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

doi:10.1038/s41588-024-01842-3

Review

. 2024 Sep;56(9):1772-1779.

doi: 10.1038/s41588-024-01842-3. Epub 2024 Jul 29.

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

Caroline F Wright^#¹, Luke N Sharp^#², Leigh Jackson², Anna Murray², James S Ware^{3

4

5}, Daniel G MacArthur^{6

7}, Heidi L Rehm^{5

8}, Kashyap A Patel^#², Michael N Weedon^#⁹

Affiliations

¹ Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK. caroline.wright@exeter.ac.uk.
² Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK.
³ National Heart and Lung Institute and MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
⁴ Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK.
⁵ Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia.
⁷ Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK. m.n.weedon@exeter.ac.uk.

^# Contributed equally.

PMID: 39075210
DOI: 10.1038/s41588-024-01842-3

Review

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

Caroline F Wright et al. Nat Genet. 2024 Sep.

. 2024 Sep;56(9):1772-1779.

doi: 10.1038/s41588-024-01842-3. Epub 2024 Jul 29.

Authors

Caroline F Wright^#¹, Luke N Sharp^#², Leigh Jackson², Anna Murray², James S Ware^{3

4

5}, Daniel G MacArthur^{6

7}, Heidi L Rehm^{5

8}, Kashyap A Patel^#², Michael N Weedon^#⁹

Affiliations

¹ Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK. caroline.wright@exeter.ac.uk.
² Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK.
³ National Heart and Lung Institute and MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
⁴ Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK.
⁵ Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia.
⁷ Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK. m.n.weedon@exeter.ac.uk.

^# Contributed equally.

PMID: 39075210
DOI: 10.1038/s41588-024-01842-3

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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Cited by

Dissecting the Reduced Penetrance of Putative Loss-of-Function Variants in Population-Scale Biobanks.
Blair DR, Risch N. Blair DR, et al. medRxiv [Preprint]. 2024 Oct 7:2024.09.23.24314008. doi: 10.1101/2024.09.23.24314008. medRxiv. 2024. PMID: 39399029 Free PMC article. Preprint.

References

1. Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020). - PubMed - PMC - DOI
1. Kingdom, R. & Wright, C. F. Incomplete penetrance and variable expressivity: from clinical studies to population cohorts. Front. Genet. 13, 920390 (2022).
1. Roberts, A. M. et al. Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease–gene relationships — allelic requirement, inheritance modes and disease mechanisms. Genet. Med. 26, 101029 (2024). - PubMed - DOI
1. Otto, P. A. & Horimoto, A. R. V. R. Penetrance rate estimation in autosomal dominant conditions. Genet. Mol. Biol. 35, 583–588 (2012). - PubMed - PMC - DOI
1. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017). - PubMed - PMC - DOI

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[1] Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020). - PubMed - PMC - DOI

[2] Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020). - PubMed - PMC - DOI

[3] Kingdom, R. & Wright, C. F. Incomplete penetrance and variable expressivity: from clinical studies to population cohorts. Front. Genet. 13, 920390 (2022).

[4] Kingdom, R. & Wright, C. F. Incomplete penetrance and variable expressivity: from clinical studies to population cohorts. Front. Genet. 13, 920390 (2022).

[5] Roberts, A. M. et al. Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease–gene relationships — allelic requirement, inheritance modes and disease mechanisms. Genet. Med. 26, 101029 (2024). - PubMed - DOI

[6] Roberts, A. M. et al. Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease–gene relationships — allelic requirement, inheritance modes and disease mechanisms. Genet. Med. 26, 101029 (2024). - PubMed - DOI

[7] Otto, P. A. & Horimoto, A. R. V. R. Penetrance rate estimation in autosomal dominant conditions. Genet. Mol. Biol. 35, 583–588 (2012). - PubMed - PMC - DOI

[8] Otto, P. A. & Horimoto, A. R. V. R. Penetrance rate estimation in autosomal dominant conditions. Genet. Mol. Biol. 35, 583–588 (2012). - PubMed - PMC - DOI

[9] Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017). - PubMed - PMC - DOI

[10] Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017). - PubMed - PMC - DOI

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

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

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