Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

Impact of early genomic prediction for recurrent selection in an upland rice synthetic population.

Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.

Identification of Loci Through Genome-Wide Association Studies to Improve Tolerance to Sulfur Deficiency in Rice.

Sulfur (S) is an essential nutrient for plant growth and development; however, S supply for crop production is decreasing due to reduced inputs from atmospheric deposition and reduced application of S-containing fertilizers. Sulfur deficiency in soil is therefore becoming a widespread cause of reduced grain yield and quality in rice (Oryza sativa L). We therefore assessed the genotypic variation for tolerance to S deficiency in rice and identified loci associated with improved tolerance. Plants were grown in nutrient solution with either low (0.01 mM) or high (1.0 mM) supply of S. Plants grown under low-S treatment showed a reduction in total biomass, mainly due to a marked reduction in shoot biomass, while root biomass and root-to-shoot ratio increased, relative to plants under high-S treatment. Genome-wide association studies (GWAS) identified loci associated with root length (qSUE2-3, qSUE4, and qSUE9), and root (qSUE1, qSUE2-1, and qSUE3-1 and qSUE3-2) or total dry matter (qSUE2, qSUE3-1, and qSUE11). Candidate genes identified at associated loci coded for enzymes involved in secondary S metabolic pathways (sulfotransferases), wherein the sulfated compounds play several roles in plant responses to abiotic stress; cell wall metabolism including wall loosening and modification (carbohydrate hydrolases: beta-glucosidase and beta-gluconase) important for root growth; and cell detoxification (glutathione S-transferase). This study confirmed the existence of genetic variation conferring tolerance to S deficiency among traditional aus rice varieties. The advantageous haplotypes identified could be exploited through marker assisted breeding to improve tolerance to S-deficiency in modern cultivars in order to achieve sustainable crop production and food security.