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Using large-scale population-based data to improve disease risk assessment of clinical variants

Nature Genetics volume 57, pages 1588–1597 (2025)Cite this article

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Abstract

Understanding the disease risk of genetic variants is fundamental to precision medicine. Estimates of penetrance—the probability of disease for individuals with a variant allele—rely on disease-specific cohorts, clinical testing and emerging electronic health record (EHR)-linked biobanks. These data sources, while valuable, each have limitations in quality, representativeness and analyzability. Here, we provide a historical account of the currently accepted pathogenicity classification system and data available in ClinVar, a public archive that aggregates variant interpretations but lacks detailed data for accurate penetrance assessment, highlighting its oversimplification of disease risk. We propose an integrative Bayesian framework that unifies pathogenicity and penetrance, leveraging both functional and real-world evidence to refine risk predictions. In addition, we advocate for enhancing ClinVar with the inclusion of high-priority phenotypes, age-stratified data and population-based cohorts linked to EHRs. We suggest developing a community repository of population-based penetrance estimates to support the clinical application of genetic data.

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Fig. 1: Development of a variant interpretation framework from pathogenicity to population-based disease risk using a real-world example of BRCA2 c.68-7T>A.

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Acknowledgements

We thank S. Plon for helpful comments on the manuscript. R.D. is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836). W.K.C. is supported by the National Human Genome Research Institute of the NIH (R01-HG010365). K.-L.H. is supported by the National Institute of General Medical Sciences of the NIH (R35-GM138113).

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Author notes

  1. These authors jointly supervised this work: Daniel M. Jordan, Ron Do.

Authors and Affiliations

  1. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Iain S. Forrest, Daniel M. Jordan & Ron Do

  2. Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Iain S. Forrest

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Iain S. Forrest, Kuan-Lin Huang, Daniel M. Jordan & Ron Do

  4. Center for Genomic Interpretation, Sandy, UT, USA

    Julie M. Eggington

  5. Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Wendy K. Chung

  6. Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Daniel M. Jordan & Ron Do

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Contributions

I.S.F., D.M.J. and R.D. wrote the first draft of the manuscript with substantial comments and revisions from K.-L.H., J.M.E. and W.K.C. All authors contributed to drafting the manuscript.

Corresponding authors

Correspondence to Daniel M. Jordan or Ron Do.

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Competing interests

R.D. reported being a scientific cofounder, consultant and equity holder for Pensieve Health (pending) and a consultant for Variant Bio and Character Bio. J.M.E. reported being a cofounder, board member and executive of the nonprofit Center for Genomic Interpretation, with part of its mission overlapping with the interests of this work, specifically the mission to encourage careful stewardship of clinical genetics. J.M.E. is also the founder of and a consultant for Grandview Consulting LLC, not related to this work. K.-L.H. is a founder of Open Box Science, not related to this work. W.K.C. is on the Board of Directors of Prime Medicine and Rallybio, not related to this work. The other authors declare no competing interests.

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Forrest, I.S., Huang, KL., Eggington, J.M. et al. Using large-scale population-based data to improve disease risk assessment of clinical variants. Nat Genet 57, 1588–1597 (2025). https://doi.org/10.1038/s41588-025-02212-3

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