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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Few genome-wide association studies GWAS account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction GxSMK on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution. Yet most studies have ignored environmental exposures with possibly large impacts on the trait variance 1 , 2. Variants that exert genetic effects on obesity through interactions with environmental exposures often remain undiscovered due to heterogeneous main effects and stringent significance thresholds. Thus, studies may miss genetic variants that have effects in subgroups of the population, such as smokers 3. Men and women gain weight rapidly after smoking cessation and many people intentionally smoke for weight management It remains unclear why smoking cessation leads to weight gain or why long-term smokers maintain weight throughout adulthood, although studies suggest that tobacco use suppresses appetite 12 , 13 or alternatively, smoking may result in an increased metabolic rate 12 , Identifying genes that influence adiposity and interact with smoking may help us clarify pathways through which smoking influences weight and central adiposity A comprehensive study that evaluates smoking in conjunction with genetic contributions is warranted. By accounting for smoking status, we focus both on genetic variants observed through their main effects and GxSMK effects to increase our understanding of their action on adiposity-related traits. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that smoking may alter the genetic susceptibility to overall adiposity and body fat distribution. For primary analyses, we conducted meta-analyses across ancestries and sexes. For secondary analyses, we conducted meta-analyses in European-descent studies alone and sex-specific meta-analyses Tables 1 , 2 , 3 , 4 , Supplementary Data 1 — 6. We considered four analytical approaches to evaluate the effects of smoking on genetic associations with adiposity traits Fig. All association results are reported with effect estimates oriented on the trait increasing allele in the current smoking stratum. Approaches 2 and 3 use the SMK-stratified meta-analyses. Approach 4 screens loci based on Approach 1, then uses SMK-stratified results to identify loci with significant interaction effects Methods section. Loci are ordered by greater magnitude of effect in smokers compared to nonsmokers and labelled with the nearest gene. We provide a comprehensive comparison with previously-identified loci 1 , 2 by trait in supplementary material Supplementary Data 7 , Supplementary Note 1. Given high correlations among waist, hip and height, these results are not surprising. Several additional loci were identified for Approaches 1 and 2 in secondary meta-analysis Table 2 , Supplementary Data 1 6 , Supplementary Fig. A comprehensive summary of nearby genes for all novel loci and their potential biological relevance is available in Supplementary Note 2. Figure 3 presents analytical power for Approaches 1 and 2 while Supplementary Table 8 and Supplementary Fig. A heat map cross-tabulates P -values for Approaches 1 and 2 along with Approach 3 examining interaction only Supplementary Fig. We demonstrate that the two approaches yield valid type 1 error rates and that Approach 1 can be more powerful to find associations given zero or negligible quantitative interactions, whereas Approach 2 is more efficient in finding associations when interaction exists. Shown is the power to identify adjusted Approach 1, dashed black lines , joint Approach 2, dotted green lines and interaction Approach 3 and 4, solid magenta and orange lines effects for various combinations of SMK- and NonSMK-specific effects and assuming 50, smokers and , nonsmokers. For b , d , f , the effect in nonsmokers was fixed to the small, medium and large BMI effects, respectively, and varied in smokers. Conditioning on these variants attenuated our interaction effect but did not eliminate it Supplementary Table 7 , suggesting a complex relationship between smoking, obesity, heart disease, and genetic variants in this region. Nicotine has differing effects on the body and brain, causing changes in metabolism and feeding behaviours These findings suggest smoking exposure may modify genetic effects on 15q to influence smoking-related diseases, such as obesity, through distinct pathways. Approach 4 SNPscreen; Fig. Power calculations demonstrate that Approach 4 has increased power to identify SNPs that show i an effect in one stratum smokers or nonsmokers and a less pronounced but concordant effect in the other stratum, or ii an effect in the larger nonsmoker stratum and no effect in smokers Fig. In contrast, Approach 3 has increased power for SNPs that show i an effect in the smaller smoker stratum and no effect in nonsmokers, or ii an opposite effect between smokers and nonsmokers Fig. Our findings for both approaches agree with these power predictions, supporting using both analytical approaches to identify GxSMK interactions. When examining the smoking specific effects for BMI and WCadjBMI loci in our meta-analyses, no significant enrichment of genetic effects by smoking status were noted. Differences in variance explained were greatest for BMI differences ranged from 1. These results suggest that smoking may increase genetic susceptibility to overall adiposity, but attenuate genetic effects on body fat distribution. This contrast is concordant with phenotypic observations of higher overall adiposity and lower central adiposity in smokers 4 , 6 , 7. Additionally, smoking increases oxidative stress and general inflammation in the body 19 and may exacerbate weight gain Many genes implicated in BMI are involved in appetite regulation and feeding behaviour 1. For waist traits, our results adjusted for BMI likely highlight distinct pathways through which smoking alters genetic susceptibility to body fat distribution. Overall, our results indicate that more loci remain to be discovered as more variance in the trait can be explained as we drop the threshold for significance. We systematically explored the potential role of our novel loci in affecting gene expression both with and without accounting for the influence of smoking behaviour Methods section, Supplementary Note 3 , Supplementary Tables 10— Supplementary Notes 1 and 3. Knockout and mutant forms of KIF1B in mice resulted in multiple brain abnormalities, including hippocampus morphology 22 , a region involved in food memory and cognition Human adipocytes express functional ARSA , which turns dopamine sulfate into active dopamine. Dopamine regulates appetite through leptin and adiponectin levels, suggesting a role for ARSA in regulating appetite Conversely, in mouse models, CDdeficient mice show decreased weight gain on high-fat diets, increased energy expenditure, improved glucose profile and decreased inflammation Several novel loci harbour genes involved in unique biological functions and pathways including addictive behaviours and response to oxidative stress. These potential candidate genes near our association signals are highly expressed in relevant tissues for regulation of adiposity and smoking behaviour for example, brain, adipose tissue, liver, lung and muscle; Supplementary Note 2 , Supplementary Table Disruption of this pathway has been associated with dysregulation of adiponectin in adipocytes of obese mice, implicating this pathway in downstream effects on weight regulation 27 , This finding is especially important due to the compounded stress adiposity places on the body as it increases chronic oxidative stress itself GRIN2A , near rs, controls long-term memory and learning through regulation and efficiency of synaptic transmission 31 and has been associated with heroin addiction There are no established relationships between GRIN2A and obesity-related phenotypes in the literature, yet memantine and ketamine, pharmacological antagonists of GRIN2A activity 34 , 35 , are implicated in treatment for obesity-associated disorders, including binge-eating disorders and morbid obesity ClinicalTrials. Memantine is under clinical investigation for treatment of nicotine dependence ClinicalTrials. Alternate forms of the oligomers may form in response to oxidative stress caused by copper exposure Copper is present in cigarette smoke and elevated in the serum of smokers, but is within safe ranges 38 , Another gene near rs, SLC23A2 Solute Carrier Family 23 Ascorbic Acid Transporter , Member 2 , is essential for the uptake and transport of Vitamin C, an important nutrient for DNA and cellular repair in response to oxidative stress both directly and through supporting the repair of Vitamin E after exposure to oxidative agents 40 , SLC23A2 is present in the adrenal glands and murine models indicate that it plays an important role in regulating dopamine levels This region is associated with success in smoking cessation and is implicated in addictive behaviours in general 43 , Our tag SNP is located within an active enhancer region marked by open chromatin marks, DNAse hypersentivity, and transcription factor binding motifs ; this regulatory activity appears tissue specific sex-specific tissues and lungs; HaploReg and UCSC Genome Browser. NAD is necessary for cellular repair following oxidative stress. Upregulation of NMNAT protects against damage caused by reactive oxygen species in the brain, specifically the hippocampus Given high phenotypic correlation between WC and WHR with height, and established shared genetic associations that overlap our adiposity traits and height 1 , 2 , 51 we expect cross-trait associations between our novel loci and height. Therefore, we conducted a look-up of all of our novel SNPs to identify overlapping association signals Supplementary Data 8. Finally, as smoking has a negative weight decreasing effect on BMI, it is likely that smoking-associated genetic variants have an effect on BMI in current smokers. We looked up published smoking behaviour SNPs 49 , 50 , 10 variants in 6 loci, in our own results. Therefore, we did not see a strong enrichment for low interaction P values among previously identified smoking loci. A possible limitation of our study may be the definition and harmonization of smoking status. We chose to stratify on current smoking status without consideration of type of smoking for example, cigarette, pipe for two reasons. First, focusing on weight alone, former smokers tend to return to their expected weight quickly following smoking cessation 7 , 13 , Second, this definition allowed us to maximize sample size, as many participating studies only had current smoking status available. Thus, results may differ with alternative harmonization of smoking exposure. Another limitation may be potential bias in our effect estimates when adjusting for a correlated covariate for example, collider bias This phenomenon is of particular concern when the correlation between the outcome and the covariate is high and when significant genetic associations occur with both traits in opposite directions. WHR has a correlation of 0. At these loci, the genetic effect estimates should be interpreted with caution. Additionally, there are no loci identified in Approach 1 SNPadjSMK that are associated with any smoking behaviour trait and that exhibit an opposite direction of effect from that identified in our adiposity traits Supplementary Data 8. We therefore preclude potential collider bias and postulate true gain in power through SMK-adjustment at these loci. Therefore, despite potential limitations, much is gained by accounting for environmental exposures in GWAS studies. To better understand the effects of smoking on genetic susceptibility to obesity, we conducted meta-analyses to uncover genetic variants that may be masked when the environmental influence of smoking is not considered, and to discover genetic loci that interact with smoking on adiposity-related traits. While many of our newly identified loci support the hypothesis that smoking may influence weight fluctuations through appetite regulation, these novel loci also have highlighted new biological processes and pathways implicated in the pathogenesis of obesity. Importantly, we identified nine loci with convincing evidence of GxSMK interaction on obesity-related traits. The majority of these loci harbour strong candidate genes for adiposity with a possible role for the modulation of effects through tobacco use. Our analyses did not allow us to determine whether these discoveries are due to different subsets of subjects included in the analyses compared to previous studies 1 , 2 or due only to adjusting for current smoking. Adjustment for current smoking in our analyses, however, did reveal novel associations. Specifically after accounting for smoking in our analyses, all novel BMI loci exhibit P -values that are at least one order of magnitude lower than in previous GIANT investigations, despite smaller samples in the current analysis 2. Thus, adjustment for smoking may have indeed revealed new loci. Further, loci identified in Approach 2, including nine novel loci, suggest that accounting for interaction improves our ability to detect these loci even in the presence of only modest evidence of GxSMK interaction. There are several challenges in validating genetic associations that account for environmental exposure. In addition to exposure harmonization and potential bias due to adjustment for smoking exposure, differences in trait distribution, environmental exposure frequency, ancestry-specific LD patterns and allele frequency across studies may lead to difficulties in replication, especially for gene-by-environment studies Despite these challenges, we were able to detect consistent direction of effect in an independent sample for all novel loci. While we found that effects were not significantly enriched in smokers for BMI, there is a greater proportion of variance in BMI explained by variants that are significant for Approach 1 SNPadjSMK , which may be expected given that there are a greater number of variants with higher effect estimates in smokers. For WHRadjBMI, there were significantly more loci that exhibit greater effects in nonsmokers, and this pattern was mirrored in the variance explained analysis. These differences in effect estimates between smokers and nonsmokers may help explain inconsistent findings in previous analyses that show central adiposity increases with increased smoking, but is associated with decreased weight and BMI 5 , 9 , Our results support previous findings that implicate genes involved in transcription and gene expression, appetite regulation, macronutrient metabolism, and glucose homeostasis. Many our newly identified loci highlight novel biological functions and pathways where dysregulation may lead to increased susceptibility to obesity, including response to oxidative stress, addictive behaviour, and newly identified regulatory functions. There is a growing body of evidence that supports the notion that exposure to oxidative stress leads to increased adiposity, risk of obesity, and poor cardiometabolic outcomes 27 , Eighteen of these remained significant in our validation with the UK Biobank sample. We confirmed most established loci in our analyses after adjustment for smoking status in smaller samples than were needed in previous discovery analyses. A typical approach in large-scale GWAS meta-analyses is not to adjust for covariates such as current smoking; our findings highlight the importance of accounting for environmental exposures in genetic analyses. We applied four approaches to identify genetic loci that influence adiposity traits by accounting for current tobacco smoking status Fig. Our primary meta-analyses focused on results from all ancestries, sexes combined. Secondary meta-analyses were performed using the European-descent populations only, as well as stratified by sex men-only and women-only in all ancestries and in European-descent study populations. The GIANT consortium was formed by an international group of researchers interested in understanding the genetic architecture of anthropometric traits Supplemental Tables 1—4 for study sample sizes and descriptive statistics. In instances where studies submitted both Metabochip and GWAS data, these were for non-overlapping individuals. Family studies used linear mixed effects models to account for familial relationships and also conducted analyses for men and women combined including sex in the model. Phenotype residuals were obtained from the adjustment models and were inverse normally transformed subsequently to facilitate comparability across studies and with previously published analyses. The trait transformation was conducted separately for smokers and nonsmokers for the SMK-stratified model and using all individuals for the SMK-adjusted model. The participating studies have varying levels of information on smoking, some with a simple binary variable and others with repeated, precise data. Since the effects of smoking cessation on adiposity appear to be immediate 7 , 8 , 52 , a binary smoking trait current smoker versus not current smoker is used for the analyses as most studies can readily derive this variable. For each studies except those that employed directly typed MetaboChip genotypes , genome-wide chip data was imputed to the HapMap II reference data set. Studies with family data also conducted analyses with these models for men and women combined after accounting for dependency among family members as a function of their kinship correlations. We assumed an additive genetic model. The aggregated summary statistics were quality-controlled according to a standardized protocol These included checks for issues with trait transformations, allele frequencies and strand. To test for issues with relatedness or overlapping samples and to correct for potential population stratification, the study-specific standard errors and association P values were genomic control GC corrected using lambda factors Supplementary Fig. While we established this criterion, no study results were removed for this reason. Meta-analyses used study-specific summary statistics for the phenotype associations for each of the above models. We used a fixed-effects inverse variance weighted method for the SNP main effect analyses. For secondary meta-analyses, we conducted meta-analyses in European-descent studies alone, and sex-specific meta-analyses. There were two reasons for conducting secondary meta-analyses. Second, by including populations from multiple ancestries in our primary meta-analyses, we may be introducing heterogeneity due to differences in effect sizes, allele frequencies, and patterns of linkage disequilibrium across ancestries, potentially decreasing power to detect genetic effects. See Supplementary Fig. Briefly, this software implements a two-sample, large sample test of equal regression parameters between smokers and nonsmokers 59 for SNPint and the two degree of freedom test of main and interaction effects for SNPjoint Figure 1 outlines the four approaches that we used to identify novel SNPs. The left side of Fig. For this approach, the null hypothesis was that there is no main and no interaction effect on the outcome. Thus, rejection of this hypothesis could be due to either a main effect or an interaction effect or to both. The right side of Fig. Approach 3 used the SMK-stratified results to directly contrast the regression coefficients for a test of interaction SNPint These variants were then carried forward for a test of interaction, comparing the SMK-stratified specific regression coefficients in the second step SNPscreen. We performed analytical power computations to demonstrate the usefulness and characteristic of the two interaction Approaches. GCTA uses associations from our meta-analyses and LD estimates from reference data sets containing individual-level genotypic data to perform the conditional analyses. To calculate the LD structure, we used two U. Many of the SNPs identified in the current analyses were nearby SNPs previously associated with related anthropometric and obesity traits for example, height, visceral adipose tissue. In order to illustrate the validity of the approaches with regards to type 1 error, we conducted simulations. We applied the four approaches to the simulated stratum-specific association results and inferred type 1 error of each approach by visually examining QQ plots and by calculating type 1 error rates. The type 1 error rates shown reflect the proportion of nominally significant simulation results for the respective approach. Analytical power calculations to identify effects for various combinations of SMK- and NonSMK-specific effects by the Approaches 1—4 again assumed 50, smokers and , nonsmokers. Second, we assumed fixed small, medium and large effects in nonsmokers and varied the effect in smokers. Snipper v1. We used two approaches to systematically explore the role of novel loci in regulating gene expression. Additional details on the methods, including study references can be found in Supplementary Note 3. Second, since public databases with eQTL data do not have information available on current smoking status, we also conducted a cis-eQTL association analysis using expression results derived from fasting peripheral whole blood using the Human Exon 1. The raw expression data were quantile-normalized, log2 transformed, followed by summarization using Robust Multi-array Average 66 and further adjusted for technical covariates, including the first principal component of the expression data, batch effect, the all-probeset-mean residual, blood cell counts, and cohort membership. Additional details can be found in Supplementary Note 3. We estimated the phenotypic variance in smokers and nonsmokers explained by the association signals. In order to determine if any of the loci identified in the current study are associated with smoking behaviour, we conducted a look-up of all lead SNPs from novel loci and Approach 3 in existing GWAS of smoking behaviour 3. The analysis consists of phasing study-specific GWAS samples contributing to the smoking behaviour meta-analysis, imputation, association testing and meta-analysis. Each region was analysed for three smoking related phenotypes: i Ever vs Never smokers, ii Current vs Non-current smokers and iii a categorical measure of smoking quantity Each increment represents an increase in smoking quantity of 10 cigarettes per day. There were 10, Never smokers, 13, Ever smokers, 11, Non-current smokers, 6, Current smokers and 11, samples with the SQ phenotypes. To further investigate the identified genetic variants in this study and to gain additional insight into their functionality and possible effects on related cardiometabolic traits, we searched for previous SNP-trait associations nearby our lead SNPs. Additionally, we used an F statistic to test whether the residual sum of squares RSS for the full model including GRSxSMK interaction was significantly different from the reduced model. How to cite this article: Justice, A. Genome-wide meta-analysis of , adults accounting for smoking behaviour identifies novel loci for obesity traits. Locke, A. Genetic studies of body mass index yield new insights for obesity biology. Nature , — Shungin, D. New genetic loci link adipose and insulin biology to body fat distribution. Taylor, A. PLoS Genet. Article Google Scholar. Pistelli, F. Weight gain after smoking cessation. Monaldi Arch. Chest Dis. Morris, R. Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. BMJ Open 5 , e Kim, J. Cigarette smoking increases abdominal and visceral obesity but not overall fatness: an observational study. Owen-Smith, V. Stopping smoking and body weight in women living in the United Kingdom. CAS Google Scholar. Lahmann, P. Sociodemographic factors associated with long-term weight gain, current body fatness and central adiposity in Swedish women. 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Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4 , — Billingsley, P. Probability and Measure 2nd edn. Wiley Kutalik, Z. Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability. Delaneau, O. Improved whole-chromosome phasing for disease and population genetic studies. Methods 10 , 5—6 Howie, B. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Download references. A full list of acknowledgments appears in the Supplementary Note 4. Co-author A. Anne E. Justice, Thomas W. Winkler, Mary F. Feitosa, Misa Graff, Virginia A. Ruth J. Loos, Tuomas O. Borecki, Kari E. North and L Adrienne Cupples: These authors jointly supervised this work. Thomas W. Winkler, Mathias Gorski, Martina E. Mary F. Virginia A. Fisher, Xuan Deng, Julius S. Population Health Research Institute, St. TransMed Systems, Inc. Tarunveer S. Ahluwalia, Marit E. Audrey Y. Chu, Nancy L. Heard-Costa, Caroline S. Chu, Shafqat Ahmad, Paul M. Ridker, Lynda M. John D. Eicher, Jennifer E. Lindgren, Mark I. Department of Nutrition, Harvard T. Lawrence F. Bielak, Jennifer A. Smith, Min A. Jhun, Patricia A. Jennifer Bragg, Anne U. Jackson, Heather M. Scott Coggeshall, Traci M. Joel Eriksson, Anna L. Jennifer E. Hastie, Alan F. Marcus E. Kleber, Graciela E. Delgado, Tanja B. Ong, Robert A. Scott, Nicholas J. Wareham, Ruth J. Rita P. Middelberg, Nicholas G. Rona J. Sander W. Peter J. Jana V. Sailaja L. Jacqueline M. Vink, Eco J. John Beilby, Alan L. Lori L. Bonnycastle, Francis S. Collins, Narisu Narisu, Amy J. Catharina A. Louis, Missouri, USA. Andrew C. Heath, Pamela A. Madden, D. Lynne J. Hocking, Sandosh Padmanabhan, David J. Box , FI, Finland. Box 20, FI, Oulu, , Finland. Tuomo Rankinen, Mark A. Andre G. Department of Epidemiology, Harvard T. You can also search for this author in PubMed Google Scholar. The association and interaction results were contributed by S. AtheroExpress ; A. AGES study ; K. ARIC study ; J. B58C study ; G. BHS study ; C. BioMe ; T. BLSA ; B. CHS ; Y. Croatia-Korcula study ; V. Croatia-Vis study ; L. Ely study ; C. EPIC , J. ERF study ; I. Family Heart Study ; J. Fenland study ; F. FramHS ; M. Gendian ; D. Generation Scotland ; L. GOOD study ; T. GOYA study ; B. GxE ; M. Health06 study ; Y. HRS study ; A. HUNT2 study ; M. Inter99 study ; T. Lifelines ; W. MEC study ; A. MESA study ; M. MrOS ; M. NFBC66 study ; L. NHS study ; D. NSHD study ; J. NTR study ; P. Prevend ; S. QFS study ; N. SardiNIA study ; R. SHIP study ; B. SPT ; S. TwinsUK study ; A. WGHS study ; N. WHI study ; M. Whitehall study ; L. YFS study. Correspondence to Anne E. Justice or L Adrienne Cupples. The remaining authors declare no competing financial interests. BMI association results for all ancestries meta-analyses. Summary statistics are presented from stage1 plus stage 2 results. XLSX 68 kb. WCadjBMI association results for all ancestries meta-analyses. XLSX 73 kb. XLSX 49 kb. BMI association results for European meta-analyses. XLSX 58 kb. XLSX 67 kb. XLSX 53 kb. Lookup of known main effects SNPS. XLSX kb. XLSX 20 kb. This work is licensed under a Creative Commons Attribution 4. Reprints and permissions. Justice, A. Nat Commun 8 , Download citation. Received : 23 June Accepted : 15 February Published : 26 April Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. Download PDF. Subjects Epidemiology Genetics research Genome-wide association studies Obesity. Abstract Few genome-wide association studies GWAS account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Causal effects from tobacco smoking initiation on obesity-related traits: a Mendelian randomization study Article 26 August Quantification of the overall contribution of gene-environment interaction for obesity-related traits Article Open access 13 March An approach to identify gene-environment interactions and reveal new biological insight in complex traits Article Open access 22 April Full size table. Figure 1: Summary of study design and results. Full size image. Figure 3: Power comparison across Approaches. Figure 4: Stratum specific estimates of variance explained. Discussion To better understand the effects of smoking on genetic susceptibility to obesity, we conducted meta-analyses to uncover genetic variants that may be masked when the environmental influence of smoking is not considered, and to discover genetic loci that interact with smoking on adiposity-related traits. Methods Study design overview We applied four approaches to identify genetic loci that influence adiposity traits by accounting for current tobacco smoking status Fig. Cohort descriptions and sample sizes The GIANT consortium was formed by an international group of researchers interested in understanding the genetic architecture of anthropometric traits Supplemental Tables 1—4 for study sample sizes and descriptive statistics. Defining smokers The participating studies have varying levels of information on smoking, some with a simple binary variable and others with repeated, precise data. Genotype identification and imputation Studies with GWAS array data or Metabochip array data contributed to the results. Additional information How to cite this article: Justice, A. References Locke, A. Article Google Scholar Pistelli, F. Article Google Scholar Clair, C. Article Google Scholar Pirie, P. Article Google Scholar Yang, J. Article Google Scholar Yang, H. Article Google Scholar Borcherding, D. Article Google Scholar Furukawa, S. Article Google Scholar Rose, J. Article Google Scholar Ioannidis, J. Article Google Scholar Wei, Y. Article Google Scholar Willer, C. Article Google Scholar Winkler, T. Article Google Scholar Irizarry, R. Article Google Scholar Billingsley, P. Article Google Scholar Delaneau, O. Acknowledgements A full list of acknowledgments appears in the Supplementary Note 4. Author information Author notes Anne E. Anton J. Borecki St. Strachan TransMed Systems, Inc. Smith St. Staessen Belgium Jan A. Hirschhorn Department of Epidemiology, Harvard T. Justice View author publications. View author publications. Ethics declarations Competing interests B. Supplementary information. Supplementary Data 1 BMI association results for all ancestries meta-analyses. Rights and permissions This work is licensed under a Creative Commons Attribution 4. About this article. Cite this article Justice, A. Copy to clipboard. Young Andrew F. Olshan Julie R. Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search. Close banner Close. Email address Sign up. Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing.

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