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Improving genomic predictions with inbreeding and non-additive effects in two admixed maize hybrid populations in single and multi-environment contexts.

Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial non-additive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modelling of inbreeding and non-additive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and non-additive effects with the natural and orthogonal interaction approach (NOIA) in single and multi-environment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multi-environment context, we found that the inclusion of non-additive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and non-additive parameters following the NOIA approach to increase prediction accuracy in admixed populations.