Article|Articles in Press, 100862

eyeVarP: a computational framework for the identification of pathogenic variants specific to eye disease

Published:April 20, 2023DOI:
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      Disease-specific pathogenic variants prediction tools that predict pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared to other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.


      We developed eyeVarP, an eye disease-specific variant prioritization tool, which applied the Random Forest (RF) algorithm to the dataset of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and InDels and predict their pathogenicity from Exome or genome sequencing data, which provides a complete computational procedure.


      eyeVarP outperformed pan-disease-specific tools in identifying eye disease-specific pathogenic variants under the top ten. VarP outperformed twelve pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and InDels. The complete pipeline would help to develop disease-specific tools for other genetic disorders.


      eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedures would significantly improve the clinical variant interpretation for specific diseases.


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