A statistical package for evaluation of hybrid performance in plant breeding via genomic selection

Abstract

Abstract Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin (Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.