Genomic prediction of hybrid crops allows disentangling dominance and epistasis

Abstract

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme – e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropiate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.

Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber (Cucumis sativus L.)

“…The GCA model has been developed by González-Diéguez et al ( 2021 ), which can trace genetic effects of male and female parents (“according to origin”) based on the GCA and SCA theoretical framework. Moreover, it can also estimate the orthogonal variance components.…”

“…In this study, the GCA models developed by González-Diéguez et al ( 2021 ) were used for model training. In the full GCA model, additive, dominance, additive-by-additive epistatic, and residual genetic effects were all considered.…”

“…where SCA is the SCA effect vector of hybrids, g D is the dominance effect of hybrids, which is assumed as g D ~ . G AA and G D genetic relationship matrices are constructed followed by González-Diéguez et al ( 2021 ).…”

“…In previous studies, GCA and SCA have been used in cucumber breeding (Golabadi et al, 2015 ; Moradipour et al, 2017 ; Ene et al, 2019 ), which indicates that both GCA and SCA are good indicators for evaluating either the inbred lines or hybrid lines. Furthermore, the accurate estimation of combining ability relies on the orthogonal a priori hypothesis of variance components, and the GCA model has been developed for the single-cross breeding system (González-Diéguez et al, 2021 ). In the GCA model, the genetic relationship matrices of additive, dominance, additive-by-additive, and residual genetic effects are defined orthogonally.…”

“…In the GCA model, the genetic relationship matrices of additive, dominance, additive-by-additive, and residual genetic effects are defined orthogonally. The GCA model has shown its potential in a maize single-cross breeding program (Technow et al, 2014 ; González-Diéguez et al, 2021 ). In this study, we investigated whether the GCA model is suitable for the cucumber single-cross breeding system.…”