| Title: | Benchmarking computational variant effect predictors by their ability to infer human traits |
| Journal: | Genome Biology |
| Published: | 1 Jul 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38951922/ |
| DOI: | https://doi.org/10.1186/s13059-024-03314-7 |
Publication 11744
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Abstract
BackgroundComputational 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.ResultsAlphaMissense 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.ConclusionWe 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.</p>
11 Authors
- Daniel R. Tabet
- Da Kuang
- Megan C. Lancaster
- Roujia Li
- Karen Liu
- Jochen Weile
- Atina G. Coté
- Yingzhou Wu
- Robert A. Hegele
- Dan M. Roden
- Frederick P. Roth
1 Application
| Application ID | Title |
|---|---|
| 51135 | Evaluating the impact of computational and experimental measures of rare variant deleteriousness on burden-based association testing |
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