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|a Khera, Amit V.
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|a Massachusetts Institute of Technology. Department of Biology
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|a Broad Institute of MIT and Harvard
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|a Chaffin, Mark
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|a Aragam, Krishna G.
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|a Haas, Mary E.
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|a Roselli, Carolina
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|a Choi, Seung Hoan
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|a Natarajan, Pradeep
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|a Lander, Eric Steven
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|a Lubitz, Steven A.
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|a Ellinor, Patrick T.
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|a Kathiresan, Sekar
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|a Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
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|b Springer Science and Business Media LLC,
|c 2020-05-21T19:54:56Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/125390
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|a 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.
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|a Article
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|t Nature Genetics
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