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Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

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Low temperature or cold stress is one of the major constraints of rice production and productivity in temperate rice-growing countries and high-altitude areas in the tropics. Even though low temperature affects the rice plant in all stages of growth, the percent seed set is damaged severely by cold and this reduces the yield potential of cultivars significantly. In this study, a new source of cold-tolerant line, IR66160-121-4-4-2, was used as a donor parent with a cold-sensitive cultivar, Geumobyeo, to produce 153 F(8) recombinant inbred lines (RILs) for quantitative trait locus (QTL) analysis. QTL analysis with 175 polymorphic simple sequence repeat (SSR) markers and composite interval mapping identified three main-effect QTLs (qPSST-3, qPSST-7, and qPSST-9) on chromosomes 3, 7, and 9. The SSR markers RM569, RM1377, and RM24545 were linked to the identified QTLs for cold tolerance with respect to percent seed set using cold-water (18-19 degrees C) irrigation in the field and controlled air temperature (17 degrees C) in the greenhouse. The total phenotypic variation for cold tolerance contributed by the three QTLs was 27.4%. RILs with high percent seed set under cold stress were validated with linked DNA markers and by haplotype analysis that revealed the contribution of progenitor genomes from the tropical japonica cultivar Jimbrug (Javanica) and temperate japonica cultivar Shen-Nung89-366. Three QTLs contributed by the cold-tolerant parent were identified which showed additive effect on percent seed set under cold treatment. This study demonstrated the utility of a new phenotyping method as well as the identification of SSR markers associated with QTLs for selection of cold-tolerant genotypes to improve temperate rice production.