Benchmarking computational variant effect predictors by their ability to infer human traits.

ABSTRACT:

Background

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.

Results

AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants i

SUBMITTER: Tabet DR 

PROVIDER: S-EPMC11218265 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature