Cordoba Novoa, H. A.; Hoyos-Villegas, V.

The study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has the potential to accelerate breeding programs through selection and purging of deleterious mutations (DelMut). Here, we investigated how predicting DelMut in breeding populations can improve genomic prediction (GP) and inform strategies to increase the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model incorporating phylogenetic and protein information. Deleterious scores from the RF model were mostly around 0.25, with the top 1% (highly DelMut) of variants scoring between 0.78 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 for variant prioritization and weighting based on predicted deleteriousness in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how different mating schemes based variable numbers of DelMut per parent affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Yield predictions were 4.4712.3% more accurate when markers were weighted by effect and deleterious score; no consistent improvement was observed for flowering time. Simulated breeding cycles showed that selecting parents with fewer highly DelMut consistently increases the rate of genetic gain. 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 assessed in other species to improve the efficacy of crop improvement.