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Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study.

ABSTRACT: BACKGROUND:Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored. METHODS:As part of the 'Population Architecture using Genomics and Epidemiology (PAGE)' Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA) and 39,716 European Americans (EA)). We assessed effect modification by testing an interaction term for each SNP and smoking (current vs. former/never) in the linear regression and by stratified analyses. RESULTS:We did not observe strong evidence for interactions and only observed two interactions with p-values <0.1: for rs6548238/TMEM18, the risk allele (C) was associated with BMI only among AA females who were former/never smokers (? = 0.018, p = 0.002), vs. current smokers (? = 0.001, p = 0.95, p(interaction) = 0.10). For rs9939609/FTO, the A allele was more strongly associated with BMI among current smoker EA females (? = 0.017, p = 3.5 x 10(-5)), vs. former/never smokers (? = 0.006, p = 0.05, p(interaction) = 0.08). CONCLUSIONS:These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results. CLINICAL TRIAL REGISTRATION:NCT00000611.

SUBMITTER: Fesinmeyer MD

PROVIDER: S-EPMC3564691 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study.

Fesinmeyer Megan D MD North Kari E KE Lim Unhee U Bůžková Petra P Crawford Dana C DC Haessler Jeffrey J Gross Myron D MD Fowke Jay H JH Goodloe Robert R Love Shelley-Ann SA Graff Misa M Carlson Christopher S CS Kuller Lewis H LH Matise Tara C TC Hong Ching-Ping CP Henderson Brian E BE Allen Melissa M Rohde Rebecca R RR Mayo Ping P Schnetz-Boutaud Nathalie N Monroe Kristine R KR Ritchie Marylyn D MD Prentice Ross L RL Kolonel Lawrence N LN Manson JoAnn E JE Pankow James J Hindorff Lucia A LA Franceschini Nora N Wilkens Lynne R LR Haiman Christopher A CA Le Marchand Loic L Peters Ulrike U

BMC medical genetics 20130111

<h4>Background</h4>Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored.<h4>Methods</h4>As part of the 'Population Architecture using Genomics and Epidemiology (PAGE)' Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies ...[more]

PMID: 23311614

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Project description:<p>By relating hundreds of thousands of genotypes to only a few phenotypes, mostly in individuals of one ancestry, genome-wide association (GWA) studies are identifying a rapidly growing number of associations. The Population Architecture using Genomics and Epidemiology (PAGE) Study is designed to further characterize the most promising variants along an epidemiological dimension that is substantial in its sample size, ethnic diversity, breadth of phenotypes, and exposures. PAGE includes scientists and population samples from large ongoing cohort studies: CALiCo (Causal Variants Across the Life Course, a consortium of ARIC, CARDIA, CHS, HCHS/SOL, Strong Heart Cohort Study, Strong Heart Family Study), EAGLE (Epidemiologic Architecture for Genes Linked to Environment, based on 3 National Health and Nutrition Examination Surveys (NHANES), MEC (Multiethnic Cohort) and WHI (Women's Health Initiative), with logistical and scientific support contributed by a Coordinating Center and the NHGRI Office of Population Genomics. These studies combined include over 270,000 participants, and populations represented include Asian Americans, African Americans, European Americans, Hispanic Americans, Native Hawaiians and American Indians. Health outcomes and traits of interest are prioritized, followed by replication of trait-genotype associations and generalization of their effects across population groups and environmental contexts. </p> <p>The first phase of genotyping focused on 901 high-profile SNPs having replicated associations with phenotypes related to diabetes, obesity, cardiovascular disease, lipids, cancers, menopause/menarche, inflammation and autoimmunity that are being genotyped in over 121,000 participants, as available for trait-specific analyses. SNP genotyping, quality control and population-specific association analyses are performed within each cohort, followed by meta-analyses using harmonized phenotypes and standardized analytic methods.</p> <p>The second phase of genotyping will be done using the Metabochip. The Metabochip is a custom large-scale (~200K SNPs) Illumina chip designed for association testing of several metabolic-related phenotypes. Genotyping will be performed at each study site and centralized analysis will be managed through the coordinating center.</p> <p>PAGE is generating 3 types of data: tier 1 (minimally curated phenotypes and analyses, all SNPs by all available phenotypes, used for Phenome-wide association study (PheWAS analysis)), tier 2 (carefully curated analyses of phenotypes available at only one of the four PAGE sites) and tier 3 (carefully curated phenotypes for multi-site analyses, data specifically generated for manuscripts). All tiers of data are submitted to the CC, where they undergo QC prior to submission to dbGaP.</p>

Dataset Information

Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study.

Publications

Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study.

Similar Datasets

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