Genomic Prediction across Structured Hybrid Populations and Environments in Maize

Abstract

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.