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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Benchmarking computational variant effect predictors by their ability to infer human traits.

Daniel R Tabet1,2,3,4, Da Kuang1,2,3,4, Megan C Lancaster5

  • 1Donnelly Centre, University of Toronto, Toronto, ON, Canada.

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Summary

AlphaMissense best predicts human traits from rare missense variants, outperforming 23 other computational variant effect predictors. This study provides an unbiased method for evaluating these tools for clinical genetics applications.

Keywords:
All of UsBenchmarkingPersonal genomicsRare missense variationUK BiobankVariant effect predictors

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Interpreting human genetic variation is crucial, but evaluating computational variant effect predictors is challenging due to bias and circularity.
  • Unbiased benchmarking requires large, population-level cohorts with genetic and phenotypic data not used in predictor training.

Purpose of the Study:

  • To develop and apply an unbiased method for evaluating and comparing computational variant effect predictors.
  • To assess the performance of 24 predictors using real-world human genetic and trait data.

Main Methods:

  • Utilized curated human gene-trait associations with reported rare-variant burden.
  • Evaluated 24 computational variant effect predictors by correlating their predictions with human traits.
  • Employed the UK Biobank and All of Us population cohorts for unbiased benchmarking.

Main Results:

  • AlphaMissense demonstrated superior performance in inferring human traits from rare missense variants across both cohorts.
  • A significant positive correlation was observed in the rankings of the evaluated predictors between the two cohorts.
  • The developed method provides a reliable framework for assessing predictor accuracy.

Conclusions:

  • The proposed assessment method effectively overcomes limitations of previous evaluations.
  • This approach is generalizable for future computational variant effect predictors.
  • Findings can inform the selection of predictors for personal and clinical genetics.