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

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.