Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Abstract

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2,3,4,5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study design and workflow.
Fig. 2: Risk for CAD according to GPS.
Fig. 3: Risk gradient for disease according to the GPS percentile.

References

  1. Green, E. D. & Guyer, M. S., National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Proc. R. Soc. Edinb. 52, 99–433 (1918).

    Google Scholar 

  3. Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135–145 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Golan, D., Lander, E. S. & Rosset, S. Measuring missing heritability: inferring the contribution of common variants. Proc. Natl Acad. Sci. USA 111, E5272–E5281 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Abul-Husn, N. S. et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354, pii: aaf7000 (2016).

    Article  Google Scholar 

  7. Nordestgaard, B. G. et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur. Heart J. 34, 3478–3490a (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014).

    Article  PubMed  Google Scholar 

  10. Chatterjee, N. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400–405 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhang, Y. et al. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future. Preprint at https://www.biorxiv.org/content/early/2017/08/11/175406 (2017).

  12. Ripatti, S. et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376, 1393–1400 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at https://www.biorxiv.org/content/early/2017/07/20/166298 (2017).

  16. Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. January, C. T. et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. Circulation 130, e199–e267 (2014).

    PubMed  PubMed Central  Google Scholar 

  23. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1545–1602 (2016).

    Article  Google Scholar 

  24. Knowler, W. C. et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 346, 393–403 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Abraham, C. & Cho, J. H. Inflammatory bowel disease. N. Engl. J. Med. 361, 2066–2078 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pharoah, P. D., Antoniou, A. C., Easton, D. F. & Ponder, B. A. Polygenes, risk prediction, and targeted prevention of breast cancer. N. Engl. J. Med. 358, 2796–2803 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Khera, A. V. & Kathiresan, S. Is coronary atherosclerosis one disease or many? Setting realistic expectations for precision medicine. Circulation 135, 1005–1007 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Christophersen, I. E. et al. Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nat. Genet. 49, 946–952 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  35. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

UK Biobank analyses were conducted via application 7089 using a protocol approved by the Partners HealthCare Institutional Review Board. The analysis was supported by a KL2/Catalyst Medical Research Investigator Training award from Harvard Catalyst funded by the National Institutes of Health (TR001100 to A.V.K.), a Junior Faculty Research Award from the National Lipid Association (to A.V.K.), the National Heart, Lung, and Blood Institute of the US National Institutes of Health under award numbers T32 HL007208 (to K.G.A.), K23HL114724 (to S.A.L.), R01HL139731 (to S.A.L.), RO1HL092577 (to P.T.E.), R01HL128914 (to P.T.E.), K24HL105780 (to P.T.E.), and RO1 HL127564 (to S.K.), the National Human Genome Research Institute of the US National Institutes of Health under award number 5UM1HG008895 (to E.S.L. and S.K.), the Doris Duke Charitable Foundation under award number 2014105 (to S.A.L.), the Foundation Leducq under award number 14CVD01 (to P.T.E.), and the Ofer and Shelly Nemirovsky Research Scholar Award from Massachusetts General Hospital (to S.K.). The authors thank D. Altshuler (Vertex Pharmaceuticals, Boston, MA) for comments on an earlier version of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

A.V.K., M.C., and S.K. conceived and designed the study. A.V.K., M.C., K.G.A., M.E.H., C.R., S.H.C., and S.A.L. acquired, analyzed, and interpreted the data. A.V.K., M.C., E.S.L., and S.K. drafted the manuscript. A.V.K., M.C., P.N., E.S.L., P.T.E., and S.K. critically revised the manuscript for important intellectual content.

Corresponding author

Correspondence to Sekar Kathiresan.

Ethics declarations

Competing interests

A.V.K. and S.K. are listed as co-inventors on a patent application for the use of genetic risk scores to determine risk and guide therapy. S.K. and P.T.E. are supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of myocardial infarction and atrial fibrillation.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Risk gradient for coronary artery disease across the distribution of the genome-wide polygenic score and two previously published scores.

ac, Three polygenic scores for coronary artery disease were calculated within the UK Biobank testing dataset of 288,978 participants: a previously published score comprising 50 variants that had achieved genome-wide levels of statistical significance in previous studies (Eur. Heart J. 37, 561–567, 2016) (a); a previously published score comprising 49,310 variants derived from a Metabochip GWAS (Eur. Heart J. 37, 3267–3278, 2016) (b); and the newly derived genome-wide polygenic score comprising 6,630,150 variants (c). For each score, the population was divided into 100 bins according to percentile of the score and prevalence of coronary artery disease within each bin plotted. The prevalence of coronary artery disease across score percentiles ranged from 1.4% to 5.9% for the 50-variant score, 1.0% to 7.2% for the 49,310-variant score, and 0.8% to 11.1% for the 6,630,150-variant genome-wide polygenic score.

Supplementary Figure 2 Predicted versus observed prevalence of coronary artery disease according to genome-wide polygenic score percentile.

For each individual within the UK Biobank testing dataset, the predicted probability of disease was calculated using a logistic regression model with only the genome-wide polygenic score (GPS) as a predictor. The predicted prevalence of disease within each percentile bin of the GPS distribution was calculated as the average predicted probability of all individuals within that bin. The shape of the predicted risk gradient was consistent with the empirically observed risk gradient, reflected by black and blue dots, respectively.

Supplementary Figure 3 Predicted versus observed prevalence of four diseases according to genome-wide polygenic score percentile.

ad, For each individual within the UK Biobank testing dataset, the predicted probability of disease was calculated using a logistic regression model with only the genome-wide polygenic score (GPS) as a predictor. The predicted prevalence of disease within each percentile bin of the GPS distribution was calculated as the average predicted probability of all individuals within that bin. The shape of the predicted risk gradient was consistent with the empirically observed risk gradient, reflected by black and blue dots, respectively, for each of four diseases: atrial fibrillation (a), type 2 diabetes (b), inflammatory bowel disease (c), and breast cancer (d). Breast cancer analys is was restricted to female participants.

Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Tables 1–10

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khera, A.V., Chaffin, M., Aragam, K.G. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50, 1219–1224 (2018). https://doi.org/10.1038/s41588-018-0183-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-018-0183-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing