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Benchmarking computational variant effect predictors by their ability to infer human traits
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Benchmarking computational variant effect predictors by their ability to infer human traits
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Journal Article
Benchmarking computational variant effect predictors by their ability to infer human traits
2024
Overview
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 in UK Biobank and
All of Us
participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.
Conclusion
We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.
Publisher
BioMed Central,Springer Nature B.V,BMC
