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

  1. James B. Meigs1,6,*
  1. 1Harvard Medical School, Boston, MA
  2. 2Section of General Internal Medicine, VA Boston Healthcare System, Boston, MA
  3. 3Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA
  4. 4Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
  5. 5Division of Endocrinology, Department of Medicine, Université de Sherbrooke, Sherbrooke, QC
  6. 6General Medicine Division, Massachusetts General Hospital, Boston, MA
  7. 7Division of Endocrinology and Metabolic Diseases, Department of Medicine, University of Verona Medical School and Hospital Trust of Verona, Verona, Italy
  8. 8Diabetes Research Center (Diabetes Unit), and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
  9. 9Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
  10. 10Department of Biostatistics, Boston University School of Public Health
  11. 11National Heart, Lung, and Blood Institute's Framingham Heart Study
  12. 12Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA
  13. 13Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX
  14. 14Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
  15. 15Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA
  1. *Corresponding author: James B. Meigs, e-mail: jmeigs{at}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 in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared to previous less inclusive GRSt; 2) separate β-cell and insulin resistance GRS (GRSβ and GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1650 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); it 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.

  • Received October 31, 2013.
  • Accepted February 2, 2014.

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