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Diabetes. 2014 Jun;63(6):2172-82. doi: 10.2337/db13-1663. Epub 2014 Feb 11.

Polygenic type 2 diabetes prediction at the limit of common variant detection.

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  • 1Harvard Medical School, Boston, MASection of General Internal Medicine, VA Boston Healthcare System, Boston, MADivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA.
  • 2Harvard Medical School, Boston, MADepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MADivision of Endocrinology, Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada.
  • 3General Medicine Division, Massachusetts General Hospital, Boston, MA.
  • 4Harvard Medical School, Boston, MAGeneral Medicine Division, Massachusetts General Hospital, Boston, MADivision of Endocrinology and Metabolic Diseases, Department of Medicine, University of Verona Medical School and Hospital Trust of Verona, Verona, Italy.
  • 5Harvard Medical School, Boston, MADiabetes Research Center (Diabetes Unit), and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MAProgram in Medical and Population Genetics, Broad Institute, Cambridge, MA.
  • 6Department of Biostatistics, Boston University School of Public Health, Boston, MANational Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA.
  • 7Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA.
  • 8Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX.
  • 9Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
  • 10Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA.
  • 11Harvard Medical School, Boston, MAGeneral Medicine Division, Massachusetts General Hospital, Boston, MA jmeigs@partners.org.

Abstract

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.

© 2014 by the American Diabetes Association.

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