Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
Name
nihms-977254.pdf
Description
Accepted version
Size
824.86 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Khera, Amit V.
Chaffin, Mark
Aragam, Krishna G.
Haas, Mary E.
Roselli, Carolina
Choi, Seung Hoan
Natarajan, Pradeep
Lander, Eric Steven
Lubitz, Steven A.
Ellinor, Patrick T.
Date Issued
August 2018
Journal
Nature Genetics
Publisher
Springer Science and Business Media LLC
Citation
Khera, Amit V. et al. "Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations." Nature Genetics 50, 9 (August 2018): 1219-1224 © 2018 The Author(s)
Version
Author's final manuscript
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 variation 1 . 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 nature 2–5 , 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 risk 6 . We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Broad Institute of MIT and Harvard
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/s41588-018-0183-z
