Physiological and genetic dissection of rice tolerance to water-deficit stress

Rice (Oryza sativa L.) is the world's most important staple food crop, especially in Asia. As a semi-aquatic crop species, water-scarcity and increasing severity of water-deficit stress owing to climate change, are a major threat to sustaining irrigated rice production. Improving the rice adaptation to water-deficit is, therefore, a primary breeding target. The main goal of this dissertation is to study the morphological, anatomical, physiological and genetic basis for responses of a rice plant to water-deficit stress. To give leads into how water-deficit tolerant rice should behave, a comparative study were conducted, whereby representative rice genotypes was compared at the same moisture stress during the vegetative stage with genotypes of wheat, a dryland cereal wheat (Triticum aestivum L.) known to be more tolerant to water-deficit than rice. Under-water-deficit, rice genotypes (IR64 & Apo) developed thinner roots allowing rapid water-acquisition, whereas wheat followed a water-conserving strategy through developing thicker leaves and roots, and moderate tillering. Root anatomy such as root diameter, xylem and stele diameter and xylem number were more plastic in wheat than in rice under-water-deficit. The methodology and findings from those representative genotypes were then projected to a diverse panel of nearly 300 rice genotypes. Such a panel was previously constructed by the International Rice Research Institute as a potential means of discovery of novel beneficial alleles for diverse phenotypic traits and their plasticity, with 46K high-quality single nucleotide polymorphisms (SNPs). A genome-wide association study (GWAS) was undertaken to identify the genomic regions regulating the morphological, physiological and root anatomical traits in rice, based on a large-scale greenhouse phenotyping of these traits. The genetic basis of these traits was different in control and water-deficit stress (strong quantitative trait loci [QTL] × environment interaction), in line with novel loci detected for the plasticity of traits. Key a priori candidate genes near to these genetic loci were also identified. Rice grain yield is strongly affected by water-deficit stress coinciding with sensitive reproductive stage. Strong genotypic variability for grain yield as well as yield components and related traits were observed in the same rice indica diversity panel, under control and reproductive stage water-deficit stress in field conditions across two years. The GWAS analysis identified the core loci of rice genome governing the grain yield and related traits. Most of the genomic loci were specific to treatment and year, indicating strong QTL × environment interactions. To enable GWAS findings to be used for better designing of genotypes by breeding, an existing process-based crop model GECROS was used in a case study, where grain yield of the same indica diversity panel (267 rice genotypes) from the control treatment in one season was dissected into eight physiological parameters. Some parameters had a stronger effect on grain yield than other parameters. Using these parameters, the model showed the ability to predict the genotypic variation of rice diversity panel for grain yield under different field conditions. Further, the GWAS analysis was extended to model-input parameters on randomly chosen 213 genotypes as a training dataset. The SNP-based estimates of parameter values calculated from the additive allelic effect of the loci were used as input to the crop model GECROS. Although the SNP-based modelling approach demonstrated the ability to predict the genotypic variation in training datasets under different environments, the prediction accuracy was lower in the remaining 54 genotypes used as a testing dataset. In addition, the prediction accuracy of grain yield was also lower using either parameter or SNP-based GECROS model in completely new season. However, the model-based sensitivity analysis effectively identified the different SNPs between control and water-deficit environments. Virtual ideotypes designed based on pyramiding the SNPs identified by modelling had a higher yield than those based on SNPs for yield per se.</p

Genome-wide association reveals novel genomic loci controlling rice grain yield and its component traits under water-deficit stress during the reproductive stage

A diversity panel comprising of 296 indica rice genotypes was phenotyped under non-stress and water-deficit stress conditions during the reproductive stage in the 2013 and 2014 dry seasons (DSs) at IRRI, Philippines. We investigated the genotypic variability for grain yield, yield components, and related traits, and conducted genome-wide association studies (GWAS) using high-density 45K single nucleotide polymorphisms. We detected 38 loci in 2013 and 64 loci in 2014 for non-stress conditions and 69 loci in 2013 and 55 loci in 2014 for water-deficit stress. Desynchronized flowering time confounded grain yield and its components under water-deficit stress in the 2013 experiment. Statistically corrected grain yield and yield component values using days to flowering helped to detect 31 additional genetic loci for grain yield, its components, and the harvest index in 2013. There were few overlaps in the detected loci between years and treatments, and when compared with previous studies using the same panel, indicating the complexity of yield formation under stress. Nevertheless, our analyses provided important insights into the potential links between grain yield with seed set and assimilate partitioning. Our findings demonstrate the complex genetic architecture of yield formation and we propose exploring the genetic basis of less complex component traits as an alternative route for further yield enhancement

Genetic control of plasticity in root morphology and anatomy of rice in response to water deficit

Elucidating the genetic control of rooting behavior under water-deficit stress is essential to breed climate-robust rice (Oryza sativa) cultivars. Using a diverse panel of 274 indica genotypes grown under control and water-deficit conditions during vegetative growth, we phenotyped 35 traits, mostly related to root morphology and anatomy, involving 45,000 root-scanning images and nearly 25,000 cross sections from the root-shoot junction. The phenotypic plasticity of these traits was quantified as the relative change in trait value under water-deficit compared with control conditions. We then carried out a genome-wide association analysis on these traits and their plasticity, using 45,608 high-quality single-nucleotide polymorphisms. One hundred four significant loci were detected for these traits under control conditions, 106 were detected under water-deficit stress, and 76 were detected for trait plasticity. We predicted 296 (control), 284 (water-deficit stress), and 233 (plasticity) a priori candidate genes within linkage disequilibrium blocks for these loci. We identified key a priori candidate genes regulating root growth and development and relevant alleles that, upon validation, can help improve rice adaptation to water-deficit stress. (Résumé d'auteur

Association mapping and genetic dissection of drought-induced canopy temperature differences in rice

Drought-stressed plants display reduced stomatal conductance, which results in increased leaf temperature by limiting transpiration. In this study, thermal imaging was used to quantify the differences in canopy temperature under drought in a rice diversity panel consisting of 293 indica accessions. The population was grown under paddy field conditions and drought stress was imposed for 2 weeks at flowering. The canopy temperature of the accessions during stress negatively correlated with grain yield (r= –0.48) and positively with plant height (r=0.56). Temperature values were used to perform a genome-wide association (GWA) analysis using a 45K single nucleotide polynmorphism (SNP) map. A quantitative trait locus (QTL) for canopy temperature under drought was detected on chromosome 3 and fine-mapped using a high-density imputed SNP map. The candidate genes underlying the QTL point towards differences in the regulation of guard cell solute intake for stomatal opening as the possible source of temperature variation. Genetic variation for the significant markers of the QTL was present only within the tall, low-yielding landraces adapted to drought-prone environments. The absence of variation in the shorter genotypes, which showed lower leaf temperature and higher grain yield, suggests that breeding for high grain yield in rice under paddy conditions has reduced genetic variation for stomatal response under drought

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.</p

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments

Biomarkers for grain yield stability in rice under drought stress

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.</p

Data underlying the publication "Genetic Mapping of the Root Mycobiota in rice and its Role in Drought Tolerance"

Microbial and genomic data from root samples from rice plants grown under flooded and drought conditions, to unravel the genetic factors involved in the rice-microbe interaction,&nbsp;and whether genetics play a role in rice drought tolerance. Root mycobiota was characterized (18S SSU rRNA) in 296 rice accessions (Oryza sativa L. subsp. indica) under control and drought conditions. Genome wide association mapping (GWAS) resulted in the identification of ten significant single nucleotide polymorphisms (SNPs) associated with root-associated fungi.&nbsp;</p

Data underlying the publication "Genetic Mapping of the Root Mycobiota in rice and its Role in Drought Tolerance"

Microbial and genomic data from root samples from rice plants grown under flooded and drought conditions, to unravel the genetic factors involved in the rice-microbe interaction,&nbsp;and whether genetics play a role in rice drought tolerance. Root mycobiota was characterized (18S SSU rRNA) in 296 rice accessions (Oryza sativa L. subsp. indica) under control and drought conditions. Genome wide association mapping (GWAS) resulted in the identification of ten significant single nucleotide polymorphisms (SNPs) associated with root-associated fungi.&nbsp;</p