Genomic selection & association mapping in rice: effect of trait genetic architecture, training population composition, marker number & statistical model on accuracy of rice genomic selection in elite, tropical rice breeding

Authors: SPINDEL, JENNIFER - Cornell University; BEGUM, HASINA - International Rice Research Institute; AKDEMIR, DENIZ - Cornell University; VIRK, PARMINDER - International Center For Tropical Agriculture (CIAT); COLLARD, BERTRAND - International Rice Research Institute; REDONA, EDILBERTO - International Rice Research Institute; ATLIN, GARY - Gates Foundation; Jannink, Jean-Luc; MCCOUCH, SUSAN - Cornell University

Submitted to: PLoS Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2015
Publication Date: 2/17/2015
Publication URL: http://DOI: 10.1371/journal.pgen.1004982
Citation: Spindel, J., Begum, H., Akdemir, D., Virk, P., Collard, B., Redona, E., Atlin, G., Jannink, J., Mccouch, S.R. 2015. Genomic selection & association mapping in rice: effect of trait genetic architecture, training population composition, marker number & statistical model on accuracy of rice genomic selection in elite, tropical rice breeding. PLoS Genetics. 11(6):e1005350.

Interpretive Summary: 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 GS on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program. 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 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 suggested that using one marker every 0.2 cM was sufficient for genomic selection in this population. The best statistical method to use depended on whether GWAS analyses indicated one or few large effect loci existed or not. Our results suggest that GS, guided by analyses of genetic architecture from GWAS, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Technical Abstract: 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 midsized 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.