Phylogenetic analysis and machine learning identify signatures of selection and predict deleterious mutations in common bean (Phaseolus vulgaris L.).

Abstract

Background: Mutations are a continuous source of new alleles and genetic diversity in populations. Domestication and selection influence the accumulation of alleles occurring across a range of deleteriousness. Evidence suggests that mildly deleterious mutations (DelMut) can be purged out of breeding populations, increasing favorable allele accumulation.

Results: We used phylogeny-based analyses among 36 legume genomes to identify selection signatures and trained a random forest (RF) model using conservation and protein information for the prediction of DelMut in common bean populations, including a newly developed multiparent advanced generation intercrossed (MAGIC) population of black beans. Genes involved in nitrogen metabolism showed signs of positive selection in the Middle American genome, whereas genes related to phosphorylation were positively selected in the Andean genome. By combining conservation and protein information with machine learning (ML) for high-dimensional feature analysis, we characterized 82,442 sites in the MAGIC founders (36,558 polymorphic) and 4753 sites evenly sequenced among RILs that could be potentially deleterious. Variation in the number of highly DelMut (high predicted deleterious scores) among lines was observed and later correlated with agronomic traits. Phenotypic analyses showed that calculated genetic load (and number of highly DelMut) was negatively correlated with flowering time, maturity, and yield. A detailed in silico analysis of predicted mutations showed presence in highly conserved protein regions, which is likely to affect protein functionality.

Conclusions: Our results show that variation in genetic load can be observed in breeding populations and potentially impact plant performance. These results contribute to understanding the genome-wide accumulation patterns of DelMut in breeding populations. Our study supports future development of strategies to reduce genetic load in promising germplasm and accelerate breeding programs.