Using deep learning models as a genetic architecture for the simulation of breeding schemes

Abstract

Abstract In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.