Genome-wide joint meta-analysis for interaction between genetic variants and smoking on waist circumference. A. E. Justice1, T. W. Winkler2, J. Ngwa3, K. L. Young1,4, D. Hadley5, M. Graff1, J. M. Vink6, L. Xue3, T. S. Ahluwalia7, T. Lehtimäki8, R. J. Strawbridge9, M. C. Zillikens10, M. F. Feitosa11, N. L. Heard-Costa12,13, J. H. Zhao14, J. Luan14, N. Direk15, H. Tiemeier15, H. J. Grabe16, T. B. Harris17, R. P. S. Middelberg18, J. V. van Vliet-Ostaptchouk19,20, I. M. Nolte20, J. Kaprio21, T. O. Kilpeläinen7, I. B. Borecki11, R. J. F. Loos22, K. E. North1, L. A. Cupples3,12 on behalf of the GIANT Consortium 1) Department of Epidemiology, University of North Carolina, Chapel Hill, NC; 2) Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany; 3) Department of Biostatistics, School of Public Health, Boston University, Boston, MA; 4) Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC; 5) Pediatric Epidemiology Center, University of South Florida, Tampa, FL; 6) Department of Biological Psychology, Neuroscience Campus, VU University & VU medical center Amsterdam; 7) The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 8) Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland; 9) Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet; 10) Department of Internal Medicine, Erasmus MC Rotterdam; 11) Department of Genetics, School of Medicine, Washington University; 12) Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA; 13) Department of Neurology, Boston University School of Medicine, Boston, MA; 14) MRC Epidemiology Unit, University of Cambridge, Cambridge, UK; 15) Department of Epidemiology, Erasmus Medical Centre; 16) Department of Psychiatry, University Medicine of Greifswald, Germany; 17) Intramural Research Program, National Institute on Aging, Bethesda, MD; 18) Genetic Epidemiology Unit, Queensland Institute of Medical Research; 19) Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; 20) Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; 21) Institute for Molecular Medicine, University of Helsinki; 22) The Charles Bronfman Institute for Personalized Medicine, The Mincdich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY.
Cigarette smokers often display lower body weight than non-smokers, and both men and women gain weight after smoking cessation causing changes in central adiposity, as assessed with waist circumference (WC). Identifying genes that influence WC and whose effects are modified by smoking will help us understand the complex interplay between genetic susceptibility, smoking and central adiposity. While many loci have been associated with WC, little is known about whether current smoking status (SMK) influences these genetic associations. We aim to discover genetic loci that interact with smoking to influence WC adjusted for BMI (WCa), and to increase the power to detect genetic main effects that may be hidden when the environmental influence of smoking is not considered. To address our aims we evaluated results from 42 studies with GWAS data available in the GIANT (Genetic Investigation of ANthropometric Traits) Consortium providing up to 113,587 individuals. We conducted inverse-variance weighted fixed-effects meta-analyses of the study specific results for four association models separately and combined across sex: 1) SNP main effect on WCa, stratified by SMK; 2) SNP main effect on WCa, adjusted for SMK; 3) SNP x SMK interaction effect on WCa; and 4) we evaluated a joint meta-analysis of the SNP main effect and SNP x SMK interaction effect. A total of 63 loci reached Genome-Wide significance (GWS) (p<5x10-8) in one or more strata, with the greatest number of significant results coming from the joint effects model. Of the 63, seven have been previously associated with waist traits, 11 are near SNPs previously associated with other adiposity traits (e.g. BMI, visceral adiposity, extreme obesity, adiponectin), and one is near a SNP previously associated with smoking behavior. A novel association near PRNP reached GWS for SMK interaction (model 3) in women. PRNP is highly expressed in the nervous system and is important in the functioning of cell signaling, memory, and immune response. Other GWS SNPs lie near strong biological candidates important in early growth and development (e.g. DNMT3A, TBX15, FGFR4) and nervous system functioning (e.g. CABLES1, DOCK3). We have greatly increased the number of loci associated with central adiposity and highlight the influence of the nervous system and developmental processes on adiposity-related traits. These results underscore the importance of accounting for SMK when investigating the genetic basis of central adiposity.
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