Prioritization of deleterious mutations informs genomic prediction and increases the rate of gain in common bean (Phaseolus vulgaris L.), a simulation study.

Abstract

Key message: Predicted deleterious mutations (SNPs) have different distributions of effects compared to random SNPs based on population composition. Variant prioritization of markers based on deleterious scores can improve the prediction of yield. Favoring mating schemes between parents with fewer highly deleterious mutations can increase the rate of genetic gain. The study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has potential to accelerate breeding programs through selection and purging deleterious mutations (DelMut). We investigated how predicting DelMut in breeding populations informs genomic prediction (GP) increasing the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model for common bean incorporating phylogenetic and protein information. Deleterious scores from the RF model were around 0.25, with the top 1% (highly DelMut) of variants scoring between 0.78 and 0.82 among populations. All populations showed variation in the number of highly DelMut per line (max. 13-197) and in genetic load. We assessed the impact of incorporating a priori information on DelMut for variant prioritization and weighting in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how designing mating schemes based on variable numbers of DelMut per parent can affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Simulated breeding cycles showed that selecting parents with fewer highly DelMut consistently increases the rate of genetic gain, and depending on the population, can be superior to phenotypic selection. These results highlight the potential of DelMut information for variant prioritization and the optimization of common bean breeding programs. The approaches we developed can be applied to other species to improve the efficacy of crop improvement.