Prediction of human missense variant effects from functional evidence
TL;DR
FuncVEP, a family of variant effect predictors trained on diverse functional data to predict the functional impact of missense variants, provides a robust, scalable solution for variant interpretation, advancing both diagnostic precision and gene discovery.
Abstract
Prediction of missense variant effects remains a critical bottleneck in both research and diagnostic genetics. Current predictors typically rely on clinical outcomes or population patterns rather than direct measures of functional impact, leading to limited generalizability and data circularity. Here we present FuncVEP, a family of variant effect predictors trained on diverse functional data to predict the functional impact of missense variants. FuncVEP generalizes across datasets and outperforms 48 existing predictors across a wide range of benchmarks, improving accuracy from 78.8% to 84.6% on functional benchmarks and from 90.1% to 92.4% on clinical benchmarks. From a discovery perspective, we identified 210 new gene–phenotype associations involving 494 genes linked to inborn errors of immunity in the UK Biobank and the Mount Sinai Million Health Discoveries Program. FuncVEP substantially improved the discovery rate relative to state-of-the-art predictors. Overall, FuncVEP provides a robust, scalable solution for variant interpretation, advancing both diagnostic precision and gene discovery. This study presents FuncVEP, a tool trained on functional data to improve missense variant interpretation and identify new gene–phenotype associations in population cohorts.