A Bivariate Genome-Wide Approach to Metabolic Syndrome
- Aldi T. Kraja1⇓,
- Dhananjay Vaidya2,*,
- James S. Pankow3,
- Mark O. Goodarzi4,
- Themistocles L. Assimes5,
- Iftikhar J. Kullo6,
- Ulla Sovio7,
- Rasika A. Mathias2,
- Yan V. Sun8,
- Nora Franceschini9,
- Devin Absher10,
- Guo Li11,
- Qunyuan Zhang1,
- Mary F. Feitosa1,
- Nicole L. Glazer11,
- Talin Haritunians12,
- Anna-Liisa Hartikainen13,
- Joshua W. Knowles5,
- Kari E. North14,
- Carlos Iribarren15,
- Brian Kral2,
- Lisa Yanek2,
- Paul F. O’Reilly16,
- Mark I. McCarthy17,
- Cashell Jaquish18,
- David J. Couper19,
- Aravinda Chakravarti20,
- Bruce M. Psaty21,
- Lewis C. Becker2,
- Michael A. Province1,
- Eric Boerwinkle22,
- Thomas Quertermous5,
- Leena Palotie23,†,
- Marjo-Riitta Jarvelin24,25,26,27,
- Diane M. Becker2,
- Sharon L.R. Kardia8,
- Jerome I. Rotter12,*,
- Yii-Der Ida Chen28,* and
- Ingrid B. Borecki1,*
- 1Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, Missouri
- 2The GeneSTAR Research Program, Johns Hopkins University, Baltimore, Maryland
- 3Department of Epidemiology, University of Minnesota, Minneapolis, Minnesota
- 4Division of Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center, Los Angeles, California
- 5Department of Medicine, Stanford University School of Medicine, Stanford, California
- 6Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
- 7Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
- 8Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
- 9Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- 10HudsonAlpha Institute for Biotechnology, Huntsville, Alabama
- 11Cardiovascular Health Research Unit and Departments of Medicine, University of Washington, Seattle, Washington
- 12Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- 13Institute of Clinical Medicine, University of Oulu, Oulu, Finland
- 14Department of Epidemiology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- 15Kaiser Foundation Research Institute, Oakland, California
- 16Department of Biostatistics and Epidemiology, School of Public Health, Imperial College, Faculty of Medicine, London, U.K.
- 17Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Churchill Hospital, Oxford, U.K.
- 18Division of Epidemiology & Clinical Applications, National Heart, Lung, and Blood Institute, Bethesda, Maryland
- 19Department of Biostatistics and Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- 20McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- 21Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- 22The University of Texas Health Science Center at Houston, Human Genetics Center, Houston, Texas
- 23Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, U.K.
- 24Department of Biostatistics and Epidemiology, School of Public Health, Imperial College, Faculty of Medicine, London, U.K.
- 25Institute of Health Sciences, University of Oulu, Oulu, Finland
- 26Biocenter Oulu, University of Oulu, Oulu, Finland
- 27National Institutes of Health and Welfare, Oulu, Finland
- 28Cedars-Sinai Medical Center and University of California, Los Angeles, California
- Corresponding author: Aldi T. Kraja, .
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.
* D.V., J.I.R., Y.-D.I.C., and I.B.B. are from the STAMPEED MetS Steering Committee.
This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db10-1011/-/DC1.
- Received August 27, 2010.
- Accepted December 21, 2010.
- © 2011 by the American Diabetes Association.
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