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Rice yield potential has been stagnant since the Green Revolution in the late 1960s, especially in tropical rice cultivars. We evaluated the effect of two major genes that regulate grain number, Gn1a/OsCKX2 and IPA1/WFP/OsSPL14, in elite indica cultivar backgrounds. The yield-positive Gn1a-type 3 and OsSPL14WFP alleles were introgressed respectively through marker-assisted selection (MAS). The grain numbers per panicle (GNPP) were compared between the recipient allele and the donor allele groups using segregating plants in BC3F2 and BC3F3 generations. There was no significant difference in GNPP between the two Gn1a alleles, suggesting that the Gn1a-type 3 allele was not effective in indica cultivars. However, the OsSPL14WFP allele dramatically increased GNPP by 10.6–59.3% in all four different backgrounds across cropping seasons and generations, indicating that this allele provides strong genetic gain to elite indica cultivars. Eventually, five high-yielding breeding lines were bred using the OsSPL14WFP allele by MAS with a conventional breeding approach that showed increased grain yield by 28.4–83.5% (7.87–12.89 t/ha) vis-à-vis the recipient cultivars and exhibited higher yield (~64.7%) than the top-yielding check cultivar, IRRI 156 (7.82 t/ha). We demonstrated a strong possibility to increase the genetic yield potential of indica rice varieties through allele mining and its application.

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.

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., Author Summary Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.