An Untapped Genetic Resource for Abiotic Stress Tolerant Traits for Rice Improvement

Mississippi State University Scholars Junction

Theses and Dissertations Theses and Dissertations

8-6-2021

Weedy rice (Oryza sativa ssp.): an untapped genetic resource for abiotic stress tolerant traits for rice improvement

Shandrea D. Stallworth [email protected]

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Recommended Citation Stallworth, Shandrea D., "Weedy rice (Oryza sativa ssp.): an untapped genetic resource for abiotic stress tolerant traits for rice improvement" (2021). Theses and Dissertations. 5272. https://scholarsjunction.msstate.edu/td/5272

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Weedy rice (Oryza sativa ssp.): an untapped genetic resource for abiotic stress

tolerant traits for rice improvement

By TITLE PAGE Shandrea D. Stallworth

Approved by:

Te-Ming (Paul) Tseng Daniel B. Reynolds Edilberto Redona Shien Lu Daniel G. Peterson Michael S. Cox (Graduate Coordinator) Scott T. Willard (Dean, College of Agriculture and Life Sciences)

A Dissertation Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Weed Science in the Department of Plant and Soil Sciences

Mississippi State, Mississippi

August 2021

2021

Name: Shandrea D. Stallworth ABSTRACT Date of Degree: August 6, 2021

Institution: Mississippi State University

Major Field: Weed Science

Major Professor: Te-Ming (Paul) Tseng

Title of Study: Weedy rice (Oryza sativa ssp.): an untapped genetic resource for abiotic stress tolerant traits for rice improvement

Pages in Study 106

Candidate for Degree of Doctor of Philosophy

Rice (Oryza sativa) is the staple food for more than 3.5 billion people worldwide. As the population continues to grow, rice yield will need to increase by 1% every year for the next 30 years to keep up with the growth. In the US, Arkansas accounts for more than 50% of rice production. Over the last 68 years, rice production has continued to grow in Mississippi, placing it in fourth place after Arkansas, Louisiana, and California. Due to increasing rice acreage, regionally and worldwide, the need to develop abiotic stress-tolerant rice has increased.

Unfortunately, current rice breeding programs lack genetic diversity, and many traits have been lost through the domestication of cultivated rice. Currently, stressors stemming from the continued effects of climate change continue to impact rice. To counteract the impacts of climate change, research has shifted to evaluating wild and weedy relatives of rice to improve breeding techniques. Weedy rice (Oryza sativa ssp.) is a genetically similar, noxious weed in rice with increased competitive ability. Studies have demonstrated that weedy rice has increased genetic variability and inherent tolerance to abiotic stressors. The aims of this study were to 1) screen a weedy rice mini-germplasm for tolerance to cold, heat, and complete submergence-stress, 2) utilize simple sequence repeat (SSR) markers and single nucleotide polymorphisms to evaluate

the genetic diversity of the weedy rice population, and 3) use genome-wide association (GWAS) to identify SNPs associated with candidate genes within the population.

DEDICATION

This body of work is dedicated to my family, closest friends, and MANRRS. Thank you all for your continued support and encouragement. If it were not for you all, I would not have been able to complete my degree. To my parents, Edwin O. Little and Tifni Jennings, thank you for constantly pushing me to strive for the best. To my grandparents, thank you for opening doors and never giving up. To my best friends, thank you for understanding my absences and missed milestones. Finally, to my brothers, Edwin II and Kenneth, for believing that your sister would always do great things.

ACKNOWLEDGEMENTS

I would like to thank Dr. Te-Ming Tseng for his continued encouragement, guidance, and leadership during my time at Mississippi State University. Thank you for never giving up on me and providing me with a creative space to answer many research questions. Your patience and wisdom have always pushed me to flourish as a weed scientist. To my dissertation committee:

Drs. Daniel Reynolds, Ed Redona, Shien Lu, and Daniel Peterson, thank you for always being available during this journey. Your feedback will always be invaluable. To Dr. Brian Baldwin, thank you for seeing the things that most could not and for always being a resource, no matter the subject matter. To Dr. Scott Willard, thank you for always having my back. You have written numerous recommendation letters that have projected me into a promising career. Thank you to the administrative staff in the Department of Plant and Soil Sciences for being my extended family and celebrating my wins as if they were your own.

I would also like to thank the Weed Physiology research group: Swati Shrestha, Gourav

Sharma, Ziming Yue, Brooklyn Schumaker, Grace Fuller, and many undergraduate student works, visiting scholars, and research technicians who never hesitated to assist me with my research.

To my support system: Christien, Johannah, Nicole, Shameca, Nikole, Tai, Lateshia,

Brandon, and Jont’e, I cannot thank you all enough. From the unexpected packages and flowers to random phone calls just because thank you for loving me and believing I could do this.

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TABLE OF CONTENTS

DEDICATION ...... ii

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

CHAPTER

I. LITERATURE REVIEW ...... 1

1.1 Introduction ...... 1 1.2 Evolutionary Background ...... 2 1.3 Competitive Ability ...... 4 1.4 References ...... 6

II. SCREENING DIVERSE WEEDY RICE (ORYZA SATIVA SSP.) MINI GERMPLASM FOR TOLERANCE TO COLD, HEAT, AND COMPLETE SUBMERGENCE STRESS DURING SEEDLING STAGE ...... 10

2.1 Abstract ...... 10 2.2 Introduction ...... 11 2.3 Materials and Methods ...... 16 2.3.1 Plant Materials ...... 16 2.3.2 Cold Tolerance Screening ...... 17 2.3.3 Heat Tolerance Screening ...... 18 2.3.4 Submergence Tolerance Screening ...... 18 2.3.5 Statistical Analysis ...... 19 2.4 Results and Discussion ...... 20 2.4.1 Cold Tolerance Screening ...... 20 2.4.2 Heat Tolerance Screening ...... 22 2.4.3 Submergence Tolerance Screening ...... 26 2.5 Conclusion ...... 30 2.6 References ...... 43

III. ASSESSING THE GENETIC DIVERSITY OF WEEDY RICE MINI-GERMPLASM USING SSR MARKERS AND SNPs ...... 50

3.1 Abstract ...... 50 3.2 Introduction ...... 51 3.3 Materials and Methods ...... 53 3.3.1 Plant Materials ...... 53 3.3.2 DNA Extraction ...... 54 3.3.3 PCR Amplification using SSR Markers ...... 54 3.3.4 SNP Genotyping ...... 55 3.3.5 Data Analysis ...... 56 3.3.5.1 SSR Marker Analysis ...... 56 3.3.5.2 SNP Genotyping Analysis ...... 56 3.4 Results and Discussion ...... 57 3.4.1 Genetic Diversity Using SSR Markers ...... 57 3.4.1.1 Genetic Diversity among Cold-Stress Subpopulation ...... 57 3.4.1.2 Genetic Diversity among Heat-Stress Subpopulation ...... 59 3.4.1.3 Genetic Diversity among Complete Submergence-Stress Subpopulation ...... 60 3.4.2 Genetic Diversity Using SNP Data ...... 62 3.4.2.1 Genetic Diversity among Cold-Stress Subpopulation ...... 63 3.4.2.2 Genetic Diversity among Heat-Stress Subpopulation ...... 64 3.4.2.3 Genetic Diversity among Complete Submergence-Stress Subpopulation ...... 66 3.5 Conclusion ...... 67 3.6 References ...... 78

IV. UTILIZATION OF GENOME-WIDE ASSOCIATION STUDIES (GWAS) TO DISCOVER SNPs RELATED TO CANDIDATE GENES FOR RICE BREEDING IMPROVEMENT ...... 82

4.1 Abstract ...... 82 4.2 Introduction ...... 83 4.3 Materials and Methods ...... 85 4.3.1 Plant Materials ...... 85 4.3.2 Next Generation Sequencing and Processing ...... 85 4.3.3 Genome-Wide Association Studies and SNP Identification ...... 86 4.4 Results and Discussion ...... 86 4.5 Conclusion ...... 88 4.6 References ...... 93

APPENDIX

A. SUPPLEMENTARY TABLES ...... 96

A.1 Supplementary Materials ...... 97

LIST OF TABLES

Table 3.1 SSR markers selected for genetic diversity using PCR amplification...... 68

Table 3.2 Genetic variation among the cold stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h)...... 69

Table 3.3 Genetic variation among the heat-stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h)...... 70

Table 3.4 Genetic variation among the submergence-stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h)...... 71

Table 4.1 Significant markers SNP markers with False Discovery Rate (FDR) with respect to cold stress treatment...... 89

Table 4.2 Significant markers SNP markers with False Discovery Rate (FDR) with respect to heat-stress treatment...... 90

Table A.1 Agronomic traits and morphological characteristics of the 54 weedy rice accessions...... 97

Table A.2 List of rice cultivars and rice breeding lines with supplemental information...... 100

Table A.3 The correlation coefficient of means separated by Fisher’s LSD for cold stress treated accessions...... 101

Table A.4 The correlation coefficient of means separated by Fisher’s LSD for heat- stress treated accessions...... 103

Table A.5 The correlation coefficient of means separated by Fisher’s LSD for submergence-stress treated accessions...... 105

LIST OF FIGURES

Figure 2.1 Mean height reduction 0 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height...... 31

Figure 2.2 Mean height reduction 7 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height...... 32

Figure 2.3 Mean height reduction 28 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height...... 33

Figure 2.4 Principal component analysis (PCA) of 16 agronomic traits, including plant height reduction and plant biomass reduction 28 days after cold-stress treatment...... 34

Figure 2.5 Mean height reduction 14 days after heat-stress treatment arranged from lowest reduction to the highest reduction in height...... 35

Figure 2.6 Mean height reduction 28 days after heat-stress treatment arranged from lowest reduction to the highest reduction in height...... 36

Figure 2.7 Mean biomass reduction 28 days after heat-stress treatment arranged from lowest reduction to the highest biomass reduction...... 37

Figure 2.8 Principal component analysis (PCA) of 16 agronomic traits, including plant height reduction and plant biomass reduction 28 days after heat-stress treatment...... 38

Figure 2.9 Mean height reduction 14 days after complete submergence-stress treatment arranged from lowest reduction to the highest reduction in height...... 39

Figure 2.10 Mean height reduction 28 days after complete submergence-stress treatment arranged from lowest reduction to the highest reduction in height...... 40

Figure 2.11 Mean biomass reduction 28 days after complete submergence-stress treatment arranged from lowest reduction to the highest biomass reduction...... 41

Figure 2.12 Principal component analysis (PCA) of 16 agronomic traits plus plant height reduction 28 days after complete submergence-stress treatment and plant biomass reduction 28 days after complete submergence-stress treatment...... 42

vii

Figure 3.1 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to cold stress treatment...... 72

Figure 3.2 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to heat-stress treatment...... 73

Figure 3.3 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to submergence-stress treatment...... 74

Figure 3.4 DISTRUCT output of allele frequencies based on inferred clusters with respect to cold stress treatment...... 75

Figure 3.5 DISTRUCT output of allele frequencies based on inferred clusters with respect to heat-stress treatment...... 76

Figure 3.6 DISTRUCT output of allele frequencies based on inferred clusters with respect to submergence-stress treatment...... 77

Figure 4.1 Genome-wide Manhattan plot of SNPs for cold stress...... 91

Figure 4.2 Genome-wide Manhattan plot of t SNPs for heat-stress...... 92

viii

CHAPTER I

LITERATURE REVIEW

1.1 Introduction

The unique hardiness of weedy rice species allows them to thrive in dynamic and stressful environments, but this flourishing results in up to 80% yield reductions in cultivated rice and an estimated $2.8 billion in mitigation costs and lost revenue for producers. Weedy rice thrives because it has retained traits such as the potential to grow taller, produce more tillers, and consume more nutrients. For example, broad-spectrum resistance and novel genes in blast races have been identified in U.S. weedy rice (Lee et al., 2011). These findings collectively suggest that weedy rice is an untapped source of novel abiotic and biotic stress-resistant genes that can be used in rice breeding programs. However, the extent of weedy rice varieties that exhibit tolerance to these and other stresses such as unfavorable temperatures, drought, submergence, blast disease, and blight disease is unknown, as are the genetic pathways potentially generating that tolerance. Thus, there is a critical need to identify the specific weedy rice accessions tolerant to abiotic and biotic stresses and the precise mechanisms through which these varieties generate this tolerance. The likely consequences of leaving this need unmet will further limitations in breeding efforts to enhance competitive vigor and yield of cultivated rice, proving to be an ongoing economic burden for rice producers.

Weedy rice, or red rice, is the unwanted plants from the same genus and species as rice

(Oryza sativa) that infests and competes with rice and other crops and produces a distinct rough

and/or red pericarp (Chauhan, 2013; Azmi et al., 2012; Busconi et al., 2012). This direct competition can cause a loss in crop yield and quality of the crop, and research has shown that weedy rice infests fields worldwide (Wang et al., 2014). Usually controlled by flooding methods, weedy rice has proven to withstand current weed management practices and is challenging to manage without causing damage to cultivated rice (Burgos et al., 2014).

As a noxious weed, weedy rice has been found to infest fields in Asia, North and South

America, Africa, and Southern Europe (Ziska et al., 2015). Dating back to the domestication of cultivated rice, weedy rice is thought to arise from selection pressure for favorable rice traits.

These traits range from fewer instances of seed shattering to desirable grain size (Lu et al.,

2016). Weedy rice has reemerged in the last century, proving to be a more significant issue in areas where direct seeding and less management practice occur (Zhang et al., 2012). Weed density in fields can play a crucial role in crop reduction, ranging from 5 -100%, sometimes forcing farmers to abandon their fields altogether (He et al., 2014; Abraham and Jose, 2014).

Because the definition for weedy rice is so broad and can include wild rice species in the genus Oryza, researchers have used Wang et al.’s (2015) definition of weedy rice: “rice plants with strong seed shattering and weediness that only occur inside and in the vicinity of rice fields, and can only reproduce in the human-disturbed environment.” Wang’s definition excludes wild species that can live and reproduce in natural and managed habitats and solely focuses on Oryza sativa f. spontanea as it deals with cultivated rice (Lu et al., 2016). Weedy rice is characterized by its tall stature, pubescence, seed shattering, and dormancy (Gealy et al., 2003).

1.2 Evolutionary Background

Research shows four distinct pathways for the evolution of weedy rice. The first identifies weedy populations that arose from the evolution of areas undisturbed by humans 2

harboring wild relatives, O. rufipogon and O. nivara. The second pathway encompasses weedy species that are natural hybrids from the crosses of cultivated rice and its reproductively compatible wild relatives. The third pathway consists of the hybridization of distant indica and japonica rice varieties resulting in recombination. This recombination has resulted in the loss of domestication traits and the reemergence of wild-type phenotypes (Song et al., 2014). The final pathway is attributed to the de-domestication or loss of domesticated traits in cultivated rice due to mutations (Yao et al., 2015). Pathways three and four are evolutionary pathways that show most varieties genetically being made up mostly of cultivated rice with only a few traits evolving or mutating from its weedy counterpart (Gross et al., 2010).

Weedy rice infests fields worldwide, with significant infestations in China, India, Japan, and the Southeastern United States (Mortimer et al., 2000; Suh et al., 1997). In the United States, weedy rice primarily affects rice fields located in the delta regions of Arkansas and Mississippi.

Arkansas is the number one rice-producing state in the US (Gealy et al., 2001). According to

USDA NRCS and the Arkansas State Plant Board, weedy rice is an introduced, native species in the lower 48 states as well as Puerto Rico and Hawaii, but it has not made an appearance on the federal noxious weed list (Arkansas State Plant Board, 1997; USDA 2012).

Due to the close similarities between weedy rice and cultivated rice, it is challenging to control weedy rice. Both cultivated and weedy rice comprise 12 pairs of chromosomes while also inhabiting the same agroecosystem, making it very difficult to control weedy rice (Gealy et al.,

2003). Completed research in cultivated rice shows the discovery of some allelopathic chemicals to suppress some weeds, such as barnyardgrass, but no research has been conducted on weedy rice (Zhang et al., 2000). At this time, weedy rice can be safely controlled by hand weeding or mechanical weeding techniques as its genetic makeup is closely related to that of cultivated rice.

Treatment by herbicides can cause injury to cultivated rice, and flooding techniques do not work as weedy rice thrives in these conditions just as cultivated rice does (Ferrero, 2003).

1.3 Competitive Ability

Competitively speaking, weedy rice is strongly characterized by its high seed shattering and ability to persist for extended periods of up to 10 years in the seed bank (Cao et al., 2006).

Weedy rice can reduce yields and affect the quality of harvested cultivated rice while withstanding early and late flowering dates and high seed shattering (Ferrero, 2003; Nagao and

Takahashi, 1963). In a rice hybridization study completed by Langevin et al. (1990), weedy rice dominant hybrid florets opened between 8 am and 9 am, while cultivated rice florets did not open until 10 am or later. These weedy rice hybrids were also taller than cultivated rice and flowered 20 -30 days later than cultivated rice. Nagao and Takahashi (1963) discovered that seed shattering came from the formation of abscission tissue formed by three layers of cells between the spikelet and the pedicel absent in cultivated rice. Weedy rice also possesses competitive characteristics to produce more tillers and culms and be taller than rice (Kwon et al., 1992).

Kwon et al. (1992) demonstrated that weedy rice and cultivated rice produced tillers at 20 days after emergence, but weedy rice produced more tillers at 40 days after emergence than cultivated rice. At 60 days after emergence, weedy rice was 46 to 55% taller than cultivated rice, reducing grain yields late in the season. Studies have also shown that weedy rice can accumulate more nitrogen and sucrose, leading to increased biomass production and growth characteristics

(Burgos et al., 2006; Sales et al., 2011). The research equated a larger N uptake capacity in weedy rice due to its ability to produce more extensive roots and greater root surface after being supplied with N following a deficit. Sucrose concentrations were also significantly higher in

weedy rice than in cultivated rice following an increase in N application (Burgos et al., 2006;

Sales et al., 2011).

Morphologically, weedy rice has proven highly variable and presents itself as an intermediate between wild and cultivated rice (Lu et al., 2016). Genetic diversity studies have shown that weedy rice is genetically and phenotypically highly variable (Burgos et al., 2014; Cao et al., 2006; Gealy et al., 2002; He et al., 2014). Burgos et al. (2014) were able to identify seven different weedy rice hull colors in imazethapyr-resistant weedy rice, indicating the phenotypic diversity of weedy rice populations. In the same study, research pointed to six different genetic groups based on seed hull color, which vastly outweighed the cultivated rice present in the surveyed fields. Cao et al. (2006) used AMOVA analysis to confirm that approximately 35% of total genetic variation existed among regions of weedy rice. Utilizing microsatellite markers,

Gealy et al. (2002) were able to find an average genetic diversity of 0.63 across 89 weedy rice accessions in Arkansas, while He et al. (2014) used microsatellite markers and found genetic diversity to be 0.62. Studies also point to a large amount of diversity amongst and within weedy rice populations and between weedy rice and cultivated rice (Londo and Schaal, 2007; Ferrero et al., 2003). Using cultivated rice, inter-varietal competition between weedy rice and cultivated rice was more important than intra-varietal competition within the rice types. By tapping into the vast amount of genetic variability present in weedy rice, tolerant genes can be used to improve cultivated rice.

1.4 References

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Arkansas State Plant Board. 1997. Regulations on plant diseases and pests (20 October 2003). Arkansas State Plant Board.

Azmi, M., Azlan, S., Yim, K. M., George, T. V., & Chew, S. E. (2012). Control of red rice in direct-seeded rice using the Clearfield production system in Malaysia. Pak J Weed Sci Res, 18, 49-53.

Bevilacqua, C. B., Basu, S., Pereira, A., Tseng, T. M., Zimmer, P. D., & Burgos, N. R. (2015). Analysis of stress-responsive gene expression in cultivated and weedy rice differing in cold stress tolerance. PloS one, 10(7), e0132100.

Broman, K. W., and Sen, S. 2009. A Guide to QTL Mapping with R/qtl. Springer, New York.

Burgos, N. R., R. J. Norman, D. R. Gealy and H. L. Black. 2006. Competitive N uptake between rice and red rice. Field Crops Research 99:96-105.

Burgos, N. R., Singh, V., Tseng, T. M., Black, H., Young, N. D., Huang, Z., & Caicedo, A. L. (2014). The impact of herbicide-resistant rice technology on phenotypic diversity and population structure of United States red rice. Plant physiology, 166(3), 1208-1220.

Busconi, M., Rossi, D., Lorenzoni, C., Baldi, G. and Fogher, C. (2012), Spread of herbicide- resistant red rice (red rice, Oryza sativa L.) after 5 years of Clearfield rice cultivation in Italy. Plant Biology, 14: 751–759.

Cao, Qianjin, Bao-Rong Lu, Hui Xia, Jun Rong, Francesco Sala, Alberto Spada, Fabrizio Grassi; Genetic Diversity and Origin of Red rice (Oryza sativa f. spontanea) Populations Found in North-eastern China Revealed by Simple Sequence Repeat (SSR) Markers. Ann Bot 2006; 98 (6): 1241-1252.

Chauhan, B. S. (2013). Strategies to manage red rice in Asia. Crop Protection, 48, 51-56.

Ferrero, A. (2003). Red rice, biological features and control. Weed Management for

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Glaubitz, J. C., Casstevens, T. M., Lu, F., Harriman, J., Elshire, R. J., Sun, Q., & Buckler, E. S. (2014). TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PloS one, 9(2), e90346.

Gross, B. L., M. Reagon, S.-C. Hsu, A. L. Caicedo, Y. Jia, and K. M. Olsen 2010. Seeing red: the origin of grain pigmentation in US red rice. Molecular Ecology 16:3380–3393.

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He, Z. X., X. Q. Jiang, D. Ratnasekera, F. Grassi, U. Perera, and B.-R. Lu 2014. Seed-mediated gene flow promotes genetic diversity of red rice within populations: implications for weed management. PLoS ONE 9:e112778.

Jia, Y., and Liu, G. 2011. Mapping quantitative trait loci for resistance to rice blast. Phytopathology 101:176-181. 10.1094/PHYTO-06-10-0151

Kwon, S. L., R. J. Smith, and R. E. Talbert. 1992. Comparative growth and development of red rice (Oryza sativa) and rice. Weed Sci. 40:57-62.

Langevin, A.S., Clay, K. & Grace, B.J. 1990. The incidence and effect of hybridization between cultivated rice and its related weed, red rice (Oryza sativa L.). Evolution 44: 1000-1008.

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Lu, B.-R., Yang, X. and Ellstrand, N. C. (2016), Fitness correlates of crop transgene flow into weedy populations: a case study of red rice in China and other examples. Evol Appl, 9: 857–870.

McCouch, S. R., Wright, M. H., Tung, C. W., Maron, L. G., McNally, K. L., Fitzgerald, M., ... & Greenberg, A. J. (2016). Open access resources for genome-wide association mapping in rice. Nature communications, 7.

Mortimer M, Pandey S, Piggin C. Baki BB, Chin DV, Mortimer M. Red rice: approaches to ecological appraisal and implications for research priorities, Proceedings of Wild and Red rice in Rice Ecosystems in Asia. A review, 2000Los Banos, Philippines International Rice Research Institute(pg. 97-105).

Nagao, S. & Takahashi, M. 1963. Trial construction of twelve linkage group in Japanese rice. J. Fac. Agric. Hokkaido Univ., 53: 72-130.

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Sales, M. A., N. R. Burgos, V. K. Shivrain and N. Murphy. 2011. Morphological and physiological responses of red rice (Oryza sativa L.) and cultivated rice (O. sativa) to N. Supply. American Journal of Plant Sciences 2:569-577.

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Song, Z. J., Z. Wang, Y. Feng, N. Yao, J. Yang, and B.-R. Lu 2015. Genetic divergence of red rice populations associated with their geographic location and coexisting conspecific crop: implications on adaptive evolution of agricultural weeds. Journal of Systematics and Evolution53:330–338.

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CHAPTER II

SCREENING DIVERSE WEEDY RICE (ORYZA SATIVA SSP.) MINI GERMPLASM

FOR TOLERANCE TO COLD, HEAT, AND COMPLETE SUBMERGENCE

STRESS DURING SEEDLING STAGE

Portions of this chapter have been accepted for publication in Frontiers Journal.

2.1 Abstract

Rice is a staple food for more than 3.5 billion people worldwide, with Asia producing almost 90% of the global rice yield. In the US, rice is primarily produced in four regions:

Arkansas Grand Prairie, Mississippi Delta, Gulf Coast, and Sacramento Valley of California.

Arkansas currently accounts for more than 50% of the rice produced in the US. As global temperatures continue to rise and fluctuate, crop-breeding programs must continue to evolve.

Unfortunately, sudden submergence due to climate change and unpredictable flash flooding can cause yield reduction up to 100% and affect 20 million ha of agricultural farmlands. Similarly, it has been demonstrated that temperatures higher than 34°C can cause spikelet infertility resulting in up to 60% reduction in yield. Comparable effects can also be seen in cold stress situations where temperatures below 17°C resulted in poor germination, seedling injury, and reduced yield.

One major drawback to developing abiotic stress-tolerant rice is the loss of critical traits such as vegetative vigor, fertility, and quality, which are essential in increasing rice’s economic return for farmers. To replace traits lost in past breeding endeavors, weedy rice (WR) has been proposed as a source for novel trait discovery to improve rice-breeding programs. Therefore, the 10

goal of this study was to screen and identify heat and submergence tolerant WR accessions. A

WR mini germplasm was exposed to cold (18⁰C), heat (38°C), and complete submergence for 21 days. After each treatment, height was recorded every seven days for 28 days, and biomass was collected 28 days after treatment.

In the cold-stress treatment, the average height reduction was 10, 9, and 17% at 0, 7, and

28 days after treatment (DAT). The average height reduction across all accessions was 19 and

21% at 14 and 28 DAT for the heat-stress treatment. The average height reduction across all accessions was 25 and 33% for the complete submergence-stress. The average biomass reduction across all accessions was 18 and 21% for heat and complete submergence-stress, respectively.

Morphologically, 46% of WR accessions with less than 20% height reduction had straw-colored hulls without awns at 28 DAT versus just 28% of the surviving WR accessions in the heat-stress treatment with less than 20% height reduction presenting straw-colored hull types with no awns present. When morphologically comparing WR accessions in the complete submergence-stress treatment, 33% of the WR accessions presented with black hull coloring and no awns, suggesting that these specific biotypes may play a role in the increased resilience of WR populations. The results presented in this paper will highlight elite WR accessions that can survive the effects of climate change.

2.2 Introduction

Rice, Oryza sativa, is a major food staple and arguably one of the most essential crops feeding close to half of the world’s population and providing more than 50% of the daily calories

(Dong and Xiao, 2016; Kim et al., 2015; Muthayya et al., 2014). Rice is primarily grown in major river deltas of Asia and Vietnam, and it is a rapidly growing food source in Africa

(Kuenzer and Knauer, 2013). Cultivated rice is separated into three subspecies: japonica, indica, 11

and O. glaberrima, with origins from North Asia, South Asia, and West Africa, respectively

(Vaughan et al., 2008). Of these three subspecies, the most common to the US are japonica and indica. The japonica subspecies can be described as sticky rice and adapted to distinct temperate and tropical regions. In contrast, the indica subspecies are long-grained, drought-tolerant, and early maturing (Civáň and Brown, 2018). China and India consume more than 50% of the rice produced worldwide, while most rice produced in North America is exported (Ferrero, 2003;

Mortimer, 2000).

In the US, rice yields continue to increase annually due to the adoption of optimum management practices and the introduction and adoption of hybrid rice varieties, but rice production systems still struggle against increased weed pressure and impacts of climate change

(Fargione et al., 2018; Espe et al., 2016). Of the significant abiotic stresses, rice can be impacted by salinity, drought, flooding, and high/low temperatures (Hasanuzzaman et al., 2018). In crops such as maize, soybeans, and rice, climate extremes explain 18-43% of the yield variance from year to year (Vogel et al., 2019). In rice, temperatures above 33⁰C can negatively impact all plant growth stages and development, with temperatures above 37⁰C causing a complete loss of plants

(Kilasi et al., 2018; Aghamolki et al., 2014; Jagadish et al., 2007). Temperatures above this threshold are typical in the summer months in the southern US (Chiang et al., 2018).

Although many studies have been conducted to study the impacts of heat-stress at the reproductive stage due to rice’s increased sensitivity to heat and the direct correlation to grain yield, Kilasi et al. screened rice seedlings for heat-stress tolerance (37⁰C) as a comparative means to identify genes controlling heat tolerance (2018). By opting to screen at the seedling stage, Kilasi et al. used the root and the shoot growth as a percentage of the control to identify strong-performing lines that can be genotyped quickly. Bewley noted that stored reserves are

mobilized during post-germination, radicle cells elongate, cells divide, and DNA is synthesized

(1997). These processes happen rapidly and can be considerably impacted by heat stress (Kilasi et al., 2018). In the case of complete submergence, Adkins et al. reported that submergence- stress in rice is associated with the seedling stage as older plants are more tolerable than younger plants (1990). Additionally, it has been noted that most rice plants that are submerged for more than three days are severely injured or die, and few cultivars can survive complete submergence for 10-14 days (Gao et al., 2007). Survival and tolerance of these rice cultivars have been tied to the introduction of semi-dwarf rice cultivars, suppressed leaf elongation, and decreased carbohydrate consumption while submerged (Gao et al., 2007).

While rice originates from tropical and sub-tropical areas, erratic climate change has lead to cold-sensitive rice cultivars. Even though cold damage can occur at any growth stage, chilling injury at the early seedling stage can lead to slow growth, delayed crop maturity, poor establishment, and decreased yield (Zhao et al., 2015). In a universal screening method developed by Shirasawa et al. (2012), rice plants are maintained in a cold deep-water irrigated pool during the entire booting stage, and the completion, spikelet fertility is used to determine cold tolerance in the population (Sun et al., 2018). The parameters of germination percent, germination index, root, shoot, seedling length, and seedling vigor are usually observed in cold stress experiments (Cruz & Milach, 2004). When evaluating rice genotypes for cold stress tolerance at the seedling stage, Rahul et al. (2017) identified three rice cultivars with decreased seedling vigor to two cold environments, 8⁰C and 13⁰C, after germinating for 28 days.

Evaluation of cold temperatures at the reproductive stage has pointed to spikelet sterility (90%), reduced numbers of spikelets (41%), and a decrease in panicle number per tiller (43%) when the duration of cold is greater than a day (Jacobs & Pearson, 1999).

In an effort to mitigate the effects of climate change, there has been a shift in research to study genetically similar weed species to uncover stress-tolerant traits to improve crop systems

(Ghanizadeh et al., 2019; Hopper et al., 2019; Barrett, 1983). In rice, studies have turned to weedy rice (WR), Oryza sativa ssp. (also commonly referred to as red rice or weedy red rice), to understand the evolutionary pathways that lead to increased competitiveness within rice fields

(Olsen et al., 2007). In the US, WR infestations are most prominent in the Mississippi delta leading to a reduction in rice yield by up to 80% (Estorninos et al., 2005). As one of the major weeds in rice, WR can reduce both the quality and quantity of cultivated rice while also adapting to multiple environments (Jia and Gealy, 2018).

WR has numerous characteristics similar to that of cultivated rice, but the contrasting differences such as increased height, shattering, and seed dormancy, coupled with its ability to mimic cultivated rice, make WR challenging to control while offering a source of genetic variation previously lost through the domestication of cultivated rice (Nadir et al., 2017;

Bevilacqua et al., 2015). WR also possesses competitive characteristics to produce more tillers and culms and be taller than rice while accumulating more nitrogen and sucrose, leading to increased biomass production and growth characteristics (Burgos et al., 2006; Kwon et al.,

1992).

Due to its competitive nature, WR grows taller and faster than cultivated rice even when planted deeper, suggesting that it may house some traits that could overcome cold stress (Gealy et al., 2000). Research has shown that weedy rice has increased tolerance to cold stress by identifying two indica varieties and one japonica rice cultivar. However, although these varieties were identified as cold tolerant, they still presented a height reduction of 29 -37% compared to untreated controls (Bevilacqua et al., 2015). In a QTL study by Oh et al. (2004), recombinant

inbred lines with one WR parent demonstrated cold tolerance contributed desirable effects in 11 of 14 QTLs reported than that of the recurrent parent. Currently, the US primarily grows japonica rice cultivars that are more tolerant to cold stress than indica type rice varieties.

However, farmers have to adhere to a strict rice planting window to ensure proper rice field stand

(Xu et al., 2008). While japonica rice cultivars are planted in the US, Ziska et al. (2015) determined that more than 90% of WR found in the US is of the indica biotype. Challenges exist in the hybridization of japonica x indica rice that produces many sterile offspring when breeding for cold tolerance (Borjas et al., 2016). To combat these challenges present in rice breeding, it may be useful to cross cold-sensitive indica rice cultivars with cold-tolerant WR biotypes.

WR had also shown tolerance to high salinity during germination, with levels only dipping below 50% germination when salinity was greater than 16 dS m-1 (decisiemens per meter) (Hakim et al., 2011). In the same study by Hakim et al. (2011), WR species exhibited less than 20% shoot length reduction at 4 dS m-1, and increased shoot lengths were recorded up to 24 dS m-1. WR also boasts broad-spectrum resistance to rice blast disease, with approximately 18 of the 60 screened WR accessions were resistant to 3-10 blast races (Liu et al., 2015). Of the 18 resistant WR accessions, one accession demonstrated complete resistance to 10 blast races, although the 14 blast races present had a virulence rate of 98.3% (Liu et al., 2015).

When chemical control is used to manage weedy rice in cultivated rice fields, studies have shown varying levels of control (30-100% injury to WR) when the commercial rate of glyphosate (900 g a.e. ha-1) is used in the field (Burgos et al., 2011). Moreover, studies have shown that weedy rice is highly variable, genetically and phenotypically (Burgos et al., 2014;

Cao et al., 2006; Gealy et al., 2002; Chen et al., 2014). Burgos et al. (2014) were able to identify seven different WR hull colors in imazethapyr-resistant WR, indicating the phenotypic diversity

of WR populations. In the same study, research pointed to six different genetic groups, based on seed hull color, which outweighed the cultivated rice present in the surveyed fields. Cao et al.

(2006) used AMOVA analysis to confirm that approximately 35% of total genetic variation existed among WR regions. Using microsatellite markers, an average genetic diversity of 0.63 across 89 weedy rice accessions in Arkansas was reported (Gealy et al., 2002). Studies also point to a significant amount of diversity amongst and within weedy rice populations and between weedy rice and cultivated rice (Ferrero, 2003; Londo and Schaal, 2007; Tseng et al., 2013).

This study aims to identify and characterize heat and submergence tolerant WR accessions and highlight these competitive weeds' efficacy.

2.3 Materials and Methods

2.3.1 Plant Materials

Seeds of 200 different WR accessions were collected from all the major rice-growing counties in Arkansas in 2008-09 and grown at the USDA-ARS Dale Bumpers National Rice

Research Center, Stuttgart, AR. Various morphological characteristics were measured (Tseng et al. 2013), and using these morphological data, 54 most competitive weedy rice accessions

(Supplementary Table 1) were selected based on exceptional field performance in the areas of plant height, high tillering, and increased panicle shattering, which is generally linked with aggressive and persistent nature of weedy rice. Four rice cultivars specific to the southeastern

United States (CRL-Thad, CRL-Rex, CRL-CL163, and CRL-PM), and six rice-breeding lines

(RBL) with traits for cold, heat, and submergence, were obtained from the Delta Research and

Extension Center, Mississippi State University, Stoneville, Mississippi, for inclusion in the study. Each experiment consisted of 54 weedy rice lines (Supplementary Table 1), two rice- breeding lines (heat-stress specific), and three cultivated rice lines. After completing one run 16

with all 54 WR accessions, ten WR accessions with less than 20% height reduction and biomass reduction, and five (four in the case of the heat-stress treatment) accessions with greater than

80% height reduction and biomass reduction were selected for runs two and three.

2.3.2 Cold Tolerance Screening

Cold stress treatments were carried out three different treatments in 2018, 2019, and 2020 in the weed physiology lab and greenhouse located on Mississippi State University's campus

(MS State, MS). Each treatment consisted of three treated replicates and three untreated controls in a completely randomized design. Four seeds from each WR accession, rice-breeding line

(RBL), and cultivated rice line (CRL) were sown in a 72-well planting tray (Greenhouse Mega

Store) containing potting soil (SunGro Professional Growing Mix; 3.8 cu. ft.). Plants were covered with clear domes (Propogation domes; Greenhouse Mega Store) and grown under optimum conditions (28°C /16 hours light, 24°C /8 hours dark) in a growth chamber (Percival

Scientific, Perry, IA) until germination, after which the clear domes were removed. After 21 days, plants were thinned to maintain two plants per well, and plant height was measured in cm.

Treated plants were transferred to a different growth chamber set to 18°C and incubated for 21 days with a 16/8h light/dark cycle. After 21 days, plants were returned to the growth chamber set to optimum growing conditions, and plant height was measured every 7 days for 28 days after treatment (DAT). At 28 DAT, plant shoots were harvested and dried in an oven for 7 days at

50°C to record vegetative biomass production. Weights were collected using an analytical balance with milligram accuracy.

2.3.3 Heat Tolerance Screening

These experiments were repeated in triplicate years in 2016, 2017, and 2019 in the weed physiology lab located on Mississippi State University's campus (MS State, MS). Each experiment consisted of three treated replicates and three untreated controls in a completely randomized design. Four seeds from each WR accession, rice-breeding-line, and cultivated rice line (CRL) were sown in a 72-well planting tray (Greenhouse Mega Store) containing potting soil (SunGro Professional Growing Mix; 3.8 cu. ft.). Plants were covered with clear domes

(Propogation domes; Greenhouse Mega Store) and grown under optimum conditions (28°C /16 hours light, 24°C /8 hours dark) in a growth chamber (Percival Scientific, Perry, IA) until germination, after which the clear domes were removed. After 21 days, plants were thinned to maintain two plants per well, and plant height was measured. Treated plants were transferred to a different growth chamber set to 38°C and incubated for 21 days with a 16/8h light/dark cycle.

After 21 days, plants were returned to the growth chamber set at optimum growing conditions, and plant height was measured at 14 and 28 days after treatment (DAT). Plant height was recorded in cm from the soil surface to the end of the healthiest, fully emerged leaf blade. After the experiment (28 DAT), plants were harvested at soil level and dried in an oven for 7 days at

50°C to record vegetative biomass production. Weights were collected using an analytical balance with milligram accuracy.

2.3.4 Submergence Tolerance Screening

Like the heat tolerance screening, these experiments were also repeated in triplicate years in 2016, 2017, and 2019 in the weed physiology lab located on Mississippi State University's campus (MS State, MS). Each experiment consisted of three treated replicates and three untreated controls in a completely randomized design. Approximately four seeds from each WR 18

accession, rice-breeding-line (RBL-60 and RBL-64), and cultivated rice line (CRL-Rex, CRL-

CL163, and CRL-PM) were sown into one 72-well planting tray (Greenhouse Mega Store) containing potting soil (SunGro Professional Growing Mix; 3.8 cu. ft.). Plants were covered with clear propagation domes (Greenhouse Mega Store) and grown under optimum conditions (28°C

/16 hours light, 24°C /8 hours dark) in a growth chamber (Percival Scientific, Perry, IA) until emergence, after which, the clear domes were removed. Once emerged, plants were trimmed to maintain two plants per well and covered with a modified 72-well tray. Wells were cut from the bottom of the 72-well tray to provide an opening for plants to continue to grow through. Twenty- one days after planting, plants were completely submerged for 21 days within 27-gallon black totes (Command XL; Lowes) to reduce algae growth. Water levels were maintained to ensure complete coverage of plants throughout the experiment. After 21 days of submergence, water was drained to maintain 5 -10 cm depth in each plastic tote, simulating normal flood conditions.

Plant heights were recorded at 0 DAT and every 7 days for 28 days. Plant height was recorded in cm from the soil surface to the end of the healthiest, fully emerged leaf blade. After the experiment (28 DAT), plants were harvested at soil level and dried in an oven for 7 days at 50°C to record vegetative biomass production. Weights were collected using an analytical balance with milligram accuracy.

2.3.5 Statistical Analysis

Data was collected from a completely randomized design with three replicates and two plants per replicate to evaluate the effects of cold, heat, and complete submergence on a WR population. The reduction in plant height was obtained by subtracting the individual heights of the treated plants (HTP) from the height of the control plants (HCP), then dividing by the HCP and multiplying by 100 to obtain a percent (Equation 2.1). For biomass, Equation 2.1 was 19

employed where height is replaced by biomass of the corresponding plant. Comparisons were made between WR accession and corresponding untreated controls, rice-breeding lines, and cultivated rice lines based on individual mean height and biomass reduction using ANOVA and

Tukey’s multiple comparisons test in JMP 14 ©. The LSD values were separated at a 0.05 level of significance. Additionally, the data were subjected to principal component analysis (PCA) with 16 agronomic traits and mean height and biomass reduction, and K-means clustering with hierarchical clusters was used to separate the WR population into groups based on these traits.

Equation 2.1.

퐻푒𝑖푔ℎ푡 푟푒푢푐푡𝑖표푛 (%) [퐻푒𝑖푔ℎ푡 표푓 퐶표푛푡푟표푙 푃푙푎푛푡푠 (퐻퐶푃) − 퐻푒𝑖푔ℎ푡 표푓 푇푟푒푎푡푒푑 푃푙푎푛푡푠 (퐻푇푃)] = (2.1) 퐻푒𝑖푔ℎ푡 표푓 퐶표푛푡푟표푙 푃푙푎푛푡푠 (퐻퐶푃) × 100

2.4 Results and Discussion

2.4.1 Cold Tolerance Screening

Many studies have highlighted WR’s ability to tolerate cold stress compared to rice cultivars (Nadir et al., 2017). At the same time, the exact mechanism of cold stress tolerance is unknown in WR, research points to a combination of metabolites, antioxidants, and the accumulation of antifreeze proteins (Wang et al., 2013). In rice, temperatures below 20⁰C can lead to reduced leaf expansion, wilting, chlorosis, and sometimes plant death (Gothandam,

2012). Plant growth is negatively affected by cold stress, and the measurement of quantitative traits such as root and shoot biomass can evaluate differing populations (Cruz et al., 2013). In a study by Aghaee et al. (2011), two rice cultivars were exposed to 10⁰C temperatures for 14 days, and they found that both cultivars demonstrated chilling stress sensitivity as a reduction of plant height and root and shoot biomass compared to control plants. In one cultivar (IRCTN34), a reduction in shoot biomass and height was attributed to avoidance mechanisms to survive cold 20

stress (Aghaee et al., 2011). Similar results were observed when screening WR accessions alongside cultivated rice lines (CRLs) and rice-breeding lines (RBLs).

In this study, 80% (12/15) of WR had a height reduction at 0 DAT (Figure 2.1). In comparison, at 0 DAT, all CRL and RBL had less than 20% height reduction. The number of

WR accessions with less than 20% height reduction increased to 87% (13/15) at 7 DAT (Figure

2.2). There was a significant decrease in WR accessions with 20% height reduction at 28 DAT with only 67% (10/15) WR having less than 20% height reduction at 28 DAT (Figure 2.3).

According to Ashaee et al. (2011), this could be characteristic of increased tolerance to cold stress. There was no significant difference between tolerant WR accessions, CRLs, or RBLs at 0,

7, or 28 DAT, suggesting that WR may have mechanisms of resistance to cold stress similar to cultivated rice.

At 0 DAT, CRLs Rex and CL163 had less than 1% reduction in plant height after being exposed to 18⁰C temperatures for 21 days. CRL-Rex is a conventional, southern, long-grain cultivar that has been well adapted to southern USA growing conditions (Solomon et al., 2012).

Although CRL-Rex is grown throughout the southeast, it is highly susceptible to sheath blight, blast, and bacterial panicle blight, making it a difficult line to consider in breeding programs

(Lee et al., 2016). CRL-163 is a semi-dwarf long-grain Clearfield variety with high yield.

However, it is also highly susceptible to sheath blight, blast, and bacterial panicle blight, which can cause some issues in the field and breeding experiments as well (Hardke et al., 2013).

Similar to the CRLs, WR accessions S9 (1.8%), S18 (2.1%), and S42 (2.8%) had less than 3% reduction in height 0 DAT. These lines were not statistically different from CRL and had less height reduction than both RBLs, 57 and 66. Although these RBLs had less than 20% height reduction at 0 DAT, the average height reduction of the two was approximately 10%. WR

accessions S9, S18, and S42 are straw-hull colored without awns. The reduction of seedling growth during cold stress is a good indicator of tolerance (Bevilacqua et al., 2015). At 7 DAT,

WR accession S42 had less height reduction than both CRLs, and at 28 DAT, CRL-Rex had no height reduction compared to highly tolerant WR accession S42 with just 2% height reduction.

To associate cold stress tolerance with other agronomic traits, a principal component analysis was employed with two components to assess variability (Figure 2.4). Hierarchical clustering data shows two separate clusters based on 16 agronomic traits. Component 1 explained 28% of the population's variability and accounted for cluster 1 containing tolerant WR accessions S9, S18, and S42. The traits associated with this cluster are culm length, thousand kernel weight, grain width, and leaf width. Sensitive WR accessions such as B5 and B86 present decreased leaf and grain width as well as black hull colors.

2.4.2 Heat Tolerance Screening

Yoshida (1981) identified the threshold temperature for rice seedlings to be 35⁰C.

Additionally, heat stress is characterized by irreversible damage that impacts plant growth and activity (Xiao et al., 2011). Due to the increased competitiveness demonstrated by WR, seedlings were treated at 38⁰C for 21 days. Affected seedlings showed reduced plant height and biomass reduction at 14 and 28 DAT. The WR population showed considerable variation in response to a consistent temperature of 38⁰C during the seedling stage. The effects of these sweltering temperatures can be observed in cultivated rice plants' low growth after extended exposure and were confirmed in a study with cultivated rice showing a reduction in height by up to 90%

(Yoshida, 1981). In this study, three lines (BKN6624-46-2, IR7478-2-6, and N22) were stunted and showed a significant decrease in spikelet fertility in a range of just 0 – 10% fertility at temperatures from 35-31⁰C (Yoshida, 1981). In a controlled environment study of rice at 22

temperatures just above 25⁰C, both spikelet number and tillering rate decreased as temperatures began to increase (Yoshida, 1973). Similar effects were observed in a genetic study by Cheng et al. (2012) that recorded a reduction in spikelet fertility from 50-80% at 38⁰C during flowering time. A study of rice mutants using crosses between heat-sensitive, high-yielding rice variety,

IR64, and drought- and heat-tolerant mutant, aus variety, N22, found that mutants had a 13% increase in plant height at 38⁰C when compared to parents (Poli et al., 2013). Increases in plant height can increase the transpiration cooling effect seen in many plants under heat stress

(Hasanuzzaman et al., 2013; Weerakoon et al., 2008). Similar results may be valid in heat- tolerant WR as two (2) WR lines had no height reduction at 28 DAT. In this study, height reduction was a significant focus as plant height is a critical characteristic for breeding rice cultivars with increased yield (Jeng et al., 2012).

Approximately 57% (8 of 14 WR accessions) and 42% (6 of 14 WR accessions) of the

WR population had a reduction of 20% or less in plant height at 14 and 28 DAT, respectively. In an experiment designed to assess the seedling survival rate in rice mutants, Bahuguna et al.

(2014) found that mean temperatures ranging from 36-45 C resulted in a loss of 53% of the seedling population just 15 days after transplanting. From this study, only eight seedlings had a survival rate of 60% (Bahuguna et al., 2014). In another study, Kilasi et al. (2018) found that aus variety N22 had approximately 30% longer root and shoot lengths than the heat-sensitive variety

IR64 after only being exposed to heat stress (37⁰C) for four days during germination. These results are similar to findings in this study as rice seedlings were just 21 days old before the onset of heat stress.

In this study, mean plant height reduction ranged from approximately 1 to 33% at 14

DAT (Figure 2.1). At 14 DAT, RBL-61 (0.8%) presented the least reduction in height, followed

by WR accession S108 (10%) and CRL-CL163 (10%). Rice-breeding line, RBL-61, is a commercial indica type rice from Pakistan often used as a check demonstrating increased tolerance to heat stress (Pervaiz et al., 2010). Additionally, RBL-61 is characterized by a medium time frame to maturity ranging from 116-130 days and a dwarf variety standing less than or equal to 110 cm tall at maturity (Pervaiz et al., 2010). WR accession S108 is characterized as a high-yielding, straw-colored, awn-less biotype collected from Randolph County, Arkansas.

Because WR samples were randomly collected from fields across the rice-producing Delta region in Arkansas, few characteristics are known about surviving accessions. WR accession S9 showed the most height reduction at 33% at 14 DAT. This WR accession was collected from

Arkansas County, Arkansas, and is characterized as a straw-colored, awn-less biotype. Similar instances of stunting were reported in a study by Poli et al. (2013) when heat-stressed rice cultivar, N22, and rice mutant, NH219, were screened. Findings showed that plant height increased by only 5% in the rice cultivar than the rice mutant, whose height increased by more than 12% (Poli et al., 2013). In comparison, the WR data showed numerous plants whose heights were not reduced after the increase in temperatures for 21 days.

At 28 DAT, mean plant height reduction ranged from 6 to 41% (Figure 2.2). This continued increase in height reduction can be attributed to additional heat-stress exposure, such as reduced photosynthetic processes due to cell death and decreased root lengths. Injury and cell death due to extreme heat stress can occur within a few minutes of exposure to heat and is characterized by foliar senescence and inhibition of root and shoot growth (Nievola et al., 2017).

Wu et al. (2016) found that high-temperature treatments reduced exposed panicles' lengths by approximately 60%. In this study, WR accession S108 had the least reduction in height (6%), while CRL-PM had the most reduction (41%). As previously mentioned, WR accession S108 is

noted to be a high-yielding accession that continued to grow taller after being exposed to heat stress for 21 days. The height reduction for S108 was 10% at 14 DAT compared to the untreated control, which decreased to 6% at 28 DAT, suggesting that this WR accession continued to grow even after exposure to above optimum heat conditions. CRL-PM is commonly used in breeding programs to distinguish cross changes in early generation breeding programs (Gravois &

Moldenhauer, 2005). This line lodges readily and has an increased degree of seed dormancy but has been used as the parental line in many breeding studies from the early 90s to 00s (Roche et al., 2005).

After 21 days of exposure to 38⁰C temperatures, the mean biomass reduction ranged from

0 to 55%. Rice is a hearty grain crop that has demonstrated the ability to continue to grow new leaves after the loss of older leaves, known as senescence (Inada et al., 1999). Extended leaf exposure to increased temperatures leads to the degradation of chloroplasts and the rapid decline of soluble proteins and chlorophyll throughout the rice crop's life cycle (Inada et al., 1999). Due to this constant cycle of increased and decreased proteins and chlorophyll, some WR plants demonstrated the ability to produce vegetation even after exposure to high temperatures continuously. WR accessions B83 and S124 had no reduction in mean biomass reduction compared to the 55% mean biomass reduction in RBL-58 (Figure 2.3). WR accessions B83 and

S124 both had height reductions of less than 20%, showing a decline from 14 DAT to 28 DAT.

Interestingly, CRL-PM had the highest height reduction at 28 DAT but had no biomass reduction at 28 DAT. This effect could be due to the selected line's age and its continued use in breeding programs that lends to its increased biomass production even after exposure to increased temperatures.

To further assess the variability and performance of those lines with low and high height and biomass reduction, a principal component analysis (PCA) was employed using hierarchical clustering (Tseng, 2013). In the PCA, 16 agronomic traits were used to access the variability captured in components 1 and 2. The PCA revealed that 36% variation in heat-stress WR accessions was attributed to component 1 compared to just 23% in component 2 (Figure 2.4).

When PCA is paired with hierarchical cluster analysis, WR accessions S108 and S109 form an independent group (Group 1) associated with leaf width (mean = 1.4 cm). While WR accession

S108 did have a reduction in biomass 28 DAT, the accession consistently had less than 20% reduction in height at 14 and 28 DAT. Group 1 is also comprised of biotypes with straw-colored hulls without awns. A second grouping (Group 2) consisting of accessions B30, B37, and B38 had above average measurements in plant flowering time (mean = 105 days), grain length (mean

= 6 cm), and culm length (mean = 129 cm), but the PCA reveals that these agronomic traits are not indicative of heat-stress tolerance. These selected agronomic traits are associated with increased plant height (Jeng et al., 2012). Additionally, agronomic associations can be made related to increased ligule length, panicle length, and leaf length in similar PCA planes. Similar results are observed in Arkansas weedy rice as PCA attributes hull color as the primary trait separating the population (Kanapeckas et al., 2017).

2.4.3 Submergence Tolerance Screening

Although rice is well known for withstanding flooded conditions due to its ability to germinate without CO2 and aerobically escape slow rising waters, issues still arise in rice’s ability to escape and survive sudden and complete submergence due to flash flooding (Jackson &

Ram, 2003). Severe injury has been reported when rice coleoptiles, leaves, or stems cannot escape flooding conditions resulting in sustained complete submergence exposure (Pearce & 26

Jackson, 1991). In instances of sudden flooding, it has been reported that rice height can increase by 25 cm per day, but drastic increases in water levels can reduce plant survival (Vergara et al.,

1976). Rice seedlings have demonstrated the ability to survive complete submergence for up to

20 days, but the seedling survival rate strongly depends on the age of seedlings and the growth stage that seedlings are impacted (Richharia & Parasuram, 1963). Kotera & Nawata (2006) found that yield loss was substantial (≥ 60%) when complete submergence took place 17 days after transplanting. Although plants were only completely submerged for just two days, yield losses continued to increase rapidly (Kotera & Nawata, 2006).

While many studies have been performed to assess rice populations' survival rate and performance when exposed to complete submergence-stress at the seedling stage, few studies have a WR populations' performance at the seedling stage. Thus, this study aimed to evaluate rice and WR accession responses to complete submergence stress at the seedling growth stage.

This population was subjected to complete submergence for 21 days to simulate the effects of unexpected, long-standing flash flooding. Affected plants were unable to escape flooded conditions resulting in plant death, and those that did survive showed increased instances of reduced plant height and biomass production at 14 and 28 DAT. Accessions that could survive showed clear signs of exposure to longer durations of complete submergence-stress, such as decreased dry matter accumulation, decreased leaf extension, and chlorosis in fully expanded leaves due to an increase in ethylene production and decreased respiration (Jackson et al., 1987).

At 14 DAT, approximately 33% (five out of fifteen WR accessions) of the WR accessions had no height reduction, while 53% (eight out of fifteen WR accessions) of WR had less than 20% height reduction (Figure 2.5). The accessions with no height reduction, S21, B38,

B45, B49, and B51, are predominantly black-pericarp, awned WR accessions. Although Setter &

Laureles (1996) found a negative correlation between shoot elongation and plant survival, stating that increased elongation in flooded rice was characteristic of low surviving seedlings after exposure to complete submergence for 14 days, the effects have not been studied in WR. In this study, varying effects were observed where those accessions that had no reduction in height continued to survive the effects of submergence-stress 28 DAT. This trend was observed at 7 and

14 DAT, but at 28 DAT, 40% of the WR had less than 20% reduction in height, but no WR accessions had no height reduction (Figure 2.6). Moreover, it is worth noting that those lines that had less than 20% height reduction at 14 DAT, surviving weedy rice accessions seemed to recover after de-submergence and maintain less than 20% height reduction at 28 DAT while

CRL-Rex and RBL-60 remained stunted at 28 DAT.

Unsurprisingly, when these WR accessions were compared to cultivated rice lines and rice breeding lines, CRL-REX and RBL-60 had more than 70% height reduction 28 DAT. It is worth mentioning that tolerant rice cultivars are commonly known to suppress leaf elongation and maintain higher carbohydrate levels under complete submergence, but it is unknown if this is true about its weedy counterpart (Gao et al., 2007). This ability may account for the increase in height reduction compared to untreated controls. CRL-REX is a Mississippi developed, conventional, semi-dwarf cultivar with excellent yield performance, good straw strength, milling, and standability (Solomon et al., 2012). CRL-REX is also known to be susceptible to rotten neck blast and sheath blight disease (Hardke et al., 2013). Disease susceptibility could also be why increased height reduction in this rice cultivar as flooded waters can increase these diseases.

RBL-60 is a submergence-tolerant rice breeding line developed at the Delta Research and

Extension Center in Stoneville, MS. RBL-60 is a derivative of IR49830 developed as a converted

mega variety with the Sub1 gene to increase submergence-stress tolerance (Septiningsih et al.,

2009).

The mean biomass reduction at 28 DAT showed 27% of WR having no reduction in accessions S21, B30, B38, and B45, while 67% of the WR accessions had less than 20% reduction in biomass (Figure 2.7). Again, those accessions with no biomass reduction were predominantly black-pericarp, awned biotypes. In submergence tolerant rice, biomass was shown to increase by 60% compared to sensitive biotypes after exposure to complete submergence (Das et al., 2009). Das et al. (2009) also found that biomass reduction (23%) was only observed when silt was introduced into the flooding environment. In this study, plants were confined to a growing box shared with 19 other plants. As treated plants escaped the submerged environment, they could affect the shaded regions of other accessions.

The PCA and hierarchal clustering completed on the WR population to better understand the association of 16 agronomic traits to complete submergence-stress tolerance uncovered two distinct clusters (Figure 2.8). Cluster 1 was comprised of those WR accessions with less than

20% height and biomass reduction 28 DAT, while cluster 2 consisted of those WR accessions with an increase in height and biomass reduction 28 DAT. Component 1 explained 28% of the variation in the data, while component 2 captured 22%. Because rice is known to escape complete submergence with decreased survival rates, agronomic traits such as awn length and grain length/weight may not be significant indicators of submergence-stress tolerance.

Submergence-stress tolerant WR accessions did have increased culm length values (mean = 134 cm) and grain yield (mean = 97 g), suggesting that these can be agronomic indicators of submergence-stress tolerance.

2.5 Conclusion

This study aimed to highlight the increased competitiveness of a diverse WR population compared to cultivated rice lines and developmental rice breeding lines. By analyzing plant height and biomass reduction and associations of agronomic traits, it is easy to see why weedy rice continues to be a growing challenge within the southeastern US. Although challenging, surviving WR accessions could prove to be a beneficial genetic resource in the future to improve rice-breeding programs.

Figure 2.1 Mean height reduction 0 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height.

The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.2 Mean height reduction 7 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height. The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.3 Mean height reduction 28 days after cold-stress treatment arranged from lowest reduction to the highest reduction in height.

The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.4 Principal component analysis (PCA) of 16 agronomic traits, including plant height reduction and plant biomass reduction 28 days after cold-stress treatment.

Figure 2.5 Mean height reduction 14 days after heat-stress treatment arranged from lowest reduction to the highest reduction in height.

The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.6 Mean height reduction 28 days after heat-stress treatment arranged from lowest reduction to the highest reduction in height.

The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.7 Mean biomass reduction 28 days after heat-stress treatment arranged from lowest reduction to the highest biomass reduction. The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.8 Principal component analysis (PCA) of 16 agronomic traits, including plant height reduction and plant biomass reduction 28 days after heat-stress treatment.

Figure 2.9 Mean height reduction 14 days after complete submergence-stress treatment arranged from lowest reduction to the highest reduction in height. The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.10 Mean height reduction 28 days after complete submergence-stress treatment arranged from lowest reduction to the highest reduction in height.The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

Figure 2.11 Mean biomass reduction 28 days after complete submergence-stress treatment arranged from lowest reduction to the highest biomass reduction. The line at 20% represents all lines with ≥ 20% reduction in height. RBL, Rice breeding line; CRL, cultivated rice line.

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CHAPTER III

ASSESSING THE GENETIC DIVERSITY OF WEEDY RICE MINI-GERMPLASM

USING SSR MARKERS AND SNPs

3.1 Abstract

As one of the most important staple food crops globally, rice (Oryza sativa L.) feeds more than half of the global population. Rice currently provides approximately 50% of the daily caloric intake for millions living in poverty and is a model plant organism that evolved from tropical and subtropical areas. With a modest genome size of about 400 million base pairs that have been completely sequenced using a map-based approach, rice has been intensely studied through functional genomic approaches. These approaches include numerous genome-wide association studies, quantitative trait loci studies, and simple sequence repeat (SSR) studies to access genetic diversity and uncover candidate genes for increased tolerance to abiotic stresses.

While many studies have used rice to discover candidate genes, there are limitations due to the loss of beneficial traits during rice domestication. To overcome this limitation, research studies have turned to utilizing weedy species for new trait discovery. Given its increased stress tolerance, weedy rice (WR) may serve as a resource for unique stress tolerance traits. While studies have identified significant genetic similarities between rice and WR, this study aims to evaluate the genetic diversity of a weedy rice mini-germplasm collected from Arkansas and

South Carolina and rice cultivars after exposure to cold (18°C), heat (38°C), and complete submergence using a standard rice molecular marker panel consisting of 30 simple sequence

repeat (SSR) markers. Molecular marker analysis using the 30 SSR markers determined that all markers were 100% polymorphic. The mean genetic diversity (h) was 0.47 in the cold-stress treatment and 0.43 for both heat and complete submergence. In the cold screening study, h was

0.51, 0.30, and 0.36 amongst tolerant WR accessions, sensitive WR accessions, and sensitive rice lines, respectively. In the heat study, h was 0.26, 0.50, and 0.36 amongst tolerant WR accessions, sensitive WR accessions, and sensitive rice lines, respectively. The submergence study identified genetic diversity (h) as 0.46, 0.33, and 0.36 amongst tolerant WR accessions, sensitive WR accessions, and sensitive rice lines, respectively. Genotyping-by-sequencing (GBS) was also employed to assess the genetic diversity based on single nucleotide polymorphisms (SNPs). A total of 199,441 SNPs were uncovered, and when subjected to Structure Harvester, three distinct clusters to four were found in all stress treatments separating tolerant WR accessions, sensitive

WR accessions, and cultivated rice.

3.2 Introduction

Rice (Oryza sativa) is held as one of the most important grain crops as it provides more than 50% of the caloric intake to approximately one-third of the world (Han et al., 2020). It is predicted that rice yields need to increase by 1% annually to continue to feed the growing population (Normile, 2008). Rice is a model plant with high levels of genetic diversity both within and among biotypes (Singh et al., 2013). As climate change continues to impact crops negatively, it is imperative to discover abiotic stress-tolerant traits for rice improvement.

Although rice is a staple, it is sensitive to stresses such as cold, heat, and complete submergence

(Gao et al., 2007).

As rice has continued to evolve, so have rice weeds and mimics. One weed of importance is weedy rice (Oryza sativa ssp.) as it is widely distributed throughout Asia, North and South 51

America, and Europe (Cao et al., 2006). Weedy rice (WR) belongs to the same species as cultivated rice and naturally competes for nutrients within field settings reducing yield and grain quality (Tseng et al., 2018). Morphologically, WR demonstrates rapid growth and development, increased tillering, varying pericarp coloring, and increased seed dormancy (Kanapeckas et al.,

2018). In a field study by Sanches-Olguin et al. (2007), the vegetative and reproductive development of sixteen weedy rice biotypes and five rice cultivars was screened. Weedy rice grew taller and faster while also maturing earlier than rice varieties (Sanchez-Olguin et al.,

2007). When weedy rice germination was evaluated, weedy rice germinated faster, and roots developed earlier than cultivated rice in temperatures ranging from 25-30⁰C, suggesting that weedy rice has the ability to withstand temperature extremes (Olajumoke et al., 2016). In a study to evaluate the effects of water depth at emergence, weedy rice was not sensitive to the depth at 9 cm while cultivated rice was unable to emerge (Suh and Ha, 1993).

As many of these studies have demonstrated the increased competitive ability of WR, the genetic variability within these weedy populations can be large and unknown. The evolutionary pathways of WR have been narrowed down to four supported hypotheses: 1) weedy rice originated from the selection and adaptation of wild rice in rice-growing areas, 2) weedy rice originated from the de-domestication of cultivated rice with an accumulation of mutants, 3) contaminated seed stocks, or 4) the hybridization between cultivated rice and wild Oryza species

(Nadir et al., 2017). To better understand the relationship between weedy rice and cultivated rice, SSR markers have been used (Gealy et al., 2002; Vaughan et al., 2001). Gealy et al. (2002) identified 18 SSR markers able to differentiated awned and awnless biotypes of red rice.

Vaughan et al. (2001) used the same SSR markers to identify close relatives of red rice to O. sativa ssp. japonica, indica, and wild relatives O. rufipogon and nivara. In rice, resources such

as http://www.gramene.org/, ready-to-use SSR markers, and MAS (marker-assisted selection) kits have been developed to assist in background selection for marker-assisted breeding in rice

(Gonzaga et al., 2015).

While molecular markers allow for rapid and precise biotype identification, the use of single nucleotide polymorphisms (SNPs) to assess genetic diversity has become more popular due to their higher frequency compared to SSR markers and the ability to use high-throughput systems to analyze (Singh et al., 2013). SNPs are a source of a large amount of genomic variation and can be used in association mapping, genetic diversity analysis, and marker-assisted selection (Gonzaga et al., 2015). SNP markers used to evaluate genetic variability and identify candidate genes in rice have gained popularity due to the full sequencing of the reference genome sequence from Nipponbare (MSU7) increasing genomic resources (Schatz et al., 2014).

The aim of this study is to use a standard rice panel of SSR markers

(http://www.gramene.org/) and SNP data to evaluate the genetic diversity of a weedy rice mini- germplasm. The objectives of the study are to identify and associate highly polymorphic SSR markers and SNPs with WR subpopulations demonstrating increased tolerance or sensitivity to cold, heat, and complete submergence in an effort to understand the genetic similarities between

WR and cultivated rice.

3.3 Materials and Methods

3.3.1 Plant Materials

Three different phenotypic studies (cold, heat, and complete submergence) were previously conducted on a weedy rice mini-germplasm containing 54 weedy rice accessions from the Weed Physiology Lab at the University of Arkansas, Fayetteville, AR; 6 stress-specific rice- breeding lines from the Delta Research and Extension Center (DREC), Stoneville, MS; and, 2 53

commercially available rice cultivars, Rex and CL163, also from DREC to identify weedy rice lines with increased sensitivity or tolerance to cold (18°C), heat (38⁰C) and complete submergence.

From each phenotypic study, 15 (14 in the heat evaluation) weedy rice accessions

(Appendix A, Supplementary Table 1) with varying levels of tolerance and sensitivity based on percent height and biomass reduction to cold, heat, and complete submergence were selected along with two cultivated rice lines (Rex and CL163) for the SSR study. In the case of the SNP analysis, 54 WR accessions, 7 cultivated rice lines, and 8 rice-breeding lines were used.

3.3.2 DNA Extraction

The DNA was extracted from fresh tissues harvested from 21-day old seedlings propagated in 72 well trays (Greenhouse Megastore, Danville, IL), filled with a potting soil mix

(SunGro Professional Growing Mix; 3.8 cu. ft.), and maintained under optimal conditions

(28⁰C/24⁰C, 16h/8h day/light, 55% humidity) in a growth chamber (Percival Scientific, Perry,

IA). The DNA was extracted using the CTAB method with some modifications (Doyle, 1987).

The quality and quantity of extracted DNA were quantified using the NanoDrop 2000

Spectrophotometer (ND-2000, Thermo-Fisher Scientific, Waltham, MA), and the extracted DNA was stored at -20⁰C to be used in polymerase chain reaction (PCR) amplification.

3.3.3 PCR Amplification using SSR Markers

The extracted DNA samples were diluted to 200 ng/µL before PCR. Thirty (30) SSR marker primer pairs from the standard panel of 50 markers used by the Generation Challenge

Program for rice diversity analysis were selected for the rapid screening of the cold, heat, and complete submergence-stress selected accessions. The primer pair information was derived from

McCouch et al. (2002) (Table 2.1). The selected markers have shown the ability to compare the genetic distance and difference between Oryza species with the AA genome (Yang et al., 1994).

PCR reactions were carried out in 96 well trays (VWR International, LLC, Radnor, PA) with a total volume of 25 µL. The reaction mixture consisted of 12.5 µL Taq 2x master mix (Taq polymerase, dATP, dGTP, dCTP, dTTP, and MgCl2) (New England Biolabs, Inc., Ipswich, MA),

1 µL each forward and reverse primer, 2 µL DNA, and 8.5 µL nuclease-free water. The reactions were run in a PTC-100 Peltier Thermo Cycler (Marshall Scientific, LLC, Hampton, NH) using the following profile for amplification: initial DNA denaturing at 94⁰C for 5 minutes, 40 cycles of DNA denaturing at 94⁰C for 30 seconds, specific primer annealing temperature from 55⁰C to

67⁰C dependent on the marker for 45 seconds, polymerase extension at 72⁰C for 2 minutes. After

40 cycles, a final extension was performed for 10 minutes at 72⁰C, with final reactions held at

4⁰C infinitely. PCR products were analyzed on 6% polyacrylamide gels for 45 minutes at 180 volts. The gels were stained with ethidium bromide and visualized under UV light.

3.3.4 SNP Genotyping

Genotyping-by-sequencing (GBS) was carried out on the selected population consisting of 54 weedy rice accessions, 7 cultivated rice lines, and 8 rice-breeding lines using the technique outlined by Burgos et al. (2014) with slight modifications. Approximately 100 mg of tissues were collected from 21-day-old seedlings propagated in 72 well trays (Greenhouse Megastore,

Danville, IL), filled with a potting soil mix (SunGro Professional Growing Mix; 3.8 cu. ft.), and maintained under optimal conditions (28⁰C/24⁰C, 16h/8h day/light, 55% humidity) in a growth chamber (Percival Scientific, Perry, IA). Once collected, fresh tissues were shipped on ice to

BGI (Cambridge, MA) for next-generation sequencing and processing using their proprietary

protocols. Raw reads were submitted to the Institute for Genomics, Biocomputing, and

Biotechnology at Mississippi State University (Mississippi State, MS) for processing.

3.3.5 Data Analysis

3.3.5.1 SSR Marker Analysis

Individual bands were scored based on a 500 bp ladder using Cross Checker 2.91

(Buntjier, 1999), and markers were considered co-dominant. For scoring, bands were subjected to binary characters where 1 is the presence of bands and 0 is the absence of bands. Using

POPGENE version 1.32 (Yeh, 2000), the following variables were observed: alleles per locus

(na), percent polymorphic loci (P), Nei’s genetic diversity (h) (Nei, 1973), and neighbor-joining trees using Nei’s genetic distance and Dedroscope (Huson et al., 2007) for visual representation.

The listed variables were used to compare genetic diversity among and within the cold, heat, and complete submergence subpopulations.

3.3.5.2 SNP Genotyping Analysis

The TASSEL 5 GBS v2 SNP-calling pipeline (Glaubitz et al., 2014; Bradbury et al.,

2007) and the rice reference genome, Nipponbare (MSU7), was used for alignment. The sequences were subjected to initial data processing by BGI, then using TASSEL, further filtering was done to remove SNPs that had more than 10% missing data and individuals with more than

95% missing data. The whole data set was comprised of 199,441high-quality SNPs were retained. Population structure was determined using STRUCTURE 2.3.4 and Structure Harvester using SNP data derived from TASSEL. Inferred clusters based on the K-value identified in

Structure Harvester, where the optimum number of inferred clusters (K) was selected as outlined by Muthukumar et al. (2015), were used with DISTRUCT (Rosenberg, 2004) to analyze the

genetic diversity present within predetermined subpopulations consisting of tolerant and sensitive WR, rice-breeding lines, and cultivated rice lines.

3.4 Results and Discussion

3.4.1 Genetic Diversity Using SSR Markers

To reduce type I and II errors in association mapping, it is necessary to determine the population structure (Pritchard et al., 2000). Population structure has been used to separate highly diverse populations of rice and weedy from various locations (Jin et al., 2010; Gealy et al., 2002). Population structure using SSRs has also been employed to detect biotype level differences in a diverse population (Zhang et al., 2011). Compared to other markers, SSR markers have great potential to discriminate between rice genotypes (Xiao et al., 1996). This study was aimed at the identification of genetic diversity present within rice subpopulations characterized as tolerant and sensitive cold, heat, and complete submergence.

3.4.1.1 Genetic Diversity among Cold-Stress Subpopulation

Using 30 SSR markers, a subpopulation containing 15 WR accessions with varying responses to cold-stress and two rice cultivars were used to determine the genetic diversity within the population. Of the 15 WR accessions, 10 of the WR accessions were determined to be cold stress-tolerant at 18⁰C after 21 days, while the remaining 5 WR accessions were determined to be cold stress-sensitive, creating three subpopulations (tolerant WR, sensitive WR, and cultivated rice). For the SSR analysis, the population genetic diversity (h) was evaluated as a whole and found to be 0.47, with all SSR markers being 100% polymorphic (Table 3.2). The observed number of alleles (na) per marker ranged from 2-3, with the average number of observed alleles being 2.7. The mean effective number of alleles (ne) was 1.98. The lowest h

(0.18) was observed in markers M12, M15, and M18, while the highest h (0.66) was observed in marker M8. The genetic diversity (h) for tolerant WR, sensitive WR, and cultivated rice was

0.51, 0.30, and 0.36, respectively. When the subpopulations were analyzed separately, only the tolerant WR accessions showed 100% polymorphic loci compared to 63% in sensitive WR and cultivated rice.

Nei’s genetic diversity (h) is measured on a scale of 0 to 1 and is used to measure the heterozygosity between and within populations (Shrestha et al., 2018; Nei, 1973). In a study to assess the genetic diversity of U.S. weedy rice and rice cultivars, rice cultivars had the highest genetic diversity (h=0.462) compared to weedy rice with an average genetic diversity of 0.27, while hybrid rice had a mean genetic diversity of 0.623 (Gealy et al., 2009). Based on the genetic diversity presented in this study, tolerant WR accessions were comparable to hybrid rice, suggesting potential introgression of alleles between cultivated rice and WR due to outcrossing.

Tolerant WR accessions are more diverse than cultivated rice and sensitive WR accessions.

Similar results were observed in a study by Londo & Schaal (2007), where weedy rice accessions had a mean genetic diversity of 0.48.

When Nei’s genetic distance (1978) was analyzed and a neighbor-joining tree was constructed, four clusters were observed (Figure 3.1). Cluster 1 consisted of cold-tolerant, straw hull-colored WR accessions S9, S94, and S97, while cluster 2 consisted of highly sensitive WR accession B86. Cluster 3 contained cultivated rice lines with some WR intermixed. The clustering of cultivated rice lines together highlights the lack of genetic diversity (h=0.36) within rice cultivars and the genetic similarities that allow for WR accessions to compete in field environments (Chauhan, 2013). Cluster 4 was comprised of the remaining WR accessions with intermediate tolerance and sensitivity to cold stress.

3.4.1.2 Genetic Diversity among Heat-Stress Subpopulation

To assess the genetic diversity present within a subpopulation consisting of tolerant WR

(14 accessions), sensitive WR (5 accessions), and cultivated rice (2 accessions), 30 SSR markers from a standard rice panel (McCouch et al., 2002) were used. Tolerant and sensitive accessions were selected based on percent height and biomass reduction after exposure to 38⁰C for 21 days.

All loci were 100% polymorphic for the heat treatment subpopulation, and the mean genetic diversity (h) was 0.43 with ranges from high (h=0.66) for marker M8 to low (h=0.11) for markers M6, M7, M12, M15, and M18 (Table 3.3). The observed number of alleles (na) ranged from 2-3, with the mean number of observed alleles being 2.7. The mean effective number of alleles (ne) was 1.98. The genetic diversity (h) of the subpopulations was 0.26, 0.50, and 0.36 for tolerant WR, sensitive WR, and cultivated rice lines, respectively. When the subpopulations were analyzed, the sensitive WR subpopulation was the only population with 100% polymorphic loci compared to 77 and 63% in the tolerant WR and cultivated rice lines, respectively.

In an analysis of genetic diversity between two rice cultivars, N22 and Uma, 200 SSR markers were used to screen heat tolerant and susceptible lines. The mean genetic diversity uncovered was 0.30, which is very similar to h values for tolerant WR accessions (h=0.26) and cultivated rice lines (h=0.36) in this study (Zhang et al., 2009). Vikram et al. (2011) screened four rice varieties using 264 SSR markers and found an average h of 42% that is again similar to values for tolerant WR accessions and cultivated rice lines. The similarities in genetic diversity and percent polymorphic loci hints at possible gene flow between tolerant WR accessions and cultivated rice lines. Additionally, tolerant WR accessions had similar values in height and biomass reduction to cultivated rice, further suggesting that these subpopulations are genetically similar. Surprisingly, heat-sensitive WR lines had the highest amount of genetic diversity

(h=0.50) compared to tolerant WR and cultivated rice. Although studies have not been completed to assess the within-population genetic diversity within WR populations with respect to heat-stress tolerance and susceptibility, the high amount of genetic diversity present within sensitive WR accessions may be key in the rapid screening and identification of sensitive rice lines in breeding programs.

Using Nei’s genetic distance (1978), a neighbor-joining tree was constructed to visualize the relationship between tolerant WR, sensitive WR, and cultivated rice. Five different clusters were identified (Figure 3.2). Of these five clusters, cluster 1 distinctly separated sensitive WR accessions, ALR-1 and B37, from the population. Both of these accessions had height and biomass reductions of above 20%. Cluster 2 was comprised of 50% of the tolerant WR subpopulation, while cluster 3 consisted of cultivated rice lines. While the remaining clusters (4 and 5) did not show a clear differentiation between heat sensitive and tolerant WR accessions, this is not uncommon, as studies have shown that population structure analysis cannot differentiate between straw hull colored weedy rice accessions and indica cultivars (Reagon et al., 2010). Moreover, weedy rice in the U.S. consists of independently evolved groups from closely related cultivated relatives that can explain the inability of SSR markers to separate WR accessions from cultivated rice lines (Thurber et al., 2013).

3.4.1.3 Genetic Diversity among Complete Submergence-Stress Subpopulation

As with the cold and heat subpopulations, 30 SSR markers were used to assess the genetic diversity of a subpopulation of rice and WR with varying levels of tolerance and sensitivity. The subpopulation was comprised of 15 WR accessions and 2 cultivated rice lines.

The WR accessions (10 tolerant and 5 susceptible) were selected based on percent height and biomass reduction after a larger population was exposed to complete submergence for 21 days. 60

The mean genetic diversity (h) for the subpopulation was 0.72. All 30 SSR markers were 100% polymorphic. Mean genetic diversity ranged from low (h=0.10) in markers M12, M15, and M18 to high (h=0.65) in marker M8 (Table 3.4). The observed number of alleles (na) ranged from 2-3, and the average was 2.7. The mean effective allele size (ne) was 1.94. When analyzing the subpopulations, the genetic diversity was 0.46, 0.33, and 0.36 for tolerant WR, sensitive WR, and cultivated rice lines, respectively. Among the subpopulations, the tolerant WR accessions were the only ones to have 100% polymorphic loci, while sensitive WR and cultivated rice lines only had 63% polymorphic loci.

Using Nei’s genetic distance (1978), a neighbor-joining tree was constructed to visualize the relationships between the WR and rice in the subpopulations (Figure 3.3). Four separate clusters were observed. Clusters 1 and 3, though on opposite ends of the tree, accounted for 30% of the tolerant WR accessions, while cluster 2 did not represent any significant clusters.

Although clusters 1 and 3 are separate, two of the WR accessions represented in the cluster are both straw hull color.

When using 30 SSR markers linked with drought-tolerant QTLs to analyze the genetic diversity of indigenous rice varieties, Anupam et al. (2017) found the mean genetic diversity (h) to range from 0.06 to 0.79, with the mean diversity to be 0.55. They found that the large range of genetic diversity could be related to small population size and selection of SSR markers, but their data ranges were very similar to the submergence subpopulation used here (Anupam et al.,

2017). Surprisingly, although only 30 SSR markers were used to screen the submergence subpopulation, the mean genetic diversity was much higher (h=0.72). Though mean genetic diversity was high, there were still some markers with low diversity (h=0.10). When assessing the genetic diversity of the subpopulations within the submergence population (tolerant and

sensitive WR and cultivated rice), there was a low amount of genetic diversity (less than 50%) within each subpopulation. Ferreira and Grattapaglia (1998) suggest that this could be due to the subpopulations having similar ecotypes or origins as the majority of the weedy rice accessions used in this study were collected from rice-producing fields in Arkansas. While this could be true, one of the WR accessions (ALR-1) in cluster 1 represents South Carolina, while the other

WR accession is a straw hull color biotype from Arkansas. Additional SSR markers may be able to discriminate between those WR accessions with different origins (Gealy et al., 2002).

3.4.2 Genetic Diversity Using SNP Data

As population growth continues and crops are met with pressure from sudden climatic changes, it is imperative to identify traits associated with abiotic stress tolerance quickly. It is essential to improve the genetic diversity of rice genotypes, possibly through the introduction of genes from wild or distant relatives (Mogga et al., 2018). To quickly identify these genes, the powerful tool, genotyping-by-sequencing (GBS), has been leveraged due to increased sequencing output, the development of reference genomes, and improved bioinformatics (Ali et al., 2018). GBS is a next-generation sequencing (NGS) tool used for high throughput genotyping of large numbers of individuals with an increased number of SNP markers (Glaubitz et al.,

2014). GBS has become more common in breeding practices due to its ability to be applied to highly diverse and large species such as rice, whose genome has a total length of 430 Mb (He et al., 2014; Eckardt, 2000).

GBS can also be leveraged to assess the genetic diversity of a population at an increased frequency compared to SSR markers while also being cost-effective and less labor-intensive

(Bhattarai & Subudhi, 2018). In a rice GBS study, GBS data uncovered 166,418 SNP loci, which is very similar to the number of SNP loci uncovered in this study where 199,441 SNP loci were 62

uncovered in a population consisting of WR, rice-breeding lines, and cultivated rice lines

(Nazzicari et al., 2016). In this study, these SNP loci were used to analyze the genetic diversity of a population consisting of WR, rice-breeding lines, and cultivated rice. The population was screened for tolerance to cold, heat, and complete submergence and separated into smaller subpopulations based on reduction in height and biomass after 21 days.

3.4.2.1 Genetic Diversity among Cold-Stress Subpopulation

Using NGS, GBS methods were used to assess the genetic diversity of a subpopulation of tolerant and sensitive WR and cultivated rice after screening for tolerance to cold stress. A total of 199,441 SNPs were used to assess the population structure using STRUCTURE and Structure

Harvester. Using DISTRUCT (Rosenberg, 2004) analysis paired with predetermined subpopulations, genetic diversity was visually graphed (Figure 3.4). The predetermined subpopulations were cultivated-sensitive rice (POP1), weedy-sensitive (POP2), and weedy- tolerant (POP3) with inferred clusters 1, 2, and 3. Hypothetically, POP1 should be primarily made of cluster 1 with red coloring, POP2 should primarily be cluster 2 with green coloring, and

POP3 should consist of cluster 3 with blue clustering. DISTRUCT (Rosenberg, 2004) is a program used to estimate the relatedness of a population as a portion 1 or 100% based on allele frequencies from a reference genome. Using inferred clusters, the percentage of shared blocks between clusters can be visualized.

POP1 consisted of three cultivated rice lines and two rice-breeding lines. The majority of the lines in POP1, with the exception of CRL-CL163 and CRL-Rex, have an average of 60% red and 40% blue split between inferred clusters 1 & 3. These lines are genetically similar and share mixes of similar alleles, while CRL-CL163 was 100% of cluster 1 and CRL-Rex was 100% of cluster 3. POP2 consisted of five sensitive WR accessions. While two accessions, S124 and 63

S113, were genetically similar, with an average mix of cluster 3 (91%) and cluster 1 (9%). Two of the WR accessions, B5 and B86, in this subpopulation had a 53% make up of cluster 1, and a

47% make up of cluster 2, while the remaining WR accession, B8, was 100% made up of cluster

2. POP3 was comprised of 70% of the tolerant WR accessions that consisted of more than 95% of cluster 3. These lines are genetically similar based on allele frequencies suggesting that they are very different from cultivated rice lines that showed a mixture of clusters 1 and 3. One WR accession, B60, had a makeup of approximately all three inferred clusters with about 44% cluster

1, 35% cluster 2, and 19% cluster 3. Additionally, WR B34 was genetically similar to WR B8 and may have varying levels of tolerance to cold stress. WR ALR-4 was comprised of 82% cluster 3 and 18% cluster 1. The sharing of cluster 3 with cultivated rice line Rex and those WR accessions in the tolerant subpopulations in POP3 suggest that there are many shared alleles between these lines, and even may suggest that these WR accessions are related to cultivated rice, Rex.

These results are similar to a study by Gross et al. (2010) that uncovered neutral markers indicating a close relationship between U.S. red (weedy) rice and cultivated rice. When Thurber et al. (2010) studied the molecular evolution of the shattering loci in U.S. weedy rice, they found that weedy rice and cultivated rice shared a number of alleles that suggest divergence then recombination with cultivated rice in the field. This could also be the reason for the overlap in clusters in the present study.

3.4.2.2 Genetic Diversity among Heat-Stress Subpopulation

Similar to the cold stress subpopulation, STRUCTURE and Structure Harvester were used to analyze allele frequencies in a heat-stress subpopulation. Structure Harvester found the

K-value to be 4, and 4 inferred clusters were used with DISTRUCT (Rosenberg, 2004) to assess 64

the genetic diversity of the subpopulation (Figure 3.5). The subpopulations were cultivated- sensitive rice (POP1), weedy-sensitive (POP2), and weedy-tolerant (POP3) with inferred clusters

1(red), 2 (green), 3 (blue), and 4 (purple).

POP1 consisted of three rice lines (CRL-PM, RBL-58, and RBL-61) with similar approximate distributions of cluster 2 (15%), 3 (29%), and 4 (56%). Interestingly, CRL-CL163 was determined to have 100% of cluster 2 while CRL-Rex was 100 cluster 3. POP2 consisted of two straw hull-colored WR accessions (B37 and B30), both of which had an average of 99% cluster 1. In the previous screening study, these accessions had approximately a 25% reduction in height 28 days after the heat-stress treatment. These lines were noted as “sensitive” lines due to their height and biomass reduction being above 20%, but the genetic diversity suggests that they may have intermediate levels of tolerance to heat stress. WR accession ALR-1, was comprised of clusters 1 (50%), 2 (11%), and 3 (39%). Approximately 67% of POP3 shared similar distributions of more than 90% cluster 3. The genetic pattern observed here was similar to that of

CRL-Rex. Those WR accessions with similar genetic patterns are straw hull colored without awns. These WR accessions had a mean height and biomass reduction of less than 25% and were considered highly tolerant to heat stress. WR accessions B81 and B83 had higher percentages of cluster 1 (28%), while WR accession B38 was 99% cluster 1. Those WR accessions with more than 40% cluster 1 may actually be sensitive to heat stress, as the genetic patterns are very alike.

The distinction between clusters of WR accessions with tolerance and sensitivity was similar to

Burgos et al. (2014) and their identification of the separation of black hull colored rice and straw hull colored rice. The genetic diversity present here does distinctly separate the biotypes where black hull WR is sensitive to heat stress and straw hull WR has increased tolerance. These biotypes also share similar genetic patterns to one another. These genetic patterns were also

reported by Chung and Park (2010) and suggested that WR accessions with similarities to cultivated rice may come from similar geographic regions. This could explain the genetic overlap between CRL-Rex and tolerant WR accessions.

3.4.2.3 Genetic Diversity among Complete Submergence-Stress Subpopulation

As with the cold and heat-stress subpopulations, STRUCTURE and Structure Harvester were paired with DISTRUCT (Rosenberg, 2004) to assess the genetic diversity of a submergence-stress subpopulation. Structure Harvester found the K-value to be K=3. The K- value (K=3) was used to create inferred clusters within DISTRUCT (Rosenberg, 2004) and understand genetic patterns based on allele frequencies of three subpopulations. POP1 consisted of cultivated-sensitive rice, POP2 consisted of weedy-sensitive accessions, and POP3 consisted of weedy-tolerant accessions (Figure 3.6). The inferred clusters were cluster 1 (red), cluster 2

(green), and cluster 3 (blue).

POP1 consisted of lines CRL-CL163 and RBL-60, with 100% cluster 2 suggesting they have similar allele frequencies, while CRL-Rex was genetically different with 100% cluster 1.

CRL-PM and RBL-64 had similar makeups of cluster 1 (mean=53%) and cluster 2 (mean=46%).

POP2 was comprised of three WR accessions (B5, B8, and B34) with similarities with cluster 2

(mean=33%) and cluster 3 (mean=67%). WR accessions S5 consisted of 98% cluster 1 while

WR B37 was 100% cluster 3. The large amount of cluster 3 present within POP2 suggests that sensitive WR accessions, while genetically different from cultivated rice, have their own patterns of allelic frequencies that increase the sensitivity to submergence-stress. In the tolerant subpopulation (POP3), tolerant WR accessions, S9, S21, and S42 complete consisted of cluster

1, while those WR accessions with varying levels of tolerance (B51, B49, B38, and B30) completely consisted of cluster 3, suggesting that they may have genetic patterns more consistent 66

with those lines with increased sensitivity to submergence. This pattern was also observed in

CRL-Rex, whose height and biomass was reduced by more than 50% in the screening study.

Those lines with increased tolerance to submergence-stress were also of the straw hull colored biotype signifying increased tolerance to submergence-stress. Although height and biomass reduction were used to differentiate between sensitive and tolerant biotypes, it is worth mentioning that the most contributing factor to population structure seems to be controlled by hull color, as observed by Tseng (2013), who found similar results when assessing genetic diversity and seed dormancy.

3.5 Conclusion

Understanding population structure and historical hierarchical relationships among rice and weedy rice is an imperative step in identifying abiotic stress-tolerant traits in weedy populations. In this study, the genetic diversity and population structure were assessed in subpopulations with varying responses to cold, heat, and submergence-stress using SSR markers and Next-Generation Sequencing techniques. While SSR marker M8 was able to assess the highest amount of genetic diversity, there were low amplifying markers that will need to be removed from the screening assay. Research suggests using hypervariable SSR (hvSSR) markers to better assess genetic diversity (Singh et al., 2013; Narshimulu et al., 2011). Although hvSSRs are suggested, this study used a panel of 30 markers known to assess rice genetic diversity to screen weedy rice accessions as well. Research studies focused on the genetic relationship between weedy rice and cultivated rice are steadily evolving, and this study’s aim was to assess similarities between cultivated and weedy rice. The genetic diversity was also assessed using

SNP markers and visualized graphically. This method is becoming the method of choice due to increasingly lower costs and high-throughput efficiency. SNP analysis using structure was used 67

to compare the genetic patterns in subpopulations and pointed to a relationship between weedy rice and cultivated rice, Rex. These genetically similar allele frequencies may be key in future breeding programs.

Table 3.1 SSR markers selected for genetic diversity using PCR amplification.

Annealing PCR Chromosome Forward Primer Reverse Primer Temperature Cycles 1 aatccaaggtgcagagatgg caacgatgacgaacacaacc 55 30 1 gtctacatgtacccttgttggg cggcatgagagtctgtgatg 61 30 1 caaatcccgactgctgtcc tgggaagagagcactacagc 55 30 1 tcctgcgaactgaagagttg agagcaaaaccctggttcac 55 30 2 accctctccgcctcgcctcctc ctcctcctcctgcgaccgctcc 61 30 2 ctgatcgagagcgttaaggg gggatcaaaccacgtttctg 61 30 3 catttgtgcgtcacggagta agccacagcgcccatctctc 53 40 3 cacaggagcaggagaagagc ggcaaaccgatcactcagtc 55 40 3 agattgatctcccattcccc cacgagcatattactagtgg 55 30 4 atcgtctgcgttgcggctgctg catggatcaccgagctcccccc 67 30 5 cttaagctccagccgaaatg ctcaccctcatcatcgcc 55 30 5 ggcgattcttggatgaagag tccccaccaatcttgtcttc 53 30 5 tgcagatgagaagcggcgcctc tgtgtcatcagacggcgctccg 61 30 6 ttggattgttttgctggctcgc ggaacacggggtcggaagcgac 63 30 6 gccagcaaaaccagggatccgg caaggtcttgtgcggcttgcgg 61 30 7 atcagcagccatggcagcgacc aggggatcatgtgccgaaggcc 63 30 7 aacaacccaccacctgtctc agaaggaaaagggctcgatc 57 30 7 ccaatcggagccaccggagagc cacatcctccagcgacgccgag 67 30 8 caacgagctaacttccgtcc actgctacttgggtagctgacc 55 30 8 gaaaccaccacacctcaccg ccgtagaccttcttgaagtag 53 40 8 acgggcaatccgaacaacc tcgggaaaacctaccctacc 53 30 8 atctctgatactccatccatcc cctgtacgttgatccgaagc 55 30 8 tgcgctgaactaaacacagc agacaaacctggccattcac 53 40 8 Cccttgtgctgtctcctctc acgggcttcttctccttctc 55 30 9 ctagttgggcatacgatggc acgcttatatgttacgtcaac 55 30 9 caaaatggagcagcaagagc tgagcacctccttctctgtag 55 30 10 tcagatctacaattccatcc tcggtgagacctagagagcc 55 30 10 tctccctcctcaccattgtc tgctgccctctctctctctc 55 30 11 tctctcctcttgtttggctc acacaccaacacgaccacac 55 30 12 cggtcaaatcatcacctgac caaggcttgcaagggaag 55 30

Table 3.2 Genetic variation among the cold stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h).

Marker na ne h M1 3 1.60 0.38 M2 3 2.89 0.65 M3 3 1.83 0.45 M4 3 2.91 0.66 M5 2 1.94 0.48 M6 2 1.45 0.31 M7 2 1.45 0.31 M8 3 2.95 0.66 M9 2 1.54 0.35 M10 2 1.99 0.50 M11 3 2.81 0.64 M12 2 1.23 0.19 M13 3 1.83 0.45 M14 3 2.81 0.64 M15 2 1.23 0.19 M16 3 1.83 0.45 M17 3 2.91 0.66 M18 2 1.23 0.19 M19 3 2.80 0.64 M20 3 1.83 0.45 M21 3 1.72 0.42 M22 3 1.72 0.42 M23 3 2.81 0.64 M24 3 1.48 0.33 M25 3 2.02 0.51 M26 3 2.91 0.66 M27 3 1.72 0.42 M28 3 2.02 0.51 M29 3 2.91 0.66 M30 3 1.72 0.42

Mean 2.7333 2.07 0.47

Table 3.3 Genetic variation among the heat-stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h).

Mean 2.7333 2.07 0.47

Table 3.4 Genetic variation among the submergence-stress subpopulation indicated through observed alleles number of alleles (na), effective allele number (ne), and Nei’s genetic diversity (h).

Marker na ne h M1 3 1.48 0.33 M2 3 2.79 0.64 M3 3 1.84 0.46 M4 3 2.75 0.64 M5 2 1.87 0.47 M6 2 1.34 0.26 M7 2 1.34 0.26 M8 3 2.86 0.65 M9 2 1.34 0.26 M10 2 1.99 0.50 M11 3 2.72 0.63 M12 2 1.12 0.10 M13 3 1.84 0.46 M14 3 2.72 0.63 M15 2 1.12 0.10 M16 3 1.84 0.46 M17 3 2.75 0.64 M18 2 1.12 0.10 M19 3 2.64 0.62 M20 3 1.84 0.46 M21 3 1.24 0.19 M22 3 1.36 0.27 M23 3 2.72 0.63 M24 3 1.47 0.32 M25 3 2.04 0.51 M26 3 2.75 0.64 M27 3 1.36 0.27 M28 3 2.04 0.51 M29 3 2.75 0.64 M30 3 1.36 0.27

Mean 2.73 1.95 0.43

Figure 3.1 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to cold stress treatment.

Figure 3.2 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to heat- stress treatment.

Figure 3.3 Neighbor-joining tree obtained from Nei’s genetic distance calculated using 30 SSR markers representing the relationship among accessions with respect to submergence-stress treatment.

Figure 3.4 DISTRUCT output of allele frequencies based on inferred clusters with respect to cold stress treatment.

Figure 3.5 DISTRUCT output of allele frequencies based on inferred clusters with respect to heat-stress treatment.

Figure 3.6 DISTRUCT output of allele frequencies based on inferred clusters with respect to submergence-stress treatment.

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CHAPTER IV

UTILIZATION OF GENOME-WIDE ASSOCIATION STUDIES (GWAS) TO DISCOVER

SNPs RELATED TO CANDIDATE GENES FOR RICE BREEDING IMPROVEMENT

4.1 Abstract

Rice (Oryza sativa L.) continues to be one of the most important crops globally. While there are numerous constraints to rice production, the most impactful is climate change. As breeding programs aim to increase tolerance to cold and heat, a fast method of identification of genes controlling tolerance is necessary. Past research has focused on the use of simple sequence repeat (SSR) markers to identify regions in the genome responsible for stress tolerance traits.

Still, the evolution of Next-Genome Sequencing (NGS) and the development of reference genomes has made genome-wide association studies (GWAS) a more favorable method. GWAS has the ability to identify loci controlling stressors of interest using a diverse rice panel. In this study, GWAS was used to identify single nucleotide polymorphisms (SNPs) associated with cold and heat tolerance in a diverse population of cultivated rice, rice-breeding lines, and weedy rice accessions. Approximately 199,441 SNPs were identified in the complete dataset of 69 weedy rice accessions, rice-breeding lines, and cultivated rice lines. Of these SNPs, 8, 23, and 15,143 significant SNPs were identified in the cold and heat subpopulations. These SNPs could be key to the discovery of candidate genes for rice breeding improvement.

4.2 Introduction

Rice is a food staple responsible for feeding more than half of the world population while also serving as a model plant organism for cereal crops (Terada et al., 2002). The need to improve rice-breeding programs was a highlight in the 2004 meeting of the United Nations, where rice was recognized as an important commodity for its role in providing food security and eliminating poverty (Gnanamanickam, 2009). Currently, the U.S. grows approximately 1.3 million ha of rice and exports more than 40% of the rice produced (Childs and Burdett, 2000).

Unfortunately, rice-growing areas are negatively impacted by a problematic weed that is genetically similar called weedy rice (Oryza sativa ssp.). Weedy rice is a common weed in rice- growing areas such as Asia, North and South America, and Africa (Shivrain et al., 2007). Gealy et al. (2003) have noted that red rice, also weedy rice, is a dominant weed in temperate rice production due to its competitive nature and red pericarp decreasing marketability. Additionally, weedy rice is the same genus and species as rice and is naturally compatible with rice in the field

(Gealy et al., 2003).

In southern states, weedy rice (WR) is characterized as being taller than rice cultivars with either straw-colored hulls without awns or black-colored hulls with awns present (Gealy et al., 2012). There are negative traits associated with the weed, such as the potential for weed-crop outcrossing, increased seed shattering, and red color seed pericarp (Gealy et al., 2003). In addition to these negative traits, WR has a competitive advantage within the field due to its increased height over shorter rice cultivars (Gealy et al., 2012). WR can cause up to 80% yield loss and reduce grain quality while being difficult to identify within rice fields due to phenotypic similarities (Tseng, 2013; Shivrain et al., 2010).

The transition from transplanted rice to direct-seeded rice (DSR) production systems has led to WR becoming one of the most difficult weeds to control due to its ability to emerge flooded environments (Ziska et al., 2015). WR has increased weediness with respect to earlier and longer flowering time, tall stature, and increased tillers that highlight rapid growth and high biomass production, and increased seed dormancy with increased ability to survive within the seed bank (Kanapeckas et al., 2017). These variable traits have made it difficult to pinpoint the specific evolution of WR and studies suggest that the evolution of WR can vary across different sites and regions (Huan et al., 2017). WR seems to have originated from the domestication of

Asian rice (O. sativa) that was then de-domesticated, reverting to weedy traits (Kanapeckas et al., 2016). Additionally, hybridization between cultivated rice and weedy rice has led to an increase in genetic diversity between weedy biotypes (Shivrain et al., 2009).

This increase in genetic diversity makes WR a strong resource for untapped traits related to abiotic stress tolerance (Londo and Schaal, 2007; Gealy et al., 2002). Using SSR markers,

Shivrain et al. (2010) found three markers with unique alleles specific to awned brown hull colored WR and awnless straw hull colored rice. This has further been demonstrated in a large- scale genotyping project using SSR markers that clearly demonstrate the differentiation between weedy rice and cultivated (Gealy et al., 2006). In another study to assess amylose content in red rice compared to commercial rice, a rice marker, RM190, had a distinct allele size associated with red rice, demonstrating a higher amylose content (Gealy and Bryant, 2008).

In recent studies, genome-wide association studies (GWAS) have been used to identify candidate trait loci associated with chilling stress (Moghimi et al., 2019), rice architecture (Yano et al., 2019), and salt tolerance (Yuan et al., 2020). GWAS can be used to make associations between phenotype and genotype in diverse populations (Mai et al., 2021). Six associated trait

loci were found for grain length and width in a GWAS study of 270 rice germplasms (Ma et al.,

2019). Mai et al. (2020) found 36 significant SNP markers using GWAS that led to the identification of 158 candidate genes associated with 21 QTLs. Wang et al. (2020) have used

GWAS on a weedy rice population to uncover13 associations tied to red pericarp color that significantly distinguished the weed from cultivated rice.

Given the recent identification of candidate genes using weedy rice, this study aims to identify SNP markers associated with cold, heat and complete submergence in a weedy rice mini-germplasm.

4.3 Materials and Methods

4.3.1 Plant Materials

Fifty-four weedy rice (WR) accessions from the Crop, Soils and Environmental Sciences

Department at the University of Arkansas (Fayetteville, AR) were selected from a larger core collection of WR collected in 2008 and 2009 (Tseng, 2013). These WR accessions were selected based on increased height, early flowering time, and maturity. In addition to the weedy rice, eight rice-breeding lines from the Delta Research and Extension Center (Stoneville, MS) and seven rice cultivars were also included in the study.

4.3.2 Next Generation Sequencing and Processing

Fresh tissues were harvested from 21-day old seedlings propagated in 72 well trays

(Greenhouse Megastore, Danville, IL), filled with a potting soil mix (SunGro Professional

Growing Mix; 3.8 cu. ft.), and maintained under optimal conditions (28⁰C/24⁰C, 16h/8h day/light, 55% humidity) in a growth chamber (Percival Scientific, Perry, IA). The tissues were shipped on ice to BGI Genomics (Cambridge, MA) for next-generation whole-genome re-

sequencing and processing using their proprietary protocols. Raw reads were submitted to the

Institute for Genomics, Biocomputing, and Biotechnology at Mississippi State University

(Mississippi State, MS) for processing.

Raw data were processed using TASSEL GBS as outlined by Glaubitz et al. (2014). SNP calls were made based on the Nipponbare reference sequence (MSU7). As outlined by Liu et al.

(2015), SNPs with greater than 10% missing data, individuals with greater than 95% missing data, non-polymorphic SNPs with missing data, and SNPs with more than two alleles were removed. This filtering resulted in 199,441 SNPs.

4.3.3 Genome-Wide Association Studies and SNP Identification

GWAS was executed using TASSEL 5 and software package LinkImpute based on the k- nearest neighbor genotype imputation method, LD KNNi, with default options as outlined by

Money et al. (2015). A weighted mixed linear model (MLM) analysis using principal component analysis (PCA) and a kinship matrix was used to estimate the association of SNPs and two stress- tolerant traits (Table 4.1 and Table 4.2). The PCA analysis was carried out with three components to explain the population structure. Significant SNPs were identified using the False

Discovery Rate (FDR) where FDR = 1/total number of SNPs for each stress (Table 4.1and Table

4.2) (Wang et al., 2016). Manhattan plots were developed to visualize significant SNP markers for cold and stress graphically.

4.4 Results and Discussion

Using GWAS under control of a weight mixed linear model, a weedy rice mini- germplasm was analyzed for SNPs associated with cold, heat, and complete submergence. The total number of SNPs identified for each stress is presented in Table 4.1. To identify significant

SNPs, the false discovery rate (Wang et al., 2016) was calculated, and true SNPs were then filtered. The number of significant SNP markers identified was 8, 23, and 15,143 for cold, heat, and complete submergence, respectfully. For the cold-stress analysis, SNPs were located on chromosomes 1, 2, 4, 5, 6, and 7 (Figure 4.1). For the heat-stress analysis, SNPs were located on chromosomes 3, 4, 5, 6, 8, 9, 10, 11, and 12 (Figure 4.2).

In genome-wide association mapping of cold stress in rice, 22 QTLs were associated with cold stress at the seedling stage (Shakiba et al., 2017). While this is more than the eight SNPs identified in this study, the SNP markers are significant because this association study was carried out in a majority weedy rice population. The identified SNPs may be used to identify cold-tolerant or sensitive weedy rice accessions for marker-assisted breeding. Ham et al. (2021) recently identified four QTLs on chromosomes 1, 4, and 5 with similar overlapping to the location of SNPs in the cold stress analysis when assessing the basis of cold tolerance at the seedling stage. In a study to identify candidate genes for rice seedling response to high- temperature stress, two candidate gene loci were identified (Wei et al., 2020). SNPs were found to be associated with shoot length, fresh biomass, and dry biomass after exposure to high- temperature stress suggesting that there are genes controlling response to high temperatures at the seedling stage. These traits were found on chromosome 8, similar to this study, suggesting that there are candidate genes related to high-temperature stress. In a heat-sensitive response

GWAS study, Lei et al. (2013) identified five QTLs on chromosomes 1, 2, 3, and 8 that were highly associated with a heat-sensitive rice cultivar derived from wild rice. In the current study, the population was not separated into subpopulations, and SNPs are located on chromosomes 3 and 8 as well, suggesting that there may be some genes controlling sensitivity to heat.

4.5 Conclusion

GWAS has been used in recent studies to identify candidate genes for abiotic stress tolerance. This study identified 8 SNPs associated with cold stress and 23 SNPs associated with heat stress. These regions will need to be further analyzed and screened in larger populations for gene validation using. These genes could be key in identifying new candidate genes for abiotic stress tolerance in cultivated rice. Moreover, the identification of these genes in weedy rice could increase the genetic diversity present within current rice breeding programs.

Table 4.1 Significant markers SNP markers with False Discovery Rate (FDR) with respect to cold stress treatment.

Chromosome Marker Position p-value Number

S1_19950672 1 19950672 0 S1_19950708 1 19950708 0 S2_12019125 2 12019125 2.6928E-08 S4_8052301 4 8052301 1.0492E-09 S5_13453843 5 13453843 5.3718E-11 S6_25264 6 25264 2.0056E-07 S7_25200054 7 25200054 7.2819E-14 S7_25200065 7 25200065 7.2819E-14

FDR 6.61E-06 Note: the nomenclature of SNPs includes the information of chromosome (“s” followed by chromosome number) and physical positioning (following chromosome number).

Table 4.2 Significant markers SNP markers with False Discovery Rate (FDR) with respect to heat-stress treatment. Chromosome Marker Position p-value Number S3_34816946 3 34816946 0 S3_24834172 3 24834172 3.94E-08 S3_24834196 3 24834196 3.94E-08 S4_12009688 4 12009688 3.94E-08 S4_12009720 4 12009720 3.94E-08 S5_29789218 5 29789218 0 S6_12998481 6 12998481 3.52E-17 S8_18417732 8 18417732 2.15E-07 S9_5142260 9 5142260 2.59E-09 S9_5142266 9 5142266 2.59E-09 S9_5142294 9 5142294 2.59E-09 S9_1951343 9 1951343 3.94E-08 S10_8074568 10 8074568 4.63E-24 S10_15864855 10 15864855 3.94E-08 S10_15864878 10 15864878 3.94E-08 S11_4943238 11 4943238 0 S11_8314750 11 8314750 0 S11_15573614 11 15573614 0 S11_27983677 11 27983677 4.63E-24 S11_15928906 11 15928906 2.59E-09 S12_13793320 12 13793320 0 S12_13793330 12 13793330 0 S12_4723017 12 4723017 7.59E-07

FDR 6.61E-06 Note: the nomenclature of SNPs includes the information of chromosome (“s” followed by chromosome number) and physical positioning (following chromosome number).

Figure 4.1 Genome-wide Manhattan plot of SNPs for cold stress.

Figure 4.2 Genome-wide Manhattan plot of t SNPs for heat-stress.

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APPENDIX A

SUPPLEMENTARY TABLES

A.1 Supplementary Materials

Table A.1 Agronomic traits and morphological characteristics of the 54 weedy rice accessions.

1 HT, STALR-1 Intermediate 103.3 118.8 33.5 88.3 24.8 41.8 110.6 16.2 2 CTALR-4 Straw 140.3 63.0 35.2 102.5 26.5 0.0 150.8 15.6 3 B2 Black 102.8 130.0 35.7 123.3 24.2 24.0 129.9 14.7 4 B3 Black 111.3 48.8 40.6 104.8 29.7 0.0 87.4 14.4 5 CT, STB5 Black 118.0 40.0 39.0 126.0 23.0 0.0 93.6 16.6 6 STS5 Straw 140.0 63.0 35.0 99.0 27.2 0.0 65.7 17.0 7 STS6 Straw 146.0 80.8 40.1 105.5 27.2 17.3 63.9 15.6 8 CT, STB8 Black 122.0 97.2 34.4 111.0 25.3 33.6 157.7 15.5 9 CT, HT, STS9 Straw 128.7 74.7 45.3 111.0 27.6 0.0 144.9 19.7 10 S11 Straw 142.2 66.0 34.4 110.5 26.0 0.0 149.4 15.5 11 B14 Black 116.0 40.0 35.1 109.0 26.0 0.0 50.9 15.6 12 S14 Straw 91.5 67.5 29.1 97.3 25.7 0.0 149.4 18.6 13 B15 Black 106.0 64.0 37.8 105.5 25.2 10.3 50.9 17.1 14 B18 Black 121.2 57.0 47.4 109.5 27.5 14.9 50.9 21.1 15 CTS18 Straw 123.7 58.2 40.9 96.5 27.5 0.0 149.4 18.1 16 B20 Black 100.0 111.0 33.6 122.6 24.8 33.5 129.9 14.2

Table A.1 (Continued)

Serial Accession Hull color Culm Culm Leaf Flowering Panicle Awn Grain Thousand Number Name length number length (days after length length yield Kernel (cm) (cm) planting) (cm) (cm) (g/plant) Weight (g) 17 B21 Black 93.8 120.0 33.9 125.8 25.1 17.8 129.9 12.0 18 CT, HT, STS21 Straw 125.4 73.0 39.0 94.8 24.1 0.0 153.6 17.6 19 HTS29 Straw 118.0 64.2 41.2 96.2 25.9 0.0 153.6 18.9 20 HT, STB30 Black 143.0 81.2 34.5 105.8 24.0 0.0 87.4 18.2 21 B32 Black 122.5 53.3 31.0 105.3 23.5 0.0 87.4 19.6 22 S33 Straw 127.0 57.5 35.7 103.0 25.7 0.0 126.4 17.5 23 CT, STB34 Black 100.4 105.0 35.6 119.6 25.0 0.0 87.4 13.8 24 HT, STB37 Black 142.0 101.3 35.4 108.0 22.7 0.0 87.4 18.1 25 HT, STB38 Black 141.6 100.4 34.1 108.8 24.5 0.0 87.4 19.1 26 CT, STS42 Straw 121.8 62.5 36.0 90.3 24.3 0.0 100.1 20.5 27 B43 Black 120.0 82.3 44.7 94.7 24.4 0.0 71.0 14.3 28 B44 Black 117.0 86.0 43.1 100.6 23.7 0.0 71.0 14.4 29 STB45 Black 127.8 97.5 42.8 95.8 25.7 0.0 71.0 16.0 30 S46 Straw 121.3 76.3 41.1 107.7 26.0 0.0 100.1 17.9 31 STB49 Black 133.8 105.0 32.4 111.3 22.2 0.0 71.0 19.3 32 STB51 Black 135.4 91.0 28.8 109.4 22.8 0.0 93.6 19.7 33 CTS59 Straw 116.3 74.0 42.2 108.0 26.9 10.8 65.7 20.1 34 CTB60 Intermediate 102.0 106.3 33.8 98.3 20.6 21.4 65.6 15.3 35 S65 Straw 135.0 71.0 32.7 86.3 25.6 13.9 63.9 16.4 36 B75 Black 139.4 77.6 40.4 107.2 26.9 20.1 137.1 14.9 37 B80 Black 123.8 58.8 32.1 105.0 21.7 4.0 157.7 14.7 38 HTB81 Black 137.2 85.0 32.3 105.0 22.9 23.6 157.7 14.6

Table A.1 (Continued)

Serial Accession Hull color Culm Culm Leaf Flowering Panicle Awn Grain Thousand Number Name length number length (days after length length yield Kernel (cm) (cm) planting) (cm) (cm) (g/plant) Weight (g) 39 HTB83 Intermediate 133.0 60.0 39.9 94.7 23.4 0.0 157.7 16.3 40 B84 Intermediate 118.8 68.8 32.7 102.8 22.1 9.0 157.7 15.7 41 HTS84 Straw 129.0 79.3 46.9 91.3 26.3 0.0 117.0 17.3 42 B85 Black 123.8 43.3 33.6 102.3 23.3 0.0 157.7 15.7 43 CTB86 Intermediate 113.3 71.7 43.8 96.0 28.5 10.9 157.7 16.9 44 B87 Black 112.3 52.0 42.8 109.0 27.2 9.8 157.7 16.3 45 B88 Black 111.4 69.0 37.5 111.3 26.3 15.0 157.7 14.8 46 CTS94 Straw 130.2 77.2 35.5 105.6 28.0 0.0 144.9 19.9 47 CTS97 Straw 111.0 98.0 32.4 106.0 21.2 0.0 144.9 17.1 48 S105 Straw 117.8 60.8 41.1 93.0 25.6 0.0 149.4 19.9 49 HTS108 Straw 121.3 56.7 43.5 94.0 25.1 0.0 149.4 17.9 50 HTS109 Straw 128.8 86.8 41.8 93.8 26.4 0.0 149.4 17.6 51 HTS110 Straw 147.8 101.3 39.3 101.5 26.8 0.0 149.4 15.6 52 CTS113 Straw 124.0 79.2 42.6 99.0 25.7 0.0 149.4 15.3 53 S118 Straw 106.3 102.5 31.6 86.5 20.5 0.0 149.4 15.6 54 CT, HTS124 Straw 109.0 59.6 38.7 88.4 25.4 0.0 149.4 17.5 CT Cold treatment selected accessions HT Heat treatment selected accessions. ST Submergence treatment selected accessions.

Table A.2 List of rice cultivars and rice breeding lines with supplemental information.

Serial Number Accession Code Accession Name aTrait

57 CT6510-24-1-2 RBL-57 Cold

58 CT6946-9-1-2-M-1P RBL-58 Heat

60 IR49830-7-1-2-2 RBL-60 Submergence

61 IR 6 PAKISTAN RBL-61 Heat

64 IR88633:1-66-B-1-B RBL-64 Submergence

66 PALMAR 18 RBL-66 Cold

67 LOCAL CRL-Thad None

68 LOCAL CRL-Rex None

69 LOCAL CRL-CL163 None

73 PI408449 CRL-PM None aLines 57, 58, 60, 61, 64, and 66 are rice breeding lines from the International Rice Research Institute (IRRI) and are developmental lines with selected traits used solely for testing purposes. Those with none have not been selected based on a specific trait.

100

Table A.3 The correlation coefficient of means separated by Fisher’s LSD for cold stress treated accessions.

101

Table A.3 (Continued)

A) Cold stress treated accessions height reduction 0 days after treatment (DAT), B) Cold stress treated accessions height reduction 7 days after treatment (DAT), and C) Cold stress treated accessions height reduction 28 days after treatment (DAT). An * is significant at P = 0.05; values without an asterisk are not significant.

102

Table A.4 The correlation coefficient of means separated by Fisher’s LSD for heat-stress treated accessions.

B) Accession CRL-PM S9 S84 S29 RBL-58 B37 S21 ALR-1 B30 B38 S109 B83 CRL-CL163 S124 RBL-61 B81 CRL-Thad S110 CRL-Rex S108 CRL-PM - -11.63 -11.14 -2.63 -2.73 -0.37 -0.04 0.48* 1.03* 1.65* 4.98* 6.62* 8.94* 10.83* 9.35* 12.01* 11.81* 12.37* 12.19* 18.33* S9 - -15.18 -6.56 -6.68 -4.27 -3.95 -3.45 -2.87 -2.26 1.07* 2.71* 5.02* 6.90* 5.31* 8.11* 7.89* 8.46* 8.24* 14.38* S84 - -9.18 -9.27 -6.91 -6.59 -6.07 -5.51 -4.90 -1.57 0.07* 2.40* 4.28* 2.81* 5.46* 5.27* 5.82* 5.64* 11.78* S29 - -14.25 -11.81 -11.49 -11.00 -10.41 -9.80 -6.47 -4.83 -2.54 -0.65 -2.33 0.57* 0.33* 0.92* 0.67* 6.81* RBL-58 - -12.38 -12.05 -11.56 -10.98 -10.36 -7.04 -5.39 -3.10 -1.21 -2.85 0.00 -0.23 0.36* 0.11* 6.26* B37 - -13.19 -12.71 -12.11 -11.50 -8.17 -6.53 -4.24 -2.36 -4.07 -1.13 -1.37 -0.78 -1.05 5.10* S21 - -13.04 -12.44 -11.82 -8.49 -6.85 -4.57 -2.68 -4.39 -1.46 -1.70 -1.10 -1.37 4.77* ALR-1 - -13.52 -12.90 -9.58 -7.93 -5.64 -3.76 -5.44 -2.54 -2.78 -2.18 -2.44 3.70* B30 - -12.90 -9.57 -7.93 -5.64 -3.76 -5.47 -2.53 -2.77 -2.18 -2.45 3.70* B38 - -10.18 -8.54 -6.26 -4.37 -6.08 -3.15 -3.39 -2.79 -3.06 3.08* S109 - -11.87 -9.59 -7.70 -9.41 -6.48 -6.72 -6.12 -6.39 -0.25 B83 - -11.23 -9.34 -11.05 -8.12 -8.36 -7.76 -8.03 -1.89 CRL-CL163 - -12.23 -13.90 -11.01 -11.24 -10.65 -10.91 -4.76 S124 - -15.79 -12.90 -13.13 -12.54 -12.80 -6.65 RBL-61 - -15.03 -15.23 -14.68 -14.86 -8.71 B81 - -13.75 -13.15 -13.42 -7.28 CRL-Thad - -13.52 -13.78 -7.63 S110 - -13.78 -7.64 CRL-Rex - -8.66 S108 -

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Table A.4 (Continued)

A) Heat-stress treated accessions height reduction 14 days after treatment (DAT), B) Heat-stress treated accessions height reduction 28 days after treatment (DAT), and C) Heat-stress treated accessions mean biomass reduction 28 days after treatment (DAT). An * is significant at P = 0.05; values without an asterisk are not significant.

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Table A.5 The correlation coefficient of means separated by Fisher’s LSD for submergence-stress treated accessions.

A) Row S5 RBL-60 CRL-PM B8 B34 CRL-Rex B5 ALR-1 S6 B37 CRL-CL163 RBL-64 S9 B30 S42 S21 B38 B45 B49 B51 S5 - -37.61 -42.05 -33.83 -26.50 -25.38 -15.39 -9.53 -9.60 -4.28 -8.72 6.83* 17.94* 17.94* 17.94* 29.06* 29.06* 29.06* 29.06* 29.06* RBL-60 - -42.05 -33.83 -26.50 -25.38 -15.39 -9.53 -9.60 -4.28 -8.72 6.83* 17.94* 17.94* 17.94* 29.06* 29.06* 29.06* 29.06* 29.06* CRL-PM - -38.27 -30.94 -29.40 -19.83 -13.97 -13.92 -8.72 -12.73 2.39* 13.5* 13.50* 13.50* 24.62* 24.62* 24.62* 24.62* 24.62* B8 - -30.28 -29.17 -19.17 -13.31 -13.39 -8.06 -12.50 3.05* 14.16* 14.16* 14.16* 25.27* 25.27* 25.27* 25.27* 25.27* B34 - -36.50 -26.50 -20.64 -20.71 -15.39 -19.83 -4.28 6.83* 6.83* 6.83* 17.94* 17.94* 17.94* 17.94* 17.94* CRL-Rex - -36.50 -30.63 -30.59 -25.38 -29.40 -14.27 -3.16 -3.16 -3.16 7.95* 7.95* 7.95* 7.95* 7.95* B5 - -31.75 -31.82 -26.50 -30.94 -15.39 -4.28 -4.28 -4.28 6.83* 6.83* 6.83* 6.83* 6.83* ALR-1 - -37.69 -32.36 -36.80 -21.25 -10.14 -10.14 -10.14 0.97* 0.97* 0.97* 0.97* 0.97* S6 - -34.60 -38.92 -23.49 -12.38 -12.38 -12.38 -1.27 -1.27 -1.27 -1.27 -1.27 B37 - -42.05 -26.50 -15.39 -15.39 -15.39 -4.28 -4.28 -4.28 -4.28 -4.28 CRL-CL163 - -30.94 -19.83 -19.83 -19.83 -8.72 -8.72 -8.72 -8.72 -8.72 RBL-64 - -26.50 -26.50 -26.50 -15.39 -15.39 -15.39 -15.39 -15.39 S9 - -37.61 -37.61 -26.50 -26.50 -26.50 -26.50 -26.50 B30 - -37.61 -26.50 -26.50 -26.50 -26.50 -26.50 S42 - -26.50 -26.50 -26.50 -26.50 -26.50 S21 - -37.61 -37.61 -37.61 -37.61 B38 - -37.61 -37.61 -37.61 B45 - -37.61 -37.61 B49 - -37.61 B51 -

B) Accession RBL-60 S5 RBL-Rex B8 B5 B34 ALR-1 RBL-64 B37 S6 CRL-PM CRL-CL163 S9 S42 B30 B45 B49 B38 S21 B51 RBL-60 - -8.48 -12.66 -6.15 2.44* 2.63* 5.70* 9.05* 14.57* 18.76* 19.57* 20.68* 26.87* 28.07* 39.92* 46.29* 48.02* 50.09* 50.29* 51.77* S5 - -39.57 -33.06 -24.48 -24.28 -21.21 -17.86 -12.35 -8.16 -7.34 -6.24 -0.04 1.15* 13.00* 19.37* 21.10* 23.18* 23.37* 24.86* RBL-Rex - -37.24 -28.66 -28.46 -25.39 -22.04 -16.52 -12.33 -11.12 -10.01 -4.22 -3.03 8.83* 15.20* 16.93* 19.00* 19.20* 20.68* B8 - -26.81 -26.61 -23.54 -20.19 -14.68 -10.49 -9.67 -8.57 -2.37 -1.18 10.67* 17.04* 18.77* 20.85* 21.04* 22.53* B5 - -35.19 -32.13 -28.77 -23.26 -19.07 -18.26 -17.15 -10.96 -9.76 2.09* 8.46* 10.19* 12.26* 12.46* 13.94* B34 - -32.32 -28.97 -23.46 -19.27 -18.45 -17.35 -11.15 -9.96 1.89* 8.26* 9.99* 12.07* 12.26* 13.74* ALR-1 - -32.04 -26.52 -22.34 -21.52 -20.41 -14.22 -13.03 -1.17 5.20* 6.93* 9.00* 9.20* 10.68* RBL-64 - -29.88 -25.69 -24.87 -23.77 -17.57 -16.38 -4.53 1.84* 3.57* 5.65* 5.84* 7.32* B37 - -31.20 -30.39 -29.28 -23.09 -21.89 -10.04 -3.67 -1.94 0.13* 0.33* 1.81* S6 - -34.58 -33.47 -27.28 -26.08 -14.23 -7.86 -6.13 -4.06 -3.86 -2.38 CRL-PM - -42.24 -36.45 -35.25 -23.40 -17.03 -15.30 -13.23 -13.03 -11.55 CRL-CL163 - -37.55 -36.36 -24.51 -18.14 -16.41 -14.33 -14.14 -12.66 S9 - -34.20 -22.35 -15.98 -14.25 -12.17 -11.98 -10.49 S42 - -23.54 -17.17 -15.44 -13.37 -13.17 -11.69 B30 - -29.02 -27.29 -25.22 -25.02 -23.54 B45 - -33.66 -31.59 -31.39 -29.91 B49 - -33.32 -33.12 -31.64 B38 - -35.19 -33.71 S21 - -33.91 B51 -

105

Table A.5 (Continued)

A) Complete submergence-stress treated accessions height reduction 14 days after treatment (DAT), B) Complete submergence-stress treated accessions height reduction 28 days after treatment (DAT), and C) Complete submergence-stress treated accessions mean biomass reduction 28 days after treatment (DAT). An * is significant at P = 0.05; values without an asterisk are not significant.

106