Phenotypic evaluation of deep learning models for classifying germline variant pathogenicity
Deep learning models for predicting variant pathogenicity have not been thoroughly evaluated on real-world clinical phenotypes. Here, we apply state-of-the-art pathogenicity prediction models to hereditary breast cancer gene variants in UK Biobank participants. Model predictions for missense variants in BRCA1, BRCA2 and PALB2, but not ATM and CHEK2, were associated with breast cancer risk. However, deep learning models had limited clinical utility when specifically applied to variants of uncertain significance.
Authors
- Ravi B. Parikh (ORCID: https://orcid.org/0000-0003-2692-6306)
- Ryan D. Chow (ORCID: https://orcid.org/0000-0002-1872-6307)
- Katherine L. Nathanson (ORCID: https://orcid.org/0000-0002-6740-0901)
Institutions
- Hospital of the University of Pennsylvania (US)
- Penn Center for AIDS Research (US)
- Philadelphia VA Medical Center (US)
- Abramson Cancer Center (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1038/s41698-024-00710-x
- Citations
- 2
- Primary Topic
- Genomic variations and chromosomal abnormalities
- Type
- article
- Field-Weighted Citation Impact
- 1.10
Funders
- Breast Cancer Research Foundation
- Gray Foundation
- National Institutes of Health
- National Cancer Institute