Abstract
Numerous computational prediction tools have been introduced to estimate the functional impact of variants in the human genome based on evolutionary constraints and biochemical metrics. However, their implementation in diagnostic settings to classify variants faced challenges with accuracy and validity. Most existing tools are pan-genome and pan-diseases, which neglected gene- and disease-specific properties and limited the accessibility of curated data. As a proof-of-concept, we developed a disease-specific prediction tool named Deafness Variant deleteriousness Prediction tool (DVPred) that focused on the 157 genes reportedly causing genetic hearing loss (HL). DVPred applied the gradient boosting decision tree (GBDT) algorithm to the dataset consisting of expert-curated pathogenic and benign variants from a large in-house HL patient cohort and public databases. With the incorporation of variant-level and gene-level features, DVPred outperformed the existing universal tools. It boasts an area under the curve (AUC) of 0.98, and showed consistent performance (AUC = 0.985) in an independent assessment dataset. We further demonstrated that multiple gene-level metrics, including low complexity genomic regions and substitution intolerance scores, were the top features of the model. A comprehensive analysis of missense variants showed a gene-specific ratio of predicted deleterious and neutral variants, implying varied tolerance or intolerance to variation in different genes. DVPred explored the utility of disease-specific strategy in improving the deafness variant prediction tool. It can improve the prioritization of pathogenic variants among massive variants identified by high-throughput sequencing on HL genes. It also shed light on the development of variant prediction tools for other genetic disorders.
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Acknowledgements
We are grateful to all students and staff who have contributed to the data curation.
Funding
This study was supported by the National Key Research and Development Program of China (2017YFC0907503) and 1 3 5 project for disciplines of excellence West China Hospital of Sichuan University (ZYJC20002).
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F. B., H. Y. and R. J.H. S. conceived the study. F. B. wrote the manuscript, with the contributions by K. T B. and H. A. M. Z., Y. L., and J. C. organized and performed the data curation. Q. C., Y. W., and X. Z. created and evaluated the model, with contributions by F. B., Q. Z., and X. L.
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Yumei Wang, Xia Zhao, and Xiarong Li were employed at GeneDock Co.Ltd. at the time of submission. No other conflicts relevant to this study should be reported.
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439_2022_2440_MOESM6_ESM.xlsx
Supplementary file6 Table S5. List of P/LP variants from DVD (v8.1), ClinVar (v20171203), and HGMD (2017q4) (XLSX 817 KB)
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Bu, F., Zhong, M., Chen, Q. et al. DVPred: a disease-specific prediction tool for variant pathogenicity classification for hearing loss. Hum Genet 141, 401–411 (2022). https://doi.org/10.1007/s00439-022-02440-1
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DOI: https://doi.org/10.1007/s00439-022-02440-1