Meta-analysis of SNP associations with body mass index in >339,000 individuals gives new genetic and biological insights into the underpinnings of obesity. E. K. Speliotes1,2, A. E. Locke3, S. Berndt4, B. Kahali1,2, A. Justice5, T. Pers6, J. Yang7, F. Day8, S. Gustafsson9, C. Powell1,2, S. Vedantam5,10,11, D. C. Croteau-Chonka12,13, T. Winkler14, A. Scherag15, I. Barroso16,17, J. S. Beckmann18,19, C. M. Lindgren20, C. J. Willer21, P. Visscher7, K. L. Mohlke12, K. E. North5, E. Ingelsson20,22, J. N. Hirschhorn5,10,11, R. J. F. Loos8,23 for the GIANT Consortium 1) Internal Med, Gastroenterology, University of Michigan, Ann Arbor, MI; 2) Department of Computational Medicine and Bioinformatics ,University of Michigan, Ann Arbor, Michigan, USA; 3) Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA; 4) Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA; 5) Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA; 6) Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA; 7) University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia; 8) MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK; 9) Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden; 10) Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, Massachusetts 02115, USA; 11) Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA; 12) Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA; 13) Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; 14) Public Health and Gender Studies, Institute of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany; 15) Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Germany; 16) Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK; 17) University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, CB2 OQQ, Cambridge, UK; 18) Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland; 19) Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland; 20) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK; 21) Department of Cardiovascular Medicine, Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA; 22) Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; 23) Mount Sinai School of Medicine, New York, NY.
Obesity is a heritable condition that affects more than a third of the U.S. population. It can predispose to development of metabolic disease but has few effective treatments. To elucidate the genetic underpinnings of obesity, we conducted the largest meta-analysis of SNP associations with body mass index (BMI), the most common measure of obesity, combining data from up to 339,224 individuals from 125 studies. We confirmed 40 established obesity loci and identified 57 new loci associated with BMI (P < 5x10-8). We used GCTA (Yang 2011) to identify second signals (P < 5x10-8) at 5 associated loci (FANCL, NLRC3/ADCY9, GPRC5B/GP2, BDNF, MC4R). Polygene analysis indicates that substantial additional heritability is captured by SNPs below the threshold of genome-wide significance. To identify genes and pathways that influence BMI, we use novel methods to integrate eQTL, functional variant, literature connection, gene expression, protein-protein interaction, mouse knockout phenotype, and pathway database information at associated loci. Our data confirm a prominent role for central nervous system regulation of BMI, and extend previous work by newly implicating additional biological processes as regulators of human body mass. These include CNS processes such as synaptic function, cell-cell adhesion and glutamate signaling (PCDH9, CADM2, NRXN3, NEGR1, GRID1), as well as peptide biology (GRP, SCG3), lipid metabolism (NPC1, DGKG), and glucose/insulin action (RPTOR, FOXO3, TCF7L2, GIPR, IRS1). Of note, one of the proposed mechanisms of topiramate, a component of a recently approved anti-obesity drug is an effect on CNS glutamate signaling, supporting this and other pathways that we identify as possible targets for obesity intervention. Interestingly, whereas most BMI associated loci have effects on related metabolic diseases/traits in expected epidemiological directions, some novel BMI SNPs display unexpected patterns of association with the BMI-increasing allele being protective for disease risk (e.g., TCF7L2 for type 2 diabetes and IRS1 for cardiovascular disease; P < 0.0005). Such pleiotropy begins to define a shared genetic etiology between BMI and metabolic disease, which may help explain why some but not all obese individuals develop metabolic disease. These results greatly enhance our understanding of BMI biology, open the door for further research into the etiology of obesity and to developing new ways to prevent and treat obesity and its complications.
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