A Bivariate Genome-Wide Approach to Metabolic Syndrome

STAMPEED Consortium

  1. Ingrid B. Borecki1,*
  1. 1Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, Missouri
  2. 2The GeneSTAR Research Program, Johns Hopkins University, Baltimore, Maryland
  3. 3Department of Epidemiology, University of Minnesota, Minneapolis, Minnesota
  4. 4Division of Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center, Los Angeles, California
  5. 5Department of Medicine, Stanford University School of Medicine, Stanford, California
  6. 6Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
  7. 7Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
  8. 8Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
  9. 9Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
  10. 10HudsonAlpha Institute for Biotechnology, Huntsville, Alabama
  11. 11Cardiovascular Health Research Unit and Departments of Medicine, University of Washington, Seattle, Washington
  12. 12Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
  13. 13Institute of Clinical Medicine, University of Oulu, Oulu, Finland
  14. 14Department of Epidemiology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
  15. 15Kaiser Foundation Research Institute, Oakland, California
  16. 16Department of Biostatistics and Epidemiology, School of Public Health, Imperial College, Faculty of Medicine, London, U.K.
  17. 17Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Churchill Hospital, Oxford, U.K.
  18. 18Division of Epidemiology & Clinical Applications, National Heart, Lung, and Blood Institute, Bethesda, Maryland
  19. 19Department of Biostatistics and Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
  20. 20McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
  21. 21Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
  22. 22The University of Texas Health Science Center at Houston, Human Genetics Center, Houston, Texas
  23. 23Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, U.K.
  24. 24Department of Biostatistics and Epidemiology, School of Public Health, Imperial College, Faculty of Medicine, London, U.K.
  25. 25Institute of Health Sciences, University of Oulu, Oulu, Finland
  26. 26Biocenter Oulu, University of Oulu, Oulu, Finland
  27. 27National Institutes of Health and Welfare, Oulu, Finland
  28. 28Cedars-Sinai Medical Center and University of California, Los Angeles, California
  1. Corresponding author: Aldi T. Kraja, aldi{at}wustl.edu.
  • Deceased.

Abstract

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS.

RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected.

RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure.

CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.

Footnotes

  • Received August 27, 2010.
  • Accepted December 21, 2010.

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  1. Diabetes vol. 60 no. 4 1329-1339
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