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Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts

Nature Genetics volume 56, pages 1772–1779 (2024)Cite this article

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Abstract

Penetrance is the probability that an individual with a pathogenic genetic variant develops a specific disease. Knowing the penetrance of variants for monogenic disorders is important for counseling of individuals. Until recently, estimates of penetrance have largely relied on affected individuals and their at-risk family members being clinically referred for genetic testing, a ‘phenotype-first’ approach. This approach substantially overestimates the penetrance of variants because of ascertainment bias. The recent availability of whole-genome sequencing data in individuals from very-large-scale population-based cohorts now allows ‘genotype-first’ estimates of penetrance for many conditions. Although this type of population-based study can underestimate penetrance owing to recruitment biases, it provides more accurate estimates of penetrance for secondary or incidental findings. Here, we provide guidance for the conduct of penetrance studies to ensure that robust genotypes and phenotypes are used to accurately estimate penetrance of variants and groups of similarly annotated variants from population-based studies.

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Fig. 1: Comparison of penetrance estimates in diabetes, calculated using the risk difference of groups of variants purportedly linked with MODY in 50,000 individuals from the UK Biobank with exome sequencing data.

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Acknowledgements

We thank A. Hattersley and numerous other colleagues and reviewers for insightful conversations and guidance. This research has been conducted using the UK Biobank resource under application numbers 49847 and 9072. The current work was supported by Diabetes UK (19/0005994), the MRC (MR/T00200X/1) and Wellcome (226083/Z/22/Z). K.A.P. is supported by a Wellcome Clinical Fellowship (219606/Z/19/Z). J.S.W. is supported by the Medical Research Council (UK), the Sir Jules Thorn Charitable Trust (21JTA), the British Heart Foundation (RE/18/4/34215) and the NIHR Imperial College Biomedical Research Centre. We acknowledge the use of the University of Exeter High-Performance Computing facility in carrying out this work. This study was supported by the National Institute for Health and Care Research Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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  1. These authors contributed equally: Caroline F. Wright, Luke N. Sharp, Kashyap A. Patel, Michael N. Weedon.

Authors and Affiliations

  1. Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK

    Caroline F. Wright, Luke N. Sharp, Leigh Jackson, Anna Murray, Kashyap A. Patel & Michael N. Weedon

  2. National Heart and Lung Institute and MRC Laboratory of Medical Sciences, Imperial College London, London, UK

    James S. Ware

  3. Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK

    James S. Ware

  4. Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    James S. Ware & Heidi L. Rehm

  5. Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia

    Daniel G. MacArthur

  6. Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

    Daniel G. MacArthur

  7. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Heidi L. Rehm

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Contributions

C.F.W. and M.N.W. conceived the study; L.N.S. and K.A.P. performed the diabetes analysis outlined in Fig. 1; C.F.W., M.N.W., L.J., A.M. and K.A.P. curated variants, genes and conditions to identify potential errors; C.F.W. wrote the first draft of the manuscript; J.S.W., D.G.M. and H.L.R. provided expert input into the manuscript; all authors contributed to revisions and the final manuscript.

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Correspondence to Caroline F. Wright or Michael N. Weedon.

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Competing interests

M.N.W. is a co-investigator on a Randox Laboratories R&D research grant and received translational industry academic funding from Randox Laboratories R&D relating to autoimmune GRS for prediction and classification of disease. M.N.W. and K.A.P. have received royalties from Randox as co-inventors of a type 1 diabetes genetic risk score product. D.G.M. is a paid advisor to GlaxoSmithKline, Insitro, Variant Bio and Overtone Therapeutics and has received research support from AbbVie, Astellas, Biogen, BioMarin, Eisai, Google, Merck, Microsoft, Pfizer and Sanofi–Genzyme. J.S.W. has received research support from Bristol Myers Squibb and has acted as a consultant for MyoKardia, Pfizer, Foresite Labs, HealthLumen and Tenaya Therapeutics. The other authors have no conflict of interest to declare.

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Wright, C.F., Sharp, L.N., Jackson, L. et al. Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts. Nat Genet 56, 1772–1779 (2024). https://doi.org/10.1038/s41588-024-01842-3

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