Machine-learning of complex evolutionary signals improves classification of SNVs.

ABSTRACT: Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant predicti

SUBMITTER: Labes S 

PROVIDER: S-EPMC8988715 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature