DOI: 10.5061/dryad.7369p
title: 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

description: 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.
description: MET_GBS_data_crfilt_.75_allchrom.hmp.txtPost-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 &gt;= .75 and all individuals with missing data &lt; .6MET_crfilt_.75_allchrom.hmp.txt.zipMET_crfilt_.90_outliers_removed_for_RRBlupPost-imputation GBS dataset used specifically for GS cross-validation in Spindel et al., 2015. Dataset contains all markers with call rates &gt;= .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.binned_subsets_2_14contains the evenly distributed genotype subsets used in Spindel et al., 2015random_subsetscontains the randomly distributed genotype subsets used in Spindel et al., 2015RYT_plotdata_by_GHID_corrected_PUBLIC_ACCESScontains the raw phenotype data for genotyped individuals for 2009-2012, dry and wet seasons, used in Spindel et al., 2015.