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.

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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.

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Author notes

  1. These authors contributed equally: Amit V. Khera, Mark Chaffin.

Affiliations

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

    • Amit V. Khera
    • , Krishna G. Aragam
    •  & Sekar Kathiresan
  2. Cardiology Division of the Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Amit V. Khera
    • , Krishna G. Aragam
    • , Pradeep Natarajan
    • , Steven A. Lubitz
    • , Patrick T. Ellinor
    •  & Sekar Kathiresan
  3. Harvard Medical School, Boston, MA, USA

    • Amit V. Khera
    • , Krishna G. Aragam
    • , Pradeep Natarajan
    • , Steven A. Lubitz
    • , Patrick T. Ellinor
    •  & Sekar Kathiresan
  4. Cardiovascular Disease Initiative of the Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Amit V. Khera
    • , Mark Chaffin
    • , Krishna G. Aragam
    • , Mary E. Haas
    • , Carolina Roselli
    • , Seung Hoan Choi
    • , Pradeep Natarajan
    • , Eric S. Lander
    • , Steven A. Lubitz
    • , Patrick T. Ellinor
    •  & Sekar Kathiresan

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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.

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.

Corresponding author

Correspondence to Sekar Kathiresan.

Integrated supplementary information

  1. 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.

  2. 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.

  3. 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.

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

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