Epistatic models and pre-selection of markers improve prediction of performance in corn

Abstract

In our previous papers, we demonstrated that the inclusion of epistatic interactions in marker models improved prediction for corn (Zea mays L.) grain quality traits. The utility of pre-selecting markers for epistatic models was not reported. In papers by other researchers, including epistatic effects in a model did not improve prediction efficacy for whole genome selection. The objectives of this study were therefore to evaluate the value of: (1) pre-selecting markers and interactions at different type 1 error levels to predict performance; (2) adding epistatic interactions to models including all markers, and (3) using marker-based models to predict performance of kernel weight (KWT), flowering date (FDT), and plant height (PHT). Data for KWT, FDT, PHT, and oil and protein concentrations were obtained for 500 S2 lines and their testcrosses from the crosses of Illinois high oil × Illinois low oil and Illinois high protein × Illinois low protein corn strains. Pre-selection using an epistatic model including both single-locus and two-locus interaction effects significant at the P = 0.05 level significantly increased prediction efficacy over selection including all markers and epistatic interactions. Adding all epistatic interactions to a model including all markers did not improve prediction. For most traits, prediction based on the P = 0.05 epistatic pre-selection model was nearly as effective as prediction based on phenotype, suggesting subsequent marker-based selection would be effective.