Population-Based Penetrance of Deleterious Clinical Variants | Breast Cancer | JN Learning | AMA Ed Hub [Skip to Content]
[Skip to Content Landing]

Population-Based Penetrance of Deleterious Clinical Variants

Educational Objective
To understand how the population-based disease risk can be determined for clinical variants in known disease-predisposition genes.
1 Credit CME
Key Points

Question  What is the population-based penetrance of pathogenic and loss-of-function clinical variants?

Findings  This cohort study included 72 434 participants from 2 biobanks who had alleles for pathogenic or loss-of-function variants reported for 157 diseases. Among the 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with less than or equal to 5% risk difference for disease in individuals with the variant allele; pathogenic variants were associated with 6.9% mean penetrance and benign variants were associated with 0.85% mean penetrance.

Meaning  In these biobanks, the estimated penetrance of pathogenic/loss-of-function variants varied, but was generally associated with a small increase in the risk of disease.

Abstract

Importance  Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches.

Objective  To evaluate the population-based disease risk of clinical variants in known disease predisposition genes.

Design, Setting, and Participants  This cohort study included 72 434 individuals with 37 780 clinical variants who were enrolled in the BioMe Biobank from 2007 onwards with follow-up until December 2020 and the UK Biobank from 2006 to 2010 with follow-up until June 2020. Participants had linked exome and electronic health record data, were older than 20 years, and were of diverse ancestral backgrounds.

Exposures  Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants).

Main Outcomes and Measures  The primary outcome was the disease risk associated with clinical variants. The risk difference (RD) between the prevalence of disease in individuals with a variant allele (penetrance) vs in individuals with a normal allele was measured.

Results  Among 72 434 study participants, 43 395 were from the UK Biobank (mean [SD] age, 57 [8.0] years; 24 065 [55%] women; 2948 [7%] non-European) and 29 039 were from the BioMe Biobank (mean [SD] age, 56 [16] years; 17 355 [60%] women; 19 663 [68%] non-European). Of 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with an RD less than or equal to 0.05. Mean penetrance was 6.9% (95% CI, 6.0%-7.8%) for pathogenic variants and 0.85% (95% CI, 0.76%-0.95%) for benign variants reported in ClinVar (difference, 6.0 [95% CI, 5.6-6.4] percentage points), with a median of 0% for both groups due to large numbers of nonpenetrant variants. Penetrance of pathogenic/loss-of-function variants for late-onset diseases was modified by age: mean penetrance was 10.3% (95% CI, 9.0%-11.6%) in individuals 70 years or older and 8.5% (95% CI, 7.9%-9.1%) in individuals 20 years or older (difference, 1.8 [95% CI, 0.40-3.3] percentage points). Penetrance of pathogenic/loss-of-function variants was heterogeneous even in known disease predisposition genes, including BRCA1 (mean [range], 38% [0%-100%]), BRCA2 (mean [range], 38% [0%-100%]), and PALB2 (mean [range], 26% [0%-100%]).

Conclusions and Relevance  In 2 large biobank cohorts, the estimated penetrance of pathogenic/loss-of-function variants was variable but generally low. Further research of population-based penetrance is needed to refine variant interpretation and clinical evaluation of individuals with these variant alleles.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 CME Credit™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Corresponding Author: Ron Do, PhD, 1468 Madison Ave, Annenberg Building, Floor 18, Room 80B, New York, NY 10029 (ron.do@mssm.edu).

Accepted for Publication: December 13, 2021.

Author Contributions: Dr Do and Mr Forrest had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Forrest, Jordan, Cho, Do.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Forrest, Cho, Do.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Forrest, Chaudhary, Petrazzini, Rocheleau, Cho.

Obtained funding: Forrest, Loos, Cho, Do.

Administrative, technical, or material support: Vy, Bafna, Cho.

Supervision: Rocheleau, Nadkarni, Cho, Do.

Conflict of Interest Disclosures: Mr Forrest reported receiving grants from the National Institute of General Medical Sciences of the National Institutes of Health (NIH). Dr Nadkarni reported receiving grants, personal fees, and nonfinancial support from and being a cofounder of and having equity in Renalytix; being a cofounder in Pensieve Health; being a cofounder and having equity in Verici; and receiving personal fees from Siemens, Reata, AstraZeneca, and BioVie outside the submitted work. Dr Do reported receiving grants from AstraZeneca and Goldfinch Bio; nonfinancial support from Goldfinch Bio; personal fees from Variant Bio; and being a scientific cofounder, consultant, and equity holder in Pensieve Health outside the submitted work. No other disclosures were reported.

Funding/Support: Mr Forrest is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (T32-GM007280). Dr Do is supported by the National Institute of General Medical Sciences of the NIH (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).

Role of the Funder/Sponsor: The National Institute of General Medical Sciences and the National Heart, Lung, and Blood Institute of the NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication; and no right to veto publication of the manuscript.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Additional Contributions: Bruce D. Gelb, MD; Sander Houten, PhD; Paz Polak, PhD; and Stuart Scott, PhD, all of whom are on the thesis advisory committee of Iain Forrest, provided critical feedback and expertise. All contributors are affiliated with the Icahn School of Medicine at Mount Sinai and no one received any additional compensation beyond usual salary for their contributions to this study.

References
1.
Miller  DT , Lee  K , Chung  WK ,  et al.  ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG).   Genet Med. 2021;23(8):1381-1390. doi:10.1038/s41436-021-01172-3Google ScholarCrossref
2.
Shendure  J , Findlay  GM , Snyder  MW .  Genomic medicine-progress, pitfalls, and promise.   Cell. 2019;177(1):45-57. doi:10.1016/j.cell.2019.02.003PubMedGoogle ScholarCrossref
3.
Landrum  MJ , Lee  JM , Riley  GR ,  et al.  ClinVar: public archive of relationships among sequence variation and human phenotype.   Nucleic Acids Res. 2014;42(Database issue):D980-D985. doi:10.1093/nar/gkt1113PubMedGoogle Scholar
4.
Shah  N , Hou  YCC , Yu  HC ,  et al.  Identification of misclassified ClinVar variants via disease population prevalence.   Am J Hum Genet. 2018;102(4):609-619. doi:10.1016/j.ajhg.2018.02.019PubMedGoogle ScholarCrossref
5.
Manrai  AK , Ioannidis  JPA , Kohane  IS .  Clinical genomics: from pathogenicity claims to quantitative risk estimates.   JAMA. 2016;315(12):1233-1234. doi:10.1001/jama.2016.1519PubMedGoogle ScholarCrossref
6.
Xiang  J , Yang  J , Chen  L ,  et al.  Reinterpretation of common pathogenic variants in ClinVar revealed a high proportion of downgrades.   Sci Rep. 2020;10(1):1-5. doi:10.1038/s41598-019-57335-5PubMedGoogle Scholar
7.
Khera  AV , Won  HH , Peloso  GM ,  et al.  Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia.   J Am Coll Cardiol. 2016;67(22):2578-2589. doi:10.1016/j.jacc.2016.03.520PubMedGoogle ScholarCrossref
8.
Mavaddat  N , Peock  S , Frost  D ,  et al; EMBRACE.  Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE.   J Natl Cancer Inst. 2013;105(11):812-822. doi:10.1093/jnci/djt095PubMedGoogle ScholarCrossref
9.
Sturm  AC , Knowles  JW , Gidding  SS ,  et al; Convened by the Familial Hypercholesterolemia Foundation.  Clinical genetic testing for familial hypercholesterolemia: JACC Scientific Expert Panel.   J Am Coll Cardiol. 2018;72(6):662-680. doi:10.1016/j.jacc.2018.05.044PubMedGoogle ScholarCrossref
10.
Lee  CH , Dershaw  DD , Kopans  D ,  et al.  Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer.   J Am Coll Radiol. 2010;7(1):18-27. doi:10.1016/j.jacr.2009.09.022PubMedGoogle ScholarCrossref
11.
Kuchenbaecker  KB , Hopper  JL , Barnes  DR ,  et al; BRCA1 and BRCA2 Cohort Consortium.  Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers.   JAMA. 2017;317(23):2402-2416. doi:10.1001/jama.2017.7112PubMedGoogle ScholarCrossref
12.
Cooper  DN , Krawczak  M , Polychronakos  C , Tyler-Smith  C , Kehrer-Sawatzki  H .  Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease.   Hum Genet. 2013;132(10):1077-1130. doi:10.1007/s00439-013-1331-2PubMedGoogle ScholarCrossref
13.
Bycroft  C , Freeman  C , Petkova  D ,  et al.  The UK Biobank resource with deep phenotyping and genomic data.   Nature. 2018;562(7726):203-209. doi:10.1038/s41586-018-0579-zPubMedGoogle ScholarCrossref
14.
Fry  A , Littlejohns  TJ , Sudlow  C ,  et al.  Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population.   Am J Epidemiol. 2017;186(9):1026-1034. doi:10.1093/aje/kwx246PubMedGoogle ScholarCrossref
15.
Van Hout  CV , Tachmazidou  I , Backman  JD ,  et al; Geisinger-Regeneron DiscovEHR Collaboration; Regeneron Genetics Center.  Exome sequencing and characterization of 49,960 individuals in the UK Biobank.   Nature. 2020;586(7831):749-756. doi:10.1038/s41586-020-2853-0PubMedGoogle ScholarCrossref
16.
Chang  CC , Chow  CC , Tellier  LCAM , Vattikuti  S , Purcell  SM , Lee  JJ .  Second-generation PLINK: rising to the challenge of larger and richer datasets.   Gigascience. 2015;4(1):7. doi:10.1186/s13742-015-0047-8PubMedGoogle ScholarCrossref
17.
McLaren  W , Gil  L , Hunt  SE ,  et al.  The Ensembl Variant Effect Predictor.   Genome Biol. 2016;17(1):122. doi:10.1186/s13059-016-0974-4PubMedGoogle ScholarCrossref
18.
Amberger  JS , Bocchini  CA , Schiettecatte  F , Scott  AF , Hamosh  A .  OMIM.org: Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic disorders.   Nucleic Acids Res. 2015;43(database issue):D789-D798. doi:10.1093/nar/gku1205PubMedGoogle Scholar
19.
Breast cancer. PheKB. Published 2018. Accessed May 31, 2020. https://phekb.org/phenotype/breast-cancer
20.
Safarova  MS , Liu  H , Kullo  IJ .  Rapid identification of familial hypercholesterolemia from electronic health records: the SEARCH study.   J Clin Lipidol. 2016;10(5):1230-1239. doi:10.1016/j.jacl.2016.08.001PubMedGoogle ScholarCrossref
21.
Papani  R , Sharma  G , Agarwal  A ,  et al.  Validation of claims-based algorithms for pulmonary arterial hypertension.   Pulm Circ. 2018;8(2):2045894018759246. doi:10.1177/2045894018759246PubMedGoogle Scholar
22.
Ye  JZ , Delmar  M , Lundby  A , Olesen  MS .  Reevaluation of genetic variants previously associated with arrhythmogenic right ventricular cardiomyopathy integrating population-based cohorts and proteomics data.   Clin Genet. 2019;96(6):506-514. doi:10.1111/cge.13621PubMedGoogle ScholarCrossref
23.
Goldberg  DS , Lewis  JD , Halpern  SD , Weiner  MG , Lo Re  V  III .  Validation of a coding algorithm to identify patients with hepatocellular carcinoma in an administrative database.   Pharmacoepidemiol Drug Saf. 2013;22(1):103-107. doi:10.1002/pds.3367PubMedGoogle ScholarCrossref
24.
Wolf  AMD , Wender  RC , Etzioni  RB ,  et al; American Cancer Society Prostate Cancer Advisory Committee.  American Cancer Society guideline for the early detection of prostate cancer: update 2010.   CA Cancer J Clin. 2010;60(2):70-98. doi:10.3322/caac.20066PubMedGoogle ScholarCrossref
25.
Ritchie  MD , Denny  JC , Zuvich  RL ,  et al; Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) QRS Group.  Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk.   Circulation. 2013;127(13):1377-1385. doi:10.1161/CIRCULATIONAHA.112.000604PubMedGoogle ScholarCrossref
26.
Simonett  JM , Sohrab  MA , Pacheco  J ,  et al.  A validated phenotyping algorithm for genetic association studies in age-related macular degeneration.   Sci Rep. 2015;5(1):12875. doi:10.1038/srep12875PubMedGoogle ScholarCrossref
27.
Kho  AN , Hayes  MG , Rasmussen-Torvik  L ,  et al.  Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.   J Am Med Inform Assoc. 2012;19(2):212-218. doi:10.1136/amiajnl-2011-000439PubMedGoogle ScholarCrossref
28.
Tier 1 genomics applications and their importance to public health. Centers for Disease Control and Prevention. Published March 6, 2014. Accessed August 25, 2021. https://www.cdc.gov/genomics/implementation/toolkit/tier1.htm
29.
Green  RC , Berg  JS , Grody  WW ,  et al; American College of Medical Genetics and Genomics.  ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing.   Genet Med. 2013;15(7):565-574. doi:10.1038/gim.2013.73PubMedGoogle ScholarCrossref
30.
Hu  C , Hart  SN , Gnanaolivu  R ,  et al.  A population-based study of genes previously implicated in breast cancer.   N Engl J Med. 2021;384(5):440-451. doi:10.1056/NEJMoa2005936PubMedGoogle ScholarCrossref
31.
Abul-Husn  NS , Manickam  K , Jones  LK ,  et al.  Genetic identification of familial hypercholesterolemia within a single US health care system.   Science. 2016;354(6319):aaf7000. doi:10.1126/science.aaf7000PubMedGoogle Scholar
32.
Park  S , Lee  S , Lee  Y ,  et al.  Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families.   BMC Med Genet. 2015;16(1):62. doi:10.1186/s12881-015-0198-6PubMedGoogle ScholarCrossref
33.
Wright  CF , West  B , Tuke  M ,  et al.  Assessing the pathogenicity, penetrance, and expressivity of putative disease-causing variants in a population setting.   Am J Hum Genet. 2019;104(2):275-286. doi:10.1016/j.ajhg.2018.12.015PubMedGoogle ScholarCrossref
34.
Khera  AV , Hegele  RA .  What is familial hypercholesterolemia, and why does it matter?   Circulation. 2020;141(22):1760-1763. doi:10.1161/CIRCULATIONAHA.120.046961PubMedGoogle ScholarCrossref
35.
Manrai  AK , Funke  BH , Rehm  HL ,  et al.  Genetic misdiagnoses and the potential for health disparities.   N Engl J Med. 2016;375(7):655-665. doi:10.1056/NEJMsa1507092PubMedGoogle ScholarCrossref
36.
Sirugo  G , Williams  SM , Tishkoff  SA .  The missing diversity in human genetic studies.   Cell. 2019;177(1):26-31. doi:10.1016/j.cell.2019.02.048PubMedGoogle ScholarCrossref
37.
de Belleroche  J , Orrell  R , King  A .  Familial amyotrophic lateral sclerosis/motor neurone disease (FALS): a review of current developments.   J Med Genet. 1995;32(11):841-847. doi:10.1136/jmg.32.11.841PubMedGoogle ScholarCrossref
38.
Murphy  NA , Arthur  KC , Tienari  PJ , Houlden  H , Chiò  A , Traynor  BJ .  Age-related penetrance of the C9orf72 repeat expansion.   Sci Rep. 2017;7(1):2116. doi:10.1038/s41598-017-02364-1PubMedGoogle ScholarCrossref
39.
Stutzmann  F , Tan  K , Vatin  V ,  et al.  Prevalence of melanocortin-4 receptor deficiency in Europeans and their age-dependent penetrance in multigenerational pedigrees.   Diabetes. 2008;57(9):2511-2518. doi:10.2337/db08-0153PubMedGoogle ScholarCrossref
40.
Cozzolino  F , Montedori  A , Abraha  I ,  et al.  A diagnostic accuracy study validating cardiovascular ICD-9io-CM codes in healthcare administrative databases: the Umbria Data-Value Project.   PLoS One. 2019;14(7):e0218919. doi:10.1371/journal.pone.0218919PubMedGoogle Scholar
Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
Close
Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close

Name Your Search

Save Search
Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Close

Lookup An Activity

or

Close

My Saved Searches

You currently have no searches saved.

Close

My Saved Courses

You currently have no courses saved.

Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close