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PLoS One. 2015 Mar 18;10(3):e0119873. doi: 10.1371/journal.pone.0119873. eCollection 2015.

Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa).

Author information

International Rice Research Institute, Los Baños, Philippines.
Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States of America.
Crop Science Cluster, University of the Philippines Los Baños, Los Baños, Philippines.
International Center for Tropical Agriculture, Cali, Colombia.


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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