularly in rainfed ecosystems

(Nguyen et al, 1997). The main reasons for decreasing water availability are deteriorating diverse- and location-specific water quality, reducing natural resources, and increasing competition between urban and industrial users (Postel, 1997).

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Due to several reasons of limited water availability, water scarcity is a serious problem affecting rice at every growth stages. Drought stress influences various physiological processes

(water use efficiency, photosynthesis and others) reducing rice productivity. Therefore, improving physiological traits such as water use efficiency (WUE) and other drought resistance related traits, is a sustainable solution to increasing water scarcity worldwide. In addition, unforeseen drought at critical stages of rice growth can cause a tremendous reduction in grain yield.

WUE is an important physiological process determining yield under drought stress, and is a component of crop drought resistance. It has been used to support rainfed plant production that can be increased per unit water used, resulting in ‘‘more crop per drop’’ (Blum, 2009; Kijne et al, 2001). Natural genetic variation for WUE has not yet been used in breeding for the improvement of water use in crop plants even though it is an important determinant of crop yield

(Passioura, 1986). Thus, improvements in WUE are often found associated with reduction in dry matter accumulation and grain yield (Matus et al, 1995). However, work with transgenic plant models has shown that WUE can be increased in rice, primarily through increase in biomass, supported by increase in photosynthesis and sugars (Karaba et al, 2007; Ambavaram et al, 2014).

WUE is based on photosynthesis and transpiration rate in the leaf where it contributes to enhancing crop productivity. Based on the basic understanding of physiological processes, genetic improvement in rice with high WUE, photosynthesis and other drought resistance related processes could enhance crop yield with less consumption of water, which has a very vital significance for food safety as well as climate change (Impa et al, 2005; Zhao et al, 2008).

The rice crop has a vast diverse germplasm that has evolved variation for WUE and drought resistance mechanisms to cope with the naturally occurring drought stress conditions.

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The diverse germplasm has wild accessions that are a good source for improving drought resistance in the rice crop using molecular breeding and advanced genomics approaches.

Understanding of the genetic basis of natural phenotypic variation in the vast diversity of rice collections has been a major interest in rice genetics research (Han and Huang, 2013). In crops, the genetic dissection of WUE and drought resistance related traits was primarily performed through bi-parental and other segregating mapping populations (Han and Huang, 2013), but with the limitation of recombination in the mapping populations and conventional genotyping technologies, the phenotyping of diverse rice germplasm and their genotypic analysis can be a good alternative method to dissect the drought resistance related traits in rice. Therefore, the screening of diverse rice germplasm for natural variation in WUE, photosynthesis, transpiration rate, stomatal conductance and other drought resistance related traits under a drought stress environment followed by a genome wide association (GWA) analysis can provide useful information for genetic identification of traits and improvement of rice genotypes for higher

WUE and drought resistance.

The USDA rice mini-core collection is a genepool collected from 76 countries and 15 geographic regions around the world covering all the subspecies of rice including O. sativa, O. glaberrima, O. nivara, O. rugipogon, O. glumaepatula and O. latifolia to represent the diversity of rice based on phenotypic traits and molecular marker variation (Agrama et al, 2009). Genetic variation among all these accessions indicates the possibility that any specific gene of interest can be isolated from the gene pool (Garris et al, 2005). Therefore, screening of this population for drought resistance related parameters, can enable this collection to become an excellent resource of natural variation for phenotypic and physiological traits involved in WUE, other drought resistance related traits and productivity for grain yield under stress. Genome

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resequencing of the whole URMC can help discover and investigate the genome-wide DNA sequence variants, and systematically catalog the natural genetic variation for drought resistance traits in rice, as shown by various studies been performed in many other organisms such as

Arabidopsis and human (Han and Huang, 2013). An evaluation of a broad array of drought resistance related traits in rice, by a genome wide association study can provide a useful tool to associate drought resistance related traits with genotypic information of the URMC.

GWA analysis, also known as whole genome association study (WGAS), is a study of a genome- wide set of genetic variants in different individuals to test if any variant is associated with a trait. It focuses on associations between single nucleotide based variants and quantitative traits, such as for major human diseases, but can be used in any organisms for multiple quantitative traits. It is used to identify QTL based on the historic recombination in a panel of diverse germplasm showing linkage disequilibrium (LD) between SNPs and QTL because of the non-random association of alleles (Yu et al, 2008; Zhang et al, 2010). A panel of high density markers that covers the genome is required in order to monitor the density of recombination breakpoints in the diverse population (Zhou et al, 2008; Flint-Garcia et al, 2003). GWAS is most commonly performed in diversity panels such as mini-core collections and collections of unrelated diverse germplasm, in order to maximize the diversity of alleles and haplotypes

(Famso et al, 2011; Huang et al, 2010, 2011; Zhao et al, 2011; Huang and Han, 2014; Yang et al,

2014). In rice, the first high-density haplotype map was constructed via resequencing of 517 landraces and cultivars, which identified 37 strongly associated loci for 14 agronomic traits via

GWAS (Huang et al, 2010). The genetic architecture of rice metabolism was also dissected via

GWA analysis, and five candidate genes were identified and annotated (Chen et al, 2014).

Recently, a high-throughput phenotyping platform was developed for GWA analysis, achieving

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good results and further promoting GWA analysis for the dissection of genetic mechanisms for other traits (Yang et al, 2014). Low coverage whole genome sequencing is an effective approach for GWA analysis in rice to reveal convergent evolution during rice domestication, and the results suggest that the aus sub species may have a domestication history independent of japonica and indica based on the three traits of amylose content, grain length and pericarp color

(Wang et al, 2016). With the advantage of GWA analysis to identify novel SNP linked QTLs and candidate genes underlying several agronomic traits, the identified QTLs and candidate genes can be validated in breeding populations before they can be used for genomics-assisted selection.

Until date, there has not been any extensive study reported on GWA analysis for WUE and drought resistance related traits in rice.

In this study, GWA analysis has been conducted for 17 plant productivity and morpho- physiological traits in 206 rice genotypes of the URMC using a large set of whole genome based single nucleotide polymorphisms (SNPs) to identify the novel QTLs and candidate genes associated with WUE and other drought resistance related traits in rice under drought stress conditions.

MATERIALS AND MEHTODS

Plant material

The United States Department of Agriculture (USDA) mini-core rice collection (UMRC) of 214 accessions of rice and 7 adapted cultivars to Arkansas, totaling 221 rice genotypes (Table

2-1) were collected from USDA ARS Dale Bumpers National Rice Research Center, Stuttgart,

Arkansas, USA (Agrama et al, 2009) in summer 2013 and used for phenotypic analysis.

However, in this study, we used only 206 rice genotypes of O. sativa out of the 221 genotypes of the URMC, and did not include genotypes from the 15 related Oryza species of glaberrima,

200

nivara, glumepetula and latifolia as the genome sequencing data are not available, and quite divergent from O. sativa genome structure.

Drought stress treatment and phenotyping at vegetative stage in the greenhouse

Rice seeds were germinated by imbibing the seeds with deionized water in an incubator in the dark at 37oC for two days. Each emerged seedling was placed in a 12.7 cm x 12.7 cm sized single PVC pot filled with a known weight of a Redi-earth potting mix (Sun Gro Horticulture

Distribution, Fayetteville, AR) (Figure 2-1). At 45 days after germination, drought stress was imposed by withholding the water until the moisture level of pots dropped to 50% of field capacity (FC) that was maintained for 10-days by weighing them daily and replenishing water lost through evapotranspiration (Batlang et al, 2013). A set of plants maintained at 100% FC served as control (Figure 2-2). In this stress period, each pot was weighed daily at a fixed time of the day, and water lost was replenished to maintain the required 50% FC (Ramegowda et al,

2014) (Chapter-II, Figure 2-2). All the rice genotypes were grown in the greenhouse at the

Altheimer Laboratory, University of Arkansas, Fayetteville. For greenhouse conditions, the temperature was maintained at 28-30oC for day and 22-23oC at night, with the light period set for light/dark of 13/11 hours with maximum light intensity of 800-1000µmol PAR m-2 s-1 that is required for rice plants. The experimental design was a complete randomized design (CRD) with

10 replications and two treatments (well-watred-WW and drought stress-DS).

After 10-days of stress period, phenotyping data of instantaneous water use efficiency

(WUEi) and 16 other drought resistant related traits (plant biomass, no of tillers, relative water content (RWC), leaf rolling (LR), photosynthesis, transpiration rate, stomatal conductance, intercellular CO2 (Ci ), the ratio of intercellular CO2 to ambient CO2 concentration (Ci/Ca ), Fv’,

Fm’, Fv’/Fm’, the efficiency of photosystem II (PhiPS II), non-photochemical quenching (qN),

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electron transport rate (ETR), and chlorophyll content were recorded according to standard protocols that were already discussed in chapter I (See Chapter I).

In the experiment, a compeletely randomized design (CRD) with 10 replications (each replication is 1 plant in greenhouse) with two treatments (WW and DS) was used. Data for water use effiency and other morpho-physiological traits were analyzed by analysis of variance

(ANOVA) using JMP version 12.0. The statistical model included the effects of genotype, treatment (WW & DS), and interaction of genotype and treatment. The Tukey’s HSD was used to compare the means of treatments (WW & DS) among all the genotypes for significant effects

(Tukey’s HSD, P < 0.05) using JMP version 12 as Tukey’s HSD can determine slight difference between the means.

SNPs detection and calling from genome sequenced data

Wang et al, 2016, sequenced the genomes of 203 rice genotypes of the URMC. The data were downloaded from the NCBI website. Other three rice genomes were sequenced in the lab

(https://plantstress-pereira.uark.edu/Pereira_Group/Home.html). The package ANGSD version

0.542 (Korneliussen et al, 2014) was used for SNP detection and calling. A set of 3 million SNPs was detected by NOVOGEN (https://en.novogene.com/ ) from the genome sequences of the 206 rice genotypes compare to the Nipponbare genome.

GWA analysis for WUE and other drought resistance related traits A panel of 3 million SNPs were detected from whole genome sequencing data using the

ANGSD approach. The SNP data were filtered and the quality check was done using visual basic codes (VB codes) in Excel to include only SNPs with a minor allele frequency (MAF) ≥ 2.0 and maximum missing SNPs ≤ 30%. Based on these quality checks, a panel of 204,262 SNPs was identified and used in GWA analysis for WUE and other drought resistance traits to find

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significant association between SNPs and traits. For GWA analysis, we performed three GWA approaches such as TASSEL (Trait Analysis by Association, Evolution and Linkage; Bradbury et al, 2007), GAPIT (Genome Association and Prediction Integratio Tool; Zhang et al, 2010) and

FarmCPU (Fixed and random model Circulating Probability Unification; Liu et al, 2016). First two models (TASSEL and GAPIT) comprise true positives and sometimes induce false positive because of overfitting the moels, where potential association can be missed. However, FarmCPU is the model that effectively controls false positives without compromising true positives.

FarmCPU is, a multiple loci linear mixed model (MLMM), divided in two parts (Liu et al, 2016): a fixed effect model (FEM) and a random effect model (REM) that are iteratively. To control overfitting the model, the REM estimates multiple markers uaed to obtain kinship and the FEM tests the markers and kinship from REM as a covariates to control false positives. At each iteration, p-values of testing markers and multiple associated maerks are unified. For GWA analysis, FarmCPU was used in R packages (Liu et al, 2016). Bonferroni multiple testing was

used to correct the genome- wide significant [-log10 (P) ≥ 7.0)], equalivent to P ≤ 0.0000001, and probable [–log10 (P) ≥ 5.0-6.9], equalivent to P ≤ 0.00001-0.000001, threshols to detect an association of SNPs with all the traits used in the study (Meng et al, 2017). In the package, four principal component analyses were performed with four principal components. The Manhattan and quantile-quantile (Q-Q) plots are useful tools for scanning the population stratification in the

GWA analysis for the traits, but the manhattan plots are shown in this study to detect associated

SNPs with WUEi and other drought resistance related traits under DS in all 206 rice genotypes of the URMC.

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Identification of candidate genes of WUEi and other drought resistance related traits

To identify the most functionally probable candidate genes for WUEi and other drought resistance related traits under drought stress, the SNPs with lowest p-values were selected and searched in a ±10kb genomic window corresponding to an average gene interval in the databases. The candidate gene predictions were performed in the databases from the Rice

Genome Annotation Project (http://rice.plantbiology.msu.edu/) and the IRGSP 1.0

(http://rapdb.dna.affrc.go.jp/).

RESULTS AND DISCUSSION

GWA analysis is a powerful analytical tool to identify candidate genes or mutations based on SNP and trait association in human as well as in plant systems. In this study, GWA analysis was performed using the FarmCPU model (Liu et al, 2016) for WUEi, photosynthresis, transpiration rate, stomatal conductance, Ci, Ci/Ca, plant biomass, number of tillers, RWC, leaf rolling, chlorophyll content, Fv’, Fm’, Fv’/Fm’, ETR, PhiPS II and qN .

GWA analysis and identification of candidate genes for WUEi, photosynthesis, transpiration rate, stomatal conductance and their components at vegetative stage drought WUEi, photosynthesis, transpiration rate, stomatal coductance and their components such as Ci and Ci/Ca are very important traits that characterize resistance to drought stress in plants.

These are physiological based traits involved in plant adaptation mechanisms under drought stress (Basu et al, 2016). No studies have been done on WUEi at the genetic level in rice.

Therefore, GWAS could be an important tool to dissect WUEi and other drought resistance related traits at the whole genome level for association to SNPs and identify candidate genes in rice. In this study, GWA analysis was performed with a panel of 204,262 SNPs and physiological traits of 206 rice genotypes of the URMC using the FarmCPU model. Based on the

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highest –log10 (p) values, the highly significant SNPs (HS-SNPs) and potential or probable SNPs

(P-SNPs) associated with WUEi, photosynthesis, transpiration rate, stomatal conductance, Ci and

Ci/Ca were chosen to identify candidate genes in genomic window of ±10kb for significant association in the reference genome (Nipponbare) using the Rice Genome Annotation Project

(http://rice.plantbiology.msu.edu/) and IRGSP 1.0 (http://rapdb.dna.affrc.go.jp/) genome sequence annotation.

Water use efficiency: For WUEi, GWA analysis identified a total of 24 SNPs associated with WUEi showing their positive and negative effects on WUEi in which 9 SNPs with –log10

(p) ≥ 7 are HS-SNPs associated with WUEi, and 15 SNPs showing –log10 (p) ≥ 5.0-6.9 are additional P-SNPs related to WUEi (Table 4-1). Genomic positions across the genome of all 24

SNPs are given in Table 4-1. All the candidate genes with locus Id and their annotated functions linked with all the 24 SNPs are shown in Supplementary Table 4-1. Out of 24 candidate genes, the list of 16 candidate genes and their annotated functions descriptions such as Os03g0139200

(Cytochrome-containing protein and root, shoot development), LOC_Os11g01690

(Retrotransposon Ty1), LOC_Os02g29040 (Ankyrin repeat), LOC_Os04g35370 (MBTB5-Bric- a-Brac), LOC_Os01g47800 (AAA-type ATPase), LOC_Os12g41690 (NITRATE, FORMATE,

IRON DEHYDROGENASE), LOC_Os12g41690 (Panicle and Seed Development, and

Regulation by Light and Abiotic Stress), Os02g0794500 (Serine carboxypeptidase II precursor/accumulated in the extracellular space), LOC_Os12g05780 (Transporter-related protein), LOC_Os01g59990 (Glucocorticoid receptor/biosynthesis of polysaccharides and glycoproteins in the plant cell wall), LOC_Os07g28920 (LSD1 zing finger), LOC_Os01g61760

(Myosin), LOC_Os04g12010 (Glycosyltransferase), LOC_Os06g44374 (Transport protein

SEC61), LOC_Os02g38220 (Eukaryotic translation initiation factor 4B) and LOC_Os05g23610

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(Dolichyl-diphosphooligosaccharidase /glycosyltransferase /protein phosphatase inhibitor 2) are shown in Figure 4-1A.

Photosynthesis: GWA analysis was conducted to identify SNPs associated with photosynthesis under drought stress, which identified 16 SNPs showing their positive and negative effects on photosynthesis. All 16 P-SNPs showing –log10 (p) ≥ 5.0-6.9 are potentially associated with photosynthesis. These 16 P-SNPs with their genomic positions across the genome are listed in Table 4-2. The 16 P-SNPs linked with 29 candidate genes, their locus IDs and annotated functions are shown in Supplementary Table 4-2. Out of 29 candidate genes, only

11 candidate genes with annotated functions are described, such as Os02g0150450

(OSIGBa0125M19.6 protein), Os02g0150600 (Pyridine nucleotide-disulphide oxidoreductase),

LOC_Os07g12830 (Cytochrome b5-like Heme/Steroid binding domain containing protein),

Os07g0232200 (Flavo-hemoprotein b5/b5R/reduced lateral root formation), Os04g0132300

(cePP protein), Os07g0572050 (Copper amine oxidase), Os07g0572100 (Amine oxidase like protein), LOC_Os10g12174 (NA), Os05g0235701 (Geranylgeranyl diphosphate synthase/biosynthesis of gibberellins), LOC_Os01g73880 (Eukaryotic translation initiation factor), Os06g0483200 (Cycloartenol synthase), LOC_Os08g27010 (APE1; ortholog of

Arabidopsis ACCLIMATION OF PHOTOSYNTHESIS TO ENVIRONMENT) and

Os11g0639000 (PHYB1 regulating elongation of sheath and stem tissues of mature plants and contribute to the light-mediated regulation of PhyA and Cab gene transcripts) are shown in

Figure 4-1B.

Transpiration rate: GWA analysis identified 26 SNPs associated with transpiration rate under drought stress, which showed their positive and negative on transpiration rate. The 26

SNPs include 8 HS-SNPs showing –log10 (p) ≥ 7.0, and 18 probable associated SNPs (PAS)

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showing –log10 (p) ≥ 5.0- 6.9 are linked with photosynthesis, whose genomic positions are listed in Table 4-3. These 26 SNPs linked with 32 candidate genes and their locus Ids and annotated functions are shown in Supplementary Table 4-3. Out of 32 candidate genes, only 23 candidate genes and their annotated functions such as LOC_Os02g24090 (NA), Os09g0420100 (Zinc finger, C6HC-type protein), Os03g0205000 (Ubiquitin system component CUE domain containing), Os05g0333200 (Guanine nucleotide-binding protein alpha-1 subunit DAIKOKU

DWARF/DWARF-1), Os05g0333500 (Heat shock protein DnaJ family protein/ Chloroplast development), LOC_Os10g21940 (Expressed protein), Os02g0762800 (DNA REPAIR

PROTEIN RAD54), Os08g0536200 (UPF0497, trans-membrane plant subgroup domain containing protein), Os08g0536300 (Hd1like, CO and TOC1 like), LOC_Os04g52970 (NBS-

LRR disease resistance protein), Os09g0497000 (Adenine nucleotide translocator 1),

Os05g0497600 (PGR5 controls the utilization of light energy in order to avoid photoinhibition during photosynthesis, and downregulation of photosystem II photochemistry in response to intense light is impaired in mutant, PGR5 encodes a novel thylakoid membrane protein that is involved in the transfer of electrons from ferredoxin to plastoquinone), Os05g0497600 (Arsenic- induced RING E3 ligase response to abiotic stress), Os03g0821100 (DnaK family protein/

HSP70), Os03g0821150 (Glutamate dehydrogenase, NAD-specific domain containing protein),

Os12g0541300 (Respiratory Burst Oxidase and NADPH oxidase 9), Os12g0541500 (Chloroplast polyprotein of Elongation factor Ts), Os03g0263000 (AP2-domain/ERF9 transcription factor),

Os04g0491200 (Oligopeptide transporter), Os01g0723400 (NADP-MALIC ENZYME 2/Malic oxidoreductase family), LOC_Os01g19850 (Amino acid permease), Os01g0304100 (Cation chloride cotransporter) and Os01g0964800 (PRAF1; Ran GTPase binding/chromatin binding/zinc ion binding) are shown in Figure 4-2A.

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Stomatal conductance: GWA analysis found 10 SNPs associated with stomatal conductance trait variation under drought stress, showing positive and negative effects on stomatal conductance. All 10 SNPs showing –log10 (p) ≥ 5.0-6.9 are probable associated (PA)

SNPs with stomatal conductance. These 16 PA-SNPs with their genomic positions across the genome are listed in Table 4-4. The 10 SNPs linked with 11 candidate genes whose locus Ids and annotated functions are shown in Supplementary Table 4-4. Out of 11 candidate genes, only 10 candidate genes and their annotated functions such as LOC_Os10g23260 (Hypothetical protein),

Os05g0220600 (Peptidase S54 rhomboid domain containing protein), Os04g0598500

(OsWAK52-OsWAK short gene/Wall-associated kinase-like protein), Os01g0150200 (Protein- tyrosine phosphatase), Os01g0150400 (3-hydroxyacyl-CoA dehydratase PASTICCINO 2A),

LOC_Os09g32010 (Ternary complex factor MIP1), Os08g0150200 (F-box domain),

Os06g0597400 (PINHEAD/ZWILLE (PNH/ZLL) response to Shoot apical meristem (SAM) maintenance and leaf development) and Os01g0716200 (IQ calmodulin-binding region domain containing protein) are shown in Figure 4-2B.

Intercellular CO2 concentration (Ci): For Ci, GWA analysis identified 19 SNPs associated with Ci under drought stress, which showed significant positive and negative effects on Ci. All

19 SNPs including 10 HS-SNPs showing –log10 (p) ≥ 7.0 and 9 probable associated PA-SNPs showing –log10 (p) ≥ 5.0- 6.9 are linked with Ci, with all HS and PA SNPs and their genomic locations listed in Table 4-5. These 19 SNPs linked with 22 candidate genes and their locus Ids and annotated functions are shown in Supplementary Table 4-5. Out of 22 candidate genes, only

21 candidate genes and their annotated functions such as Os12g0562500 (Protein kinase family protein), Os07g0564600 (Secretory carrier membrane protein), Os07g0564533 (HAT family dimerization protein), Os01g0257900 (Hypothetical gene), Os02g0273100 (Aminotransferase),

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Os03g0668900 (Acidic leucine-rich nuclear phosphoprotein), Os03g0669000 (DEAD-box RNA helicase to regulate the thermotolerant growth at high temperature), Os08g0137100 (WD40

Polycomb protein of the PRC2 complex to regulate vegetative and reproductive development),

Os08g0137200 (Actin/actin-like family protein), Os09g0396900 (Vacuolar membrane transporter, Fe and Zn translocation between flag leaves and seeds), Os06g0537500 (Zinc finger,

RING-type domain containing protein), Os05g0169300 (Tryptophan aminotransferase/ Indole-3- acetic acid (IAA) biosynthesis/grain development), Os01g0140400 (Leucine-rich repeat),

LOC_Os03g25190 (OsFBX87 - F-box domain containing protein), Os08g0477100 (Latex allergen), Os08g0341700 (Phosphatidylinositol transfer-like protein II), LOC_Os11g37260

(SEY1), Os11g0586900 (ADP-ribosylation factor-like protein 5) and Os06g0343900

(Armadillo-like helical domain containing protein) are shown in Figure 4-3A.

Intercellular CO2 to ambient CO2 concentration ratio (Ci/Ca): GWA analysis identified 19

SNPs associated with Ci/Ca under drought stress and the SNPs showed their positive and negative effect on Ci/Ca. All 19 SNPs showing –log10 (p) ≥ 5.0-6.9 are PA-SNPs with Ci/Ca.

These 19 PA-SNPs with their genomic positions are listed in Table 4-6. The 19 SNPs linked with

22 candidate genes whose locus Ids and annotated functions are shown in Supplementary Table

4-6. Out of 22 candidate genes, only 16 candidate genes and their annotated functions such as

Os03g0355600 (Adapter-related protein complex 2 beta 1 subunit (Beta-adaptin) plasma membrane adaptor), Os04g0680550 (Tyrosyl-tRNA synthetase), Os03g0292900 (Importin-beta

N-terminal domain containing protein), Os06g0104100 (Squalene/phytoene synthase domain containing protein), Os06g0104200 (Secondary wall NAC transcription factor 7), Os08g0404350

(FACT complex subunit SPT16), Os08g0404300 (Chloroplast 50S ribosomal protein L27),

Os07g0445800 (Glycolipid transfer protein ), LOC_Os02g36770 (Galactosyltransferase family

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protein), Os04g0479200 (NAD-dependent isocitrate dehydrogenase), Os07g0572100 (Amine oxidase), LOC_Os02g27710 (Disease resistance protein), Os01g0162200 (Leucine-rich repeat domain), Os10g0469300 (Leucine-rich repeat domain containing protein), Os10g0478450

(MATH domain containing protein ) and Os02g0789700 (NADPH-dependent oxidoreductase) are shown in Figure 4-3B.

GWA analysis and identification of candidate genes for plant biomass and number of tillers at vegitative stage Plant biomass and number of tillers are plant productivity components determinig plant growth and development, and ultimately enhancing biomass production. Drought causes reduction in plant biomass, number of tillers, and many other components. Therefore, to dissect the natural variation and identifying the unique alleles for plant biomass and number of tillers,

GWAS plays an important role, showing powerful potential to identify the genes involved in biomass production in plants. In this study, GWA analysis was performed with plant biomass and number of tillers in 206 rice genotypes of the URMC and a panel of 204,262 genome-wide

SNPs under drought stress unsing the FarmCPU model. Based on highest significant –log10 (p) values, the HS-SNPs (–log10 (p) ≥ 7) and P-SNPs (–log10 (p) ≥ 5.0-6.9) associated with plant biomass and number of tillers were selected to find candidate genes in a genomic window of ±10 kb in the reference genome (Nipponbare) using the Rice Genome Annotation Project

(http://rice.plantbiology.msu.edu/) and IRGSP 1.0 (http://rapdb.dna.affrc.go.jp/) genome annotation predictions.

GWA analysis identified 23 SNPs associated with plant biomass under drought stress, with positive and negative effects on plant biomass. The 23 SNPs including 14 HS-SNPs showing –log10 (p) ≥7.0 and 9 P-SNPs showing –log10 (p) ≥ 5.0- 6.9 are linked with plant biomass, with all HS-SNPs and P-SNPs with their genomic positions across the genome listed in

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Table 4-7A. These 23 SNPs linked with 25 candidate genes, their locus Ids and annotated functions are shown in Supplementary Table 4-7A. Out of the 25 candidate genes, only 20 candidate genes and their annotated functions such as LOC_Os06g38830 (Receptor kinase precursor), Os09g0323100 (RING finger ubiquitin E3 ligase/Heat tolerance/ Modulation of hydrogen peroxide-induced stomatal closure), Os09g0323000 (UDP-galactose 4-epimerase),

Os03g0405000 (NAD(P)-binding protein), Os03g0405100 (Ubiquinone biosynthesis protein

COQ9), LOC_Os09g11790 (DEFL14), LOC_Os08g25690 (Expressed protein),

LOC_Os02g25624 (Expressed protein), LOC_Os10g04770 (Expressed protein),

LOC_Os05g18280 (Kelch domain containing protein), Os01g0816000 (Pentatricopeptide),

Os02g0632800 (OsWAK14 - OsWAK receptor-like protein kinase), Os03g0226800 (Cytosine- specific methyltransferase/CHROMOMETHYLTRANSFERASE 1), Os10g0575200 (HSP

DnaJ), LOC_Os11g41330 (Expressed protein ), LOC_Os08g09410 (OsFBX265 - F-box domain containing protein), Os06g0183100 (B-type response regulator/ Cytokinin signaling),

LOC_Os07g03000 (Receptor kinase precursor) and Os10g0462800 (PTAC3-PLASTID

TRANSCRIPTIONALLY ACTIVE3) are shown in Figure 4-4A.

For the trait number of tillers, GWA analysis was done to identify significant SNPs associated with number of tillers and 7 SNPs were found associated under drought stress, with

SNPs showing their positive and negative effects localized on the genome. All 7 SNPs showing – log10 (p) ≥ 5.0-6.9 are probable associated SNPs with the numbr of tillers. These 7 P-SNPs with their genomic positions and the positive & negative effects on number of tillers are listed in

Table 4-7B. The 7 SNPs linked with 7 candidate genes, their locus Ids and annotated functions are shown in Supplementary Table 4-7B. Out of the 7 candidate genes, only 6 candidate genes and their annotated functions such as Os05g0200500 (Casein kinase 1), Os05g0200500 (Casein

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kinase 1), LOC_Os12g17780 (Expressed protein), Os01g0587400 (Serine/threonine kinase- related domain containing protein), Os12g0148700 (Adipose-regulatory protein) and

LOC_Os01g59440 (Brassinosteroid insensitive 1) are shown in Figure 4-4B.

GWA analysis and identification of candidate genes for relative water content, leaf rolling, chlorophyll content and at vegetative stage Relative water content (RWC), leaf rolling (LR), and chlorophyll content (SPAD) are leaf morpho-physiological traits indicating water status, sympthoms of water deficit conditions and amount of cholorophyll in leaf, respectively. Rice is a drought sensitive crop with drought stress affecting these leaf morph-physiological traits. To identify the candidate genes related to RWC,

LR and SPAD, these traits were dissected at the genomics level using GWAS models. For GWA analysis for these traits, we used the phenotyping data of these three leaf based traits of 206 rice genotypes of the URMC and a set of 204,262 genome-wide SNPs using the FarmCPU model.

The GWA analysis calculated –log10 (p) values that are used to identify the highly significant (– log10 (p) ≥ 7) and prob (–log10 (p) 5.0-6.9) SNPs assocaited with RWC, LR, and SPAD. The HS-

SNPs and P-SNPs were selected to identify candidate genes in genomic window of ±10kb in the reference genome (Nipponbare) for the significant associations using gene annotations from the

Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/) and IRGSP 1.0

(http://rapdb.dna.affrc.go.jp/).

GWA analysis identified 17 SNPs associated with RWC under drought stress and the

SNPs showed their positive and negative effects on RWC in the analysis. All 17 P-SNPs showing –log10 (p) ≥ 5.0-6.9 have probable association with RWC. These 17 P-SNPs with their genomic positions across the genome and positive & negative effects on RWC are listed in Table

4-8. The 17 SNPs linked with 24 candidate genes, their locus Ids and annotated functions are shown in Supplementary Table 4-8. All 24 candidate genes and their annotated functions such as

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Os05g0207900 (ulp1 protease), Os05g0208000 (2-oxoglutarate/malate translocator),

Os12g0293100 (Telomerase reverse transcriptase), Os01g0510600 (Tetratricopeptide-like helical domain containing protein), Os05g0230900 (Glyoxalase I ), Os12g0407200 (OsMADS74),

Os12g0407300 (Intron maturase, type II family protein), Os08g0525000 (Ras GTPase family protein), LOC_Os08g413500 (FAD binding domain-containing protein), Os11g0660500

(Translationally controlled tumor protein and response to mercury tolerance), Os02g0793300

(Nudix hydrolase 3, AtNUDT3)/Splice isoform 2), Os02g0793200 (Pentatricopeptide repeat protein), Os02g0793300 (Armadillo-type fold protein), Os11g0660500 (Translationally controlled tumor protein response to mercury tolerance), Os01g0611100 (GTP-binding nuclear protein Ran-2 ), Os03g0812800 (EF hand family protein), LOC_Os05g30220 (NBS-LRR protein/disease resistance RPP13-like protein 1/P-loop containing nucleoside triphosphate hydrolases), Os01g0598200 (Calcineurin B-like protein 8), Os07g0281800 (Aldehyde oxidase),

Os12g0286300 (Cytochrome P450 domain containing protein), Os01g0895300 (Auxin- responsive protein), Os01g0895500 (OsRhmbd5), LOC_Os01g67054 (Calreticulin precursor protein ) and LOC_Os11g31360 (No Apical Meristem protein-NAM) are shown in Figure 4-5A.

In this study, GWAS was an useful tool to identify 11 SNPs associated with LR under drought stress, and the SNPs showed both positive and negative effects on LR in the analysis. All

11 SNPs showing –log10 (p) ≥ 5.0-6.9 are P-SNPs associated with LR. These 11 P-SNPs with their genomic positions and their positive & negative effects on LR are listed in Table 4-9A. The

11 SNPs linked with 11 candidate genes, their locus Ids and annotated functions are shown in

Supplementary Table 4-9A. All 11 candidate genes and their annotated functions such as

Os04g0632100 (RECEPTOR-like cytoplasmic kinase 163), Os06g0114200 (ATARFB1B/GTP binding), Os06g0198500 (RmlC-like jelly roll fold domain containing protein response to leaf

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rolling in Arabidopsis), Os03g0395000 (Heme oxygenase 2), Os02g0252600 (Aminotransferase, class IV family protein), Os01g0689000 (DnaK), Os01g0375500 (Shikimate biosynthesis protein aroDE), Os02g0754000 (TLD family protein), Os02g0754000 (TLD family protein),

LOC_Os04g40510 (Glycosyl hydrolase family 5 protein) and Os02g0754000 (TLD family protein) are shown in Figure 4-5B.

For chlorophyll content, GWA analysis identified 14 SNPs associated with chlorophyll content under drought stress, which showed their positive and negative effects on chlorophyll content. The 14 SNPs include 1 HS-SNP showing –log10 (p) ≥ 7.0, and 13 P-SNPs showing – log10 (p) ≥ 5.0- 6.9 are linked with chlorophyll content, and the significant SNPs with their genomic positions are listed in Table 4-9B. These 14 SNPs linked with 17 candidate genes and their locus Ids and annotated functions are shown in Supplementary Table 4-9B. Out of 17 candidate genes, only 16 candidate genes and their annotated functions such as

LOC_Os03g36100 (NA), Os01g0617500 (Tetratricopeptide-like helical protein), Os01g0617600

(Plant organelle RNA recognition protein), Os08g0197000 (Cyclin-like F-box domain containing protein), Os08g0196700 (HAP2 SUBUNIT OF CCAAT-BOX BINDING COMPLEX/HAP2 subunit of HAP complex, Nuclear Factor Y (NF-YA) transcription factor to that confers drought stress tolerance), Os06g0707000 (MATE efflux family protein, overlapping with glycosyl transferase gene, Function: Genome-wide expression analysis revealed modulation of genes involved in plant growth, development and biotic stress in transgenic lines), LOC_Os05g27740

(Expressed protein), Os04g0415100 (Expressed protein), Os01g0592900 (Regulator of telomere elongation helicase 1), Os08g0192900 (RNA-binding nucleolin/Nucleotide-binding, alpha-beta plait domain), LOC_Os12g29434 (Wall-associated receptor kinase 3 precursor/RECEPTOR-

LIKE CYTOPLASMIC KINASE 368), Os01g0616900 (Ctps protein), Os08g0300200 (Plant

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synaptotagmin), Os03g0429900 (OSIGBa0135L04.1 protein), Os03g0430000 (Twin arginine translocation signal) and Os08g0200100 (LUC7 related family protein) are shown in Figure 4-6.

GWA analysis and identification of candidate genes for chlorophyll fluorescence and its components at vegetative stage

Chlorophyll fluorescence components such as variable fluorescence (Fv’), maximum variable (Fm’), and the ratio of Fv’ and Fm’ (Fv’/Fm’), Electron transport rate (ETR), the efficeincy of photosystem II (PhiPSII) and non-photochemical quenching (qN) are the measurements determining the light use efficiency in the plants. Drought stress affects the efficiency of these components when plants absorb different types of light wavelenghts. To identify unique alleles related to these parameters, GWA analysis was done with the phenotyping data of 206 rice genotypes of the URMC and a panel of 204,262 genome-wide SNPs using FarmCPU model. The

GWA analysis calculated –log10 (p) values that are used to determine highly siginificant ( –log10

(p) ≥ 7) and probable (–log10 (p) ≥ 5.0-6.9) SNPs associated with Fv’, Fm’ (Fv’/Fm’), ETR, ETR,

PhiPSII and (qN). The HSS- and P- SNPs were selected to find candidate genes in a genomic window of ±10kb in the reference genome (Nipponbare) for sigificant hits using from Rice

Genome Annotation Project (http://rice.plantbiology.msu.edu/) and IRGSP 1.0

(http://rapdb.dna.affrc.go.jp/).

In this study, GWA analysis identified 17 SNPs associated with Fv’ under drought stress, with the SNPs showing both positive and negative effects on Fv’ in the analysis. All 17 SNPs showing –log10 (p) ≥ 5.0-6.9 are probably associated with the Fv’. These 17 P-SNPs with their genomic positions across the genome and their positive & negative effects on Fv’ are listed in

Table 4-10. The 17 SNPs linked with 25 candidate genes, their locus Ids and annotated functions are shown in Supplementary Table 4-10. Out of 25 candidate genes, only 16 candidate genes and their annotated functions such as Os12g0156000 (OSIGBa0134P10.11 protein), New SNP (No

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locus Id), Os10g0576900 (NAD(P)-binding domain containing protein), Os04g0605500 (P-type

IIB Ca(2+) ATPase response to stress tolerance), Os10g0415800 (Prolyl oligopeptidase family protein), Os10g0415900 (General control of Amino-acid synthesis 5), Os12g0508266 (Histone deacetylase superfamily protein), Os09g0479400 (Phenylalanyl-tRNA synthetase, class IIc),

Os09g0479500 (NB-ARC protein), Os02g0705000 (NAD(P)-binding domain containing protein), Os07g0583200 (Mitochondrial transcription termination factor), Os07g0600400

(WD40/YVTN repeat), Os03g0586500 (Chloroplast post-illumination chlorophyll fluorescence increase), Os06g0704800 (Histone acetyltransferase HAC-like 2), Os06g0704900 (Cell division- like protein) and Os06g0705100 (Thylakoid lumenal 13.3 kDa protein) are shown in Figure 4-

7A.

For Fm’, a total of 15 SNPs associated with Fm’ under drought stress condition was found from the GWA analysis, and the SNPs expressed their positive and negative effects on Fm’. All

15 SNPs having –log10 (p) ≥ 5.0-6.9 have probable association with Fm’. These 15 P-SNPs with their genomic positions across the genome and their positive & negative effects on Fm’ are listed in Table 4-11. The 15 SNPs linked with 19 candidate genes, their locus Ids and annotated functions are listed in Supplementary Table 4-11. Out of 19 candidate genes, only 14 candidate genes and their annotated functions, such as Os12g0502700 (Heat shock protein DnaJ, N- terminal domain containing protein), Os12g0502800 (Ureide permease 2), Os11g0435300

(Ankyrin repeat domain containing protein), Os10g0576900 (NAD(P)-binding domain containing protein), Os09g0549500 (Nucleotide-binding, alpha-beta plait domain containing protein), Os09g0549600 (Heavy metal transport/detoxification protein domain containing protein), Os10g0450900 (Glycine-rich cell wall structural protein 2 precursor), Os05g0323100

(Rhodanese protein), Os07g0583200 (Mitochondrial transcription termination factor),

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Os08g0205150 (NB-ARC domain containing protein), Os11g0435300 (Ankyrin repeat protein),

Os02g0704900 (Inorganic pyrophosphatase-like protein), Os02g0705000 (NAD(P)-binding domain containing protein), Os02g0662700 (SCARECROW-LIKE 1, hairy meristem 1) are shown in Figure 4-7B.

GWA analysis identified 29 SNPs associated with Fv’/Fm’ under drought stress, which showed their positive and negative effects on Fv’/Fm’. All 29 SNPs including 8 HS-SNPs showing

–log10 (p) ≥ 7.0 and 21 P-SNPs showing –log10 (p) ≥ 5.0- 6.9 are linked with Fv’/Fm’ and all

HS-SNPS and P- SNPs with their genomic positions across the genome are listedin Table 4-12.

These 29 SNPs linked with 34 candidate genes and their locus Ids and annotated functions are listed in Supplementary Table 4-12. Out of 34 candidate genes, only 21 candidate genes and their annotated functions such as Os08g0180000 (mRNA cap guanine-N7 methyltransferase 1),

Os04g0605500 (P-type IIB Ca(2+) ATPase response to stress tolerance), Os07g0447800

(Alpha-D-phosphohexomutase, alpha/beta/alpha I, II and III), Os02g0622500 (Hypothetical protein), Os07g0540200 (OSIGBa0116M22.12 protein), Os04g0346000 (2OG-Fe(II) oxygenase protein), Os03g0625700 (Roothairless 1), Os11g0201540 (Hypothetical protein), Os11g0634200

(Expressed protein), Os06g0367500 (B-cell receptor-associated protein 31 protein.),

Os05g0289400 (CRN-Crooked neck) protein), Os10g0394200 (OsSub60 - Putative Subtilisin homologue), Os09g0297000 (Ferrochelatase-1, chloroplast precursor), Os02g0215900 (Protein kinase, catalytic protein), Os01g0102850 (Nitrilase 2), Os01g0102900 (Regulation of light- dependent attachment of LEAF-TYPE FERREDOXIN-NADP+ OXIDOREDUCTASE (LFNR) to the thylakoid membrane), Os01g0103100 (TGF-beta receptor, type I/II extracellular region protein), Os03g0165000 (DNA topoisomerase 3 protein), Os07g0608200 (HAP2 SUBUNIT OF

CCAAT-BOX BINDING COMPLEX), Os07g0608300 (Alpha/beta hydrolase fold-1 domain

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protein) and Os12g0514900 (Mitochondrial import inner membrane translocase) are shown in

Figure 4-8A.

For the photosystem II efficiency (PhiPS II), a total of 12 SNPs associated to PhiPS II were found, which exhibited their positive and negative effects on PhiPS II. All 12 SNPs are P-

SNPs showing –log10 (p) ≥ 5.0- 6.9 linked with PhiPS II, the P-SNPs with their genomic positions across the genome are listed in Table 4-13. These 12 SNPs linked with 17 candidate genes, their locus Ids and annotated functions are listed in Supplementary Table 4-13. All 21 candidate genes and their annotated functions such as Os01g07278000 (Peptidase S8, subtilisin- related domain containing protein ), Os01g0727840 (Subtilisin 3), Os12g0574000 (Amino acid transporter transmembrane protein), Os02g0661400 (Protein of unknown function DUF597),

Os06g0644800 (Glucosidase II beta subunit-like domain containing protein), Os06g0644700

(Amino acid transporter transmembrane protein), Os07g0531700 (NUC153 domain containing protein), Os07g0531600 (Phospholipid/Glycerol acyltransferase domain containing protein),

Os01g0220300 (Translation elongation factor EF1B), Os07g0124600 (Nucleotide-binding, alpha-beta), Os01g0911200 (Dolichyl diphosphooligosaccharide-protein Glycosyltransferase),

Os01g0911300 (ABC TRANSPORTER B FAMILY MEMBER 24), Os11g0593500

(OsFBDUF57 - F-box and DUF domain containing protein), Os01g0149350 (NB-ARC domain containing protein), Os01g0954400 (Hydroxyproline-rich glycoprotein DZ-HRGP precursor),

Os09g0439400 (Pectin lyase fold/virulence factor) and Os09g0439500 (Type II chlorophyll a/b binding protein from photosystem I precursor) are shown in Figure 4-8B.

GWA analysis identified 18 SNPs associated with ETR under drought stress, which showed their positive and negative effects on ETR. All 17 SNPs are P-SNPs showing –log10 (p)

≥ 5.0- 6.9 linked with ETR, and all these P-SNPs with their genomic positions across the genome

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are listed in Table 4-14. These 18 SNPs associated with 22 candidate genes, with locus Ids and annotated functions of the candidate genes are listed in Supplementary Table 4-14. All 22 candidate genes and their annotated functions such as New SNP (No locus found),

Os04g0640700 (Beta-D-xylosidase/Glycosyl hydrolase 3), LOC_Os02g47744 (MYB family transcription factor), Os04g0423600 (SET domain containing protein), Os04g0423800

(Peroxidase), Os11g0206400 (Mitochondria transcription termination factor), Os01g0727800

(Peptidase S8 protein), Os11g0683700 (Pectin lyase fold domain containing protein),

Os07g0124500 (Eukaryotic translation initiation factor 3 subunit 8), Os07g0124600 (Nucleotide- binding, alpha-beta plait domain containing protein), Os03g0226501 (Cation efflux family protein), LOC_Os06g07780 (Vesicle-associated membrane protein), Os01g0549400 (RNA helicase-like protein DB10), Os04g0632500 (Protein kinase, catalytic containing protein),

Os04g0632600 (Bulb-type lectin protein), Os07g0531700 (NUC153 domain containing protein),

Os12g0582700 (Cytochrome P450), Os03g0213800 (Mitochondrial substrate carrier family protein), LOC_Os04g24220 (OsWAK32-OsWAK receptor-like protein kinase), Os02g0291000

(Calcineurin B ), Os02g0290900 (GDSL-motif lipase/hydrolase family protein) and

Os04g0590400 (GRAS transcription factor response to drought stress response) are shown in

Figure 4-9A.

For non-photochemical quenching (qN), we found 19 SNPs associated with qN under drought stress in the GWA analysis, and all the SNPs showed their positive and negative effects on qN. All 19 SNPs are P- SNPs showing –log10 (p) ≥ 5.0- 6.9 linked with qN and all these P-

SNPs with their genomic positions across the genome are listed in Table 4-15. These 19 SNPs linked with 24 candidate genes, the locus Ids and annotated functions of these candidate genes are listed in Supplementary Table 4-15. All 24 candidate genes and their annotated functions

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such as Os02g0493300 (Etratricopeptide-like helical protein), Os06g0547400 (Peroxidase

P7/class III peroxidase 86/peroxidase FLXPER4/PER4), Os12g0156000 (OSIGBa0134P10.11 protein), Os04g0605500 (P-type IIB Ca(2+) ATPase response to stress tolerance/ calcium- transporting ATPase 8/plasma membrane-type), Os04g0632100 (S-locus receptor-like kinase

RLK13), Os04g0632500 (Protein kinase/catalytic protein), Os04g0605500 (P-type IIB Ca(2+)

ATPase response to stress/calcium-transporting ATPase 8/plasma membrane-type),

Os03g0823700 (Ras-related protein Rab11C), Os01g0938100 (Photosystem II protein Psb28),

Os01g0150200 (Protein-Tyrosine phosphatase/PTPLA protein), Os01g0150400 (3-hydroxyacyl-

CoA dehydratase PASTICCINO 2A), Os11g0629200 (Vacuolar sorting-associated protein 26),

Os11g0629300 (RING finger protein/RING-H2 protein 16/gamma rays-induced RING finger protein1/gamma rays-induced RING finger protein 1), Os03g0253500 (D111/G-patch protein),

Os01g0286100 (Helix-loop-helix DNA-binding protein), Os05g0226000 (Proline-rich protein),

Os06g0690700 (Cadmium/zinc-transporting ATPase response to drought tolerance),

Os04g0605500 (P-type IIB Ca(2+) ATPase response to stress tolerance), Os08g0395400

(Hypothetical protein), Os09g0515200 (Peptidase/T1 family/N-terminal nucleophile aminohydrolases), Os03g0659900 (S3 self-incompatibility locus), Os03g0366200

(CAMK_CAMK_like.18 - CAMK includes calcium/calmodulin dependent protein/Protein kinase), Os04g0162100 (ZOS4-02 - C2H2 zinc finger protein) and Os08g0543050 (NBS2-

RDG2A) are shown in Figure 4-9B.

CONCLUSIONS

Based on a review of previous studies in rice under drought stress, there has not yet been any published research on use of the USDA rice mini-core collection (URMC) of rice genotypes for GWA analysis and identification of candidate genes based on unique SNPs associated to

220

natural variation for plant productivity, leaf morphological and physiological traits contributing to high yield production in rice. In this study, GWA analysis was conducted using the phenotyping data of the URMC of 206 rice genotypes for water use efficiency, photosynthesis, transpiration rate, stomata conductance, intercellular CO2, the ratio of intercellular CO2 and ambient CO2 concentration, plant biomass, number of tillers, relative water content, leaf rolling, chlorophyll content, Fv’, Fm’, Fv’/Fv’, electron transport rate, the efficiency of photosystem II and non-photochemical quenching at vegetative stage under drought stress conditions in the greenhouse. The aim of this study was to identify SNPs significantly associated with water use efficiency, photosynthesis, transpiration rate, stomata conductance, intercellular CO2, the ratio of intercellular CO2 and ambient CO2 concentration, plant biomass, number of tillers, relative water content, leaf rolling, chlorophyll content, Fv’, Fm’, Fv’/Fv’, electron transport rate, the efficiency of photosystem II and non-photochemical quenching in the population of 206 rice genotypes of the URMC. Based on the significance level of genome-wide SNPs in rice genotypes, GWA analysis identified several SNPs linked to morpho-physiological traits such as for WUEi, 24

SNPs linked to 24 candidate genes such as Os03g0139200 (Cytochrome-containing protein regulating root & shoot development), Os04g0591900 (Panicle & seed development under abiotic stress, and a LOC_Os01g59990 (Glucocorticoid receptor synthesizes polysaccharides and glycoproteins in the plant cell wall). For photosynthesis, 16 SNPs linked to 29 candidate genes including Os11g0639000 (PHYB1 regulates elongation of sheath and stem tissues of mature plants and contributes to the light-mediated regulation of PhyA and Cab gene transcripts) were found in this study. A total of 26 SNPs linked to 32 candidate genes associating to transpiration rate such as Os05g0333500 (GS5 and the guanine nucleotide-binding protein alpha-1 determines grain size and yield in rice and controls response of stomatal opening/closure under abiotic

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stress), Os05g0333500 (Heat shock protein DnaJ regulates chloroplast development),

Os09g0497000 (Adenine nucleotide translocator 1 and PGR5 control the utilization of light energy in order to avoid photoinhibition during photosynthesis, and impairment in the downregulation of photosystem II photochemistry in response to intense light) and

Os05g0497600 (Arsenic-induced RING E3 ligase responses to abiotic stress) were found in the

GWA analysis. The GWA analysis found 10 SNPs linked to 11 candidate genes associating to stomatal conductance including Os06g0597400 (PINHEAD gene responses shoot apical meristem (SAM) maintenance and leaf development). For Ci & Ci/Ca, 19 SNPs linked to 22 candidate genes including Os03g0669000 (DEAD-box RNA helicase regulates the thermotolerant growth at high temperature), Os08g0137100 (WD40 regulates vegetative and reproductive development) and Os09g0396900 (Vacuolar membrane transporter) translocates Fe and Zn between flag leaves and seeds in Arabidopsis and rice. We also found 23 SNPs linked to

25 candidate genes associating with plant biomass in which Os10g0462800 (PTAC3-PLASTID

TRANSCRIPTIONALLY ACTIVE3 regulates plastid development for gaining more chlorophyll content in Arabidopsis) and 7 SNPs linked to 7 candidate genes associating number of tillers in which Os05g0200500 (Casein kinase 1), Os01g0587400 (Serine/threonine kinase-related domain containing protein), Os12g0148700 Adipose-regulatory protein) and LOC_Os01g59440

(Brassinosteroid insensitive 1). GWA analysis found 17 SNPs linked with 24 candidate genes associated with RWC in which LOC_Os11g31360 (No Apical Meristem NAM), 11 SNPs linked with 11 candidate genes associated with LR in which Os06g0198500 (RmlC-like jelly roll fold domain containing protein responses leaf rolling in Arabidopsis) and 14 SNPs linked with 17 candidate genes associated with chlorophyll content in which Os08g0196700 (HAP2/Nuclear

Factor Y (NF-YA) transcription factor expresses drought stress tolerance) and Os06g0707000

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(MATE efflux/ glycosyl transferase gene revealed modulation of genes involved in plant growth

& development under abiotic stress in transgenic lines in Arabidopsis). In addition, GWA analysis identified 17 SNPs linked with 25 candidate genes associated with Fv’ in which

Os03g0586500 (Chloroplast post-illumination chlorophyll fluorescence) and Os06g0704900

(Regulation of the cell division in plants), 15 SNPs linked with 19 candidate genes for Fm’ in which Os02g0662700 (SCARECROW-LIKE 1/ hairy meristem 1 regulates hairs on meristem in

Arabidopsis), 29 SNPs linked with 34 candidate genes associated with Fv’/Fm’ in which

Os04g0605500 (P-type IIB Ca(2+) ATPase expresses stress tolerance) and Os01g0102900

(LEAF-TYPE FERREDOXIN-NADP+ OXIDOREDUCTASE regulates the development of light-dependent attachment to the thylakoid membrane). In this study, we identified 12 SNPs linked with 21 candidate genes associated with PhiPS II in which Os09g0439500 (Type II chlorophyll a/b binding protein initiates the development of chlorophyll from photosystem I precursor). For ETR and qN, 17 SNPs linked with 22 candidate genes including

LOC_Os02g47744 (MYB transcription factor), Os11g0206400 (Mitochondria transcription termination factor and GRAS transcription factor responses to drought stress tolerance in

Arabidopsis & rice plants) and 19 SNPs linked with 24 candidate genes including

Os04g0605500 (P-type IIB Ca(2+) ATPase/ calcium-transporting ATPase 8/plasma membrane- type genes exhibited stress tolerance in plants) and Os01g0938100 (Psb28 gene responses photosystem II to increase photosynthesis) were identified using GWA analysis, respectively.

The results suggest that with the help of the powerful statistical tool (FarmCPU) and SNPs detection from low coverage of genomes sequencing, it is possible to analyze water use efficiency, other physiological, plant productivity and leaf morphological traits under drought stress in greenhouse conditions using GWA analysis. In this study, we show how GWA analysis

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could be a powerful tool in providing an insight into the genetic architecture of WUEi and other drought resistance related traits from a diverse collection of rice genotypes, and identify candidate genes associated with multiple traits under drought stress at the vegetative stage in greenhouse conditions. In continuation, theorefore we would like to validate candidate genes related to WUEi and drought resistance related traits in further research.

REFERENCES Agrama, H.A., W. Yan, F. Lee, R. Fjellstrom, M-H. Chen, M, Jia, A. McClung. 2009. Genetic assessment of a mini-core developed from the USDA rice gene bank. Crop Science. 49: 1336–1346.

Basu, S., V. Ramegowda, A. Kumar, and A. Pereira. 2016. Plant adaptation to drought. F1000Research, 5(F1000 Faculty Rev): 1554. (doi: 10.12688/f1000research.7678.1

Blum, A. 2009. Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. Field Crops Research 112: 119–123.

Bouman, B.A.M., E. Humphreys, T.P. Tuong and R. Barker. 2007. Rice and water. Advance Agronomy. 92: 187-237.

Bradbury, P.J., Z. Zhang, D.E. Kroon, T.M. Casstevens, Y.Ramdoss, E.S. Buckler. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 23 (19): 2633-2635.

224

Chen, W., Y. Gao, W. Xie, L. Gong, K. Lu, W. Wang et al. 2014. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nature Genetics. 46: 714–721.

Famoso, A.N., K. Zhao, R.T. Clark, C.W. Tung, M.H. Wright, C. Bustamante, L.V. Kochian, S.R. McCouch. 2011. Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genetics. 7: e1002221.

Flint-Garcia, S.A., J.M. Thornsberry, E.S. Buckler. 2003. Structure of linkage disequilibrium in plants. Annual Review of Plant Biology. 54: 357–374.

Garris, A.J., T.H. Tai, J. Coburn, S. Kresovich, and S. McCouch. 2005. Genetic structure and history, technology, and production. Crop Production Ser #6149. John Wiley & Sons New York, NY.

Han, B. and X. Huang. 2013. Sequencing-based genome-wide association study in rice. Current Opinion in Plant Biology. 16:133–138.

Huang, X. and B. Han. 2014. Natural Variations and Genome-Wide Association Studies in Crop Plants. Annual Review of Plant Biology. 65: 531-551.

Huang, X. H., X.H. Wei, T. Sang, Q. Zhao, Q. Feng, Y. Zhao, C.Y. Li, C.R. Zhu, T.T. Lu, Z. W. Zhang, M. Li, D.L. Fan, Y.L. Guo, A.H. Wang, L. Wang, L.W. Deng, W.J. Li, Y. Q. Lu, Q. J. Weng, K.Y. Liu, T, Huang, T. Y. Zhou, Y.F. Jing, W. Li, Z. Lin,E.S. Buckler, Q. Qian, Q.F. Zhang, J.Y. Li, B. Han. 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics. 42: 961–967.

Huang, X., Y. Zhao, X. Wei et al. 2011. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm, Nature. Genetics. 4: 32–39.

Impa, S. M., S. Nadaradjan, P. Boominathan. 2005. Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Science. 45: 2517–2522.

Karaba, A., S. Dixit, R. Greco, A. Aarons, K.R. Trijatmiko, N. Marsch-Martinez, A. Krishnan, K.N. Nataraja M, Udayakumar, and A. Pereira. 2007. Improvement of water use efficiency in rice by expression of HARDY, an Arabidopsis drought and salt tolerance gene. Proceeding of the National Academy of Sciences. 104:15270-75.

225

Khush, G. S. 2005. What it will take to feed 5.0 billion rice consumers by 2030. Plant Molecular Biology. 59: 1–6.

Kijne, J.W., R. Barker, D. J. Molden (Eds.). 2003. Water Productivity in Agriculture: Limits and Opportunities for Improvement. CABI, UK, p. 332.

Korneliussem, T.S., A Albrechtsen, A. Nielson. 2014. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics. 15:356.

Liu, X., M. Huang, B. Fan, E.S. Buckler, Z. Zhang. 2016. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genetics. 12: e1005767.

Mae. 1997. Physiological nitrogen efficiency in rice: Nirogen utilization, photosynthesis, and yield potential. Plant soil. 196: 201-210.

Matus, A., A.E. Slinkaed, and C.V. Kessel. 1995. Carbon-13 isotope discrimination at several growth stages in lentil, spring wheat and canola. Canadian Journal of Plant Sciences. 75: 577–581.

Meng, Q., K. Wang, X. Liu, H. Zhou, L. Xu, Z. Wang and M. Fang. 2017. Identification of growth trait related genes in a Yorkshire purebred pig population by genome-wide association studies. Asian-Australas Journal of Animal Science. 30:462-469.

Nguyen, H.T., R.C Babu and A. Blum. 1997. Breeding for drought resistance in rice: physiology and molecular genetic considerations. Crop Science. 37: 1426–1434.

Passioura, J.B. 1986. Resistance to drought and salinity: Avenues for improvement. Australian Journal of Plant Physiology. 13: 191–201.

Postel, S. 1997. Last Oasis. Facing water scarcity, New York: Norton and Company. pp 239.

Vaughan, D.A. 1989. The genus Oryza L. Current status of taxonomy. IRRI Research Paper. Serial Number 138.

Wang, H., X. Xu, F.G. Vieira, Y. Xiao, Z. Li, J. Wang, R. Nielsen, and C. Chu. 2016. The Power of Inbreeding: NGS Based GWAS of Rice Reveals Convergent Evolution during Rice Domestication. Molecular Plant. 9: 975–985. doi: 10.1016/j.

226

Yang, N., Y.L. Lu, X. H. Yang, J. Huang, Y. Zhou, F.H. Ali, W.W. Wen, J. Liu, J.S. Li and J.B. Yan. 2014. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genetics. 10: e1004573.

Yu, J., JB Holland, MD McMullen, ES Buckler. 2008. Genetic Design and Statistical Power of Nested Association Mapping in Maize. Genetics .178: 539–551.

Zhang, Z., E. Ersoz, C.Q. Lai, R. J. Todhunter, H. K. Tiwari, M.A. Gore, P. J. Bradbury, J. Yu, D.K. Arnett, J.M. Ordovas et al. 2010. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics. 42:355-360.

Zhao, K., C.W. Tung, G.C. Eizenga, M.H. Wright, M.L. Ali, A.H. Price et al. 2011. Genome- wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications. 2: 467.

Zhu, C., M. Gore, E. S. Buckler, J Yu. 2008. Status and prospects of association mapping in plants. Plant Genome 1: 5–20.

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Table 4-1. Identification of 24 SNPs associated with instantaneous water use efficiency (WUEi) under drought stress by GWA analysis. Based on highly significant SNPs (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to WUEi under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S3_2152064 3 2152064 2.67E-13** 2.4924 Os03g0139200 2147064-2157063 S11_376411 11 376411 7.97E-12** 2.00347 LOC_Os11g01690 371411-381410 S2_17197164 2 17197164 2.29E-11** 2.50286 LOC_Os02g29040 17192164-17202163 S4_21508372 4 21508372 1.60E-09** -1.9013 LOC_Os04g35370 21503372-21513371 S1_27362973 1 27362973 1.36E-08** -1.5296 LOC_Os01g47800 27357973-27367972 S12_25809127 12 25809127 3.60E-08** 1.89704 LOC_Os12g41690 25804127-25814126 S4_29962767 4 29962767 1.21E-07** -1.7313 Os04g0591900 29957767-29967766 S2_33768395 2 33768395 4.64E-07** 1.25372 Os02g0794500 33763395-33773394 S12_2663623 12 2663623 5.85E-07** 1.56658 LOC_Os12g05780 2658623-2668622 S1_34683187 1 34683187 1.63E-06* -1.4407 LOC_Os01g59990 34678187-34688186 S7_16957817 7 16957817 1.90E-06* 1.25938 LOC_Os07g28920 16952817-16962816 S1_35724127 1 35724127 2.01E-06* 1.6801 LOC_Os01g61760 35719127-35729126 S4_6595499 4 6595499 2.78E-06* 1.24582 LOC_Os04g12010 6590499-6600498 S6_26783445 6 26783445 3.92E-06* 1.15563 LOC_Os06g44374 26778445-26788444 S2_23119995 2 23119995 5.07E-06* -1.5191 LOC_Os02g38220 23114995-23124994 S5_13537890 5 13537890 6.20E-06* -1.2045 LOC_Os05g23610 13532890-13542889 S10_5652341 10 5652341 2.83E-05* 1.37577 LOC_Os10g10280 5647341-5657340 S7_22260452 7 22260452 4.23E-05* -1.1192 LOC_Os07g0557601 22255452-22265451 S8_26031982 8 26031982 4.82E-05* -1.1457 LOC_Os08g41220 26026982-26036981 S7_4264328 7 4264328 6.11E-05* 1.03412 LOC_Os07g08330 4259328-4269327 S8_17710486 8 17710486 6.91E-05* -1.1596 Os08g0377200 17705486-17715485 S12_14828417 12 14828417 7.15E-05* -1.5848 Os12g25630 14823417-14833416 S7_27710032 7 27710032 8.80E-05* -1.0195 Os07g0658300 27705032-27715031 S2_19705515 2 19705515 9.01E-05* -1.2235 Os02g0534700 19700515-19710514 ** * Note: - : Significant SNP (–log10 (p) ≥7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

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Table 4-2. Identification of 16 SNPs associated with photosynthesis under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to photosynthesis under drought stress in the URMC are listed. SNP Chr Position P.value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S2_2785855 2 2785855 1.58E-06* 4.475592 Os02g0150450 2,780,855-2,790,854 Os02g0150600 2,780,855-2,790,854 S7_7349371 7 7349371 1.79E-05* 4.106082 LOC_Os07g12830 7, 349, 371-7, 349, 371 Os07g0232200 7, 349, 371-7, 349, 371 S4_2025812 4 2025812 2.54E-05* 3.693537 Os04g0132300 2,020,812-2,030,811 S7_23101562 7 23101562 2.59E-05* -4.25398 LOC_Os07g38450 23,096,562-23,106,561 Os07g0572050 23,096,562-23,106,561 Os07g0572100 23,096,562-23,106,561 Os07g0572300 23,096,562-23,106,561 S10_6790852 10 6790852 2.63E-05* -2.84816 LOC_Os10g12174 8,298,559-8, 308,558 S5_8303559 5 8303559 2.99E-05* 3.483107 Os05g0235600 8,298,559-8, 308,558 Os05g0235701 8,298,559-8, 308,558 S1_42815226 1 42815226 3.99E-05* 3.700964 LOC_Os01g73880 42815226-42825226 S6_16422740 6 16422740 4.87E-05* 3.159923 Os06g0483200 16,417,740-16,427,739 S8_16507513 8 16507513 5.26E-05* -4.11221 LOC_Os08g27010 16507513-16517513 S9_17416588 9 17416588 5.65E-05* -5.63987 Os09g0460200 17,411,588-17,421,587 Os09g0460300 17,411,588-17,421,587 Os09g0460400 17,411,588-17,421,587 Os09g0460500 17,411,588-17,421,587 S11_25260692 11 25260692 5.87E-05* 2.607967 Os11g0639000 25,255,692-25,265,691 Os11g0639100 25,255,692-25,265,691 S8_27194211 8 27194211 7.27E-05* 2.822689 Os08g0543275 27,189,211-27,199,210 S3_14721648 3 14721648 7.85E-05* 3.273042 LOC_Os03g25720 14721648-14731678 Os03g0374100 14721648-14731678 S6_22352789 6 22352789 8.35E-05* -2.19339 LOC_Os06g37750 22, 347,789-22, 357,788 Os06g0575400 22, 347,789-22, 357,788 Os06g0575500 22, 347,789-22, 357,788 S2_22217360 2 22217360 8.96E-05* -3.01226 LOC_Os02g36830 22217360-22227360 S8_16520517 8 16520517 9.99E-05* 3.218987 LOC_Os08g27040 16520517-16530517 * Note: - : Probable SNPs (–log10 (p) ≥ 5.0-6.9)

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Table 4-3. Identification of 26 SNPs associated with transpiration rate (TR) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to TR under drought stress the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S2_13956764 2 13956764 2.28E-11** 0.38100166 LOC_Os02g24090 13,951,764-13,961,763 S9_15127510 9 15127510 6.85E-11** 0.3127728 Os09g0420100 15,122,510-15,132,509 S3_5479027 3 5479027 9.22E-09** -0.5572354 Os03g0205000 5,474,027-5,484,026 S5_15612727 5 15612727 1.57E-08** -0.2529412 Os05g0333200 15,607,727-15,617,726

Os05g0333500 15,607,727-15,617,726 S10_11315813 10 11315813 3.54E-08** 0.28094645 LOC_Os10g21940 32,125,269-32,135,268 S2_32130269 2 32130269 4.40E-08** 0.29082747 Os02g0762800 32,125,269-32,135,268 S8_26789538 8 26789538 1.81E-07** 0.26196293 Os08g0536200 26,784,538-26,794,537

Os08g0536300 26,784,538-26,794,537 S4_31555023 4 31555023 9.58E-07** -0.1630362 LOC_Os04g52970 31,550,023-31,560,022 S9_19211469 9 19211469 1.02E-06* 0.21010338 Os09g0497000 19,206,469-19,216,468 S8_28379250 8 28379250 5.75E-06* -0.2026961 Os05g0497600 28, 374,250-28, 384,249 S5_24478185 5 24478185 6.52E-06* -0.2330716 Os05g0497600 24,473,185-24,483,184 S3_34448551 3 34448551 7.11E-06* -0.2486761 Os03g0821100 34,443,551-34,453,550

Os03g0821150 34,443,551-34,453,550 S12_21656732 12 21656732 8.01E-06* -0.168189 Os12g0541300 21,651,732-21,661,731

Os12g0541500 21,651,732-21,661,731 S3_8641262 3 8641262 8.69E-06* -0.2418671 Os03g0263000 8,636,262-8,646,261 S4_24550575 4 24550575 1.24E-05* -0.3044302 Os04g0491200 24,545,575-24,555,574 S1_30167611 1 30167611 1.28E-05* -0.2385943 Os01g0723400 30,162,611-30,172,610 S1_11255929 1 11255929 1.66E-05* -0.2682197 LOC_Os01g19850 11,250,929-11,260,928

Os01g0304100 11,250,929-11,260,928 S1_42544784 1 42544784 2.36E-05* -0.2738421 Os01g0964800 42,539,784-42,549,783 S6_3235927 6 3235927 2.90E-05* -0.2544107 LOC_Os06g06850 3235927-3236927 S1_39396339 1 39396339 3.72E-05* 0.2107353 Os01g0904700 39, 391, 339-39,401, 338 S9_19624924 9 19624924 4.21E-05* -0.2127459 LOC_Os09g32930 19624924-19634924 S2_17613183 2 17613183 5.11E-05* -0.2532299 LOC_Os02g29600 17613183-17623183 S3_32572598 3 32572598 5.57E-05* 0.19396746 Os03g0784700 32,567,598-32,577,597 S7_28274358 7 28274358 5.64E-05* -0.1672714 Os07g0669100 28,269, 358-28,279, 357

Os07g0669200 28,269, 358-28,279, 357 S6_4438736 6 4438736 6.27E-05* -0.1929002 Os06g0188000 4,433,736-4,443,735 S9_3726729 9 3726729 8.39E-05* -0.2537968 LOC_Os09g07450 3,721,729-3,731,728 ** * Note: - : Significant SNP (–log10 (p) ≥ 7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

230

Table 4-4. Identification of 10 SNPs associated with stomatal conductance under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to stomatal conductance under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S10_12144290 10 12144290 1.34E-05* 0.03408593 LOC_Os10g23260 12,139,290-12,149,289 S5_7426256 5 7426256 1.38E-05* 0.03109158 Os05g0220600 7,421,256-7,431,255 S4_30200418 4 30200418 1.61E-05* 0.02504475 Os04g0598500 30,200,418-30,200,418 S1_2724329 1 2724329 1.85E-05* 0.03609905 Os01g0150200 2,719, 329-2,729, 328

Os01g0150400 2,719, 329-2,729, 328 S9_19108758 9 19108758 3.58E-05* 0.02966723 LOC_Os09g32010 19,103,758-19,113,757 S6_20591069 6 20590069 4.63E-05* -0.0266906 Os06g0545400 20590069-20600069 S8_2887257 8 2887257 5.25E-05* -0.0303098 Os08g0150200 2,882,257-2,892,256 S6_23543251 6 23543251 5.32E-05* 0.02896659 Os06g0597400 23,538,251-23,548,250 S6_23543255 6 23543255 5.52E-05* 0.02959737 Os06g0597400 23,535,755-23,545,754 S1_29805286 1 29805286 6.67E-05* 0.02685032 Os01g0716200 29,800,286-29,810,285 * Note: - : Probable SNP (–log10 (p) ≥ 5.0-6.9)

231

Table 4-5. Identified 19 SNPs associated with intercellular CO2 concentration (Ci) under drought stress by GWA analysis. Based on highly significant SNPs (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Ci under drought stress in 206 rice genotypes of the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S10_23061543 10 23061543 6.01E-12** 71.1534606 Os12g0562500 23,056,543-23,066,542 S6_7752732 6 7752732 6.29E-12** 80.1322424 New 7,747,732-7,757,731 S7_22628709 7 22628709 1.03E-11** 84.469201 Os07g0564600 22,623,709-22,633,708

Os07g0564533 22,623,709-22,633,708 S1_8631935 1 8631935 1.41E-08** 51.7949922 Os01g0257900 8,626,935-8,636,934 S2_9953428 2 9953428 1.73E-08** -67.653518 Os02g0273100 9,948,428-9,958,427 S3_26379405 3 26379405 2.16E-08** 50.9280604 Os03g0668900 26, 374,405-26, 384,404

Os03g0669000 26, 374,405-26, 384,404 S8_2082999 8 2082999 3.76E-08** -61.37128 Os08g0137100 2,077,999-2,087,998

Os08g0137200 2,077,999-2,087,998 S9_13831846 9 13831846 4.12E-08** 64.4073234 Os09g0396900 13,826,846-13,836,845 S6_20139781 6 20139781 4.28E-08** 61.8288467 Os06g0537500 20,134,781-20,144,780 S5_4156069 5 4156069 1.78E-07** 58.2247184 Os05g0169300 4,151,069-4,161,068 S1_2136231 1 2136231 2.88E-06* -41.757006 Os01g0140400 2,131,231-2,141,230 S3_14397935 3 14397935 6.32E-06* 47.2977641 LOC_Os03g25190 14, 392,935-14,402,934 S8_23503277 8 23503277 8.88E-06* -44.100074 Os08g0477100 23,498,277-23,508,276 S8_15391357 8 15391357 1.34E-05* -55.547782 Os08g0341700 15, 386, 357-15, 396, 356 S11_22008249 11 22008249 1.82E-05* -47.588835 LOC_Os11g37260 20,817,645-20,827,644 S11_22216863 11 22216863 8.75E-05* -50.683148 Os11g0586900 22,211,863-22,221,862 S1_8121101 1 8121101 9.17E-05* -49.249591 Os01g0247500 8,116,101-8,126,100 S11_22009972 11 22009972 9.79E-05* -42.338954 LOC_Os11g37260 20,817,645-20,827,644 S6_13755485 6 13755485 9.90E-05* -44.760977 Os06g0343900 13,750,485-13,760,484 ** * Note: - : Significant SNP (–log10 (p) ≥ 7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

232

Table 4-6. Identified 19 SNPs associated with the ratio of intercellular CO2 (Ci) to the ambient CO2 concentration (Ci/Ca) under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Ci/Ca under drought stress in 206 rice genotypes of the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S3_13597119 3 13597119 1.45E-05* 0.09720568 Os03g0355600 13,592,119-13,602,118 S4_34742626 4 34742626 1.73E-05* -0.1480425 Os04g0680550 34,737,626-34,747,625 S3_10197065 3 10197065 2.15E-05* 0.13518379 Os03g0292900 10,192,065-10,202,064 S6_285546 6 285546 2.44E-05* 0.11826709 Os06g0104100 280,546-290,545

Os06g0104200 280,546-290,545 S8_19315996 8 19315996 3.50E-05* 0.09448642 Os08g0404350 19, 310,996-19, 320,995

Os08g0404300 19, 310,996-19, 320,995 S7_15220513 7 15220513 3.94E-05* -0.109678 Os07g0445800 15,215,513-15,225,512 S2_22177842 2 22177842 5.57E-05* 0.09427961 LOC_Os02g36770 22,172,842-22,182,841 S4_23968453 4 23968453 5.91E-05* -0.0997987 Os04g0479200 23,963,453-23,973,452 S7_23101562 7 23101562 6.85E-05* 0.12083399 Os07g0572100 23,096,562-23,106,561 S2_16413383 2 16413383 7.31E-05* -0.0990121 LOC_Os02g27710 16,408, 383-16,418, 382 S1_3251563 1 3251563 7.36E-05* -0.137712 Os01g0162200 3,246,563-3,256,562 S10_17348982 10 17348982 7.50E-05* -0.1097677 Os10g0469300 17, 343,982-17, 353,981 S5_14717686 5 14717686 7.53E-05* 0.11024366 Os05g0317700 14,712,686-14,722,685 S10_17950439 10 17950439 7.82E-05* 0.09613482 Os10g0478450 17,945,439-17,955,438 S5_28044536 5 28044536 7.93E-05* -0.1066906 Os05g0563400 28,039,536-28,049,535

Os05g0563550 28,039,536-28,049,535 S4_16563159 4 16563159 8.15E-05* -0.106755 Os04g0347900 16,558,159-16,568,158 S2_20135849 2 20135849 8.24E-05* 0.08377045 Os02g0542100 20,130,849-20,140,848 S2_40863 2 40863 8.37E-05* -0.1067479 Os02g0100700 35,863-45,862 S1_33561657 1 33561657 9.37E-05* -0.0709446 Os02g0789700 33,556,657-33,566,656 * Note: - : Probable SNP (–log10 (p) ≥ 5.0-6.9)

233

Table 4-7A. Identification of 23 SNPs associated with plant biomass under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to plant biomass under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S6_23050318 6 23050318 2.00E-17** 1.17773948 LOC_Os06g38830 23,045, 318-23,055, 317 S9_9447344 9 9447344 6.53E-16** -1.3604606 Os09g0323100 9,442, 344-9,452, 343 Os09g0323000 9,442, 344-9,452, 343 S10_12206329 10 12206329 5.49E-11** 0.73969276 new 12,201, 329-12,211, 328 S3_16579553 3 16579553 2.76E-10** 0.73727928 Os03g0405000 16,574,553-16,584,552 Os03g0405100 16,574,553-16,584,552 S9_6589548 9 6589548 5.20E-10** -0.8201621 LOC_Os09g11790 6,584,548-6,594,547 S8_15628604 8 15628604 5.40E-10** 0.91799119 LOC_Os08g25690 15,623,604-15,633,603 S2_14979516 2 14979516 1.57E-09** 1.19877753 LOC_Os02g25624 14,974,516-14,984,515 S10_2288747 10 2288747 8.50E-09** 0.73111274 LOC_Os10g04770 2,283,747-2,293,746 S5_10528231 5 10528231 1.27E-08** 0.49328279 LOC_Os05g18280 10,523,231-10,533,230 S1_34687797 1 34687797 3.16E-07** -0.8964571 Os01g0816000 34,682,797-34,692,796 S2_25348973 2 25348973 4.46E-07** 0.65484556 Os02g0632800 25, 343,973-25, 353,972 S3_6660602 3 6660602 5.45E-07** -0.598373 Os03g0226800 6,655,602-6,665,601 S10_22873793 10 22873793 5.72E-07** 0.54365453 Os10g0575200 22,868,793-22,878,792 S11_24789164 11 24789164 7.76E-07** -0.6233382 LOC_Os11g41330 24,784,164-24,794,163 S8_5453334 8 5453334 1.33E-06* -0.5181507 LOC_Os08g09410 5,448, 334-5,458, 333 S6_4141099 6 4141099 1.73E-06* 0.41071463 Os06g0183100 4,136,099-4,146,098 S7_1118065 7 1118065 2.38E-06* -0.6971438 LOC_Os07g03000 1,113,065-1,123,064 S1_42254657 1 42254657 2.81E-05* -0.6036989 LOC_Os01g72834 42,249,657-42,259,656 S10_17052688 10 17052688 3.22E-05* -0.4877923 Os10g0462800 17,047,688-17,057,687 S9_22039317 9 22039317 5.69E-05* -0.4278833 Os09g0555400 22,034, 317-22,044, 316 S3_1555682 3 1555682 6.29E-05* 0.47933929 Os03g0127600 1,550,682-1,560,681 S8_15207579 8 15207579 6.70E-05* 0.57582332 Os08g0338200 15,202,579-15,212,578 S1_42343145 1 42343145 7.58E-05* -0.557886 Os01g0960400 42, 338,145-42, 348,144 Table 4-7B. Identification of 7 SNPs associated with number of tillers (NOT) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0- 6.9), the candidate genes associated to NOT under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S5_6285571 5 6285571 1.05E-05* 2.22870995 Os05g0200500 6285571-6385571 S5_6285571 5 6285573 1.20E-05* 2.164979147 Os05g0200500 6285573-6385573 S12_10179566 12 10179566 2.02E-05* 2.02134094 LOC_Os12g17780 10179566-10189566 S1_22884580 1 22884580 2.90E-05* 2.58978725 Os01g0587400 22,879,580-22,889,579 S12_2393692 12 2393692 3.46E-05* 2.2800516 Os12g0148700 2, 388,692-2, 398,691 S1_34376759 1 34376759 3.86E-05* -2.2723365 LOC_Os01g59440 34376759-34476759 S4_16868766 4 16868766 5.83E-05* 2.16755307 LOC_Os04g28480 16,863,766-16,873,765

234

Table 4-8. Identification of 17 SNPs associated with relative water content (RWC) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and Probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to RWC under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S5_6692790 5 6692790 2.08E-06* 10.5344473 Os05g0207900 6,687,790-6,697,789

Os05g0208000 6,687,790-6,697,789 S12_11375620 12 11375620 2.99E-06* 10.3036329 Os12g0293100 11, 370,620-11, 380,619 S1_17947180 1 17947180 6.00E-06* 9.15829942 Os01g0510600 17,942,180-17,952,179 S5_7963863 5 7963863 1.25E-05* -9.7436821 7,958,863-7,968,862 Os05g0230900 S12_12314773 12 12314773 1.76E-05* 10.0156606 12, 309,773-12, 319,772 Os12g0407200 Os12g0407300 12, 309,773-12, 319,772 * S8_26127360 8 26127360 1.81E-05 -8.8948943 Os08g0525000 26,122, 360-26,132, 359 LOC_Os08g41350 26,122, 360-26,132, 359 * S11_26512406 11 26512406 3.29E-05 -6.7135883 Os11g0660500 26,507,406-26,517,405 S2_33707704 2 33707704 3.38E-05* 9.42743163 Os02g0793300 33,702,704-33,712,703 Os02g0793200 33,702,704-33,712,703 Os02g0793300 33,702,704-33,712,703 * S11_26512405 11 26512405 3.85E-05 -6.7237606 Os11g0660500 26,507,406-26,517,405 * S1_24184124 1 24184124 5.41E-05 8.65591817 Os01g0611100 24,184,124-24,194,124 * S3_34053558 3 34053558 5.55E-05 6.71026256 Os03g0812800 34,048,558-34,058,557 S5_17515858 5 17515858 6.00E-05* 8.43719581 17,510,858-17,520,857 LOC_Os05g30220 * S1_23498161 1 23498161 6.76E-05 8.79785055 Os01g0598200 23,493,161-23,503,160 *

S7_10734154 7 10734154 7.23E-05 8.96716127 Os07g0281800 10,729,154-10,739,153 * S12_10900182 12 10900182 8.17E-05 8.13304846 Os12g0286300 10,895,182-10,905,181 S1_38930091 1 38930091 8.93E-05* 8.97077865 38,925,091-38,935,090 Os01g0895300 Os01g0895500 38,925,091-38,935,090 LOC_Os01g67054 38,925,091-38,935,090 * S11_18305099 11 18305099 9.96E-05 8.69210589 LOC_Os11g31360 18, 300,099-18, 310,098 * Note: - : Probable SNP (–log10 (p) ≥ 5.0-6.9)

235

Table 4-9A. Identification of 11 SNPs associated with leaf rolling (LR) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0- 6.9), the candidate genes associated to LR under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S4_32177181 4 32177181 1.76E-06* -0.5731214 Os04g0632100 32,172,181-32,182,180 S6_815437 6 815437 1.42E-05* -0.8652476 Os06g0114200 810,437-820,436 S6_4998668 6 4998668 2.73E-05* 0.72392384 Os06g0198500 4,993,668-5,003,667 S3_15926575 3 15926575 3.51E-05* 0.72879634 Os03g0395000 15,921,575-15,931,574 S2_8600658 2 8600658 3.56E-05* -0.587391 Os02g0252600 8,595,658-8,605,657 S1_28431075 1 28431075 5.76E-05* 0.61128610 Os01g0689000 28,431,075-28,441,075 S1_15511201 1 15511201 6.59E-05* -0.6420079 Os01g0375500 15,506,201-15,516,200 S2_31712942 2 31712942 8.17E-05* -0.4134783 Os02g0754000 31,707,942-31,717,941 S2_31712939 2 31712939 8.36E-05* -0.4138093 Os02g0754100 31,707,942-31,717,941 S4_24068096 4 24068096 9.09E-05* -0.6753551 LOC_Os04g40510 24,063,096-24,073,095 S2_31712938 2 31712938 9.70E-05* -0.4107234 Os02g0754100 31,707,938-31,717,937

Table 4-9B. Identification of 14 SNPs associated with chlorophyll content (SPAD) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to SPAD under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S3_20030159 3 20030159 5.97E-07** 3.28579843 Os03g0559900 20025159-20035158 S1_24548418 1 24548418 3.52E-06* 3.12569259 Os01g0617500 24,543,418-24,553,417

Os01g0617600 24,543,418-24,553,417 S8_5606217 8 5606217 8.87E-06* 3.02888381 Os08g0197000 5,601,217-5,611,216

Os08g0196700 5,601,217-5,611,216 S6_29879556 6 29879556 2.86E-05* 2.96969565 Os06g0707000 29,874,556-29,884,555 S5_16159253 5 16159253 3.05E-05* 2.30547189 LOC_Os05g27740 16159253-16169253 S4_20516405 4 20516405 3.19E-05* 2.85184477 Os04g0415100 20,511,405-20,521,404 S1_23185715 1 23185715 3.44E-05* 2.81320068 Os01g0592900 23,180,715-23,190,714 S8_5423771 8 5423771 3.47E-05* 1.96867393 Os08g0192900 5,418,771-5,428,770 S12_17485901 12 17485901 3.94E-05* 2.79018456 LOC_Os12g29434 17,480,901-17,490,900 S1_24505983 1 24505983 4.07E-05* 2.79717213 Os01g0616900 24,500,983-24,510,982 S8_12337252 8 12337252 4.87E-05* 3.21137891 Os08g0300200 12, 332,252-12, 342,251 S3_18012574 3 18012574 5.00E-05* 2.91512086 Os03g0429900 18,007,574-18,017,573

Os03g0430000 18,007,574-18,017,573 S8_5792517 8 5792517 5.29E-05* 2.666406 Os08g0200100 5,787,517-5,797,516 S2_23735211 2 23735211 7.45E-05* 2.490146 Os02g0605600 23,735,211-23,745,211 ** * Note: - : Significant SNP (–log10 (p) ≥ 7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

236

Table 4-10. Identification of 17 SNPs associated with variable chlorophyll fluorescence (Fv’) under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fv’ under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S12_2753389 12 2753389 1.61E-06* 185.9494 Os12g0156000 2,748, 389-2,758, 388 S7_16453787 7 16453787 4.7E-06* 167.0285 New 16,448,787-16,458,786 S10_22988517 10 22988517 8.15E-06* 159.0217 Os10g0576900 22,983,517-22,993,516 S4_30580353 4 30580353 1.34E-05* 108.6159 Os04g0605500 30,575, 353-30,585, 352 S10_14545074 10 14545074 2E-05* -141.863 Os10g0415800 14,540,074-14,550,073 Os10g0415900 14,540,074-14,550,073 S12_19538901 12 19538901 2.33E-05* 165.7553 Os12g0508266 19,533,901-19,543,900 S9_18391936 9 18391936 2.86E-05* 154.6603 Os09g0479400 18, 386,936-18, 396,935 Os09g0479500 18, 386,936-18, 396,935 S2_29091790 2 29091790 2.91E-05* 152.0999 Os02g0705000 29,086,790-29,096,789 S7_23616805 7 23616805 4.27E-05* -160.885 Os07g0583200 29,086,790-29,096,789 S7_24495721 7 24495721 5.32E-05* -185.257 Os07g0600400 24,490,721-24,500,720 S10_19105197 10 19105197 5.65E-05* -152.025 Os10g0501000 19,100,197-19,110,196 Os10g0500700 19,100,197-19,110,196 S2_729447 2 729447 5.75E-05* 114.6006 Os02g0113400 724,447-734,446 S10_21435885 10 21435885 6.11E-05* 167.9804 Os10g0547900 21,430,885-21,440,884 S10_14543245 3 14543245 8.31E-05* 149.6197 Os03g0586500 21,430,885-21,440,884 Os03g0586600 21,430,885-21,440,884 S10_14543245 10 14543245 8.32E-05* 149.6197 Os10g0415800 14,538,245-14,548,244 S3_34599611 3 34599611 8.42E-05* 132.224 Os03g0823800 34,594,611-34,604,610 Os03g0823700 34,594,611-34,604,610 Os03g0823900 34,594,611-34,604,610 S6_29784523 6 29784523 9.83E-05* 150.9795 Os06g0704800 29,779,523-29,789,522 Os06g0704900 29,779,523-29,789,522 Os06g0705100 29,779,523-29,789,522 * Note: - : Probable SNP (–log10 (p) ≥ 5.0-6.9)

237

Table 4-11. Identification of 15 SNPs associated with maximum chlorophyll fluorescence (Fm’) under drought stress by GWA analysis. Based on probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fm’ under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S12_19165020 12 19165020 1.17E-05* 236.5538097 Os12g0502700 19,160,020-19,170,019

Os12g0502800 19,160,020-19,170,019 S11_14118354 11 14118354 1.21E-05* -163.336131 Os11g0435300 14,113, 354-14,123, 353 S10_22988517 10 22988517 1.68E-05* 209.3157515 Os10g0576900 22,983,517-22,993,516 S9_21745843 9 21745843 2.67E-05* 247.8357203 Os09g0549500 21,740,843-21,750,842

Os09g0549600 21,740,843-21,750,842 S10_16428506 10 16428506 2.97E-05* 208.6904819 Os10g0450900 16,423,506-16,433,505 S5_15003381 5 15003381 3.06E-05* 207.3496503 Os05g0323100 14,998, 381-15,008, 380 S7_23616805 7 23616805 3.48E-05* -221.479360 Os07g0583200 23,611,805-23,621,804 S8_6141601 8 6141601 3.91E-05* 216.6651153 Os08g0205150 6,136,601-6,146,600 S11_14118353 11 14118353 4.04E-05* -153.375932 Os11g0435300 14,113, 353-14,123, 352 S2_29091790 2 29091790 4.20E-05* 203.1537248 Os02g0704900 29,086,790-29,096,789

Os02g0705000 29,086,790-29,096,789 S7_24495721 7 24495721 4.61E-05* -254.221556 Os07g0600400 24,490,721-24,500,720 S9_18391936 9 18391936 6.37E-05* 201.6487032 Os09g0479500 18, 386,936-18, 396,935 S2_26849133 2 26849133 6.96E-05* 205.0951123 Os02g0662700 26,844,133-26,854,132 S2_11013440 2 11013440 7.64E-05* 193.0890716 Os02g0290900 11,008,440-11,018,439

Os02g0291000 11,008,440-11,018,439 S5_4984630 5 4984630 8.03E-05* 262.8949548 Os05g0182800 4,979,630-4,989,629 * Note: - : Probable SNP (–log10 (p) ≥ 5.0-6.9)

238

Table 4-12. Identification of 29 SNPs associated with the ratio of variable chlorophyll fluorescence to the maximum chlorophyll fluorescence (Fv’/Fm’) under drought stress by GWA analysis. Based on highly significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to Fv’/Fm’ under drought stress in the URMC are listed.

SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S8_4676173 8 4676173 6.96E-15** 0.03458215 Os08g0180000 4,671,173-4,681,172 S4_30580353 4 30580353 1.58E-13** 0.02886059 Os04g0605500 30,575, 353-30,585, 352 S7_15346949 7 15346949 2.63E-13** 0.04179867 Os07g0447800 15, 341,949-15, 351,948 S2_24790042 2 24790042 7.89E-12** 0.03800746 Os02g0622500 24,785,042-24,795,041 S7_21308942 7 21308942 2.64E-09** -0.0349232 Os07g0540200 21, 303,942-21, 313,941 S4_16455437 4 16455437 1.80E-08** -0.0281212 Os04g0346000 16,450,437-16,460,436 S3_23804955 3 23804955 1.56E-07** -0.0324462 Os03g0625700 23,799,955-23,809,954 S11_5125325 11 5125325 6.80E-07** 0.02178577 Os11g0201540 5,120, 325-5,130, 324 S11_24952146 11 24952146 4.52E-06* 0.01897729 Os11g0634200 24,947,146-24,957,145 S6_15382107 6 15382107 5.19E-06* 0.02380566 Os06g0367500 15, 377,107-15, 387,106 S5_12600211 5 12600211 7.09E-06* 0.01950278 Os05g0289400 12,595,211-12,605,210 S10_13162836 10 13162836 8.88E-06* 0.01569434 Os10g0394200 13,157,836-13,167,835 S9_7202675 9 7202675 1.48E-05* -0.020462 Os09g0297000 7,197,675-7,207,674 S2_6492223 2 6492223 1.62E-05* 0.0195371 Os02g0215900 6,487,223-6,497,222 S1_174450 1 174450 1.77E-05* -0.0266727 Os01g0102850 169,450-179,449

Os01g0102900 169,450-179,449

Os01g0103100 169,450-179,449 S3_3495456 3 3495456 2.34E-05* 0.02408083 Os03g0165000 3,490,456-3,500,455 S1_1572379 1 1572379 3.64E-05* 0.02220948 Os01g0128200 1,567, 379-1,577, 378 S7_25005447 7 25005447 4.09E-05* 0.02496172 Os07g0608200 25,000,447-25,010,446

Os07g0608300 25,000,447-25,010,446 S11_10831101 11 10831101 4.43E-05* -0.025181 Os11g0295000 10,826,101-10,836,100 S2_3609346 2 3609346 5.14E-05* 0.02466831 Os02g0167000 ,604, 346-3,614, 345

Os02g0167100 ,604, 346-3,614, 345 S8_27351705 8 27351705 5.68E-05* -0.0235242 Os08g0545900 27, 346,705-27, 356,704 S8_7990930 8 7990930 6.23E-05* 0.0203427 Os08g0230900 7,985,930-7,995,929

Os08g0231400 7,985,930-7,995,929 S9_4775759 9 4775759 6.99E-05* -0.0183718 Os09g0264400 4,770,759-4,780,758 S1_316392 1 316392 7.47E-05* -0.0214953 Os01g0105900 311, 392-321, 391 S1_34324186 1 34324186 7.71E-05* -0.024806 Os01g0808400 34, 319,186-34, 329,185 S12_19950577 12 19950577 8.23E-05* -0.024035 Os12g0514900 19,945,577-19,955,576 S12_22665681 12 22665681 8.36E-05* 0.02317075 Os12g0556600 22,660,681-22,670,680 S11_14098802 11 14098802 8.96E-05* 0.01873329 Os11g0435100 14,093,802-14,103,801 S3_3495438 3 3495438 9.31E-05* 0.01972044 Os03g0165000 3,490,438-3,500,437 ** * Note: - : Highly significant SNP (–log10 (p) ≥ 7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

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Table 4-13. Identification of 12 SNPs associated with the efficiency of photosystem II (PhiPS II) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to PhiPS II under drought stress in the URMC are listed. SNP Chr Position P value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S1_30350048 1 30350048 7.21E-06* -0.0261274 Os01g0727800 30, 345,048-30, 355,047

Os01g0727840 30, 345,048-30, 355,047 S12_23683863 12 23683863 1.46E-05* -0.021113 Os12g0574000 23,678,863-23,688,862 S2_26786661 2 26786661 2.05E-05* -0.0187994 Os02g0661400 26,781,661-26,791,660 S6_26318220 6 26318220 2.22E-05* 0.02249203 Os06g0644800 26, 313,220-26, 323,219

Os06g0644700 26, 313,220-26, 323,219 S7_20813910 7 20813910 2.88E-05* 0.02164839 Os07g0531700 20,808,910-20,818,909

Os07g0531600 20,808,910-20,818,909 S1_6590427 1 6590427 3.82E-05* -0.0230095 Os01g0220300 6,585,427-6,595,426 S7_1278971 7 1278971 5.90E-05* -0.0231776 Os07g0124600 1,273,971-1,283,970 S1_39700623 1 39700623 6.60E-05* -0.0223257 Os01g0911200 39,695,623-39,705,622

Os01g0911300 39,695,623-39,705,622 S11_22610911 11 22610911 7.39E-05* 0.02278672 Os11g0593500 22,605,911-22,615,910 S1_2665209 1 2665209 7.65E-05* -0.0164909 Os01g0149350 2,660,209-2,670,208 S1_42027533 1 42027533 7.72E-05* 0.0218117 Os01g0954400 42,022,533-42,032,532 S9_16285708 9 16285708 9.23E-05* -0.0202459 Os09g0439400 16,280,708-16,290,707

Os09g0439500 16,280,708-16,290,707 * Note:- : Probable SNP (–log10 (p) ≥ 5.0-6.9)

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Table 4-14. Identification of 18 SNPs associated with electron transport rate (ETR) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to ETR under drought stress in the URMC are included. SNP Chr Position P value Effect Candidate gene Genomic window (10kb (bp) in RAPDB) S4_32238101 4 32238101 1.02E-06* 13.2697264 New 32,233,101-32,243,100 S4_32592440 4 32592440 2.34E-06* 13.4793434 Os04g0640700 32,587,440-32,597,439 S2_29192513 2 29192513 1.17E-05* -9.99647992 LOC_Os02g47744 29,187,513-29,197,512 S4_20952047 4 20952047 1.92E-05* -12.8161149 Os04g0423600 20,947,047-20,957,046

Os04g0423800 20,947,047-20,957,046 S11_5368220 11 5368220 2.07E-05* -12.9699391 Os11g0206400 5, 363,220-5, 373,219 S1_30350048 1 30350048 2.79E-05* -14.6459333 Os01g0727800 30, 345,048-30, 355,047 S11_27652883 11 27652883 3.10E-05* 15.3718998 Os11g0683700 27,647,883-27,657,882 S7_1278971 7 1278971 4.98E-05* -14.0045639 Os07g0124500 1,273,971-1,283,970

Os07g0124600 1,273,971-1,283,970 S3_6639890 3 6639890 5.41E-05* -12.3302596 Os03g0226501 6,634,890-6,644,889 S6_3775363 6 3775363 5.46E-05* -13.0847014 LOC_Os06g07780 22,142,579-22,152,578 S1_20527995 1 20527995 5.66E-05* 14.0283350 Os01g0549400 20,522,995-20,532,994 * S4_32195836 4 32195836 6.66E -05 13.7552458 Os04g0632500 32,190,836-32,200,835 Os04g0632600 32,190,836-32,200,835 S7_20813910 7 20813910 7.88E-05* 12.2565130 Os07g0531700 20,808,910-20,818,909 S12_24188546 12 24188546 8.29E-05* 12.7441182 Os12g0582700 24,183,546-24,193,545 S3_5968466 3 5968466 9.27E-05* 17.6328195 Os03g0213800 5,963,466-5,973,465 S4_13863513 4 13863513 9.33E-05* -13.1848361 LOC_Os04g24220 13,858,513-13,868,512 S2_11017148 2 11017148 9.57E-05* -14.1163109 Os02g0291000 11,012,148-11,022,147

Os02g0290900 11,012,148-11,022,147 S4_29862159 4 29862159 9.57E-05* -8.68277388 Os04g0590400 29,857,159-29,867,158

Note: - *: Probable SNP (–log10 (p) ≥ 5.0-6.9)

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Table 4-15. Identification of 19 SNPs associated with non-photochemical quenching (qN) under drought stress by GWA analysis. Based on significant (–log10 (p) ≥ 7.0) and probable SNPs (–log10 (p) ≥ 5.0-6.9), the candidate genes associated to qN under drought stress in the URMC are included SNP Chr Position P value Effect Candidate gene Genomic window (bp) (10kb in RAPDB) S2_17283722 2 17283722 2.34E-06* 0.227575565 Os02g0493300 17,278,722-17,288,721 S6_20709826 6 20709826 6.56E-06* 0.187901591 Os06g0547400 20704826-20714825 S12_2753389 12 2753389 9.64E-06* 0.203996921 Os12g0156000 2,748, 389-2,758, 388 S4_30580353 4 30580353 9.93E-06* 0.130374361 Os04g0605500 30,575, 353-30,585, 352 S4_32184706 4 32184706 1.05E-05* 0.172234511 Os04g0632100 32,179,706-32,189,705 Os04g0632500 32,179,706-32,189,705 S4_30582761 4 30582761 2.14E-05* 0.211334769 Os04g0605500 30,575, 353-30,585, 352 S3_34595672 3 34595672 3.74E-05* 0.193257003 Os03g0823700 34,590,672-34,600,671 S1_41186780 1 41186780 3.85E-05* -0.18658325 Os01g0938100 41,181,780-41,191,779 S1_2724329 1 2724329 4.09E-05* 0.21691941 Os01g0150200 2,719, 329-2,729, 328 Os01g0150400 2,719, 329-2,729, 328 S11_24643668 11 24643668 4.68E-05* -0.17239505 Os11g0629200 24,638,668-24,648,667 Os11g0629300 24,638,668-24,648,667 Os03g0253500 8,092,683-8,102,682 S1_10277809 1 10277809 5.07E-05* -0.19178135 Os01g0286100 10,272,809-10,282,808 Os05g0226000 7684997-7694996 S6_28796353 6 28796353 5.67E-05* 0.178995245 Os06g0690700 28,791, 353-28,801, 352 S4_30579153 4 30579153 5.81E-05* 0.116809885 Os04g0605500 30,574,153-30,584,152 S8_18766826 8 18766826 6.09E-05* -0.11798968 Os08g0395400 18766826-18776826 S9_20066911 9 20066911 6.54E-05* 0.147288491 Os09g0515200 20,061,911-20,071,910 S3_25847471 3 25847471 7.48E-05* 0.193749232 Os03g0659900 25,842,471-25,852,470 S3_14319019 3 14319019 9.07E-05* -0.17328137 Os03g0366200 14, 314,019-14, 324,018 S4_4282724 4 4282724 9.47E-05* 0.158061717 Os04g0162100 4,277,724-4,287,723 S8_27174999 8 27174999 9.72E-05* 0.145581607 Os08g0543050 27,169,999-27,179,998 ** * Note: - : Significant SNP (–log10 (p) ≥ 7.0); : Probable SNP (–log10 (p) ≥ 5.0-6.9)

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Figure 4-1. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (- log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A.) For instantaneous water use efficiency (WUEi), based on highest –log10 (P-values), 15 most significant SNPs associated with candidate genes were shown. B.) For photosynthesis, based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with WUEi and photosynthesis. 243

Figure 4-2. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) for transpiration rate (TR), based on highest –log10 (P-values), 19 most significant SNPs associated with candidate genes were shown. B) For stomatal conductance, based on highest –log10 (P-values), 9 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with TR and stomatal conductance.

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Figure 4-3. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (- log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For intercellular CO2 concentration (Ci), based on highest –log10 (P-values), 18 most significant SNPs associated with candidate genes were shown. B) For the ratio of intercellular CO2 to the ambient CO2 concentration (Ci/Ca), based on highest –log10 (P-values), 14 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Ci and Ci/Ca. 245

Figure 4-4. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (- log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For biomass, based on highest –log10 (P- values), 18 most significant SNPs associated with candidate genes were shown. B) For number of tillers (NOT), based on highest –log10 (P-values), 6 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with biomass and NOT.

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Figure 4-5. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For relative water content (RWC), based on highest –log10 (P-values), 17 most significant SNPs associated with candidate genes were shown. B) For leaf rolling (LR), based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with RWC and LR.

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Figure 4-6. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. For chlorophyll content (SPAD), based on highest –log10 (P-values), 13 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with chlorophyll content.

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Figure 4-7. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For variable fluorescence (Fv’), based on highest –log10 (P-values), 12 most significant SNPs associated with candidate genes were shown. B) For maximum fluorescence (Fm’), based on highest –log10 (P-values), 11 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Fv’ and Fm’.

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Figure 4-8. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For the ratio of variable fluorescence to the maximum fluorescence (Fv’/Fm’), based on highest –log10 (P-values), 18 most significant SNPs associated with candidate genes were shown. B) For the efficiency of photosystem II (PhiPSII), based on highest –log10 (P-values), 12 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with Fv’/Fm’ and PhiPS II.

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Figure 4-9. Manhattan plot from GWA analysis associated with drought resistance at vegetative stage under drought stress. The x-axis shows SNP positions across the entire rice genome by chromosome and the y-axis is –log10 (p value) of each SNP. Two thresholds [(-log10 (P) ≥ 7.0) and (-log10 (P) ≥ 5.0-6.9)] were set and SNPs above these thresholds were identified as ‘significant’ (-log10 (P) ≥ 7.0) and ‘probable’ (-log10 (P) ≥ 5.0-6.9) SNPs to find candidate genes for the traits. A) For electron transport rate (ETR), based on highest –log10 (P-values), 18 most significant SNPs associated with candidate genes were shown. B) For non-photochemical quenching (qN), based on highest –log10 (P-values), 19 most significant SNPs associated with candidate genes were shown. Black arrows indicated all the candidate genes associated with ETR and qN.

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CHAPTER-V GENOME-WIDE META ANALYSIS (GWMA) OF QTLS FOR DROUGHT RESISTANCE TRAITS AND GRAIN YIELD COMPONENTS UNDER DROUGTH STRESS

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ABSTRACT Rice (Oryza sativa L.) is, one of the most important staple food crops worldwide, and a good source of consumable energy in the form of calories for one third of the world’s population.

Rice requires a large amount of water and is sensitive to drought at all the growth stages, but the reproductive stage is very sensitive to drought stress (Lafitte et al, 2004) as it determines the yield of rice grain. In the last 14 years, a large number of quantitative trait loci (QTLs) have been mapped in different rice populations for drought resistance related traits and grain yield under drought stress conditions, by several rice drought research groups interested in improving drought resistance and higher grain yield worldwide. By collecting the vast resource of published studies on QTLs related to drought resistance and grain yield traits, compiling the data, and summarizing all the QTLs on a consensus map the QTL regions can be narrowed down to using meta-analysis to identify putative genes with more precision. In this study, we have collated data of 911 independent QTLs related to 109 different drought resistance and grain yield traits from

39 different rice populations in 58 different studies between 2000 to 2014. The 911 different

QTLs related to 109 different DR traits and GY components are distributed all over the genome, with chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 containing 156, 128, 105, 90, 49, 70, 50,

82, 69, 35, 44, and 33 QTLs respectively. The genome-wide meta-analysis identified 60 meta-

QTLs (MQTLs) averaging five major QTLs per chromosome for DR traits based on GY.

However, out of the 60 MQTLs, one MQTL on chromosome 11 was not considered as a MQTL as it contained only one QTL. All 60 MQTLs were short-listed based on position, distance and

95% confidence interval (CI) of MQTLs, considering significant parameters to identify the number of MQTLs in genome. In the meta-analysis results were useful to collate a large number of QTLs into MQTL, reducing the genetic distance on the chromosome, with lower CI. Out of the 60 MQTLs, 13 genome-wide MQTLs were found useful, which contained a higher number

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of QTLs, with more precise mapping and reduced genetic distance in the genome with lower CI.

These 13 genome-wide MQTLs could be targeted more effectively for marker-assisted selection towards development of drought resistant and high yielding rice genotypes under drought stress.

INTERODUCTION Rice (Oryza sativa L.) is one of the most important staple food crops worldwide, and a good source of consumable energy in the form of calories feeding one third of world’s population (Courtois et al, 2009). Rice requires a large amount of water even though more than

45% the rice-producing area is not permanently flooded (International Rice Research Institute,

2002). Therefore, rice is particularly more sensitive to water deficit conditions compared to other food crops. Rice sensitivity to drought stress is especially severe at all the growth stages, with the reproductive stage being the most sensitive (Lafitte et al, 2004) to drought stress as it determines the grain yield of the rice crop. In previous studies, it has been shown that in South and Southeast Asia, more than 50% of a total of 40-million hectare area is affected by drought stress conditions every year (Sarkarung and Pantuwan, 1999). In the United States of America, drought stress is also becoming a serious problem with Arkansas and California as leading states in rice production, California has been facing a drought stress problem since a few years affecting rice grain yield in field conditions. For solving this drought stress problem, the development of drought resistant and higher grain yielding rice varieties using modern molecular breeding and genomics approaches, is much needed. Several drought related traits in rice plants are suggested to contribute to grain yield and drought resistance, but none of them have worked under field conditions (Fukai and Cooper, 1995).

The fundamental success of breeding for drought resistance has become slow in the last several years (Venuprasad et al, 2009). Drought resistance therefore needs analytical approaches

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to dissect and investigate the contribution of different morpho-physiological drought resistance

(DR) related traits and grain yield (GY) components using quantitative trait loci (QTL) based models in our strategies (Trijatmiko et al, 2014). These kinds of approaches are useful for crops such as rice, whose intensive genetic linkage maps are already available publicly (Harushima et al, 1998; McCouch et al, 2002). With the availability of map-based sequencing of the rice genome (IRGSP, 2005), along with the help of dense molecular marker based linkage maps, molecular breeders and geneticists are empowered to narrow down the QTLs regions and identify candidate genes in the regions for gene cloning and validation using reverse genetics approaches (Hattori et al, 2009).

To date, several QTL-based studies have been reported from different populations for several DR related traits and GY components, putatively contributing to drought resistance and enhancing GY in rice under drought stress conditions, such as for root system related traits

(Champoux et al, 1995; Zhang et al, 2001; Venuprasad et al, 2002; Lafitte et al, 2004; Yue et al,

2006; Subashri et al, 2009; Srividhaya et al, 2011; Zhou et al, 2011; Lang et al, 2013; Sandhu et al, 2013; Trijatmiko et al, 2014), several leaf-based physiological traits such as osmotic adjustment (Zhang et al, 2001; Robin et al, 2003), water use efficiency (Katto et al, 2006; This et al, 2010; Zhou et al, 2011), photosynthesis (Zhao et al, 2008; Hu et al, 2009; Gu et al, 2012), transpiration rate (Zhao et al, 2008; Khowaja and Price, 2008), stomatal conductance (Zhao et al,

2008; Subashri et al, 2009; This et al, 2010; Gu et al, 2012), intracellular CO2 concentration

(Zhao et al, 2008), ABA concentration (This et al, 2010), carbon isotope discrimination (This et al, 2010; Takai et al, 2006) and visual leaf symptoms of drought stress such as leaf rolling

(Courtois et al, 2000; Hemamalini et al, 2000; Long Biao et al, 2004; Yue et al, 2005; Subashri et al, 2009; Gomez et al, 2010; Trijatmiko et al, 2014) and leaf dying (Courtois et al, 2000; Yue et

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al, 2005; Gomez et al, 2010). However, no clear evidence was found that these DR related traits significantly contribute to grain yield under drought conditions in the field.

Since one and half decades, GY and its components have attracted attention for QTL- based studies under drought stress conditions with a multitude of studies in different rice populations (Babu et al, 2003; Lafitte et al, 2004; Lanceras et al, 2004; Zou et al, 2005; Xu et al,

2005; Bernier et al, 2007; Zhao et al, 2008; Liu et al, 2008; Subashri et al, 2009; Virkam et al,

2011; Bimpong et al, 2011; Ghimire et al, 2012; Vikram et al, 2012; Lang et al, 2013; Swamy et al, 2013; Wang et al, 2013; Sandhu et al, 2013; Yadawa et al, 2013; Xing et al, 2014; Verma et al, 2014; Palanog et al, 2014; Sandhu et al, 2014; Dixit et al, 2014; Trijatmiko et al, 2014).

Employing molecular genetic approaches, the genetic dissection of DR related traits and GY components under drought stress have intensively been investigated by several teams worldwide for development of drought resistance in rice (Courtois et al, 2009). Based on a survey of internet based resources, between 2000 to 2014, 58 different studies for 109 different DR related traits and GY components in different rice population, have been reported in peer-reviewed journals and the results of these studies have been used in this analysis. However, the question remains of how to integrate this information together, narrow down the QTL regions related to DR related traits associated with GY components in the genetic linkage maps and find out putative candidate genes contributing to GY production of rice under field conditions.

To derive an answer to the above question, meta-analysis is an important statistical tool that integrates and analyzes multiple QTL data from different studies on similar traits. Pooling the data from different studies allows greater statistical power for QTL identification and estimates their accurate genetic effects (Goffinet and Garber, 2000). The consistent QTL identified by meta-analysis for a set of QTL at a confidence interval (CI) of 95% are called meta-

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QTL (Swamy et al, 2011). The meta-QTL with the smallest CI and having a consistent and large effect on a trait are useful in marker assisted selection or MAS (Swamy et al, 2011). QTL-based meta-analysis can therefore provide precise conclusions that are stronger than individual studies, and an insight into the genetic dissection of complex traits such as drought, salt and heat response. Meta-QTL analysis has been used to precisely summarize several agronomical traits in various crops.

In rice, Courtois et al. (2009) performed meta- analysis of 675 QTLs controlling 29 root traits including root number, maximum root length, root thickness, root/ shoot ratio, and root penetration index in 12 different rice populations from 24 published papers from 1995 to 2007, and developed a public database. They analyzed a number of root QTLs in 5-Mb segments covering the whole genome that revealed the existence of “hot spots” that encompassed a few genes. Swamy et al. (2011) reported meta-analyses associations of 53-GY QTLs from 15 different studies under drought stress conditions, and found 14 meta-QTLs on 7 chromosomes in drought tolerance for yield (qDTY) mapping populations developed by IRRI. Trijatmiko et al.

(2014) conducted meta-QTL analyses across diverse populations, showing the meta-QTL was conserved across genetic backgrounds and co-localized with QTLs for leaf rolling and osmotic adjustment. They identified a meta-QTL that has a QTL for percent seed set and grains per panicle under drought stress on chromosome 8 in the same region as a QTL for osmotic adjustment (OA) that was found previously in three different populations.

In other crops, meta QTL-analysis has been applied to study Fusarium head blight resistance (Liu et al, 2009; Löffler et al, 2009), to identify QTLs for grain protein content, pre- harvest sprouting tolerance and grain weight (Gupta et al, 2007), seed dormancy (Tyagi and

Gupta, 2012), earliness traits (Hanocq et al, 2007), and QTL related to yield and yield

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components (Zhang et al, 2010) in wheat. Moreover, it has also been employed to characterize plant height (Sun et al, 2012) and cyst nematode resistance QTL (Guo et al, 2006) in soybean, disease resistance in cacao (Lanaud et al, 2009), blight resistance and plant maturity traits in potato (Danan et al, 2011), fiber quality in cotton (Lacape et al, 2010) and traits associated to drought, cold temperatures, waterlogging, salt content and mineral availability in barley (Li et al,

2013).

The main objective of this study is to collate and integrate the data of all QTL based studies conducted from 2000 to 2014 in rice for DR related traits and GY components using

QTL-based meta-analysis tools across the rice genome. In future, we would like to convert genetic linkage maps to physical linkage maps of the used markers, identify putative candidate genes, and develop a database for the scientific community working on drought stress responses worldwide.

MATERIALS AND METHODS

Based on previous studies in rice (Courtois et al, 2009, Swamy et al, 2011; Trijatmiko et al, 2014), meta-QTL analysis employed three sequential footsteps for the identification of a cluster of QTLs called Meta QTL (MQTL), for DR related traits contributing to GY under drought stress. The first step required was to collect a review of the literature on DR related traits and GY components under drought stress, and synthesize the QTL based information. The next step needed was to create a consensus map of all the available markers (Temnykh et al, 2001) and project all the independent QTLs related to DR traits and GY components from the reviewed studies, on the 12 chromosomes of rice. The last step was to cluster all the independent QTLs and identify the MQTLs using statistical tools.

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Review of previous studies and compilation of QTL data for DR related traits and GY Previous studies conducted on mapping of QTLs for DR related traits and GY in rice under well-watered (WW) and drought stress (DS) were collected from published peer reviewed journals. More than 60 different studies were collated for synthesizing ~1500 QTL data of DR related traits and GY components under WW and DS conditions, from between the years 2000 to

2014. However, for this chapter, data from 58 different studies were used of 911 independent

QTLs for 109 different DR related traits and GY components in 39 different rice populations under drought stress in greenhouse and field conditions conducted from 2000 to 2014. The detailed information on parents used for developing mapping populations, rice types and size of populations used for QTLs mapping, country where the experiment conducted and references are given in Table 5-1.

Construction of a consensus map

In this study, the consensus map was developed for meta-QTL analysis using

BioMercator v4.2 (http://moulon.inra.fr/). The rice genetic linkage map of Temnykh et al. (2001) was used as a reference map. Chromosomes associated with fewer than two common markers to the reference map were removed before the development of the consensus map (Swamy et al,

2011). Inversions of marker sequences were filtered out by discarding inconsistent loci with the exception of very closely linked markers. After the integration of all maps, the consensus map contained 531 markers, including SSR, RFLP, AFLP markers, and genes (Tenmykh et al, 2001).

The consensus map covered a total length of 1821 cM, with an average distance of 3.5 cM between markers (Tenmykh et al, 2001).

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Integration of QTLs on the consensus map

For performing the meta-QTL analysis, projection of the QTLs required the position of the markers linked with the independent QTL, the left and right flanking markers of the QTL, logarithm of the odds (LOD) score, and phenotypic variation (R2) explained by the QTL. The projection of the QTL was completed using a simple scaling rule between the original QTL flanking marker interval and the corresponding interval on the chromosome of the consensus map (Swamy et al, 2011). For a given QTL position, the new confidence interval (CI) on the consensus linkage group was estimated with an algorithm based on a Gaussian mixture model determining the most likely QTL position. In this study, all the integrations of the QTLs for DR related traits and GY components were performed using BioMercator v4.2

(http://moulon.inra.fr/).

Performance of meta-analysis of QTLs for DR related traits and GY

The meta-QTL analysis was performed using BioMercator v4.2 (http://moulon.inra.fr/).

This statistical tool relies on a clustering algorithm based on a Gaussian mixture model and determines the likely number of clusters considered as the meta QTLs underlying the QTLs observed in a given region (Courtois et al, 2009). The tool was commended for maximum best five meta-QTLs (MQTLs) on each chromosome based on the positions, confidence intervals of the meta-QTLs and mtodel choice, which performed well in simulations (Veyrieras et al, 2007;

Courtois et al, 2009).

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RESULTS AND DISCUSSION

Development of a database of genome-wide QTLs for DR traits and GY

All the information on ~1500 QTLs controlling morpho-physiological, plant productivity traits, and grain yield components was extracted from more than 60 different studies under WW and DS conditions in rice from 1998 to 2014. However, in this study, the data of 911 QTLs limited to DR related traits and GY components under drought stress from 58 different studies for 109 different DR related traits and GY components were used. These included 39 different rice populations from 11 different countries, published between 2000 to 2014, with population size ranging between 90 to 513 (Table 5-1).

The detailed information on QTL names, chromosomes, marker intervals (cM), positional markers, left and right flanking markers, marker interval between left & right flanking markers,

LOD scores, phenotypic variation explained by QTLs, additive effect of QTL, DR related traits

& GY components, parents used for mapping population, parents type, population type & size, location used for the study, number of markers used for the study, reference, and QTL analysis methods are given in Supplementary Table 5-1. The 911 QTLs for DR traits and GY components are distributed on 12 chromosomes in rice, with chromosome 1 having the highest number of

QTLs (156) followed by chromosomes 2 (128), 3 (105), 4 (90), 8 (82), 6 (70), 9 (69), 7 (50),

5(49), 11(44), 10 (35), and 12(33), respectively (Figure 5-1). The phenotypic variation explained by QTLs varied from -3.1% to 71% among all the QTLs from the 58 different studies from 2000-

2014 (Figure 5-1).

In this study, the grain yield has the highest number of genome-wide QTLs (131) followed by plant height (78), days to flowering (44), leaf rolling (28), panicle number (24), leaf drying (21), biomass (18), harvest index (18), number of tillers (17), relative water content (17),

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carbon isotope discrimination (15), water use efficiency (15), relative grain yield (14), root volume (14), panicle exertion (13), heading days (12), osmotic adjustment, panicle length & root growth rate in depth (12), net photosynthesis & spikelet fertility (11), canopy temperature (10), deep root rate in volume, leaf blade flatness, & relative spike fertility (10), 1000-grain weight, filled grain per plant, leaf length, root growth rate in volume, & chlorophyll content (9), delay in flowering time, drought respond index, drought tolerance, flag leaf length, grain number per plant, leaf width, & stomatal conductance (8), flowering date, kernel weight, leaf area, leaf N2, root length, specific leaf area, & transpiration rate (7), dry root weight, days to heading, flag leaf width, root dry weight, & root shoot ratio (6), biological yield, difference in panicle excretion, maximum root depth, relative fertile panicles, relative grain weight, & stomata frequency (5), intercellular CO2, the ratio of Ci to the Ca, days of rolling, economic index, percent spikelet fertility, relative flag leaf width, root pulling force, relative plant height, root thickness, & shoot biomass (4), coleorhiza length, fresh root weight, fresh shoot weight, panicle fertility, relative flag leaf length, relative spikelet number, secondary branch number, shoot dry weight, spikelet number, & total grain weight (3), ABA content, dry shoot weight, fraction sterile panicle, germination percentage, germination rate, grain weight per plant, leaf erectness, the yield of PSII under flowering, plumule length, panicle neck diameter, penetrated root thickness, percent seed set, panicle weight, radical length, relative growth rate, root weight, senescence, seeds panicle, total spikelet number, & proportional water loss at time to reach leaf rolling score 4.5-DS (2), drought sensitivity under flowering, days to maturity, economic index per day, Fv’/Fm’ under flowering, length/breadth ratio, leaf dry weight, primary branch number, plot yield, productive tillers, penetrated root dry weight, recovery score, spikelet density, spikelets per panicle , & total

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root dry weight (1), respectively (Table 5-2). The symbol of each DR related traits & GY components and their associated references are given in Table 5-2.

Identification of genome-wide meta-QTLs (MQTLs) using meta-analysis

Based on previous studies on QTLs mapping, QTLs are precise and can be mapped on chromosomes using molecular mapping tools (Price, 2006; Ashikari and Matsuoka, 2006).

However, the complex nature and context dependency of the QTLs in different genetic backgrounds and environments are the problems in identification of the accurate locations of the

QTLs in the genome (Swamy et al, 2011). The identification of precise QTLs having major effects in different genetic backgrounds and environments can be successful, and can be used in molecular breeding for development of drought resistant rice genotypes using marker-assisted selection (Swamy et al, 2011). For identification of accurate positioned QTLs for DR traits and

GY components associating with other traits, meta-QTL analysis can help to identify accurate and concise QTLs from different studies in different genetic backgrounds and different locations worldwide, and the concise QTLs can be helpful to identify candidate genes. In this study, genome wide meta-QTL analysis has been carried out to identify meta-QTLs for DR related traits and GY under drought stress in rice. Based on a review of literature from 2000-2014, a total 911 QTLs for 109 different DR traits and GY components in 39 different rice population from 58 different studies under drought stress from different geographical locations was chosen for meta-analysis.

The genome-wide 911 QTLs for DR traits and GY components were projected on 12 chromosomes on a consensus map (Figure 5-2). Meta-QTL analysis was performed using

BioMercatorv4.2 and identified 60 MQTLs showing five best QTLs per chromosome for DR traits based on GY trait. However, out of 60 MQTLs, one MQTL on chromosome 11 were not

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considered as a MQTL as it contained only one QTL. All 60 MQTLs were short-listed based on position, distance and 95% of confidence interval (CI) of MQTLs considering a significant model indicating the number of MQTLs in genome. The number of identified MQTLs along with their position (cM), distance (cM), CI, model and QTLs found in each MQTL are shown in

Table 5-3. These 60 MQTLs were found distributed genome-wide in the rice genome. Meta- analysis clustered the five best MQTLs on chromosome 1, with the five MQTLs named

MQTL1.1, MQTL1.2, MQTL1.3, MQTL1.4 and MQTL1.5 shown in Figure 5-3. In the Figure 5-

3, MQTL1.1 is positioned at 39.11 cM showing 11.38 cM distance, and 1.62 CI with model 7 and 17 different QTLs related to GY, OA, PH, RSF, RS, LR, RDW, DRW & WATERLOSS were clustered in MQTL1.1 (Table 5-3). MQTL1.2 was mapped at 108.92 cM with 7.69 cM distance having 2.57 CI with model 7 and 45 different QTLs related to GR, GY, RS, WUE, RT,

PN, PH, PE, SC, RSN, DRI, BY, EI, NOT, FLW, CL, GP, LD, CL, GP, LD, RWC, SF, DFT,

DRRV, HD, PL, DTF, OA, RY, & SPAD were found in MQTL 1.2 (Table 5-3). MQTL1.3,

MQTL1.4 and MQTL 1.5 were found at148.68, 172.02, 189.74 cM with 5.74, 4.65, 0.14 cM distance having 1.02, 1.79, 0.06 CI with model 7, respectively and 45 QTL related to RS, HD,

GY, RWC, LR, DTB, PH, PL, LBF, BIO, BY, PN, HI, TR, CID, FSP, Ci/Ca, SF, PE, OA, LN2

& DTF, 42 QTLs related to RS, LR, TGW, RWC, PH, RDV, PE, DPE, DRI, 1000-GW, NOT,

LL, SLA, DTH, DTF, DTB, RFLW, GY & RGRV and 7 different QTLs related to DTF, LR,

LD, RWC, PH, & SBIO were positioned in MQTLs 1.3, 1.4 and 1.5, respectively (Table 5-3).

On chromosome 2, the five best MQTLs named MQTLs 2.1, 2.2, 2.3, 2.4 and 2.5 were found using meta-analysis and shown in Figure 5-4. In this Figure, MQTLs 2.1, 2.2, 2.3, 2.4 and 2.5 were mapped at 17.92, 50.84, 67.82, 145.62 and 188.28 cM with 5.03, 3.42, 11.41, 7.21 and 0.78 cM distance on the chromosome showing 3.1, 1.5, 2.01, 1.5, and 0.13 CI with model 9 & 10,

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respectively and 21, 12, 26, 60 and 9 different QTLs related to LREACT, GY, GP, LD, GPP,

PN, Ci/Ca, FLL, CT & RS, LW, RGRV, DRW, LN2, RGW, PL, PN, NOT, PH, FRW, & SPAD,

GY, OA, RSN, SF, DT, LW, RGRD, RT, FD, WUE, LBF, PHOTO, SDW, HI, RFLL, PN, DTH,

RPF & NOT, WUE, RGR, CID, DPE, PH, DTH, PF, GY, RGRD, DRRV, NOT, HI, FLW, LR,

LD, CT, SF, REVS, DRI, RY, BIO, HD, PH, LL, DRW, TGW, FLL & DTF and BIO, DS,

PHOTO, NOT, PN, PF, LL & WATERLOSS were clustered in MQTLs 2.1, 2.2, 2.3, 2.4, & 2.5, respectively (Table 5-3). Meta-analysis identified the best five MQTLs named MQTLs 3.1, 3.2,

3.3, 3.4 and 3.5 on chromosome 3 and all the MQTLs are shown in Figure 5-5. In the Figure 5-4,

MQTLs 3.1, 3.2, 3.3, 3.4 and 3.5 were found at 8.62, 40.47, 105.56, 128.34 and 192.83 cM with

6.64, 8.41, 2.83, 10.62 and 1.23 cM distance having 1.8, 3.09, 6.0, 2.25, and 0.24 CI with model

8, respectively and 50, 14, 11, 5 and 25 different QTLs related to LD, DTF, RWC, HI, LR, PE,

RV, RDW, GY, RGRD, RV, PH, DTF, FLL, LD, RGW, BIO, PH, DTM, HD & SPAD, GY,

SPAD, DPE, COL, NOT, DTF & LR, DT, LBF, GY, LR, RV, STCON, NOT, SBN, BIO &

GWP, GWP, RWC, LR, RY & PH and LR, Ci, LL, GP, GY, FLL, SPAD, RSF, DTH, LD &

TGW were positioned in MQTLs 3.1, 3.2, 3.3, 3.4 and 3.5, respectively (Table 5-3). On chromosome 4, MQTLs 4.1, 4.2, 4.3, 4.4 and 4.5 were identified using meta-analysis and shown in Figure 5-6. In the Figure 5-6, MQTLs 4.1, 4.2, 4.3, 4.4, and 4.5 were found at 32.02, 92.38,

115.29, 136.31 and 150.6 cM with 8.58, 3.86, 3.53, 3.57, and 1.50 cM distance on the chromosome having 2.3, 2.55, 0.66, 2.03 and 0.04 CI with models 8, respectively and 18, 22, 30,

14, and 6 different QTLs related to Ci/Ca, CID, WUE, LBF, GY, SLA, STCON, DTF, RS, LD,

LW, RGRD, & LR,GY, TGW, EI, WUE, GR, KW, WUE, SF, PE, CID, PL, HD, FD, SBN, &

SPD,SBN, SPD, LW, LR, RPF, PN, PL, GY, PH, TSN, PND, RV, SPP, PBN, RGRD, MRD,

DTF, NOT & SN,GPP, RV, RGRD, MRD, PSF, LW, PH, FLL, FLW, SPAD, SBN & SPD and

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LD, HD, PH, CID, GY & SEN, were found in MQTLs 4.1, 4.2, 4.3, 4.4 and 4.5, respectively

(Table 5-3). Meta-analysis identified best five MQTLs named MQTLs 5.1, 5.2, 5.3, 5.4, and 5.5 on chromosome 5 and all the MQTLs are shown in Figure 5-7. The Figure 5-7 shows that

MQTLs 5.1, 5.2, 5.3, 5.4 and 5.5 were identified at 0.07, 29.92, 79.38, 93.94, and 111.26 cm on the chromosome with 3.63, 9.14, 3.9, 6.2, and 0.06 cM distance having 2.82, 3.03, 2.29, 2.28 and

0.31CI with model 5, respectively and 9, 3, 15, 12, and 9 different QTLs related to DTF, RTH,

TR, PN, LAREA, SPAD, BIO, OA & PH, PH, DTF & NOT, PN, GY, RGW, SF, LW, RPF, FD,

FLL, PH, LR, PRDW, HD, CT & RWC, SLAREA, CID, LBF, PL, LD, LR, & LN2 and FLW,

DT, RaL, LAREA, PH, FSP & RSF were positioned in MQTLs 5.1, 5.2, 5.3, 5.4 and 5.5, respectively (Table 5-3). On chromosome 6, the five best MQTLs named MQTLs 6.1, 6.2, 6.3,

6.4, and 6.5 using meta-analysis and all the MQTLs are shown in Figure 5-8. The Figure 5-8 shows that MQTLs 6.1, 6.2, 6.3, 6.4, and 6.5 were identified on 24.47, 39.76, 71.65, 107.43 and

130.51cM with 2.81, 5.68, 4.87, 3.92, and 5.11 cM distance having 0.99, 0.71, 4.55, 3.18, and

0.18 CI with model 8, respectively and 19, 8, 4, 18, and 11 different QTLs related to FSW, DTF,

RL, FRW, RTH, RV, RGR, NOT, PN, GY, DSW, LR, GY & RWC, FGP, SF, FLW, GY,

PHIPSII, PH, TGW & STCON, RWC, PH, & SPAD, DTH, LBF, FLL, FLD, KW, PF, RGRD,

BIO, EI, RSN, DTF, WUE, PN, RWC & LD, and CT, LBF, HD, CID, DT, KW, PH, EI & DRI were positioned in MQTLs 6.1, 6.2, 6.3, 6.4, and 6.5, respectively (Table 5-3). On chromosome

7, the five best MQTLs named MQTLs 6.1, 6.2, 6.3, 6.4, and 6.5 in the meta-analysis and all identified MQTLs are shown in Figure 5-9. The Figure 5-9 shows that MQTLs 6.1, 6.2, 6.3, 6.4 and 6.5 were identified at 0.11, 25.32, 41.82, 64.13, and 91.91cM with the distance of 4.94, 3.59,

4.74, 5.76, and 3.71 cM having 2.62, 3.07, 1.73, 2.29, and 0.33 with model 8, respectively and 4,

21, 5, 6, and 21 different QTLs related to PH, LD & RDW, DPE, GY, RSF, RV, RGRD, FLL,

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LR, LD, RGY, PE, DRRV, DTF, RGW & PNOT, OA, KW, RW & PH, TGW, GP, SF, LR &

LD, and SF, HI, FTD, SLW, FDE, OA, FLL, DRRV, Ci, DTF & LD were positioned in MQTLs

6.1, 6.2, 6.3, 6.4 and 6.5 on chromosome 7, respectively (Table 5-3). In the meta-analysis, the five best MQTLs we found were named MQTLs 8.1, 8.2, 8.3, 8.4, and 8.5 on chromosome 8 and all the MQTLs are shown in Figure 5-10. The Figure 5-10 shows that MQTLs 8.1, 8.2, 8.3, 8.4, and 8.5 were found at 57.0, 63.59, 75.30, 84.78 and 107.04 cM with the distance of 51.16, 3.25,

2.62, 5.17 and 2.24 showing 0.71, 2.73, 2.03, 2.54, and 0.14 CI with model 7 respectively and

30, 5, 11, 8, and 17 different QTLs related to RL, RDW, SF, RS, CID, DLR, RL, RSF, PN,

NOT, DTH, GPP, PSS, OA, PW, HD, GY, RGY, DTF & Ci, RGRD, SF, RV, PN & WUE,

RGRD, KW, GPP, PSS, CT, PH, DRI, WUE, CID & OA, RPF, PSS, RSF, DPE, RGY, OA, DT,

PN & SN and PL, PE, BY, PSS, NOT, RV, LR, SBN, TGW, PH, DRW, LD & PE were found in

MQTLs 8.1, 8.2, 8.3, 8.4, and 8.5, respectively (Table 5-3). On chromosome 9, MQTLs 9.1, 9.2,

9.3, 9.4 and 9.5 were found in the meta-analysis and shown in Figure 5-11. In the Figure 5-10,

MQTLs named 9.1, 9.2, 9.3, 9.4 and 9.5 were identified at 1.25, 13.55, 62.62, 78.98, and 110.7 cM with distance of 1.84, 7.87, 4.49, 9.45, and 5.20 cM having 1.02, 4.62, 2.59, 1.47, and 0.4 CI with model 10, 8 and 7, respectively and 212, 6, 13, 16, and 6 different QTLs related to LR,

BIO, DTF, HI, RGY, SF, RGW, RFLW, PH, SDW, RL, PL & PW, DRRV, RSF, RGRD, DLR,

MRD & PSS, PHOTO, SLAREA, LD, COLL, TR, TGW, PE, STCON, PH, Fv’/Fm’ & RSF,

PN, LR, DTF, RL, DRI, RWC, Ci, BY, DTH, DRW, GP, FGP, PL & PRT, and SN, RV, RL,

RWC, HD & DTF were clustered in MQTLs 9.1, 9.2, 9.3, 9.4, and 9.5 , respectively (Table 5-3).

MQTLs 10.1, 10.2, 10.3, 10.4, and 10.5 were identified on chromosome 10 and shown in Figure

5-12. In the Figure 5-12, MQTLs 10.1, 10.2, 10.3, 10.4, and 10.5 were located at 13.75, 44.92,

54.82, 70.84, and 91.8 cM with the distance of 6.62, 2.24, 2.75, 2.95, and 1.95 having 4.87, 4.43,

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2.72, 3.56, 3.03 CI with model 6, respectively and 7, 4, 11, 2, and 7 different QTLs associated with PH, PE, HD , GY & TRDW, LN2, RFLW, PH & PE, DRRV, PHOTO, DRI, DTF, DTH,

SLAREA, SF, PN & RSF, PL & LD, and HI, FLL, PH, RGY & HD were found in MQTLs 10.1,

10.2, 10.3, 10.4, and 10.5 on chromosome 10, respectively (Table 5-3). On chromosome 11, the four best MQTLs were named MQTLs 11.1, 11.3, 10.4, and 11.5, however MQTL 11.2 were not considered as MQTL as it had only one QTL related to LDW. All the MQTLs are shown in

Figure 5-13. The Figure 5-13 shows that MQTLs 11.2, 11.3, 11.4, and 11.5 were located at

45.78, 74.7, 105.03, and 120.6 cm with the distance of 2.07, 8.42, 5.1, and 0.5 cM showing 2.63,

1.85, 3.15, and 0.28 on chromosome 11, respectively and 16, 8, 9, and 4 different QTLs related to LBF, MRD, PH, LN2, FGP, PL, SPN, LD, SBIO, LBR, RGRD & PN, PN, PH, SF, TGW,

STCON, TR & RPF, TR, CT, PE, RWC, PHOTO, RV, SEN & DTF, and PH, PND, LR & SPAD were positioned in MQTLs, 11.1, 11.3, 11.4, and 11.5 on chromosome 11, respectively (Table 5-

3). Meta-analysis identified the five best MQTLs named MQTLs 12.1, 12.2, 12.3, 12.4, and 12.5 on chromosome 12 and all the MQTLs are shown in Figure 5-14. In the Figure 5-14, MQTLs

12.1, 12.2, 12.3, 12.4, and 12.5 were located at 23.96, 61.16, 73.38, 101.54, and 111.45 cM with the distance of 12.93, 5.57, 7.68, 2.45, and 0.45 CI with model 7 on the chromosome 12, respectively and 6, 5, 4, 8, and 4 different QTLs related to WUE, RWC, FD, RSN & GY, GY,

DTF & PH, SLAREA, LD, RWC & SBIO, PH, ABA, HD, RW, RFP & LR, and LL, LR & LW were found in MQTLs 12.1, 12.2, 12.3, 12.4, and 12.5 on chromosome 12, respectively (Table 5-

3).

In the meta-analysis results, based on number of QTLs in MQTL, lower genetic distance on the chromosome, and lower CI, out of 60 MQTLs, 13 genome-wide MQTLs were found useful, which are containing higher number of QTLs, lower genetic distance in the genome with

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lower CI were found. On chromosome 1, there were three MQTLs named MQTLs 1.2, 1.3, 1.4 having 7.69, 5.74, and 4.65 cM genetic distance with 2.57, 1.02, and 1.79 CI, containing 45, 45, and 42 different QTLs related to different DR traits and GY components, respectively (Table 5-

3). Three MQTLs named MQTLs 2.1, 2.3, and 2.4 contained 21, 26, and 60 different QTL related to different DR traits and GY components and showed 5.03, 11.41, and 7.21 genetic distance with 3.1, 2.01, and 1.5 CI on chromosome 2. Two MQTLs 3.1 and 3.5 had 50 and 25 different QTLs related to DR and GY traits on chromosome 3 and these MQTLs showed 6.64 and 1.23 genetic distance with 1.28 and 0.24 CI, respectively. On chromosomes 4, 7, 8 and 9, there were MQTLs 4.2 & 4.3, 7.2, 8.1, and 9.1 containing 22 & 30, 21, 30, and 21 different

QTLs showing 3.86 & 3.53, 3.59, 1.16, and 1.84 cM genetic distance on the chromosomes with

2.55 & 0.66, 3.07, 0.71, and 1.02 CI, respectively (Table 5-3). Therefore, these 13 genome-wide

MQTLs could be useful for marker assisted selection for development of drought resistant and high yielding rice genotypes under drought stress. The information on these MQTLs will be useful to approach candidate genes associated with drought resistance and higher grain yield under drought stress conditions.

CONCLUSIONS

Meta-analysis of a large number of independent QTLs for DR related traits and GY components is an effective statistical approach for accurate identification of QTLs in the genome. In this study, we used the data of 911 QTLs extracted for DR related traits and GY components under drought stress, from 58 different studies for 109 different DR related traits and GY components of 39 different rice populations from 11 different countries published between 2000 to 2014, with the population size ranging from 90 to 513. The 911 different QTLs related to 109 different DR traits and GY components were distributed in the genome in which

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the chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 each contain 156, 128, 105, 90, 49, 70,

50, 82, 69, 35, 44 and 33, QTLs, respectively. For meta-analysis of these QTLs, a consensus map was developed using BioMercator v4.2 software with the map containing 531 markers, including

SSR, RFLP, AFLP markers, and genes (Tenmykh et al, 2001). The consensus map covered a total length of 1821 cM, with an average genetic distance of 3.5 cM between markers (Tenmykh et al, 2001). After identification, the 911 different QTLs for DR and GY components were projected on a consensus map for meta-analysis. The genome-wide meta-analysis identified 60

MQTLs, averaging five best QTLs per chromosome for DR traits based on GY. However, out of

60 MQTLs, one MQTL on chromosome 11 was not considered as a MQTL as it contained only one QTL. All 60 MQTLs were short-listed based on position, distance and 95% confidence interval (CI) of MQTLs considering a significant parameters to identify the number of MQTLs in genome. In the meta-analysis results based on the number of QTLs in MQTL, a lower cumulative genetic map distance on the chromosome, and lower CI, out of 60 MQTLs, 13 genome-wide MQTLs were found most useful, which contain a higher number of QTLs, and lower genetic distance in the genome with lower CI. On chromosome 1, there were three MQTLs named MQTLs 1.2, 1.3, 1.4 having 7.69, 5.74, and 4.65 cM genetic distance with 2.57, 1.02, and

1.79 CI, containing 45, 45, and 42 different QTLs related to different DR traits and GY components, respectively (Table 5-3). Three MQTLs named MQTLs 2.1, 2.3, and 2.4 contained

21, 26, and 60 different QTL related to different DR traits and GY components and showed 5.03,

11.41, and 7.21 genetic distance with 3.1, 2.01, and 1.5 CI on chromosome 2. Two MQTLs 3.1 and 3.5 had 50 and 25 different QTLs related to DR and GY traits on chromosome 3 and these

MQTLs showed 6.64 and 1.23 genetic distance with 1.28 and 0.24 CI, respectively. On chromosomes 4, 7, 8 and 9, there were MQTLs 4.2 & 4.3, 7.2, 8.1, and 9.1 containing 22 & 30,

270

21, 30, and 21 different QTLs showing 3.86 & 3.53, 3.59, 1.16, and 1.84 Cm genetic distance on the chromosomes with 2.55 & 0.66, 3.07, 0.71, and 1.02 CI, respectively (Table 5-3). Therefore, these 13 genome-wide MQTLs could be useful in marker assisted based selection for development of drought resistant and high yielding rice genotypes under drought stress. The information on these MQTLs will be useful to identify candidate genes associated with drought resistance and higher grain yield under drought stress conditions. For further analysis, we will convert the genetic position (cM) to physical map position (bp) of each marker used in this study and project all these QTLs with our GWAS based significant SNPs on a consensus map. The information on physical position of each QTL and SNP will enable us to identify candidate genes and chromosomal regions controlling drought resistance and grain yield under drought stress.

REFERENCES

Ashikari, M., M. Matsuoka. 2006. Identification, isolation and pyramiding of quantitative trait loci for rice breeding. Trends in Plant Science. 11:344-350.

Babu, R. Chandra, Bay D. Nguyen, Varapong Chamarerk, P. Shanmugasundaram, P. Chezhian, P. Jeyaprakash, S. K. Ganesh, A. Palchamy, S. Sadasivam, S. Sarkarung, L. J. Wade, and Henry T. Nguyen. 2003. Genetic Analysis of Drought Resistance in Rice by Molecular Markers: Association between Secondary Traits and Field Performance. Crop Science. 43:1457–1469.

Bernier, Jerome, Arvind Kumar, Venuprasad Ramaiah, Dean Spaner, and Gary Atlin. 2007. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Science. 47:507–518.

Bimpong, Isaac Kofi, Rachid Serraj, Joong Hyoun Chin, Joie Ramos, Evelyn M. T. Mendoza, Jose E. Hernandez, Merlyn S. Mendioro, Darshan S. Brar. 2011. Identification of QTLs for drought-related traits in alien introgression lines derived from crosses of rice (Oryza sativa cv. IR64) × O. glaberrima under lowland moisture stress. Journal of Plant Biology. 54:237–250.

Chakraborty, Supriya, Zhao-Bang Zeng. 2011. QTL mapping for days to flowering under drought condition in rice (Oryza sativa L.) genome. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 39(1):58-63.

271

Champoux, M.C., G. Wang , S. Sarkarung , D.J. Mackill , J.C. O’Toole, N. Huang , S.R. McCouch. 1995. Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers. Theoretical Applied Genetics. 90: 969–981. doi:10.1007/BF00222910.

Courtois, B., G. McLaren, P.K. Sinha, K. Prasad, R. Yadav and L. Shen. 2000. Mapping QTLs associated with drought avoidance in upland rice. Molecular Breeding. 6: 55–66, 2000.

Courtois, B., N. Ahmadi, F. Khowaja, A. Price, J.F. Rami, J. Frouin, C. Hamelin, M. Ruiz. 2009. Rice root genetic architecture: meta-analysis from a QTL database improves resolution to a few candidate genes. Rice. 2:115-128.

Dixit, Shalabh, Anshuman Singh, Ma Teresa Sta Cruz, Paul T Maturan, Modesto Amante and Arvind Kumar. 2014. Multiple major QTL lead to stable yield performance of rice cultivars across varying drought intensities. BMC Genetics, 15:16.

Fukai, S. and M. Cooper. 1995. Development of drought resistant cultivars using physiomorphological traits in rice. Field Crops Research. 40:67– 87.

Liu, G.L., H.W. Mei b , X.Q. Yu, G.H. Zou, H.Y. Liu, S.P. Hu, M.S. Li , J.H. Wu , L. Chen, L.J. Luo. 2008. QTL analysis of panicle neck diameter, a trait highly correlated with panicle size, under well-watered and drought conditions in rice (Oryza sativa L.). Plant Science 174:71–77.

Goffinet, B., S. Gerber. 2000. Quantitative trait loci: a meta-analysis. Genetics.155:463–73.

Gomez, S. M., N. Manikanda Boopathi, S. Satheesh Kumar, T. Ramasubramanian, Zhu Chengsong , P. Jeyaprakash , A. Senthil , R. Chandra Babu. 2010. Molecular mapping and location of QTLs for drought-resistance traits in indica rice (Oryza sativa L.) lines adapted to target environments. Acta Physiology Plant. 32:355–364.

Gupta, P.K., H.S. Balyan, P.L. Kulwal. 2007. QTL analysis for some quantitative traits in bread wheat. Genetics. 8(11):807-814.

272

Harushima, Y., M. Yano, A. Shomura, M. Sato, T. Shimano, Y. Kuboki, et al. 1998. A high- density rice genetic map with 2275 markers using a single F2 population. Genetics.148:479–94.

Hattori, Y., K. Nagai, S. Furukawa, X. J. Song, R. Kawano, H. Sakakibara, J. Wu, T. Matsumoto, A.Yoshimura, H. Kitano, M. Matsuoka, H. Mori, M. Ashikari. 2009. The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature 460:1026–1030. doi:10.1038/ nature08258

Hemamalini, G.S., H.E. Shashidhar, Shailaja Hittalmani. 2000. Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.). Euphytica. 112: 69–78.

Hu, Song-Ping, Ying Zhou, Lin Zhang, Xiu-Dong Zhu, Lin Li, Li-Jun Luo, Guo-Lan Liu, Qing- Ming Zhou. 2009. Correlation and quantitative trait loci analyses of total chlorophyll content and photosynthetic rate of rice (Oryza sativa L.) under water stress and well- watered conditions. Journal of Integrative Plant Biology. Pp1-10.

International Rice Genome Sequencing Project. 2005. The map-based sequence of the rice genome. Nature. 436 :793–800. doi:10.1038/nature03895

International Rice Research Institute. 2002. Rice almanac, 253 pp.

Gu, Junfei, Xinyou Yin, Paul C. Struik, Tjeerd Jan Stomph, Huaqi Wang. 2012. Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions. Journal of Experimental Botany. 63(1):455–469.

Kato, Yoichiro, Sawa Hirotsu, Keisuke Nemoto, Junko Yamagishi. 2008. Identification of QTLs controlling rice drought tolerance at seedling stage in hydroponic culture. Euphytica.160:423–430.

Khowaja, Farkhanda S. and Adam H. Price. 2008. QTL mapping rolling, stomatal conductance and dimension traits of excised leaves in the Bala x Azucena recombinant inbred population of rice. Field Crops Research 106:248–257.

Krishna Hari Ghimirea, Krishna Hari, Lenie A. Quiatchona, Prashant Vikram, B.P. Mallikarjuna Swamy, Shalabh Dixit, Helaluddin Ahmed, Jose E. Hernandez, Teresita H. Borromeoa, Arvind Kumar. 2012. Identification and mapping of a QTL (qDTY1.1) with a consistent effect on grain yield under drought. Field Crops Research 131: 88–96.

273

QTLs across diverse environments in a Gossypium hirsutum x G. barbadense RIL population. BMC Plant Biology. 10:132.

Lafitte, H. R., A. H. Price, B. Courtois. 2004. Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theoretical Applied Genetics.109: 1237–1246.

Lafitte, H.R., A.H. Price, B. Courtois. 2004. Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theoretical Applied Genetics. 109(6):1237–1246. doi: 10.1007/s00122-004-1731-8

Lanaud, C., O. Fouet, D. Clément, M. Boccara, A. M. Risterucci, S. Surujdeo-Maharaj, T. Legavre, and X. Argout .2009. A meta–QTL analysis of disease resistance traits of Theobroma cacao L. Molecular Breeding. 24(4):361-374.

Lanceras, Jonaliza C., Grienggrai Pantuwan, Boonrat Jongdee, and Theerayut Toojinda. 2004. Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiology. 135: 384–399.

Lang, N.T., C.T. Nha, P.T.T. Ha, B.C. Buu. 2013. Quantitative trait loci (QTLs) associated with drought tolerance in rice (Oryza sativa L.). SABRAO Journal of Breeding and Genetics. 45(3): 409-421.

Li, W.T., C. Liu, Y.X. Liu, Z.E. Pu, S.F. Dai, J.R. Wang, X.J. Lan, Y.L. Zheng, Y.M. Wei. 2013. Meta-analysis of QTL associated with tolerance to abiotic stresses in barley. Euphytica. 189(1):31-49.

Löffler, M., C.C. Schön, T. Miedaner. 2009. Revealing the genetic architecture of FHB resistance in hexaploid wheat (Triticum aestivum L.) by QTL meta-analysis. Molecular Breeding. 23(3):473-488.

Long- Biao, Guo, Qian Qian, Zeng Da-Li, Dong Guao-Jun, Teng Sheng, Zhu Li-Huang. 2004. Genetic dissection of leaf correlative traits in rice (Oryza sativa L.). Acta Genetica Sinica. 31(3): 275-280.

Mardani, Zahra, Babak Rabiei, Hossein Sabouri, Atefeh Sabouri. 2013. Mapping of QTLs for germination characteristics under non-stress and drought stress in rice. Rice Science. 20(6): 391−399.

274

McCouch, S.R., L. Teytelman, Y. Xu, K.B. Lobos, K. Clare, M. Walton, B. Fu, R. Maghirang, Z. Li, Y. Xing, Q. Zhang, I. Kono, M. Yano, R. Fjellstrom, G. DeClerck, D. Schneider, S. Cartinhour, D. Ware, L. Stein. 2002. Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Research. 9(6):199–207. doi:10.1093/dnares/9.6.199

Palanoga, Alvin D., B.P. Mallikarjuna Swamy, Noraziyah Abd Aziz Shamsudin, Shalabh Dixit, Jose E. Hernandez, Teresita H. Boromeoa, Pompe C. Sta. Cruz, Arvind Kumar. 2014. Grain yield QTLs with consistent-effect under reproductive-stage drought stress in rice. Field Crops Research 161:46–54.

Price, A.H. 2006. Believe it or not, QTLs are accurate. Trends in Plant Science. 11:213-216. Robin, S., M.S. Pathan, B. Courtois, R. Lafitte, S. Carandang, S. Lanceras, M. Amante, H.T. Nguyen, Z. Li, 2003. Mapping osmotic adjustment in an advanced backcross inbred population of rice. Theoretical Applied Genetics. 107(7):1288–1296.

Sandhu, Nitika, Anshuman Singh, Shalabh Dixit, Ma Teresa Sta Cruz, Paul Cornelio Maturan, Rajinder Kumar Jain and Arvind Kumar. 2014. Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genetics. 15:63.

Sandhu, Nitika, Sunita Jain, Arvind Kumar, Balwant Singh Mehla, Rajinder Jain. 2013. Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation. BMC Genetics. 14:104.

Sangodele, E.A., R.R.Hanchinal, N.G.Hanamaratti, Vinay Shenoy and V.Mohan Kumar. 2014. Analysis of drought tolerant QTL linked to physiological and productivity component traits under water-stress and non-stress in rice (Oryza sativa L.). International Journal of Current Research Academic Review. 2(5): 108-113.

Sarkarung, S., G. Pantuwan. 1999. Improving rice for drought-prone rainfed lowland environments. In Ito O, O’Toole J, Hardy B (eds) Genetic improvement of rice for water- limited environments, Proceedings of the workshop on genetic improvement of rice for water-limited environments held at Los Banos, Philippines, 1–3 Dec 1998. International Rice Research Institute, Los Banos, pp 57–70

Sellamuthu, Reena, Chandrababu Ranganathan, and Rachid Serraj. 2015. Mapping QTLs for reproductive-stage drought resistance traits using an advanced backcross population in upland rice. Crop Science. 55:1–13.

Srinivasan, S., S. Michael Gomez, S. Satheesh Kumar, S. K. Ganesh, K. R. Biji, A. Senthil, R. Chandra Babu. 2008. QTLs linked to leaf epicuticular wax, physio-morphological and plant production traits under drought stress in rice (Oryza sativa L.). Plant Growth Regulation. Pp 1-12. 275

Srividhya, A., L. R. Vemireddy, P. V. Ramanarao, S. Sridhar, M. Jayaprada, G. Anuradha, B. Srilakshmi, H. K. Reddy, A. S. Hariprasad, E. A. Siddiq. 2011. Molecular mapping of QTLs for drought related traits at seedling stage under PEG induced stress conditions in rice. American Journal of Plant Sciences. 2: 190-201.

Subashri, M., S. Robin, K. K. Vinod, S. Rajeswari, K. Mohanasundaram, T. S. Raveendran. 2009. Trait identification and QTL validation for reproductive stage drought resistance in rice using selective genotyping of near flowering RILs. Euphytica. 166:291–305.

Suji, K.K., K. R. Biji, R. Poornima, K. Silvas Jebakumar Prince, K. Amudhan, S. Kavitha, Sumeet Mankar, R. Chandra Babu. 2012. Mapping QTLs for plant phenology and production traits using indica rice (Oryza sativa L.) lines adapted to rainfed environment. Molecular Biotechnology. 52:151–160.

Sun, Y., H. Luan, Z. Qi, D. Shan, C. Liu, G. Hu, Q. Chen. 2012. Mapping and meta-analysis of height QTLs in soybean. Legume Genomics and Genetics. 3(1)1-7.

Swamy, BP Mallikarjuna, Prashant Vikram, Shalabh Dixit, HU Ahmed and Arvind Kumar.2011. Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genomics. 12:319.

Swamy, Mallikarjuna B. P., Helal Uddin Ahmed, Amelia Henry, Ramil Mauleon, Shalabh Dixit, Prashant Vikram, Ram Tilatto, Satish B. Verulkar, Puvvada Perraju, Nimai P. Mandal, Mukund Variar, Robin S., Ranganath Chandrababu, Onkar N. Singh, Jawaharlal L. Dwivedi, Sankar Prasad Das, Krishna K. Mishra, Ram B. Yadaw, Tamal Lata Aditya, Biswajit Karmakar, Kouji Satoh, Ali Moumeni, Shoshi Kikuchi, Hei Leung, Arvind Kumar. 2013. Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega variety IR64 under drought. PLOS ONE. 8(5): e62795.

Takai, Toshiyuki, Yoshimichi Fukuta, Atsuko, Sugimoto, tatsuhiko Shiraiwa, Takeshi Horie. 2006. Mapping of QTLs controlling carbon isotope discrimination in the photosynthetic system using recombinant inbred lines derived from two different rice cultivars. Plant Production Science. 9(3): 271-280.

Temnykh, S., G. DeClerck, A. Lukashova, L. Lipovich, S. Cartinhour, S.R. McCouch. 2001. Computational and experimental analysis of microsatellite in rice (Oryza sativa L.): frequency, length variation, transposon associations and genetic marker potential. Genome Research. 1:1441-1452.

This, Dominique, Jonathan Comstock, Brigitte Courtois, Yunbi Xu, Nourollah Ahmadi, Wendy M. Vonhof, Christine Fleet, Tim Setter, Susan McCouch. 2010. Genetic analysis of water use efficiency in rice (Oryza sativa L.) at the leaf level. Rice. 3:72–86.

276

Trijatmiko, K.R., Supriyanta, J. Prasetiyono, M. J. Thomson, C. M. Vera Cruz, S. Moeljopawiro, A. Pereira. 2014. Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population. Molecular Breeding. 34:283–295.

Tyagi, S.P., and P.K. Gupta. 2012. Meta-analysis of QTLs involved in pre-harvest sprouting tolerance and dormancy in bread wheat. Triticeae Genomics and Genetics. 3(2):9-24.

Venuprasad, R., C.O. Dalid, M. Del Valle, D. Zhao, M. Espiritu, M.T. Sta Cruz, M. Amante, A. Kumar, G.N. Atlin. 2009. Identi- fication and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theoretical Applied Genetics. 120(1): 177–190. doi:10.1007/s00122-009-1168- 1

Verma, Sunil Kumar, Ritu Ravi Saxena, Ravi Ratna Saxena, Mary Suchita Xalxo, Satish Balkrishna Verulkar. 2014. QTL for grain yield under water stress and non-stress conditions over years in rice (Oryza sativa L.). Australian Journal of Crop Science. 8(6):916-926.

Veyrieras, J.B., B. Goffinet, A. Charcosset. 2007. Meta QTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinformatics.8:49.

Vikram, Prashant, BP Mallikarjuna Swamy, Shalabh Dixit, Helal Uddin Ahmed, Ma Teresa Sta Cruz, Alok Kumar Singh and Arvind Kumar. 2011. BMC Genetics. 12:89.

Vikrama, Prashant, B.P. Mallikarjuna Swamya, Shalabh Dixit , Helaluddin Ahmeda, M.T. Sta Cruz, Alok K. Singh, Guoyou Yec, Arvind Kumar. 2012. Bulk segregant analysis: “An effective approach for mapping consistent-effect drought grain yield QTLs in rice”. Field Crops Research 134:185–192.

Wang, Yun, Jinping Zang, Yong Sun, Jahuar Ali, Jianlong Xu, and Zhikang Li. 2013. Background-independent quantitative trait loci for drought tolerance identified using advanced backcross introgression lines in rice. Crop Science. 53: 435-441.

Xing, Wang, Hongwei Zhao, and Detang Zou. 2014. Detection of main-effect and epistatic QTL for yield-related traits in rice under drought stress and normal conditions. Candian Journal of Plant Science. 94: 633641.

Xu, J.L. H. R. Lafitte, Y. M. Gao, B. Y. Fu, R. Torres, Z. K. Li. 2005. QTLs for drought escape and tolerance identified in a set of random introgression lines of rice. Theoretical Applied Genetics. Pp 1-10.

277

Yadawa, Ram Baran, Shalabh Dixit, Anitha Ramana, Krishna Kumar Mishra, Prashant Vikrama, B.P. Mallikarjuna Swamya, Ma. Teresa Sta Cruz, Paul T. Maturana, Madhav Pandeyc, Arvind Kumar. 2013. A QTL for high grain yield under lowland drought in the background of popular rice variety Sabitri from Nepal. Field Crops Research 144: 281– 287.

Yue, B., L. Xiong, W. Xue, Y. Xing, L. Luo, C. Xu. 2005. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil. Theoretical Applied Genetics 111(6):1127–1136. Doi: 10.1007/s00122-005-0040-1

Yue, Bing, Lizhong Xiong, Weiya Xue, Yongzhong Xing, Lijun Luo, Caiguo Xu. 2005. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil. Theoretical Applied Genetics.111: 1127–1136.

Zhang, J., H.G. Zheng, A. Aarti, G. Pantuwan , T.T. Nguyen, T.N. Tripathy, A.K. Sarial, S. Robin, R.C. Babu, D.B. Nguyen, S. Sarkarung, A. Blum, H.T. Nguyen. 2001. Locating genomic regions associated with components of drought resistance in rice: comparative mapping within and across species. Theoretical Applied Genetics. 103(1):19–29. doi:10.1007/s001220000534

Zhao, Xiu-Qin, Jian-Long Xu, Ming Zhao, Renee Lafitte, Ling-Hua Zhu, Bin-Ying Fu, Yong- Ming Gao, Zhi-Kang Li. 2008. QTLs affecting morph-physiological traits related to drought tolerance detected in overlapping introgression lines of rice (Oryza sativa L.). Plant Science. 174: 618–625.

Zhou, GuangSheng, Fang Liu, JinHua Cao, Bing Yue, LiZhong XIiong. 2011. Detecting quantitative trait loci for water use efficiency in rice using a recombinant inbred line population. Crop Genetics. 56(14): 1481-1487.

Zou, G.H., H. W. Mei, H. Y. Liu, G. L. Liu, S. P. Hu, X. Q. Yu, M. S. Li, J. H. Wu, L. J. Luo. 2005. Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers. Theoretical Applied Genetics. 112: 106–113.

278

Table 5-1. Drought stress related studies in rice included in the study S.No Parents Type Pop. Pop. size Country Reference (Lines) 1 IR64xAzucena IxTRJ DH 135 Phil Courtois et al, 2000 2 IR64 x Azucena IxTRJ DH 135 Ind Hemamalini et al, 2000 3 CT9993 x IR62266 AJxI DH 154 USA Zhang et al,2001 4 IR64 x Azucena IxTRJ DH 90 Ind Venuprasad et al, 2002 5 CT9993 x IR62266 AJxI DH 154 Ind Babu et al, 2003 6 IR62266-42-6-2 x IxIm-TRJ BILs 150 Phil Robin et al, 2003 IR60080-46-A 7 Bala x Azucena IxTRJ RILs 205 Phil Lafitte et al, 2004 8 CT9993-5-10-1-M x AJxI DH 154 Thai Lanceras et al, 2004 IR62266-42-6-2 9 Zhaiyeqing8 x JxI DH 127 China Long Biao et al, 2004 Jingxi17 10 Zhenshan 97B x IxTRJ RILs 187 China Zou et al, 2005 IRAT109 11 Zhenshan 97B x IxTRJ RILs 180 China Yue et al, 2005 IRAT109 12 Teqing x Lemont IxTRJ BILs 133 China Xu et al, 2005 13 Milyang 23 x IxJ RILs 126 Phil Takai et al, 2006 Adhikari 14 Bala x Azucena IxTRJ RILs 177 Ind Gomez et 1l, 2006 15 Zhenshan 97B x IxTRJ RILs 180 China Yue et al, 2006 IRAT109 16 CT9993-5-10-1-M x AJxI DH 105 Ind Kumar et al, 2007 IR62266-42-6-2 17 Way Rarem x IxA F3 436 Phil Bernier et al, 2007 Vandana 18 Zhenshan 97B x IxTRJ RILs 180 China Yue et al, 2008 IRAT109 19 Tequing x Lemont IxTRJ BILs 254 China Zhao et al, 2008 20 Adhikari x IRAT 109 TEJxTRJ BILs 106 Japan Katto et al, 2008 21 Bala x Azucena IxTRJ RILs 176 UK Khawaja & Price, 2008 22 Zhenshan 97B x IxTRJ RILs 187 China Liu et al, 2008 IRAT109 23 CT9993-5-10-1-M x AJxI DH 154 Ind Srinivasan et al, 2008 IR62266-42-6-2 24 Zhenshan 97B x IxTRJ RILs 195 China Hu et al, 2009 IRAT110 25 IR64 x Norungan IxTRJ RILs 380 Ind Subashri et al, 2009 26 IR20 x Nootripathu IxI RILs 250 Ind-USA Gomez et al, 2010 27 IR 64 x Azucena IxTRJ DH 91 USA This et al, 2010 28 IR 64 x Azucena IxTRJ RILs 165 USA This et al, 2010 29 Swarna x N22 IxI RILs 292 Phil Virkam et al, 2011 30 IR 64 x N22 IxI RILs 289 Phil Virkam et al, 2011 31 MTU 1010 x N22 IxI RILs 362 Phil Virkam et al, 2011 32 IR 64 x INRC10192 IxI RILs 140 Phil Srividhya et al, 2011

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Table 5-1. Continued. 33 IR64 x RAM 40 and IxTRJ BILs 513 Phil Bimpong et al,2011 RAM 90 34 CT9993-5-10-1-M x AJxI DH 154 USA Chakraborty and Zeng, 2011 IR62266-42-6-2 35 Maybelle x Baiyeqiu JxI DH 251 China Qun et al,2011 36 Zhenshan 97B x IxTRJ RILs 180 China Zhou et al, 2011 IRAT109 37 CT9993-5-10-1-M x AJxI DH 135 Phil-Ind Sellamuthu et al,2011 IR62266-42-6-2 38 Shennong265 x JxI BILs 94 China Gu et al, 2012 Haogelao 39 Swarna x IxI F3:5 259 Phil Ghimire et al, 2012 Dhagaddeshi 40 IR62266 x Norungan IxI RILs 232 Ind Suji et al, 2012 41 Swarna x Basmati 334 IxI RILs 203-367 Phil Vikram et al, 2012 42 Sabarti x IR4371-46- IxI BILs 294 Phil-Nep Mishra et al, 2013 1-1(Sokha Dhan-1) 43 OM1490 x WAB880- IxI BILs 229 Viet Lang et al, 2013 1-38-18-20-P1-HB 44 IR64 x IR77298-5-6- IxI BILs - Phil Swammy et al, 2013 B-18(Aday Sel) 45 IR 64 x Tarom molaei IxJ BILs 72 USA Wang et al,2013 46 Pusa Basmati1460 x IxI F2:3 94 Phil Sandhu et al, 2013 MASARB 25 47 HKR47 x MAS26 IxI F2:3 100 Phil Sandhu et al, 2013 48 Sabitri x IR77298-5- IxI BILs 294 Phil Yadaw et al, 2013 6-18 49 Gharib x Sepidroud IxI F2:4 148 Iran Mardani et al, 2013 50 Xiaobaijingzi x IxI F2:7 220 China Xing et al, 2014 Kongyu131 51 Danteshwari x IxI RILs 122 Ind Verma et al, 2014 Dagaddeshi 52 IR64 x Kali IxA BILS 300 Phil Palanong et al, 2014 53 MTU1010 x Kali IxA BILS 300 Phil Palanong et al, 2014 54 IR64 x Kali IxA BILS 300 Phil Sandhu et al, 2014 55 MTU1010 x Kali IxA BILS 300 Phil Sandhu et al, 2014 56 Swarna x WAB 450 IxI BILs 188 Ind Sangodele et al, 2014 57 TDK1 x IR55419-04 IxI BILs 365 Phil Dixit et al, 2014 58 IR64X IRAT177 IxTRJ RILs 154 Phil Trijatmiko et al, 2014 Abbreviations- Pop.-population, IxTRJ-Indica xTropical japonica; IxI-Indica xIndica; AJxI- African japonica x Indica JxI-japonica xIndica; IximTRJ-Indica x Improved Tropical japonica; Indi-India; Phil-Philippines, Nep-Nepal; Thai- Thailand; Viet-Vietnam; RILs-Recombinant inbred lines; BILs-Backcross inbred lines; DH-Double haploid

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Table 5-2. Drought stress related traits for which QTLs were identified included in the meta- analysis study. S.No. Symbol Traits under drought stress No of Reference QTLs 1 GY Grain yield 131 Babu et al, 2003, Lafitte et al, 2004, Lanceras et al, 2004, Zou et al, 2005, Xu et al, 2005, Bernier et al, 2007, Zhao et al, 2008, Liu et al, 2008, Subashri et al, 2009, Virkam et al, 2011, Bimpong et al, 2011, Ghimire et al, 2012 Vikram et al, 2012, Lang et al, 2013, Swamy et al, 2013, Wang et al, 2013, Sandhu et al, 2013, Yadaw et al, 2013, Xing et al, 2014, Verma et al, 2014, Palanong et al, 2014 , Sandhu et al, 2014 , Dixit et al, 2014, Trijatmiko et al, 2014 2 PH Plant height 78 Venuprasad et al, 2002, Babu et al, 2003, Lafitte et al, 2004, Lanceras et al, 2004, Xu et al, 2005, Gomez et la, 2006, Bernier et al, 2007, Yue et al, 2008, Srinivasan et al, 2008, Subashri et al, 2009 Gomez et al, 2010, Virkam et al, 2011, Bimpong et al, 2011, Sellamuthu et al, 2011, Lang et al, 2013, Sandhu et al, 2013, Xing et al, 2014, Sandhu et al, 2014 , Dixit et al, 2014, Trijatmiko et al, 2014 3 DTF Days to flowering 44 Venuprasad et al, 2002, Lanceras et al, 2004, Gomez et la, 2006, Kumar et al, 2007, Bernier et al, 2007, Virkam et al, 2011, Chakraborty & Zeng, 2011, Sellamuthu et al, 2011, Ghimire et al, 2012 Yadawa et al, 2013, Palanog et al, 2014, Sandhu et al, 2014, Dixit et al, 2014 4 LR Leaf rolling 28 Courtois et al, 2000, Hemamalini et al, 2000, Long Biao et al, 2004, Yue et al, 2005, Subashri et al, 2009, Gomez et al, 2010, Trijatmiko et al, 2014 5 PN Panicle number 24 Lanceras et al, 2004, Zou et al, 2005, Bernier et al, 2007, Sellamuthu et al, 2011, Wang et al, 2013, Sandhu et al, 2013, Xing et al, 2014 6 LD Leaf drying 21 Courtois et al, 2000, Yue et al, 2005, Gomez et al, 2010 7 BIO Biomass 18 Yue et al, 2006, Bernier et al, 2007, Gomez et al, 2010, Virkam et al, 2011, Bimpong et a, 2011 8 HI Harvest index 18 Venuprasad et al, 2002, Lafitte et al, 2004, Lanceras et al, 2004, Yue et al, 2006, Subashri et al, 2009, Virkam et al, 2011, Bimpong et al, 2011 9 NOT No. of tillers 17 Gomez et al, 2006, Subashri et al, 2009, Gomez et al, 2010, This et al, 2010, Bimpong et al, 2011, Sandhu et al, 2013

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Table 5-2. Continued. S.No. Symbol Traits under drought stress No of Reference QTLs 10 RWC Relative water content 17 Courtois et al, 2000, Babu et al, 2003 Srinivasan et al, 2008,Subashri et al, 2009, Sangodele et al, 2014 11 CID Carbon isotope discriminations 15 Takai et al, 2006, This et al, 2010 12 WUE Water use efficiency 15 Katto et al, 2008, This et al, 2010, Zhou et al, 2011 13 RGY Relative grain yield 14 Yue et al, 2005, 2006, Suji et al, 2012 14 RV Root volume 14 Venuprasad et al, 2002, Yue et al, 2006, Subashri et al, 2009, Sandhu et al, 2013 15 PE Panicle exertion 13 Yue et al, 2008, Subashri et al, 2009 16 HD Heading days 12 Xu et al, 2005 17 OA Osmotic adjustment 12 Zhang et al, 2001, Robin et al, 2003 18 PL Panicle length 12 Lafitte et al, 2004,Liu et al, 2008, Subashri et al, 2009, Sandhu et al, 2013 19 RGRD Root growth rate in depth 12 Yue et al, 2006 20 PHOTO Net Photosynthesis 11 Zhao et al, 2008, Hu et al, 2009, Gu et al, 2012 21 SF Spikelet fertility 11 Lafitte et al, 2004, Zou et al, 2005, Wang et al, 2013 22 CT Canopy Temperature 10 Yue et al, 2005, Srinivasan et al, 2008 Gomez et al, 2010, Sangodele et al, 2014 23 DRRV Deep root rate in volume 10 Yue et al, 2006 24 LBF Leaf blade flatness 10 This et al, 2010, This et al, 2010 25 RSF Relative Spike Fertility 10 Yue et al, 2005, Yue et al, 2006 26 1000-GW 1000-grain weight 9 Subashri et al, 2009, Wang et al, 2013, Sangodele et al, 2014 27 FGP Filled grain per plant 9 Wang et al, 2013 28 LL Leaf length 9 This et al, 2010 29 RGRV Root growth rate in volume 9 Yue et al, 2006 30 SPAD Chlorophyll content 9 Zhou et al, 2008, Hu et al, 2009 31 DFT Delay in Flowering Time 8 Yue et al, 2005 32 DRI Drought Respond Index 8 Yue et al, 2005, Yue et al, 2006, Bernier et al, 2007 33 DT Drought tolerance 8 Qun et al, 2011 34 FLL Flag leaf length 8 Yue et al, 2008 35 GPP Grain number per plant 8 Xing et al, 2014, Sangodele et al, 2014 Trijatmiko et al, 2014 36 LW Leaf width 8 This et al, 2010 37 STCON Stomatal conductance 8 Zhao et al, 2008, Subashri et al, 2009, This et al, 2011, Gu et al, 2012 38 FD Flowering date 7 Lafitte et al, 2004, Kumar et al, 2007 39 KW Kernel weight 7 Zhou et al, 2011 40 LAREA Leaf area 7 Zhao et al, 2008 41 LN2 Leaf N2 7 This et al, 2010

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Table 5-2. Continued. S.No. Symbol Traits under drought stress No of Reference QTLs 42 RL Root length 7 Subashri et al, 2009, Lang et al, 2013, Sandhu et al, 2013 43 SLAREA Specific leaf area 7 This et al, 2010 44 TR Transpiration rate 7 Zhao et al, 2008, Khowaja and Price, 2008 Gu et al, 2012 45 DRW Dry root weight 6 Lang et al, 2013, Sandhu et al, 2013 46 DTH Days to heading 6 Babu et al, 2003, Takai et al, 2006 Bimpong et al, 2011, Trijatmiko et al, 2014 47 FLW Flag leaf width 6 Yue et al, 2008 48 RDW Root dry weight 6 Venuprasad et al, 2002, Srividhya et al, 2011 49 RSR Root shoot ratio 6 Srividhya et al, 2011 50 BY Biological yield 5 Lanceras et al, 2004, Subashri et al, 2009 51 DPE Difference in panicle excretion 5 Yue et al, 2008 52 MRD Maximum root depth 5 Yue et al, 2006 53 RFP Relative fertile panicles 5 Yue et al, 2006 54 RGW Relative grain weight 5 Yue et al, 2006 55 STOF Stomata frequency 5 Zhao et al, 2008 56 Ci Intercellular CO2 4 Zhao et al, 2008 57 Ci/Ca The ratio of Ci to the Ca 4 This et al, 2011 58 DLR Days of rolling 4 Yue et al, 2006 59 EI Economic index 4 Zhou et al, 2011 60 PSF Percent spikelet fertility 4 Lanceras et al, 2004 61 RFLW Relative flag leaf width 4 Yue et al, 2008 62 RPF Root pulling force 4 Zhang et al, 2001, Trijatmiko et al, 2014 63 RPH Relative plant height 4 Yue et al, 2008 64 RTH Root thickness 4 Lafitte et al, 2004, Sandhu et al, 2013 65 SBIO Shoot biomass 4 This et al, 2010 66 COLL Coleorhiza length 3 Mardani et al, 2013 67 FRW Fresh root weight 3 Sandhu et al, 2013 68 FSW Fresh shoot weight 3 Sandhu et al, 2013 69 PF Panicle fertility 3 Bimpong et al, 2011 70 RFLL Relative flag leaf length 3 Yue et al, 2008 71 RSN Relative spikelet number 3 Yue et al, 2006 72 SBN Secondary branch number 3 Liu et al, 2008 73 SDW Shoot dry weight 3 Srividhya et al, 2011 74 SN Spikelet number 3 Zou et al, 2005 75 TGW Total grain weight 3 Zou et al, 2005 76 ABA ABA content 2 This et al, 2010 77 DSW Dry shoot weight 2 Sandhu et al, 2013 78 FSP Fraction sterile panicle 2 Lafitte et al, 2004

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Table 5-2. Continued. S.No. Symbol Traits under drought stress No of Reference QTLs 79 GP Germination percentage 2 Mardani et al, 2013 80 GR Germination rate 2 Mardani et al, 2013 81 GWP Grain weight per plant 2 Sangodele et al, 2014 82 LRECT Leaf erectness 2 This et al, 2010 83 PHIPSII The yield of PSII under 2 Gu et al, 2012 flowering 84 PLuL Plumule length 2 Mardani et al, 2013 85 PND Panicle neck diameter 2 Liu et al, 2008 86 PRT Penetrated root thickness 2 Zhang et al, 2001 87 PSS Percent seed set 2 Trijatmiko et al, 2014 88 PW Panicle weight 2 Sangodele et al, 2014 89 RaL Radical length 2 Mardani et al, 2013 90 RGR Relative growth rate 2 Katto et al, 2008, Subashri et al, 2009 91 RW Root weight 2 Zhou et al, 2011 92 SEN Senescence 2 Subashri et al, 2009 93 SPN Seeds panicle 2 Sandhu et al, 2013 94 TSN Total spikelet number 2 Lanceras et al, 2004 95 WATERLOSS Proportional water loss at time 2 Khowaja and Price, 2008 to reach leaf rolling score 4.5- DS 96 DS Drought sensitivity under 1 Gu et al, 2012 flowering 97 DTM Days to maturity 1 Venuprasad et al, 2002 98 EIPD Economic index per day 1 Zhou et al, 2011 99 Fv'/Fm' Fv’/Fm’ under flowering 1 Gu et al, 2012 100 LBR Length/breadth ratio 1 Sandhu et al, 2013 101 LDW Leaf dry weight 1 Khowaja and Price, 2008 102 PBN Primary branch number 1 Liu et al, 2008 103 PGY plot grain yield 1 Subashri et al, 2009 104 PNOT Productive tillers 1 Sangodele et al, 2014 105 PRDW Penetrated root dry weight 1 Zhang et al, 2001 106 REVS Recovery score 1 Gomez et al, 2010 107 SD Spikelet density 1 Liu et al, 2008 108 SPP Spikelets per panicle 1 Trijatmiko et al, 2014

109 TRDW Total root dry weight 1 Zhang et al, 2001 Total QTLs for DR traits and GY components 911

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Table 5-3. List of Meta-QTLs (MQTLs) in which drought resistance related QTLs from different populations were identified. S.No MQTL Chr MQTL MQTL MQTL MQTL No. of Traits related QTLs in MQTL position distance model CI QTLs in (cM) (cM) MQTL 1 MQTL1.1 1 39.11 11.38 7 1.62 17 GY, OA, PH, RSF, RS, LR, RDW, DRW, & WATERLOSS 2 MQTL1.2 1 108.92 7.69 7 2.57 45 GR, GY, RS, WUE, RT, PN, PH, PE, SC, RSN, DRI, BY, EI, NOT, FLW, CL, GP, LD, CL, GP, LD, RWC, SF, DFT, DRRV, HD, PL, DTF, OA, RY, & SPAD 3 MQTL1.3 1 148.68 5.74 7 1.02 45 RS, HD, GY, RWC, LR, DTB, PH, PL, LBF, BIO, BY, PN, HI, TR, CID, FSP, Ci/Ca, SF, PE, OA, LN2 & DTF 4 MQTL1.4 1 172.02 4.65 7 1.79 42 RS, LR, TGW, RWC, PH, RDV, PE, DPE, DRI, 1000- GW, NOT, LL, SLA, DTH, DTF, DTB, RFLW, GY & RGRV 5 MQTL1.5 1 189.74 0.14 7 0.06 7 DTF, LR, LD, RWC, PH, & SBIO 6 MQTL2.1 2 17.92 5.03 10 3.1 21 LREACT, GY, GP, LD, GPP, PN, Ci/Ca, FLL, CT & RS 7 MQTL2.2 2 50.84 3.42 10 1.5 12 LW, RGRV, DRW, LN2, RGW, PL, PN, NOT, PH, FRW, & SPAD 8 MQTL2.3 2 67.82 11.41 10 2.01 26 GY, OA, RSN, SF, DT, LW, RGRD, RT, FD, WUE, LBF, PHOTO, SDW, HI, RFLL, PN, DTH, RPF & NOT 9 MQTL2.4 2 145.62 7.21 9 1.5 60 WUE, RGR, CID, DPE, PH, DTH, PF, GY, RGRD, DRRV, NOT, HI, FLW, LR, LD, CT, SF, REVS, DRI, RY, BIO, HD, PH, LL, DRW, TGW, FLL & DTF 10 MQTL2.5 2 188.28 0.78 10 0.13 9 BIO, DS, PHOTO, NOT, PN, PF, LL & WATERLOSS 11 MQTL3.1 3 8.62 6.64 8 1.28 50 LD, DTF, RWC, HI, LR, PE, RV, RDW, GY, RGRD, RV, PH, DTF, FLL, LD, RGW, BIO, PH, DTM, HD & SPAD 12 MQTL3.2 3 40.47 8.41 8 3.09 14 GY, SPAD, DPE, COL, NOT, DTF & LR 13 MQTL3.3 3 105.56 2.83 8 6 11 DT, LBF, GY, LR, RV, STCON, NOT, SBN, BIO & GWP 14 MQTL3.4 3 128.34 10.62 8 2.25 5 GWP, RWC, LR, RY & PH 15 MQTL3.5 3 192.83 1.23 8 0.24 25 LR, Ci, LL, GP, GY, FLL, SPAD, RSF, DTH, LD & TGW

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Table 5-3. Continued. S.No MQTL Chr MQTL MQTL MQTL MQTL No. of Traits related QTLs in MQTL position distance model CI QTLs in (cM) (cM) MQTL 16 MQTL4.1 4 32.02 8.58 8 2.3 18 Ci/Ca, CID, WUE, LBF, GY, SLA, STCON, DTF, RS, LD, LW, RGRD, & LR 17 MQTL4.2 4 92.38 3.86 7 2.55 22 GY, TGW, EI, WUE, GR, KW, WUE, SF, PE, CID, PL, HD, FD, SBN, & SPD 18 MQTL4.3 4 115.29 3.53 7 0.66 30 SBN, SPD, LW, LR, RPF, PN, PL, GY, PH, TSN, PND, RV, SPP, PBN, RGRD, MRD, DTF, NOT & SN 19 MQTL4.4 4 136.31 3.57 7 2.03 14 GPP, RV, RGRD, MRD, PSF, LW, PH, FLL, FLW, SPAD, SBN & SPD 20 MQTL4.5 4 150.6 1.50 7 0.04 6 LD, HD, PH, CID, GY & SEN 21 MQTL5.1 5 0.07 3.63 5 2.82 9 DTF, RTH, TR, PN, LAREA, SPAD, BIO, OA & PH 22 MQTL5.2 5 29.92 9.14 5 3.03 3 PH, DTF & NOT 23 MQTL5.3 5 79.38 3.9 5 2.29 15 PN, GY, RGW, SF, LW, RPF, FD, FLL, PH, LR, PRDW, HD, CT & RWC 24 MQTL5.4 5 93.94 6.2 5 2.28 12 SLAREA, CID, LBF, PL, LD, LR, & LN2 25 MQTL5.5 5 111.26 0.06 5 0.31 9 FLW, DT, RaL, LAREA, PH, FSP & RSF 26 MQTL6.1 6 24.47 2.81 8 0.99 19 FSW, DTF, RL, FRW, RTH, RV, RGR, NOT, PN, GY, DSW, LR, GY & RWC 27 MQTL6.2 6 39.76 5.68 8 0.71 8 FGP, SF, FLW, GY, PHIPSII, PH, TGW & STCON 28 MQTL6.3 6 71.65 4.87 8 4.55 4 RWC, PH, & SPAD 29 MQTL6.4 6 107.43 3.92 8 3.18 18 DTH, LBF, FLL, FLD, KW, PF, RGRD, BIO, EI, RSN, DTF, WUE, PN, RWC & LD 30 MQTL6.5 6 130.51 5.11 7 0.18 11 CT, LBF, HD, CID, DT, KW, PH, EI & DRI 31 MQTL7.1 7 0.11 4.94 8 2.62 4 PH, LD & RDW 32 MQTL7.2 7 25.32 3.59 8 3.07 21 DPE, GY, RSF, RV, RGRD, FLL, LR, LD, RGY, PE, DRRV, DTF, RGW & PNOT 33 MQTL7.3 7 41.82 4.74 8 1.73 5 OA, KW, RW & PH 34 MQTL7.4 7 64.13 5.76 8 2.29 6 TGW, GP, SF, LR & LD 35 MQTL7.5 7 91.91 3.71 8 0.33 12 SF, HI, FTD, SLW, FDE, OA, FLL, DRRV, Ci, DTF & LD 36 MQTL8.1 8 57 1.16 7 0.71 30 RL, RDW, SF, RS, CID, DLR, RL, RSF, PN, NOT, DTH, GPP, PSS, OA, PW, HD, GY, RGY, DTF & Ci 37 MQTL8.2 8 63.59 3.25 7 2.73 5 RGRD, SF, RV, PN & WUE 38 MQTL8.3 8 75.3 2.62 7 2.03 11 RGRD, KW, GPP, PSS, CT, PH, DRI, WUE, CID & OA

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Table 5-3. Continued. S.No MQTL Chr MQTL MQTL MQTL MQTL No. of Traits related QTLs in MQTL position distance model CI QTLs in (cM) (cM) MQTL 39 MQTL8.4 8 84.78 5.17 7 2.54 8 RPF, PSS, RSF, DPE, RGY, OA, DT, PN & SN 40 MQTL8.5 8 107.04 2.24 7 0.14 17 PL, PE, BY, PSS, NOT, RV, LR, SBN, TGW, PH, DRW, LD & PE 41 MQTL9.1 9 1.25 1.84 10 1.02 21 LR, BIO, DTF, HI, RGY, SF, RGW, RFLW, PH, SDW, RL, PL & PW 42 MQTL9.2 9 13.55 7.87 8 4.62 6 DRRV, RSF, RGRD, DLR, MRD & PSS 43 MQTL9.3 9 62.62 4.49 8 2.59 13 PHOTO, SLAREA, LD, COLL, TR, TGW, PE, STCON, PH, Fv’/Fm’ & RSF 44 MQTL9.4 9 78.98 9.45 8 1.47 16 PN, LR, DTF, RL, DRI, RWC, Ci, BY, DTH, DRW, GP, FGP, PL & PRT 45 MQTL9.5 9 110.7 5.20 7 0.4 6 SN, RV, RL, RWC, HD & DTF 46 MQTL10.1 10 13.75 6.62 6 4.87 7 PH, PE, HD , GY & TRDW 47 MQTL10.2 10 44.92 2.24 6 4.43 4 LN2, RFLW, PH & PE 48 MQTL10.3 10 54.82 2.75 6 2.72 11 DRRV, PHOTO, DRI, DTF, DTH, SLAREA, SF, PN & RSF 49 MQTL10.4 10 70.84 2.95 6 3.56 2 PL & LD 50 MQTL10.5 10 91.8 1.95 5 3.03 7 HI, FLL, PH, RGY & HD 51 MQTL11.1 11 45.78 2.07 9 2.63 16 LBF, MRD, PH, LN2, FGP, PL, SPN, LD, SBIO, LBR, RGRD & PN 52 MQTL11.2 11 61.12 3.85 6 7.42 1 LDW 53 MQTL11.3 11 74.7 8.42 6 1.85 8 PN, PH, SF, TGW, STCON, TR & RPF 54 MQTL11.4 11 105.03 5.1 6 3.15 9 TR, CT, PE, RWC, PHOTO, RV, SEN & DTF 55 MQTL11.5 11 120.6 0.5 6 0.28 4 PH, PND, LR & SPAD 56 MQTL12.1 12 23.96 12.93 7 3.79 6 WUE, RWC, FD, RSN & GY 57 MQTL12.2 12 61.16 5.57 6 1.89 5 GY, DTF & PH 58 MQTL12.3 12 73.38 7.68 7 1.56 4 SLAREA, LD, RWC & SBIO 59 MQTL12.4 12 101.54 2.45 7 3.41 8 PH, ABA, HD, RW, RFP & LR 60 MQTL12.5 12 111.45 0.65 7 0.23 4 LL, LR & LW MQTL = Meta-QTL, Chr= Chromosome, cM= centiMorgan, No= Number, CI = confidence interval

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180

160 156

140 128 120

105 100 90 82 80 70 69 60 Drought related QTLs related Drought 49 50 44 40 35 33

0 1 2 3 4 5 6 7 8 9 10 11 12 Chromosome

Figure 5-1. Distribution of 911 QTLs related to drought response traits on rice chromosomes in the genome. The bar graphs show the frequency of drought related QTLs on individual rice chromosomes. The number on the top of bar graphs represents the total number of drought related QTLs each chromosome contains.

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Figure 5-2. Graphic presentation of integration and projection of 911 QTLs associated to 109 different drought related traits on the 12 rice chromosomes, from 58 different drought stress studies conducted in different populations published between 2000 to 2014 from different countries.

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Figure 5-3. Projection of 156 drought related QTLs and identification of Meta-QTLs (MQTLs) on chromosome 1 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on the right of the chromosome. The five best MQTLs named MQTL1.1, MQTL1.2, MQTL1.3, MQTL1.4 & MQTL1.5 highlighted in brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-4. Projection of 128 drought related QTLs and identification of MQTLs on chromosome 2 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 2. The five best MQTLs named MQTL2.1, MQTL2.2, MQTL2.3, MQTL2.4 & MQTL2.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-5. Projection of 105 drought related QTLs and identification of MQTLs on chromosome 3 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 3. Five best MQTLs named MQTL3.1, MQTL3.2, MQTL3.3, MQTL3.4 & MQTL3.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-6. Projection of 90 drought related QTLs and identification of MQTLs on chromosome 4 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 4. Five best MQTLs named MQTL4.1, MQTL4.2, MQTL4.3, MQTL4.4 & MQTL4.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-7. Projection of 49 drought related QTLs and identification of MQTLs on chromosome 5 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 5. Five best MQTLs named MQTL5.1, MQTL5.2, MQTL5.3, MQTL5.4 & MQTL5.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-8. Projection of 70 drought related QTLs and identification of MQTLs on chromosome 6 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 6. Five best MQTLs named MQTL6.1, MQTL6.2, MQTL6.3, MQTL6.4 & MQTL6.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-9. Projection of 50 drought related QTLs and identification of MQTLs on chromosome 7 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 7. Five best MQTLs named MQTL7.1, MQTL7.2, MQTL7.3, MQTL7.4 & MQTL7.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-10. Projection of 82 drought related QTLs and identification of MQTLs on chromosome 8 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 8. Five best MQTLs named MQTL8.1, MQTL8.2, MQTL8.3, MQTL8.4 &MQTL8.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

297

Figure 5-11. Projection of 69 drought related QTLs and identification of MQTLs on chromosome 9 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 9. Five best MQTLs named MQTL9.1, MQTL9.2, MQTL9.3, MQTL9.4 & MQTL9.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-12. Projection of 35 drought related QTLs and identification of MQTLs on chromosome 10 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 10. Five best MQTLs named MQTL10.1, MQTL10.2, MQTL10.3, MQTL10.4 & MQTL10.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-13. Projection of 44 drought related QTLs and identification of MQTLs on chromosome 11 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 11. Five best MQTLs named MQTL11.1, MQTL11.2, MQTL11.3, MQTL11.4 & MQTL11.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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Figure 5-14. Projection of 33 drought related QTLs and identification of MQTLs on chromosome 12 using meta-analysis clustered with grain yield trait. The projected QTLs are shown on left and all the markers with genetic distance (cM) are shown on right of chromosome 12. Five best MQTLs named MQTL12.1, MQTL12.2, MQTL12.3, MQTL12.4 & MQTL12.5 highlighting by brown, gray, orange, blue and coral colors were identified using meta-analysis.

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CHAPTER-VI OVERALL CONCLUSIONS

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Drought is one of the serious constraints affecting rice growth and grain yield, and has become a crisis threating agriculture worldwide. Rice production requires a large amount of water to complete its life cycle, which contributes to water scarcity that is further affected by growing human needs due to the increase in population and urbanization in the fast-growing economies of many countries, which altogether might further affect the increase in food production needed. A major challenge for agriculture worldwide, in crop production under drought stress conditions, is the improvement of rice genotypes for water use efficiency and other drought resistance related traits. Drought stress causes alteration in morphological, physiological, and biochemical processes, which comprise morpho-physiological and productivity traits such as photosynthesis, transpiration rate, water use efficiency (WUE), stomatal conductance, chlorophyll content, electron transport rate, number of tillers and biomass, all of which ultimately determine grain yield. Many genotypes have evolved or been selected for high water efficiency and drought tolerance, which can be further used to improve other rice genotypes for enhanced productivity with superior biomass and/or economic yield under drought stress. Based on the review of literature related for WUE and other drought resistance (DR) related traits, there are several published studies conducted in mapping populations and diverse germplasm. However, there is no study as yet conducted with the USDA rice mini-core collection (URMC) of diverse rice genotypes for WUE, DR related traits and grain yield under drought stress using high-throughput genomics tools.

Keeping the above background information in mind, the present study was conducted with the objectives 1) Phenotypic analysis of the URMC for WUE and DR related traits, 2)

Correlation analysis of rice drought response phenes for physiological traits and grain yield in the URMC, 3) Molecular genetic dissection of WUE and DR using GWA analysis in the URMC,

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and 4) Genome-wide meta-analysis (GWMA) of QTLs for DR traits and grain yield components under drought stress.

In the first research objective, the URMC of 221 rice genotypes were screened for WUE, plant biomass and number of tillers, and various morpho-physiological traits (relative water content, leaf rolling, leaf chlorophyll content, photosynthesis, transpiration rate, stomatal conductance, intercellular CO2, the ratio of intercellular CO2 and ambient CO2 concentration, Fv’,

Fm’, Fv’/Fm’, electron transport rate, the efficiency of photosystem II and non-photochemical quenching) at the vegetative stage under drought stress conditions in the greenhouse. Based on the findings of this study, our focus was to identify rice genotypes having higher WUE, photosynthesis and biomass enhancing plant productivity under drought stress. Out of the

URMC of 221 rice genotypes, 35 rice genotypes showed high levels of drought resistance shown by the levels of WUE, photosynthesis and biomass under drought stress, with less than 25% reduction in these three traits, 14 rice genotypes exhibited moderate drought resistance with a medium rate of WUE, photosynthesis and biomass under drought stress and 25-40% reduction in all these three traits, and 8 rice genotypes showed lower WUE, photosynthesis and biomass under drought stress with more than 40% reduction in all these three traits. The genotypes N22,

Vandana, 301418 and 301408 are drought resistant and genotype 310100 is drought sensitive, all adapted for flowering and producing good grain yield in Arkansas. Subsequently, F3 populations were developed from the crosses between drought resistant 301418 and drought sensitive 310100 genotypes and the F3 population advanced for developing mapping population to map the QTLs for higher WUE, photosynthesis and biomass. The information from this study can be useful to plant breeders for developing high yielding and drought resistant rice genotypes for adverse environmental conditions in the future.

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The second objective was an analysis of 81 diverse rice genotypes of the URMC that were screened for grain yield components (panicle length, number of spikelets per panicle, number of filled grains & unfilled grains per panicle) at the reproductive stage in field conditions, as well as plant productivity (plant biomass) and physiological (photosynthesis and

WUEi) traits at the vegetative stage under drought stress. After recording the data of both vegetative and reproductive screens of the rice genotypes, analysis of variance was done using

JMP 12.0. The analysis of variance showed significant variation between well-watered and drought stressed plants for the grain yield components at reproductive stage and physiological traits at vegetative stage among the 81 rice genotypes of the URMC. There was an interaction between treatment and rice genotype, which showed a reduction in all the grain yield components and physiological traits under drought. A summary of the results showed a set of 5 out of 81 rice genotypes of the URMC including 301066, 301408, 301418, N22 and Vandana are drought resistant with longer panicle length, higher numbers of spikelets per panicle, higher number of filled grains per panicle, lower number of unfilled grains per panicle with less than

25% reduction in all the grain yield components in field conditions under drought stress, as well exhibiting higher amount of plant biomass, higher rate of photosynthesis and WUEi with less than 25% reduction in all these physiological traits in rice plants in the greenhouse conditions at vegetative stage. Another set of 22 rice genotypes named 310007, 310020, 310039, 310100,

310144, 310338, 310345, 310348, 310351, 310381, 310389, 31044, 310515, 310566, 310588,

310645, 310687, 310724, 310773, 311793, 311794 and 311795 were classified drought sensitive, exhibiting smaller panicle length, lower numbers of spikelets per panicle, lower number of filled grains per panicle and higher number of unfilled grains per panicle drought stress with more than 40 % reduction of all these grain yield components in field conditions, as

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well as exhibiting higher amount of plant biomass, higher rate of photosynthesis and WUEi, with more than 40% reduction in all these physiological traits in green house conditions at the vegetative stage, respectively. Based on correlation among traits, the major grain yield components such as number of spikelets per panicle, number of filled and unfilled grains per panicle showed strong positive correlation with major physiological traits such as plant biomass, photosynthesis and WUEi. In the study, the results suggest that drought resistant genotypes having higher grain yield as well as higher photosynthesis and WUEi can be useful for plant breeders to improve Arkansas’s traditional adapted varieties for drought resistance. The information on rice stress physiology of these genotypes provide a basic understanding for dissecting the genetic architecture of photosynthesis and WUEi.

The third research objective was to identify SNPs significantly associated with the drought response parameters of WUE, photosynthesis, transpiration rate, stomata conductance, intercellular CO2, the ratio of intercellular CO2 and ambient CO2 concentration, plant biomass, number of tillers, relative water content, leaf rolling, chlorophyll content, Fv’, Fm’, Fv’/Fv’, electron transport rate, the efficiency of photosystem II and non-photochemical quenching of 206 rice genotypes of the URMC using genome-wide association (GWA) analysis. Based on the significance level of genome-wide SNPs in rice genotypes, GWA analysis identified several

SNPs linked to morpho-physiological traits such as for WUEi, 24 SNPs linked to 24 candidate genes (with predicted or known functions) such as Os03g0139200 (Cytochrome-containing protein regulating root & shoot development), Os04g0591900 (Panicle & seed development under abiotic stress), and LOC_Os01g59990 (Glucocorticoid receptor, synthesizes polysaccharides and glycoproteins in the plant cell wall). For photosynthesis, 16 SNPs were found linked to 29 candidate genes including Os11g0639000 (PHYB1, regulates elongation of

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sheath and stem tissues of mature plants and contributes to light-mediated regulation of PhyA and Cab gene transcripts) were found in this study. A total of 26 SNPs linked to 32 candidate genes were identified associating to transpiration rate such as Os05g0333500 (GS5, guanine nucleotide-binding protein alpha-1 that determines grain size and yield in rice and controls response of stomatal opening/closure under abiotic stress), Os05g0333500 (Heat shock protein

DnaJ, regulates chloroplast development), Os09g0497000 (Adenine nucleotide translocator 1 and PGR5, controls the utilization of light energy to avoid photoinhibition during photosynthesis and downregulation of photosystem II photochemistry in response to intense light) and

Os05g0497600 (Arsenic-induced RING E3 ligase, responds to abiotic stress), were found in the

GWA analysis. The GWA analysis also identified 10 SNPs linked to 11 candidate genes associating with stomatal conductance, including Os06g0597400 (PINHEAD gene, shoot apical meristem (SAM) maintenance and leaf development). For Ci & Ci/Ca, 19 SNPs linked to 22 candidate genes including Os03g0669000 (DEAD-box RNA helicase regulates thermotolerant growth at high temperature), Os08g0137100 (WD40 regulates vegetative and reproductive development) and Os09g0396900 (Vacuolar membrane transporter, translocates Fe and Zn between flag leaves and seeds in rice and Arabidopsis). We also found 23 SNPs linked to 25 candidate genes associating with plant biomass, in which Os10g0462800 (PTAC3-PLASTID

TRANSCRIPTIONALLY ACTIVE3, regulates plastid development for more chlorophyll content in Arabidopsis) and 7 SNPs linked to 7 candidate genes associating with number of tillers which include Os05g0200500 (Casein kinase 1), Os01g0587400 (Serine/threonine kinase- related domain containing protein), Os12g0148700 Adipose-regulatory protein) and

LOC_Os01g59440 (Brassinosteroid insensitive 1). GWA analysis found 17 SNPs linked with 24 candidate genes associated with RWC which include LOC_Os11g31360 (NAM, No Apical

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Meristem), 11 SNPs linked with 11 candidate genes associated with LR in which Os06g0198500

(RmlC-like jelly roll fold domain containing protein, leaf rolling response in Arabidopsis) and 14

SNPs linked with 17 candidate genes associated with chlorophyll content, including

Os08g0196700 (HAP2/Nuclear Factor Y, NF-YA transcription factor expresses drought stress tolerance) and Os06g0707000 (MATE efflux/ glycosyl transferase gene, modulation of genes involved in plant growth & development under abiotic stress in transgenic lines in Arabidopsis).

In addition, GWA analysis identified 17 SNPs linked with 25 candidate genes associated with

Fv’ including Os03g0586500 (Chloroplast post-illumination chlorophyll fluorescence) and

Os06g0704900 (Regulation of the cell division in plants), 15 SNPs linked with 19 candidate genes for Fm’ such as Os02g0662700 (SCARECROW-LIKE 1/ hairy meristem 1 regulates hairs on meristem in Arabidopsis), 29 SNPs linked with 34 candidate genes associated with Fv’/Fm’ including Os04g0605500 (P-type IIB Ca2+ ATPase, expresses stress tolerance) and

Os01g0102900 (LEAF-TYPE FERREDOXIN-NADP+ OXIDOREDUCTASE, regulates development of light-dependent attachment to thylakoid membrane). In this study, we identified

12 SNPs linked with 21 candidate genes associated with PhiPS II in which Os09g0439500 (Type

II chlorophyll a/b binding protein initiates the development of chlorophyll from photosystem I precursor). For ETR and qN, 18 SNPs linked with 22 candidate genes including

LOC_Os02g47744 (MYB transcription factor), Os11g0206400 (Mitochondrial transcription termination factor, GRAS transcription factor responses to drought stress tolerance in

Arabidopsis & rice) and 19 SNPs linked with 24 candidate genes including Os04g0605500 (P- type IIB Ca(2+) ATPase/ calcium-transporting ATPase 8/plasma membrane-type genes, exhibits stress tolerance in plants) and Os01g0938100 (photosystem II Psb28 gene to increase photosynthesis) were identified using GWA analysis, respectively. The results suggest that with

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the help of powerful statistical tool (FarmCPU) and SNP detection from low coverage genome sequencing, it is possible to analyze the genetic basis of WUE, other physiological, plant productivity & leaf morphological traits under drought stress, screened under controlled greenhouse conditions using GWA analysis. In this study, we show that GWA analysis is a powerful tool to provide an insight into the genetic architecture of WUEi and other drought resistance related traits, by screening a diverse collection of rice genotypes and identify candidate genes associated with these traits under drought stress at the vegetative stage in greenhouse conditions. These candidate genes related to WUEi and drought resistance related traits will be validated in further research.

In the fourth objective, we used the data describing 911 QTLs for DR related traits and grain yield (GY) components under drought stress, from 58 different studies for 109 different

DR related traits and GY components in 39 different rice population from 11 different countries published between 2000 to 2014, from population sizes ranging from 90 to 513. The 911 different QTLs for 109 different DR traits and GY components were distributed in the genome in which chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 containing QTL numbers of 156, 128,

105, 90, 49, 70, 50, 82, 69, 35, 44 and 33, respectively. For meta-analysis of these QTLs, a consensus map was developed using BioMercator v4.2 software, containing 531 markers, including SSR, RFLP, AFLP markers, and genes (Tenmykh et al, 2001). The consensus map covered a total length of 1821 cM, with an average genetic distance of 3.5 cM between markers

(Tenmykh et al, 2001). After identification, the 911 different QTLs for DR and GY components were projected on a consensus map for meta-analysis. The meta-analysis identified 60 genome- wide MQTLs averaging five best QTLs per chromosome for DR traits based on GY. However, out of 60 MQTLs, one MQTL on chromosome 11 was not considered as a MQTL as it contained

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only one QTL. All 60 MQTLs were short-listed based on position, distance and 95% confidence interval (CI) of MQTLs considering significant parameters, summarizing the distribution of drought stress related MQTLs in the rice genome. In the meta-analysis results redefining a number of QTLs in MQTL units, a reduced cumulative genetic map was resolved on the chromosomes with a lower CI, and out of the 60 MQTLs, 13 genome-wide MQTLs were found most useful, which contain a higher density of QTLs with lower genetic distance and CI. On chromosome 1, there are three MQTLs termed MQTL 1.2, 1.3, 1.4 with 7.69, 5.74, and 4.65 cM genetic distance and 2.57, 1.02, and 1.79 CI, containing 45, 45, and 42 different QTLs respectively, related to different DR traits and GY components. On chromosome 2 three MQTLs named MQTLs 2.1, 2.3, and 2.4 contained 21, 26, and 60 different QTL related to different DR traits and GY components and showed 5.03, 11.41, and 7.21 genetic distance with 3.1, 2.01, and

1.5 CI. Chromosome 3 has two MQTLs 3.1 and 3.5, with 50 and 25 different QTLs related to

DR and GY traits, showing 6.64 and 1.23 genetic distance, with 1.28 and 0.24 CI, respectively.

On chromosomes 4, 7, 8 and 9, there were MQTLs 4.2 & 4.3, 7.2, 8.1, and 9.1 containing 22 &

30, 21, 30, and 21 different QTLs showing 3.86 & 3.53, 3.59, 1.16, and 1.84 CM genetic distance on the chromosomes with 2.55 & 0.66, 3.07, 0.71, and 1.02 CI, respectively. Therefore, these 13 genome-wide MQTLs could be useful for marker assisted selection for the development of drought resistant and high yielding rice genotypes under drought stress. For further analysis, the genetic map position (cM) will be aligned to the physical map position (bp) of each marker used in this study and the QTLs projected with our GWA analysis based significant SNPs on an integrated consensus map. The information on physical position of each QTL and SNP will enable identification of candidate genes and chromosomal regions controlling drought resistance and grain yield under drought stress.

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