Robust Genomic Modelling Using Expert Knowledge about Additive, Dominance and Epistasis Variation

A major challenge with modelling non-additive genetic variation is that it is hard to separate non-additive variation from additive and environmental variation. In this paper, we describe how to alleviate this issue, and improve genomic modelling of additive and non-additive variation, by leveraging the ample expert knowledge available about the relative magnitude of the sources of phenotypic variation. The method is Bayesian and uses the recently introduced penalized complexity and hierarchical decomposition prior frameworks, where priors can be specified and visualized in an intuitive way, and be used to induce parsimonious modelling. We evaluate the potential impact for plant breeding through a simulated case study of a wheat breeding program. We compare different models and different priors with varying amounts of expert knowledge. The results show that the proposed priors and expert knowledge improved the robustness of the genomic modelling and the selection of the genetically best individuals in the breeding program. We observed this improvement in both variety selection on genetic values and parent selection on additive values, but the variety selection benefited the most. An improvement was not observed in the overall accuracy of estimating genetic values for all individuals and variance components. Finally, we discuss the importance of expert-knowledge priors for genomic modelling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modelling.