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

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 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.

Genomic and transcriptomic approaches toward plant selection

“…In rice, genomic selection has been applied successfully to breeding programs in an efficient pattern by using GWAS, QTLs and GBS method. This study has revealed that interpretation of GWAS in genetic architecture and population structure is a useful tool to enhance rice breeding through genomic selection (Spindel et al, 2015). In potato Slater et al (2016) reported that for four key traits with varying heritability, genomic selection is more accurate using genome-wide SNPs and can improve genetic gain (Slater et al, 2016).…”

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F₂ populations by using genomic selection models

“…However, several studies have shown that a small number of markers can be used for high-accuracy genomic selection for many traits (Bhering et al, 2015;Spindel et al, 2015). In the future, it is necessary to test the optimal number of markers and individuals, using a large number of markers.…”

“…The objective of genomic selection is to identify possible markers in linkage disequilibrium with the gene regions of interest. Since this pioneering study, several authors have used this technique to predict the genetic value in several plant species, such as corn (Beyene et al, 2015), soybeans (Zhang et al, 2016), wheat (Bassi et al, 2016), forest species (Cros et al, 2015), sugarcane (Gouy et al, 2013), and rice (Spindel et al, 2015).…”

Breaking through the feed barrier: options for improving forage genetics

“…The GS theoretical framework developed over a decade ago (Meuwissen et al 2001) is now enabled by efficiency gains in DNA marker (Davey et al 2011;Elshire et al 2011;Poland and Rife 2012) and, more recently, plant phenotypic (White and Conley 2013) data generation, management and analysis; and offers proven value in economic plant species (Massman et al 2013;Spindel et al 2015).…”