Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

Cécile Grenier,Joe Tohme,Marc Henri Châtel,Brigitte Courtois,Yolima Ospina,Constanza Quintero,Nourollah Ahmadi,Tuong-Vi Cao,Roberto Papa

doi:10.1371/journal.pone.0136594

Abstract

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

Highlights

Genomic selection (GS) arose from the combination of new high-throughput marker technologies and new statistical methods that allow the analysis of the genetic architecture of complex traits in the framework of infinitesimal model effects
GS consists of three steps: (i) inference of the genotype-phenotype relationships in a training population, (ii) prediction of genetic value based on marker genotypes or genomic estimated breeding values (GEBV) in a target population and (iii) selection of individuals based on their GEBV [1]
We evaluated the effect of different parameters on the accuracy of GEBV, in the context of rice population improvement through recurrent selection

Summary

Introduction

Genomic selection (GS) arose from the combination of new high-throughput marker technologies and new statistical methods that allow the analysis of the genetic architecture of complex traits in the framework of infinitesimal model effects. GS differs significantly from conventional marker assisted selection as it makes it possible to select individuals without prior knowledge of an association between the markers and the trait of interest. GS consists of three steps: (i) inference of the genotype-phenotype relationships in a training population, (ii) prediction of genetic value based on marker genotypes or genomic estimated breeding values (GEBV) in a target population and (iii) selection of individuals based on their GEBV [1]. GS is perfectly suited for breeding highly polygenic traits, such as yield, drought tolerance, and resource use efficiency [1,2]

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