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Abstract
Purpose
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.
Methods
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.
Results
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.
Conclusion
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.
Keyword
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Article info
Publication history
Accepted:
April 13,
2023
Received in revised form:
April 11,
2023
Received:
July 6,
2022
Publication stage
In Press Accepted ManuscriptIdentification
Copyright
© 2023 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.
