A Bivariate Genome-Wide Approach to Metabolic Syndrome

Author:

Kraja Aldi T.1,Vaidya Dhananjay2,Pankow James S.3,Goodarzi Mark O.4,Assimes Themistocles L.5,Kullo Iftikhar J.6,Sovio Ulla7,Mathias Rasika A.2,Sun Yan V.8,Franceschini Nora9,Absher Devin10,Li Guo11,Zhang Qunyuan1,Feitosa Mary F.1,Glazer Nicole L.11,Haritunians Talin12,Hartikainen Anna-Liisa13,Knowles Joshua W.5,North Kari E.14,Iribarren Carlos15,Kral Brian2,Yanek Lisa2,O’Reilly Paul F.16,McCarthy Mark I.17,Jaquish Cashell18,Couper David J.19,Chakravarti Aravinda20,Psaty Bruce M.21,Becker Lewis C.2,Province Michael A.1,Boerwinkle Eric22,Quertermous Thomas5,Palotie Leena23,Jarvelin Marjo-Riitta16242526,Becker Diane M.2,Kardia Sharon L.R.8,Rotter Jerome I.12,Chen Yii-Der Ida27,Borecki Ingrid B.1

Affiliation:

1. Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, Missouri

2. The GeneSTAR Research Program, Johns Hopkins University, Baltimore, Maryland

3. Department of Epidemiology, University of Minnesota, Minneapolis, Minnesota

4. Division of Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center, Los Angeles, California

5. Department of Medicine, Stanford University School of Medicine, Stanford, California

6. Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota

7. Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.

8. Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan

9. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

10. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama

11. Cardiovascular Health Research Unit and Departments of Medicine, University of Washington, Seattle, Washington

12. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California

13. Institute of Clinical Medicine, University of Oulu, Oulu, Finland

14. Department of Epidemiology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

15. Kaiser Foundation Research Institute, Oakland, California

16. Department of Biostatistics and Epidemiology, School of Public Health, Imperial College, Faculty of Medicine, London, U.K.

17. Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Churchill Hospital, Oxford, U.K.

18. Division of Epidemiology & Clinical Applications, National Heart, Lung, and Blood Institute, Bethesda, Maryland

19. Department of Biostatistics and Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

20. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland

21. Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, and Group Health Research Institute, Group Health Cooperative, Seattle, Washington

22. The University of Texas Health Science Center at Houston, Human Genetics Center, Houston, Texas

23. Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, U.K.

24. Institute of Health Sciences, University of Oulu, Oulu, Finland

25. Biocenter Oulu, University of Oulu, Oulu, Finland

26. National Institutes of Health and Welfare, Oulu, Finland

27. Cedars-Sinai Medical Center and University of California, Los Angeles, California

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.

Publisher

American Diabetes Association

Subject

Endocrinology, Diabetes and Metabolism,Internal Medicine

Reference50 articles.

1. Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Final report;Circulation,2002

2. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey;Ford;JAMA,2002

3. Prevalence of the metabolic syndrome and its components: findings from a Finnish general population sample and the Diabetes Prevention Study cohort;Ilanne-Parikka;Diabetes Care,2004

4. Metabolic syndrome and early-onset coronary artery disease: is the whole greater than its parts?;Iribarren;J Am Coll Cardiol,2006

5. Trends in metabolic syndrome and gene networks in human and rodent models;Kraja;Endocr Metab Immune Disord Drug Targets,2008

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3