Improvement of predictive ability in maize hybrids by including dominance effects and marker × environment models

Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with nonadditive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and is a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein we assessed modeling approaches where dominance effects and multienvironment were considered for genomic selection in maize ( Zea mays L.) hybrids. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three cycles in different locations. Hybrid genotypes were inferred in silico based on their parental inbred lines using single nucleotide polymorphism (SNP) markers obtained via a 500k SNP chip. We considered the importance to decompose additive and dominance marker effects into components that are constant across environments and deviations that are group specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30%. Contrastingly, additive models yielded better results for grain moisture. For multienvironment modeling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype × environment interactions resulted in gains in accuracy and hence could be considered for implementation in genomic selection in maize breeding programs.