Modeling Epistasis in Genomic Selection

Abstract

Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species.

Keywords: GenPred; epistasis; extended G-BLUP (EG-BLUP); genomic best linear unbiased prediction (G-BLUP); genomic selection; reproducing kernel Hilbert space regression (RKHS); shared data resource.