Data from: Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines
Spindel, Jennifer, Cornell University
Begum, Hasina, International Rice Research Institute
Akdemir, Deniz, Cornell University
Virk, Parminder, Centro Internacional de Agricultura Tropical
Collard, Bertrand, International Rice Research Institute
Redoña, Edilberto, International Rice Research Institute
Jannink, Jean-Luc, Cornell University, Agricultural Research Service
McCouch, Susan R., Cornell University
Published Jan 30, 2016 on Dryad.
Cite this dataset
Spindel, Jennifer et al. (2016). Data from: Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines [Dataset]. Dryad. https://doi.org/10.5061/dryad.7369p
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.
Post-imputation GBS data for the IRRI breeding population used in Spindel et al., 2015 and Begum and Spindel et al., 2015 (with additional filtering, see papers for details). GBS dataset contains all SNPs with call rates >= .75 and all individuals with missing data < .6
Post-imputation GBS dataset used specifically for GS cross-validation in Spindel et al., 2015. Dataset contains all markers with call rates >= .9 and lines that were included in the GS analysis (i.e., sub-population outliers are removed from this dataset). The data are formatted for use with the R rrBLUP package.
contains the evenly distributed genotype subsets used in Spindel et al., 2015
contains the randomly distributed genotype subsets used in Spindel et al., 2015
contains the raw phenotype data for genotyped individuals for 2009-2012, dry and wet seasons, used in Spindel et al., 2015.