Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Issue 4 (12th February 2022) - Roth, Morgane; Beugnot, Aurélien; Mary-Huard, Tristan; Moreau, Laurence; Charcosset, Alain; Fiévet, Julie B
Abstract: Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize ( Zea mays L.) 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 nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment 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 multienvironment context, we found that the inclusion of nonadditive 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 nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations. Abstract : Genetic admixture results from the recombination between structural groups. Successful genetic analyses of admixed populations for hybrid breeding require accounting for inbreeding and non-additive effects, which is statistically challenging. Using two admixed maize populations with contrasting levels of diversity, Roth et al . propose models for genomic prediction which efficiently exploit these effects in single and multienvironment contexts. Directional dominance was significant for most traits, which translated into strong prediction improvements when accounting for the level of homozygosity. The authors discuss the potential of admixed material for long-term diversity management in maize breeding programs and the necessary use of ad-hoc prediction models to unleash their genetic potential.