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Research Article

A Genome-Wide Association Study Reveals Variants in ARL15 that Influence Adiponectin Levels

  • J. Brent Richards mail,

    brent.richards@mcgill.ca

    Affiliations: Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada, Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

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  • Dawn Waterworth,

    Affiliation: Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America

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  • Stephen O'Rahilly,

    Affiliation: University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

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  • Marie-France Hivert,

    Affiliations: General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America

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  • Ruth J. F. Loos,

    Affiliation: Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom

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  • John R. B. Perry,

    Affiliations: Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom, Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom

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  • Toshiko Tanaka,

    Affiliations: Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, United States of America, Medstar Research Institute, Baltimore, Maryland, United States of America

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  • Nicholas John Timpson,

    Affiliation: Medical Research Council Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, United Kingdom

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  • Robert K. Semple,

    Affiliation: University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

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  • Nicole Soranzo,

    Affiliations: Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom

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  • Kijoung Song,

    Affiliation: Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America

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  • Nuno Rocha,

    Affiliation: University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

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  • Elin Grundberg,

    Affiliations: Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada, McGill University and Genome Québec Innovation Center, Montréal, Québec, Canada

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  • Josée Dupuis,

    Affiliation: Department of Biostatistics, School of Public Health, Boston University, Boston, Massachusetts, United States of America

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  • Jose C. Florez,

    Affiliations: Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, Center for Human, Genetic Research and Diabetes Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

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  • Claudia Langenberg,

    Affiliation: Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom

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  • Inga Prokopenko,

    Affiliation: Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

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  • Richa Saxena,

    Affiliations: Center for Human, Genetic Research and Diabetes Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America

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  • Robert Sladek,

    Affiliation: Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada

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  • Yurii Aulchenko,

    Affiliation: Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

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  • David Evans,

    Affiliation: Medical Research Council Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, United Kingdom

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  • Gerard Waeber,

    Affiliation: Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, Lausanne, Switzerland

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  • Jeanette Erdmann,

    Affiliation: Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany

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  • Mary-Susan Burnett,

    Affiliation: Cardiovascular Research Institute, Washington Hospital Center, Washington, District of Columbia, United States of America

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  • Naveed Sattar,

    Affiliation: British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom

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  • Joseph Devaney,

    Affiliation: Cardiovascular Research Institute, Washington Hospital Center, Washington, District of Columbia, United States of America

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  • Christina Willenborg,

    Affiliations: Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany, Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany

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  • Aroon Hingorani,

    Affiliation: Centre for Clinical Pharmacology, University College, London, United Kingdom

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  • Jaquelin C. M. Witteman,

    Affiliation: Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

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  • Peter Vollenweider,

    Affiliation: Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, Lausanne, Switzerland

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  • Beate Glaser,

    Affiliation: Medical Research Council Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, United Kingdom

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  • Christian Hengstenberg,

    Affiliation: Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany

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  • Luigi Ferrucci,

    Affiliation: Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, United States of America

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  • David Melzer,

    Affiliation: Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom

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  • Klaus Stark,

    Affiliation: Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany

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  • John Deanfield,

    Affiliation: Cardiothoracic Unit, Great Ormond Street Hospital for Children National Health Service Trust, London, United Kingdom

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  • Janina Winogradow,

    Affiliation: Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany

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  • Martina Grassl,

    Affiliation: Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany

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  • Alistair S. Hall,

    Affiliation: Leeds Institute of Genetics, Health and Therapeutics and Leeds Institute of Molecular Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom

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  • Josephine M. Egan,

    Affiliation: Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, Maryland, United States of America

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  • John R. Thompson,

    Affiliation: Departments of Health Sciences, University of Leicester, Leicester, United Kingdom

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  • Sally L. Ricketts,

    Affiliation: Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, United Kingdom

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  • Inke R. König,

    Affiliation: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany

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  • Wibke Reinhard,

    Affiliation: Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany

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  • Scott Grundy,

    Affiliation: Center for Human Nutrition, Department of Clinical Nutrition, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America

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  • H-Erich Wichmann,

    Affiliation: Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute of Medical Information Science, Biometry and Epidemiology, Ludwig-Maximilans-Universität, Munich, Germany

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  • Phil Barter,

    Affiliation: Heart Research Institute, Camperdown, Sydney, New South Wales, Australia

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  • Robert Mahley,

    Affiliation: Gladstone Institute of Neurological Disease and Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America

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  • Y. Antero Kesaniemi,

    Affiliation: Department of Internal Medicine and Biocenter Oulu, University of Oulu, Oulu, Finland

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  • Daniel J. Rader,

    Affiliation: Cardiovascular Institute and Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America

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  • Muredach P. Reilly,

    Affiliation: Cardiovascular Institute and Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America

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  • Stephen E. Epstein,

    Affiliation: Cardiovascular Research Institute, Washington Hospital Center, Washington, District of Columbia, United States of America

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  • Alexandre F. R. Stewart,

    Affiliation: Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

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  • Cornelia M. Van Duijn,

    Affiliation: Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

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  • Heribert Schunkert,

    Affiliation: Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany

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  • Keith Burling,

    Affiliation: University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom

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  • Panos Deloukas,

    Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom

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  • Tomi Pastinen,

    Affiliations: Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada, McGill University and Genome Québec Innovation Center, Montréal, Québec, Canada

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  • Nilesh J. Samani,

    Affiliation: Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom

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  • Ruth McPherson,

    Affiliation: Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

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  • George Davey Smith,

    Affiliation: Medical Research Council Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, United Kingdom

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  • Timothy M. Frayling,

    Affiliations: Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom, Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, United Kingdom

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  • Nicholas J. Wareham,

    Affiliation: Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom

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  • James B. Meigs,

    Affiliations: General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America

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  • Vincent Mooser equal contributor,

    equal contributor Contributed equally to this work with: Vincent Mooser, Tim D. Spector

    Affiliation: Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America

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  • Tim D. Spector equal contributor,

    equal contributor Contributed equally to this work with: Vincent Mooser, Tim D. Spector

    Affiliation: Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom

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  • GIANT Consortium
  • Published: December 11, 2009
  • DOI: 10.1371/journal.pgen.1000768

Abstract

The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P≤5×10−8). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P≤0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2×10−19 for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9×10−8, n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5×10−6, n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2×10−3, n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.

Author Summary

Through a meta-analysis of genome-wide association studies of 14,733 individuals, we identified common base-pair variants in the genome which influence circulating adiponectin levels. Since adiponectin is an adipocyte-derived circulating protein which has been inversely associated with risk of obesity-related diseases such as type 2 diabetes (T2D) and coronary heart disease (CHD), we next sought to understand if the identified variants influencing adiponectin levels also influence risk of T2D, CHD, and several metabolic traits. In addition to confirming that variation at the ADIPOQ locus influences adiponectin levels, our analyses point to a variant in the ARL15 (ADP-ribosylation factor-like 15) locus which decreases adiponectin levels and increases risk of CHD and T2D. Further, this same variant was associated with increased fasting insulin levels and glycated hemoglobin. While the function of ARL15 is not known, we provide insight into the tissue specificity of ARL15 expression. These results thus provide novel insights into the physiology of the adiponectin pathway and obesity-related diseases.

Introduction

Adiponectin is an adipocyte-secreted protein that increases insulin sensitivity [1],[2],[3], and has anti-diabetic [4],[5],[6] and anti-atherogenic effects [7]. Several features render adiponectin an attractive and tractable biomarker for large epidemiologic studies, such as its long half-life, high ex vivo stability, and minimal diurnal variability [8],[9].

While adiponectin levels are highly heritable (30–70%) [10],[11],[12], several well-designed studies have shown variable association between common polymorphisms in the adiponectin gene (ADIPOQ), possibly due to small sample sizes and different panels of single nucleotide polymorphisms (SNPs), ethnicities and clinical outcomes [12],[13],[14]. This has lead some observers to call for a more complete and systematic characterization of the genetic determinants of adiponectin levels [12].

Our study therefore sought to address 2 questions: first, what are the common genetic determinants of adiponectin levels both at ADIPOQ and elsewhere? And second, do the variants robustly associated with adiponectin levels influence metabolic traits and risk of metabolic disease?

To comprehensively assess the influence of common genetic variation on circulating adiponectin levels, we undertook a large-scale meta-analysis of 3 genome-wide association studies (GWAS) for circulating adiponectin levels from population-based cohorts (n = 8,531 participants). From this first stage, we chose SNPs most strongly associated with adiponectin levels (P<10−4, n = 250), and tested these for their association with adiponectin in 5 additional population-based cohorts (n = 6,202). The 5 SNPs which achieved genome-wide significance in the combined stage were then tested for their association with: type 2 diabetes mellitus (T2D) in the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium [15] (n = 10,128); indices of insulin resistance in the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) [16] (n = 24,188); risk of coronary heart disease (CHD) in a consortium of 8 cohorts with available genome-wide association data (n = 22,421); and body mass index (BMI) in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (Text S1) [17],[18] (n = 32,527) (Figure 1).

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Figure 1. Overall study design.

doi:10.1371/journal.pgen.1000768.g001

Results

Genome-Wide Association Study for Circulating Adiponectin Levels

To identify genetic variants influencing adiponectin levels, we performed a GWAS utilizing information from population-based cohorts including, in total, 14,733 subjects of European descent (Table 1). We identified 5 variants at 2 loci that achieved genome-wide significance (P≤5×10−8) for their relationship with circulating adiponectin levels (Table 2). The SNP most strongly associated with circulating adiponectin levels lies 30 kb upstream of the ADIPOQ locus (rs266717; P-combined = 9.2×10−19) (Table 2, Figure S1, Figure S2). In total, 4 SNPs at the ADIPOQ locus demonstrated genome-wide significant associations with circulating adiponectin. All 8 studies contributed to these genome-wide significant associations, with the exception of rs6444175, which demonstrated some heterogeneity across cohorts (Table 2).

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Table 1. Participant characteristics (n total for all cohorts = 14,733).

doi:10.1371/journal.pgen.1000768.t001
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Table 2. Relationship of SNPs achieving genome-wide significance for their association with adiponectin levels (n = 14,733 from the 8 studies in Table 1).

doi:10.1371/journal.pgen.1000768.t002

Our results also identified a novel intronic SNP (rs4311394) located in the ARL15 (ADP-ribosylation factor-like 15) gene whose G allele was robustly associated with decreased adiponectin levels (P = 2.9×10−8) (Table 2, Table S3, Figure 2). ARL15 is an ADP-ribosylation factor-like GTP-binding protein, whose function is unknown, yet belongs to a family of proteins involved in intracellular vesicle trafficking [19].

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Figure 2. Association between SNPs near ARL15 and adiponectin levels.

(A) −Log(P-value) measures for association between single nucleotide polymorphisms (SNPs) and chromosomal position. (B) Linkage disequilibrium in GOLD heat map Haploview 4.0 color scheme, CEPH (Centre d'Étude du Polymorphisme Humain) population. The x axis represents genomic position in Mb (A) and in kb (B). All P-values are derived from the discovery meta-analysis of CoLaus, TwinsUK, and Genetic Etiology of Metabolic Syndrome (GEMS) cohorts, except that for the lead SNP, rs4311394 (in red), which is derived from the combined P-value from the CoLaus, TwinsUK, GEMS, Framingham, InCHIANTI, Baltimore Longitudinal Study of Aging (BLSA), Avon Longitudinal Study of Parents and Children (ALSPAC), and European Prospective Investigation of Cancer-Norfolk (EPIC-Norfolk) cohorts.

doi:10.1371/journal.pgen.1000768.g002

Association with Metabolic Disease and Metabolic Traits

Since glycemia, T2D and CHD have been correlated with adiponectin levels, we tested whether genome-wide significant SNPs for adiponectin levels were associated with glycemia, indices of insulin resistance, and risk of T2D and CHD. Since 5 SNPs (which, due to linkage disequilibrium, represented 4.59 independent statistical tests [see Methods]) were tested for their association with T2D, CHD and metabolic traits, we employed a conservative Bonferroni-corrected threshold of α = 0.011 (where 0.011 = 0.05/4.59) to declare statistical significance for these metabolic diseases and traits. None of the SNPs at the ADIPOQ locus demonstrated a robust relationship with T2D, CHD, homeostasis model assessment insulin resistance (HOMA-IR), homeostasis model assessment beta-cell function (HOMA-B) or BMI (Table 3, Table 4, Table S4). However rs1648707, at ADIPOQ, was associated with a non-statistically significant trend for its relationship with CHD (P = 0.04) and T2D (P = 0.046).

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Table 3. Association of genome-wide significant SNPs with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD) (n = 10,128 for T2D; n = 22,421 for CHD).

doi:10.1371/journal.pgen.1000768.t003
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Table 4. Association of genome-wide significant SNPs with indices of insulin homeostasis.

doi:10.1371/journal.pgen.1000768.t004

In contrast, the risk allele rs4311394-G at ARL15, which was associated with lower adiponectin levels, was also associated with: an increased risk of CHD in a consortium of 7 CHD cohorts (Odds ratio [OR] = 1.12, [95% Confidence Interval [CI]: 1.06, 1.17], P = 8.5×10−6, n = 22,421); an increased risk of T2D in the DIAGRAM consortium [15] (OR = 1.11 [95% CI: 1.03, 1.18], P = 3.2×10−3, n = 10,128); and higher glycated hemoglobin in the European Prospective Investigation of Cancer-Norfolk (EPIC-Norfolk) cohort (0.025% per G allele [95% CI: 0.01, 0.04], P = 5.0×10−4, n = 14,168) (Table 3). In the MAGIC consortium [16], the rs4311394-G allele was associated with increased levels of fasting insulin (P = 2.3×10−3, n = 24,614), and demonstrated non-significant trends towards higher HOMA-IR (P = 0.01, n = 24,188) and HOMA-B (P = 0.02, n = 24,130) (Table 4). In the GIANT consortium [17], the same allele demonstrated a modest and non-significant association with decreased BMI (P = 0.016, n = 32,527) (Table S4), indicating that the disease and metabolic trait associations of rs4311394-G are unlikely to be mediated through an increase in BMI.

Thus, in sum, the G allele at rs4311394 was consistently associated with an increased risk of T2D and CHD, as well as deleterious changes in the 5 metabolic traits tested.

Expression Studies

Since the function and distribution of ARL15 expression is unknown, we assessed the level of ARL15 mRNA expression in human tissues using quantitative real-time PCR across a wide set of human tissues. We identified that ARL15 was expressed most abundantly in skeletal muscle at a level 4-fold that of the mean of all other tissues, with adipose expression detectable but low (Figure 3). Using biopsied tissue from insulin-sensitive tissues (liver, skeletal muscle and adipose tissue) in healthy volunteers, immunoblots confirmed ARL15 expression in skeletal muscle, although it was detectable in all 3 tissues (Figure 4).

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Figure 3. Tissue distribution of ARL15 expression.

mRNA levels determined by quantitative real-time PCR in a panel of human tissues.

doi:10.1371/journal.pgen.1000768.g003
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Figure 4. Western blot showing ARL15 expression in insulin-responsive tissues in humans with α-tubulin as a loading control.

HEK293 = untransfected HEK293 cells; ARL15 = HEK293 cells transiently expressing wild type human ARL15. SkM = skeletal muscle; WAT = white adipose tissue.

doi:10.1371/journal.pgen.1000768.g004

Discussion

By conducting a GWAS for the adipocyte-derived protein adiponectin, we have identified a novel susceptibility variant in ARL15, which is associated with lower adiponectin levels and increased risk of T2D and CHD. Our results also help clarify which variants at ADIPOQ influence adiponectin levels, thus expanding our understanding of the adiponectin pathway.

ARL15 is widely expressed [20]. However its function is unknown, and there have been no phenotypes previously associated with this gene. Based on its predicted protein sequence, ARL15 is structurally similar to ADP-ribosylation factors and Ras-related GTP-binding proteins which play key roles in the regulation of intracellular vesicle trafficking [19], and which have been specifically implicated in insulin signaling and insulin-stimulated glucose transport [21],[22],[23],[24]. Our preliminary data demonstrate that ARL15 is expressed in insulin-responsive tissues, including adipose tissue. Interestingly, expression was highest in skeletal muscle, which is the main site of insulin-mediated glucose disposal, but which does not synthesize adiponectin. Thus, ARL15 is a good candidate to be involved in cellular insulin resistance and/or adiponectin trafficking and secretion. Its implication in metabolic diseases by a non-hypothesis-based genetic approach provides strong impetus for further functional studies.

Our study sheds further light on the role of ADIPOQ SNPs on adiponectin levels — which has been the source of several inconsistent reports [12],[13],[14],[25] — since we have systematically tested all common HapMap CEPH (Centre d'Étude du Polymorphisme Humain) SNPs through genotyping and imputation across the ADIPOQ locus in 14,733 individuals (Figure S2). Among the SNPs previously associated with adiponectin levels at ADIPOQ, the rs1648707 SNP achieved genome-wide significance in our analysis for adiponectin. rs1648707 is in moderate linkage disequilibrium with rs266729 (r2 = 0.74), which has previously been associated with adiponectin levels, but not consistently with T2D [12]. We did not assess rare variants, and were thus unable to test the association of rs17366743 (minor allele frequency = 0.075) with adiponectin levels, which has been previously associated with T2D and with fasting glucose, but not with adiponectin levels [13].

Interestingly, ADIPOQ SNPs that showed genome-wide significant associations with adiponectin levels did not show associations with T2D or CHD. This raises the question of how ARL15 interacts with adiponectin to influence disease risk. The demonstrated relationship of ARL15 with the metabolic traits and diseases may represent adiponectin-independent effects of ARL15 — a hypothesis that could be tested by adjusting the relationship between ARL15 and CHD or T2D for adiponectin levels (which was not possible in this study, as the disease cohorts had no measured adiponectin levels). Alternatively, recent evidence suggests that adiponectin may be influenced directly by insulin exposure [26][35], allowing adiponectin to act as a surrogate marker for integrated total insulin exposure as a result of its stable half-life and relatively low diurnal variability. Consequently, ARL15 may be an upstream mediator of the relationship between insulin and adiponectin, and may thus impact upon T2D and CHD through an insulin-dependent pathway which involves, but is not entirely dependent upon, adiponectin. In addition, since we demonstrated that the ARL15 variant was associated with adiponectin levels across all age ranges, including children in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, this variant likely affects lifelong adiponectin levels, which may influence its relationship with T2D and CHD.

In conclusion, this study expands our understanding of the genetic influences on adiponectin levels. We have implicated a novel locus, ARL15, in the regulation of adiponectin levels and clarified the role of variants near ADIPOQ on adiponectin levels. Finally, we provide further evidence that the variant at ARL15 may influence risk of T2D and CHD, thus providing impetus for further study of ARL15.

Methods

We undertook a GWAS to detect SNPs which were associated with adiponectin, and tested the physiologic and clinical relevance of these SNPs by assessing their association with indices of glucose homeostasis and BMI in European populations, and with T2D and CHD in large clinical cohorts (Figure 1).

Ethical Considerations

All studies including biopsy of liver, skeletal muscle or adipose tissue from healthy volunteers for immunoblotting studies were approved by institutional ethics review committees at the relevant organizations. All participants provided informed written consent.

Study Populations

The first stage of the GWAS for adiponectin levels was performed in 3 population-based cohorts utilizing subjects of self-described European ancestry, which were not selected for diabetes, heart disease or any metabolic trait (Table 1). The discovery cohorts included CoLaus [36], TwinsUK [37],[38], and Genetic Etiology of Metabolic Syndrome (GEMS) [39]. Participants of the CoLaus study were individuals of European ancestry, randomly selected from 56,694 permanent residents of Lausanne, Switzerland, between the ages of 35 and 75 years. Recruitment took place between April 2003 and March 2006. TwinsUK is a population-based sample of British twins, which is representative of the general United Kingdom population, and is extensively phenotyped for aging-related traits [40]. GEMS is a case-control study of dyslipidemic individuals between the ages of 20 and 65 years. Cases and controls were matched based on gender and recruitment site. The GEMS and CoLaus studies were sponsored in part by GlaxoSmithKline. All participants were informed of this sponsorship, and consented for the use of their data and biologic samples by GlaxoSmithKline and its subsidiaries.

The validation cohorts included the Framingham Offspring Study (FOS) [13], Baltimore Longitudinal Study of Aging (BLSA) [41], InCHIANTI [42],[43], ALSPAC [44] and EPIC-Norfolk [45]. The FOS is a population-based sample of residents of Framingham, Massachusetts. Adiponectin was measured at exam 7 (1998–2002). BLSA is an observational study that began in 1958 to study normative aging in a cohort of healthy persons 17 years of age and older at study entry. InCHIANTI is a population-based cohort designed to study aging-related traits and disease from the Chianti geographic region (Tuscany, Italy). ALSPAC is a population-based birth cohort study consisting initially of over 13,000 women and their children recruited in the county of Avon, UK, in the early 1990s. The EPIC-Norfolk cohort is a British population-based study of white persons recruited from Norfolk, UK, between 1993 and 1997. All individuals in all replication cohorts were of self-described European descent.

Phenotyping and Genotyping for Metabolic Traits, T2D, and CHD

Only the SNPs which achieved genome-wide significance for adiponectin levels in the combined analysis of data from all 8 cohorts were assessed for their relationship with adiposity-driven diseases and traits, which included: T2D, CHD, fasting glucose, glycated hemoglobin, BMI and insulin, as well as measures of insulin resistance (HOMA-IR) and beta-cell function (HOMA-B) estimated by the homeostasis model [46].

T2D risk was estimated from the DIAGRAM consortium (a meta-analysis of 3 T2D genome-wide association scans [http://www.well.ox.ac.uk/DIAGRAM/], which included 4,107 T2D cases and 5,187 controls). The 3 populations were the Wellcome Trust Case Control Consortium (WTCCC), the Finland-United States Investigation of NIDDM [Non-Insulin-Dependent Diabetes Mellitus] Genetics (FUSION), and the Diabetes Genetics Initiative (DGI). A full description of this meta-analysis is available elsewhere [15],[47].

The association between susceptibility alleles and fasting glucose, insulin and measures of insulin resistance and beta-cell function were tested in MAGIC [16]. This consortium includes data from 36,610 individuals of European descent who were included in 4 distinct consortia: [a] The European Network for Genetic and Genomic Epidemiology (ENGAGE) project, combining data from deCODE, Northern Finland Birth Cohort 1966, Netherlands Twins Register/Netherlands Study of Depression and Anxiety and the Rotterdam study; [b] the GEMS study, which includes data from the CoLaus and TwinsUK scans; [c] DFS, which includes the DGI, FUSION and SardiNIA scans; and [d] the Framingham Heart Study. Details of all of these studies, phenotyping and genotyping protocols have been published previously [16].

The association between susceptibility alleles and CHD was tested in 8 cohorts (n = 22,421). These cohorts included PennCath [48], MedStar, the Ottawa Heart Study [49], the WTCCC coronary heart disease (CAD) study [50],[51], a case-control study of CHD nested in the EPIC-Norfolk cohort comprising participants with available genome-wide data [52], German Myocardial Infarction Family Study (GerMIFS) I and GerMIFS II [50],[53], and the Rotterdam Study [54] (Table S2). The rs4311394 SNP was assessed by imputation in the GerMIFS I cohort, and did not meet quality control criteria. Thus, results for this SNP are reported for all cohorts except GerMIFS I (Figure S3). All other SNPs were assessed in all cohorts.

Associations with BMI were tested in the GIANT consortium [17],[18], which encompasses 15 cohorts of 32,527 individuals of European descent. It has been described in detail previously, including information on genotyping and phenotyping [17].

Genotyping

Table S1 outlines the genotyping methods used for each cohort, individual and SNP exclusion thresholds, and imputation algorithms. For the CoLaus and GEMS studies, genotypes were obtained using the Affymetrix Genechip Human Mapping 500k array with the Bayesian Robust Linear Modeling using Mahalanobis distance (BRLMM) algorithm [52]. The TwinsUK samples were genotyped using the Illumina calling algorithm on the Illumina HumanHap300, HumanCNV370 Duo and HumanHap 550 [40]. The FOS employed the Affymetrix 500k and MIPS 50k genotyping arrays. Both the BLSA and InCHIANTI cohorts used the Illumina Human Hap 550 genotyping arrays, while the Illumina Human Hap300 array was used in the ALSPAC cohort. Targeted genotyping was performed in the EPIC-Norfolk cohort using TaqMan SNP genotyping assay (Applied Biosystems, Warrington, UK) according to the manufacturer's protocol. Genotype frequencies were in Hardy Weinberg Equilibrium (HWE) (P>0.50), call rates were >94% and concordances were >98% for the TaqMan assay.

Adiponectin Measurement

The TwinsUK and EPIC-Norfolk cohorts measured adiponectin levels with an in-house 2-site enzyme-linked immunosorbent assay (ELISA) using antibodies and standards from R&D Systems Europe (Abingdon, Oxford, UK) in plasma. The day-to-day coefficients of variation (CV) for adiponectin were 5.4%, 5.2%, and 5.8% at a concentration of 3.6 µg/ml, 9.2 µg/ml, and 15.5 µg/ml, respectively [38]. The FOS, CoLaus and GEMS measured adiponectin using the ELISA assay (R&D Systems, Minneapolis, Minnesota, United States of America; Intra-assay CV: 5.8%) [13]. Importantly, while CoLaus and GEMs measured adiponectin in plasma, the FOS measured adiponectin in serum. The ALSPAC cohort measured adiponectin using a commercially available ELISA kit (R&D systems, Oxon, UK) previously validated against the corresponding radio-immunoassay (RIA). The inter-assay CV for this adiponectin assay was <7.5%. The InCHIANTI and BLSA studies measured adiponectin levels using the adiponectin RIA assay of Linco Research (St. Charles, Missouri, USA). The detectable ranges for the RIA assay used in InCHIANTI and BLSA are 0.78 µg/ml–200 µg/ml.

Expression Experiments

Relative levels of ARL15 mRNA in human tissues were assessed by quantitative real-time PCR of a commercially available human tissue panel of RNA (AMS Biotechnology, Abingdon, UK). 500 ng of RNA were reverse-transcribed using 125 ng of random hexamers and 500 µM deoxynucleotide triphosphates (dNTPs) (both from Promega, Madison, Wisconsin, USA) and 500 ng of Superscript III reverse transcriptase (Invitrogen). Gene expression was quantified on an ABI7900 Real-Time PCR system (Applied Biosystems, Foster City, California, USA) in TaqMan Mastermix (Applied Biosystems). Primers and probe for ARL15 were supplied by Applied Biosystems (ABI Hs00219491_m1), and ARL15 expression was normalized to expression of PPIA (Cyclophilin A). PPIA primers (5′-ACGGCGAGCCCTTGG-3′ (sense), 5′- TTTCTGCTGTCTTTGGGACCT-3′ (antisense)) and probe (5′-[FAM] CGCGTCTCCTTTGAGCTGTTTGCA[TAMRA]-3′) were synthesized by Sigma-Aldrich. Skeletal muscle biopsies were a gift from Dr Anna Krook, from the Karolinska Institute. Frozen skeletal muscle, liver and white adipose tissue samples were homogenized in lysis buffer (50 mM Tris-HCl, pH8.0, 150 mM NaCl, 1 mM EDTA, 1% (v/v) Triton X-100, and Complete Protease Inhibitor Cocktail [Roche]), and cell debris removed by centrifugation. Cleared supernatants were boiled in sodium dodecyl sulphate (SDS) sample buffer and run on an SDS polyacrylamide gel before transfer to a polyvinylidene difluoride (PVDF) membrane (Amersham) and subsequent immunoblotting with either purified rabbit anti-human ARL15 antibody (Proteintech Group) or anti-α-tubulin antibody (sc-8035; Santa Cruz Biotechnology). Full-length human wild type ARL15 cDNA was purchased from Open Biosystems and subcloned into pCDNA 3.1 (Invitrogen) using the XhoI and HindIII restriction sites. HEK293 cells (American Type Culture Collection [ATCC]) were transiently transfected using the CalPhos Mammalian Tranfection Kit (Clontech) according to the manufacturer's instructions.

Statistical Methods

In all cohorts, the adiponectin concentrations were natural logarithm transformed to create a normally distributed phenotype. Adiponectin levels were subsequently adjusted for age, sex and BMI — important correlates of adiponectin levels [4],[5]. All results reported for association of genetic variants with adiponectin levels are adjusted for age, sex and BMI. All statistical tests assumed an additive effect of the effect allele. In the TwinsUK cohort, we found that there was little difference when comparing results both adjusted, and unadjusted, for BMI (the Spearman coefficients for the beta coefficients was 0.94 and 1.0 for P-values [P-values for both Spearman coefficients<1×10−5]).

The SNPTEST software program [51] was used to perform genome-wide association testing in the GEMS and CoLaus cohorts, while the Merlin software package [55] was used to perform association testing in the TwinsUK cohort. The meta-analysis of the discovery phase cohorts (CoLaus, TwinsUK and GEMS) was performed using Liptak-Stouffer's method for combination of independent tests, where P-values are converted to Z-scores by a standard normal curve and weighted by each study's sample size [56].

All SNPs that achieved a combined P-value of ≤10−4 in the meta-analysis (n = 250) were tested for their association in the additional cohorts (InCHIANTI, BLSA, ALSPAC and the Framingham Offspring Cohort). Two SNPs that were not near the ADIPOQ locus, and which demonstrated associations of ≤5×10−7 with adiponectin levels in the combined analysis, were further verified in an additional replication cohort (EPIC-Norfolk), where association with adiponectin was tested using a generalized linear model. For the quantitative trait analyses, individuals with known T2D were excluded. For the T2D case-control analyses, each SNP was tested for association using a logistic regression analysis, adjusted for age, sex and BMI. All analyses for the EPIC-Norfolk cohort were performed with SAS 9.1 (SAS Institute Inc., Cary, North Carolina, USA). To perform a meta-analysis of all replication and discovery cohorts, we employed inverse-variance techniques in the STATA software package (College Station, Texas, USA).

We declared statistical significance in the GWAS as P≤5×10−8, where this threshold is based on a Bonferroni correction of α = 0.05 divided by one million, the estimated number of independent common tests among common SNPs in the CEU population of the HapMap II project [57]. Using this threshold, 5 SNPs achieved genome-wide significance for their relationship with circulating adiponectin levels in the combined analysis of all adiponectin cohorts. These were subsequently tested for their association with glycated hemoglobin, indices of insulin resistance, beta-cell function and risk of T2D and CHD. The number of independent statistical tests represented by these 5 SNPs, accounting for linkage disequilibrium at ADIPOQ, was assessed by spectral decomposition of matrices of pairwise linkage disequilibrium between the 4 SNPs at the ADIPOQ locus [58]. In total, 3.59 independent statistical tests were performed at this locus, and one at the ARL15 locus. Thus, statistical significance in the follow-up studies was declared at P≤0.011 (based on a Bonferroni correction of α = 0.05 divided by 4.59, the number of statistically independent SNPs tested in the follow-up analyses).

Since 2 cohorts measured adiponectin concentrations using an RIA method (BLSA and InCHIANTI) whilst all others used an ELISA method, and since one study, ALSPAC, was based on children, rather than adults, we tested for evidence of heterogeneity in the combined analysis using the Q-test P-value [59].

Supporting Information

Figure S1.

Association between SNPs near ADIPOQ and Adiponectin levels. (A) −log (P value) measures for association between SNPs and chromosomal position. (B) Entrez Genes. (C) Linkage disequilibrium in GOLD heat map Haploview 4.0 color scheme, CEPH population. The x axis represents genomic position in Mb (A) and in kb (B,C). All P values are derived from the discovery meta-analysis, except for the genome-wide significant SNPs (Table 2), which are derived from the combined P values from all cohorts (displayed in red).

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(1.13 MB TIF)

Figure S2.

Relationship of genome-wide significant SNPs from the current study with selected previously published SNPs at the ADIPOQ locus.

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Figure S3.

Forest Plot of Association of rs4311394 with Risk of CHD (total n = 22,421).

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Table S1..

Genotyping information for the adiponectin discovery and replication cohorts.

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Table S2.

Cohort information, case and control definitions for coronary heart disease cohorts. (A) Cohort information for coronary heart disease cohorts and (B) Case and control definitions for coronary heart disease cohorts.

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Table S3.

Quality control parameters for rs4311394 at ARL15 from each cohort involved in the adiponectin GWAS.

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(0.03 MB DOC)

Table S4.

Relationship of genome-wide significant SNPs with body mass index (BMI) in the GIANT consortium.

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Text S1.

Genetic Investigation of Anthropometric Traits (GIANT) Consortium.

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(0.05 MB DOC)

Acknowledgments

We thank all study participants, volunteers, and study personnel that made this consortium possible. DELFIA Adiponectin Assays were performed by the NIHR Cambridge Biomedical Research Centre, Core Biochemical Assay Laboratory. We would like to thank Renée Atallah for her help with the manuscript. We acknowledge the contributions of the GIANT consortium, which provided summary statistics for the relationship between the genome-wide significant SNPs and BMI. Members of the consortium are listed in Text S1.

Author Contributions

Ran meta-analysis: J. B. Richards, K. Song. Ran statistical analysis in studies: J. B. Richards, D. Waterworth, M.-F. Hivert, R. J. F. Loos, J. R. B. Perry, T. Tanaka, N. J. Timpson, K. Song, C. Langenberg, I. Prokopenko, R. Saxena, Y. Aluchenko, D. Evans, J. Erdmann, C. Willenborg, J. C. M. Witteman, B. Glaser, I. R. König, C. M. Van Duijn, T. M. Frayling. Designed studies: J. B. Richards, D. Waterworth, J. Dupuis, J. C. Florez, G. Waeber, J. Erdmann, M.-S. Burnett, J. Devaney, P. Vollenweider, C. Hengstenberg, L. Ferruci, D. Melzer, K. Stark, J. Deanfield, A. S. Hall, J. M. Egan, S. Grundy, P. Barter, Y. A. Kesaniemi, D. J. Rader, M. P. Reilly, S. E. Epstein, A. F. R. Stewart, N. J. Samani, R. McPherson, N. J. Wareham, J. B. Weigs, V. Mooser, T. D. Spector. Established the consortium: J. B. Richards, D. Waterworth, C. Hengstenberg, V. Mooser, T. D. Spector. Interpreted results and critically read the manuscript: J. B. Richards, D. Waterworth, S. O'Rahilly, M.-F. Hivert, R. J. F. Loos, J. R. B. Perry, T. Tanaka, N. J. Timpson, R. K. Semple, N. Soranzo, K. Song, N. Rocha, E. Grundberg, J. Dupuis, J. C. Florez, C. Langenberg, I. Prokopenko, R. Saxena, R. Sladek, Y. Aluchenko, D. Evans, G. Waeber, J. Erdmann, M.-S. Burnett, N. Sattar, J. Devaney, C. Willenborg, A. Hingorani, J. C. M. Witteman, P. Vollenweider, B. Glaser, C. Hengstenberg, L. Ferruci, D. Melzer, K. Stark, J. Deanfield, J. Winogradow, M. Grassl, A. S. Hall, J. M. Egan, J. R. Thompson, S. L. Ricketts, I. R. König, W. Reinhard, S. Grundy, H. E. Wichmann, P. Barter, R. Mahley, Y. A. Kesaniemi, D. J. Rader, M. P. Reilly, S. E. Epstein, A. F. R. Stewart, C. M. Van Duijn, H. Schunkert, K. Burling, P. Deloukas, T. Pastinen, N. J. Samani, R. McPherson, G. Davey Smith, T. M. Frayling, N. J. Wareham, J. B. Weigs, V. Mooser, T. D. Spector. Wrote the first draft of the manuscript: J. B. Richards. Coordinated/collected phenotypic information: D. W, G. Waeber, M.-S. Burnett, N. Sattar, J. Devaney, A. Hingorani, P. Vollenweider, C. Hengstenberg, K. Stark, J. Winogradow, M. Grassl, A. S. Hall, S. L. Ricketts, W. Reinhard, S. Grundy, H. E. Wichmann, P. Barter, R. Mahley, Y. A. Kesaniemi, D. J. Rader, M. P. Reilly, S. E. Epstein, N. J. Samani, R. McPherson, G. Davey Smith, J. B. Weigs, V. Mooser, T. D. Spector. Performed expression studies: S. O'Rahilly, R. K. Semple, N. Rocha, E. Grundberg, T. Pastinen. Coordinated GWA genotyping of studies: N. Soranzo, J. Erdmann, C. Hengstenberg, K. Stark, M. P. Reilly, H. Schunkert, P. Deloukas, T. D. Spector. Obtained funding: J. Dupuis, J. C. Florez, J. Erdmann, N. Sattar, C. Hengstenberg, A. S. Hall, J. M. Egan, J. R. Thompson, D. J. Rader, M. P. Reilly, S. E. Epstein, A. F. R. Stewart, N. J. Samani, R. McPherson, G. Davey Smith, N. J. Wareham, J. B. Weigs, T. D. Spector. Performed laboratory analyses: N. Sattar, K. Stark, J. M. Egan, K. Burling, R. McPherson. Provided data for the BMI trait: GIANT Consortium.

References

  1. 1. Hivert MF, Sullivan LM, Fox CS, Nathan DM, D'Agostino RB Sr, et al. (2008) Associations of adiponectin, resistin, and tumor necrosis factor-alpha with insulin resistance. J Clin Endocrinol Metab 93: 3165–3172.
  2. 2. Hung J, McQuillan BM, Thompson PL, Beilby JP (2008) Circulating adiponectin levels associate with inflammatory markers, insulin resistance and metabolic syndrome independent of obesity. Int J Obes (Lond) 32: 772–779.
  3. 3. Wannamethee SG, Tchernova J, Whincup P, Lowe GD, Rumley A, et al. (2007) Associations of adiponectin with metabolic and vascular risk parameters in the British Regional Heart Study reveal stronger links to insulin resistance-related than to coronory heart disease risk-related parameters. Int J Obes (Lond) 31: 1089–1098.
  4. 4. Lindsay RS, Funahashi T, Hanson RL, Matsuzawa Y, Tanaka S, et al. (2002) Adiponectin and development of type 2 diabetes in the Pima Indian population. The Lancet 360: 57–58.
  5. 5. Weyer C, Funahashi T, Tanaka S, Hotta K, Matsuzawa Y, et al. (2001) Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia. J Clin Endocrinol Metab 86: 1930–1935.
  6. 6. Spranger J, Kroke A, Mohlig M, Bergmann MM, Ristow M, et al. (2003) Adiponectin and protection against type 2 diabetes mellitus. Lancet 361: 226–228.
  7. 7. Pischon T, Girman CJ, Hotamisligil GS, Rifai N, Hu FB, et al. (2004) Plasma Adiponectin Levels and Risk of Myocardial Infarction in Men. JAMA: The Journal of the American Medical Association 291: 1730–1737.
  8. 8. Hoffstedt J, Arvidsson E, Sjolin E, Wahlen K, Arner P (2004) Adipose tissue adiponectin production and adiponectin serum concentration in human obesity and insulin resistance. J Clin Endocrinol Metab 89: 1391–1396.
  9. 9. Peake PW, Kriketos AD, Campbell LV, Shen Y, Charlesworth JA (2005) The metabolism of isoforms of human adiponectin: studies in human subjects and in experimental animals. Eur J Endocrinol 153: 409–417.
  10. 10. Comuzzie AG, Funahashi T, Sonnenberg G, Martin LJ, Jacob HJ, et al. (2001) The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome. Journal of Clinical Endocrinology and Metabolism 86: 4321–4325.
  11. 11. Vasseur F, Helbecque N, Dina C, Lobbens S, Delannoy V, et al. (2002) Single-nucleotide polymorphism haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians. Hum Mol Genet 11: 2607–2614.
  12. 12. Menzaghi C, Trischitta V, Doria A (2007) Genetic influences of adiponectin on insulin resistance, type 2 diabetes, and cardiovascular disease. Diabetes 56: 1198–1209.
  13. 13. Hivert MF, Manning AK, McAteer JB, Florez JC, Dupuis J, et al. (2008) Common variants in the adiponectin gene (ADIPOQ) associated with plasma adiponectin levels, type 2 diabetes, and diabetes-related quantitative traits: the Framingham Offspring Study. Diabetes.
  14. 14. Ling H, Waterworth DM, Stirnadel HA, Pollin TI, Barter PJ, et al. (2009) Genome-wide Linkage and Association Analyses to Identify Genes Influencing Adiponectin Levels: The GEMS Study. Obesity (Silver Spring).
  15. 15. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638–645.
  16. 16. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, et al. (2008) Variants in MTNR1B influence fasting glucose levels. Nat Genet.
  17. 17. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, et al. (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41: 25–34.
  18. 18. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, et al. (2008) Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 40: 768–775.
  19. 19. Gillingham AK, Munro S (2007) The small G proteins of the Arf family and their regulators. Annu Rev Cell Dev Biol 23: 579–611.
  20. 20. Thierry-Mieg D, Thierry-Mieg J (2006) AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biology 7: S12.
  21. 21. Hofmann I, Thompson A, Sanderson CM, Munro S (2007) The Arl4 family of small G proteins can recruit the cytohesin Arf6 exchange factors to the plasma membrane. Curr Biol 17: 711–716.
  22. 22. Fuss B, Becker T, Zinke I, Hoch M (2006) The cytohesin Steppke is essential for insulin signalling in Drosophila. Nature 444: 945–948.
  23. 23. Ishiki M, Klip A (2005) Minireview: recent developments in the regulation of glucose transporter-4 traffic: new signals, locations, and partners. Endocrinology 146: 5071–5078.
  24. 24. Hou JC, Pessin JE (2007) Ins (endocytosis) and outs (exocytosis) of GLUT4 trafficking. Curr Opin Cell Biol 19: 466–473.
  25. 25. Pollin TI, Tanner K, O'Connell JR, Ott SH, Damcott CM, et al. (2005) Linkage of plasma adiponectin levels to 3q27 explained by association with variation in the APM1 gene. Diabetes 54: 268–274.
  26. 26. Basu R, Pajvani UB, Rizza RA, Scherer PE (2007) Selective downregulation of the high molecular weight form of adiponectin in hyperinsulinemia and in type 2 diabetes: differential regulation from nondiabetic subjects. Diabetes 56: 2174–2177.
  27. 27. Blumer RM, van der Crabben SN, Stegenga ME, Tanck MW, Ackermans MT, et al. (2008) Hyperglycemia prevents the suppressive effect of hyperinsulinemia on plasma adiponectin levels in healthy humans. Am J Physiol Endocrinol Metab 295: E613–617.
  28. 28. Brame LA, Considine RV, Yamauchi M, Baron AD, Mather KJ (2005) Insulin and endothelin in the acute regulation of adiponectin in vivo in humans. Obes Res 13: 582–588.
  29. 29. Imagawa A, Funahashi T, Nakamura T, Moriwaki M, Tanaka S, et al. (2002) Elevated serum concentration of adipose-derived factor, adiponectin, in patients with type 1 diabetes. Diabetes Care 25: 1665–1666.
  30. 30. Lindstrom T, Frystyk J, Hedman CA, Flyvbjerg A, Arnqvist HJ (2006) Elevated circulating adiponectin in type 1 diabetes is associated with long diabetes duration. Clin Endocrinol (Oxf) 65: 776–782.
  31. 31. Galler A, Gelbrich G, Kratzsch J, Noack N, Kapellen T, et al. (2007) Elevated serum levels of adiponectin in children, adolescents and young adults with type 1 diabetes and the impact of age, gender, body mass index and metabolic control: a longitudinal study. Eur J Endocrinol 157: 481–489.
  32. 32. Leth H, Andersen KK, Frystyk J, Tarnow L, Rossing P, et al. (2008) Elevated levels of high-molecular-weight adiponectin in type 1 diabetes. J Clin Endocrinol Metab 93: 3186–3191.
  33. 33. Semple RK, Soos MA, Luan J, Mitchell CS, Wilson JC, et al. (2006) Elevated plasma adiponectin in humans with genetically defective insulin receptors. J Clin Endocrinol Metab 91: 3219–3223.
  34. 34. Semple RK, Halberg NH, Burling K, Soos MA, Schraw T, et al. (2007) Paradoxical elevation of high-molecular weight adiponectin in acquired extreme insulin resistance due to insulin receptor antibodies. Diabetes 56: 1712–1717.
  35. 35. Bluher M, Michael MD, Peroni OD, Ueki K, Carter N, et al. (2002) Adipose tissue selective insulin receptor knockout protects against obesity and obesity-related glucose intolerance. Dev Cell 3: 25–38.
  36. 36. Firmann M, Mayor V, Vidal PM, Bochud M, Pecoud A, et al. (2008) The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 8: 6.
  37. 37. Andrew T, Hart DJ, Snieder H, de LM, Spector TD, et al. (2001) Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res 4: 464–477.
  38. 38. Richards JB, Valdes AM, Burling K, Perks UC, Spector TD (2007) Serum adiponectin and bone mineral density in women. J Clin Endocrinol Metab 92: 1517–1523.
  39. 39. Stirnadel H, Lin X, Ling H, Song K, Barter P, et al. (2008) Genetic and phenotypic architecture of metabolic syndrome-associated components in dyslipidemic and normolipidemic subjects: the GEMS Study. Atherosclerosis 197: 868–876.
  40. 40. Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, et al. (2008) Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371: 1505–1512.
  41. 41. Shock NW, Greulich RC, Andres RA, Arenberg D, Costa PT, et al. (1984) Normal Human Aging: The Baltimore Longitudinal Study of Aging. NIH Publication No 84-2450.
  42. 42. Ferrucci L, Bandinelli S, Benvenuti E, Di Iorio A, Macchi C, et al. (2000) Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc 48: 1618–1625.
  43. 43. Melzer D, Perry JR, Hernandez D, Corsi AM, Stevens K, et al. (2008) A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet 4: e1000072.
  44. 44. Golding J, Pembrey M, Jones R (2001) ALSPAC–the Avon Longitudinal Study of Parents and Children. I. Study methodology. Paediatr Perinat Epidemiol 15: 74–87.
  45. 45. Harding AH, Sargeant LA, Welch A, Oakes S, Luben RN, et al. (2001) Fat consumption and HbA(1c) levels: the EPIC-Norfolk study. Diabetes Care 24: 1911–1916.
  46. 46. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis Model Assessment - Insulin Resistance and Beta-Cell Function from Fasting Plasma-Glucose and Insulin Concentrations in Man. Diabetologia 28: 412–419.
  47. 47. Rafiq S, Melzer D, Weedon MN, Lango H, Saxena R, et al. (2008) Gene variants influencing measures of inflammation or predisposing to autoimmune and inflammatory diseases are not associated with the risk of type 2 diabetes. Diabetologia 51: 2205–2213.
  48. 48. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, et al. (2006) A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity-specific risk of myocardial infarction. Nat Genet 38: 68–74.
  49. 49. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, et al. (2007) A common allele on chromosome 9 associated with coronary heart disease. Science 316: 1488–1491.
  50. 50. Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, et al. (2007) Genomewide association analysis of coronary artery disease. N Engl J Med 357: 443–453.
  51. 51. Consortium WTCC (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.
  52. 52. Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, et al. (2008) LDL-cholesterol concentrations: a genome-wide association study. Lancet 371: 483–491.
  53. 53. Erdmann J, Großhennig A, Braund P, König I, Hengstenberg C, et al. (2009) New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nature Genetics. In Press.
  54. 54. Hofman A, Breteler MM, van Duijn CM, Krestin GP, Pols HA, et al. (2007) The Rotterdam Study: objectives and design update. Eur J Epidemiol 22: 819–829.
  55. 55. Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2002) Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30: 97–101.
  56. 56. Folks JL (1984) Combination of independent tests;. In: Krishnaiah PR, Sen PK, editors. Amsterdam, North-Holland: Elsevier Science Publ Co.
  57. 57. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.
  58. 58. Nyholt DR (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74: 765–769.
  59. 59. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. Bmj 327: 557–560.