Improving genomic selection in hexaploid wheat with sub-genome additive and epistatic models.

Abstract

The goal of wheat breeding is the development of superior cultivars tailored to specific environments, and the identification of promising crosses is crucial for the success of breeding programs. Although genomic estimated breeding values were developed to estimate additive effects of genotypes before testing as parents, application has focused on predicting performance of candidate lines, ignoring nonadditive genetic effects. However, nonadditive genetic effects are hypothesized to be especially important in allopolyploid species due to the interaction between homeologous genes. The objectives of this study were to model additive and additive-by-additive epistatic effects to better delineate the genetic architecture of grain yield in wheat and to improve the accuracy of genome-wide predictions. The data set utilized consisted of 3,740 F5:6 experimental lines tested in the K-State wheat breeding program across the years 2016 and 2018. Covariance matrices were calculated based on whole- and sub-genome marker data, and the natural and orthogonal interaction approach was used to estimate variance components for additive and additive-by-additive epistatic effects. Incorporating epistatic effects in additive models resulted in nonorthogonal partitioning of genetic effects but increased total genetic variance and reduced deviance information criteria. Estimation of sub-genome effects indicated that genotypes with the greatest whole-genome effects often combine sub-genomes with intermediate to high effects, suggesting potential for crossing parental lines that have complementary sub-genome effects. Modeling epistasis in either whole-genome or sub-genome models led to a marginal (3%) improvement in genomic prediction accuracy, which could result in significant genetic gains across multiple cycles of breeding.