Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

ARTICLE Received 23 Jun 2016 |Accepted 15 Feb 2017 |Published 26 Apr 2017 Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits Anne E. Justice et al.# 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. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. 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. Correspondence and requests for materials should be addressed to A.E.J. (email: [email protected]) or to L.A.C. (email: [email protected]). #A full list of authors and their affiliations appears at the end of the paper. DOI: 10.1038/ncomms14977 OPEN NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 1 Recent genome-wide association studies (GWAS) have described loci implicated in obesity, body mass index (BMI) and central adiposity. Yet most studies have ignored environmental exposures with possibly large impacts on the trait variance1,2. 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 smokers3. It is often noted that currently smoking individuals display lower weight/BMI and higher waist circumference (WC) as compared to nonsmokers4–6. Smokers also have the smallest fluctuations in weight over B20 years compared to those who have never smoked or have stopped smoking7,8. Also, heavy smokers (420 cigarettes per day [CPD]) and those that have smoked for more than 20 years are at greater risk for obesity than non-smokers or light to moderate smokers (o20 CPD)9,10. Men and women gain weight rapidly after smoking cessation and many people intentionally smoke for weight management11.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 appetite12,13 or alternatively, smoking may result in an increased metabolic rate12,13. Identifying genes that influence adiposity and interact with smoking may help us clarify pathways through which smoking influences weight and central adiposity13. A comprehensive study that evaluates smoking in conjunction with genetic contributions is warranted. Using GWAS data from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, we identified 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity, assessed by BMI and central obesity independent of overall body size, assessed by WC adjusted for BMI (WCadjBMI) and waist-to-hip ratio adjusted for BMI (WHRadjBMI). 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. Results GWAS discovery overview. We meta-analysed study-specific association results from 57 Hapmap-imputed GWAS and 22 studies with Metabochip, including up to 241,258 (87% European descent) individuals (51,080 current smokers and 190,178 nonsmokers) while accounting for current smoking (SMK) (Methods section, Supplementary Fig. 1, Supplementary Tables 1–4). 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 1–4, Supplementary Data 1–6). We considered four analytical approaches to evaluate the effects of smoking on genetic associations with adiposity traits (Fig. 1, Methods section). Approach 1 (SNPadjSMK) examined genetic associations after adjusting for SMK. Approach 2 (SNPjoint) considered the joint impact of main effects adjusted for SMK þ interaction effects14. Approach 3 focused on interaction effects (SNPint); Approach 4 followed up loci from Approach 1 for interaction effects (SNPscreen). Results from Approaches 1–3 were considered genome-wide significant (GWS) with a P-valueo5108while Approach 4 used Bonferroni adjustment after screening. Lead variants 4500 kb from previous associations with BMI, WCadjBMI, and WHRadjBMI were considered novel. All association results are reported with effect estimates oriented on the trait increasing allele in the current smoking stratum. Across the three adiposity traits, we identified 23 novel associated genetic loci (6 for BMI, 11 for WCadjBMI, 6 for WHRadjBMI) and nine having significant GxSMK interaction effects (2 for BMI, 2 for WCadjBMI, 5 for WHRadjBMI; Fig. 1, Tables 1–4, Supplementary Data 1–6). We provide a comprehensive comparison with previously-identified loci1,2 by trait in supplementary material (Supplementary Data 7, Supplementary Note 1). Accounting for smoking status. For primary meta-analyses of BMI (combined ancestries and sexes), 58 loci reached GWS in Approach 1 (SNPadjSMK; Supplementary Data 1, Supplementary Figs 2 and 3), including two novel loci near SOX11,andSRRM1P2 (Table 1). Three more BMI loci were identified using Approach 2 (SNPjoint), including a novel locus near CCDC93 (Supplementary Figs 4 and 5). For WCadjBMI, 62 loci reached GWS for Approach 1 (SNPadjSMK) and two more for Approach 2 (SNPjoint), including eight novel loci near KIF1B,HDLBP,DOCK3, ADAMTS3,CDK6,GSDMC,TMEM38B and ARFGEF2 (Table 1, Supplementary Data 2, Supplementary Figs 2–5). Lead variants near PSMB10 from Approaches 1 and 2 (rs14178 and rs113090, respectively) are 4500kb from a previously-identified WCadjBMI-associated variant (rs16957304); however, after conditioning on the known variant, our signal is attenuated (P Conditional ¼3.02102and P Conditional ¼5.22103), indicating that this finding is not novel. For WHRadjBMI, 32 loci were identified in Approach 1 (SNPadjSMK), including one novel locus near HLA-C, with no additional loci in Approach 2 (SNPjoint; Table 1, Supplementary Data 3, Supplementary Figs 2–5). We used GCTA15 to identify loci from our primary metaanalyses that harbour multiple independent SNPs (Methods section, Supplementary Tables 5–7). Conditional analyses revealed no secondary signals within 500 kb of our novel lead SNPs. Additionally, we performed conditional association analyses to determine whether our novel variants were independent of previous GWAS loci within 500 kb that are associated with related traits of interest. All BMI-associated SNPs were independent of previously identified GWS associations with anthropometric and obesity-related traits. Seven novel loci for WCadjBMI were near previous associations with related anthropometric traits. Of these, association signals for rs6743226 near HDLBP, rs10269774 near CDK6, and rs6012558 near ARFGEF2 were attenuated (P Conditional 41E5and bdecreased by half) after conditioning on at least one nearby height and hip circumference adjusted for BMI (HIPadjBMI) SNP, but association signals remained independent of other related SNP-trait associations. For WHRadjBMI, our GWAS signal was attenuated by conditioning on two known height variants (rs6457374 and rs2247056), but remained significant in other conditional analyses. 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. 6). For BMI, 2 novel loci were identified by Approach 1, including 1 near EPHA3 and 1 near INADL. For WCadjBMI, 2 novel loci were identified near RAI14 and PRNP. For WHRadjBMI, five novel loci were identified in secondary meta-analyses near BBX, TRBI1,EHMT2,SMIM2 and EYA4. A comprehensive summary of nearby genes for all novel loci and their potential biological relevance is available in Supplementary Note 2. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 2NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications Figure 3 presents analytical power for Approaches 1 and 2 while Supplementary Table 8 and Supplementary Fig. 7 present simulation results to evaluate type 1 error (Methods section). A heat map cross-tabulates P-values for Approaches 1 and 2 along with Approach 3 examining interaction only (Supplementary Fig. 8). 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. Modification of genetic predisposition by smoking. Approach 3 directly evaluated GxSMK interaction (SNPint; Table 3, Supplementary Data 1–6, Fig. 2, Supplementary Figs 9 and 10). For primary meta-analysis of BMI, two loci reached GWS including a previously identified GxSMK interaction locus near CHRNB4 (ref. 3), and a novel locus near INPP4B. Both loci exhibit GWS effects on BMI in smokers and no effects in nonsmokers. For CHRNB4 (cholinergic nicotine receptor B4), the variant minor allele (G) exhibits a decreasing effect on BMI in current smokers (bsmk ¼0.047) but no effect in nonsmokers (bnonsmk ¼0.002). Previous studies identified nearby SNPs in high LD associated with smoking (nonsynonymous, rs16969968 in CHRNA5)3and arterial calcification (rs3825807, a missense variant in ADAMTS7)16. 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. Importantly, the CHRNA5-CHRNA3-CHRNB4 gene cluster has been associated with lower BMI in current smokers3, but with higher BMI in never smokers3, evidence supporting the lack of association in nonsmokers as well as a lack of previous GWAS findings on 15q25 (Supplementary Data 8)1. The CHRNA5-CHRNA3-CHRNB4 genes encode the nicotinic acetylcholine receptor (nAChR) subunits a3, a5 and b4, which are expressed in the central nervous system17. Nicotine has differing effects on the body and brain, causing changes in metabolism and feeding behaviours18. These findings suggest smoking exposure may modify genetic effects on 15q24-25 to influence smoking-related diseases, such as obesity, through distinct pathways. In primary meta-analyses of WCadjBMI, one novel GWS locus (near GRIN2A) with opposite effect directions by smoking status was identified for Approach 3 (SNPint; Table 3, Supplementary Data 2, Fig. 2, Supplementary Figs 9 and 10). The T allele of rs4141488 increases WCadjBMI in current smokers and decreases it in nonsmokers (bsmk ¼0.037, bnonsmk ¼0.015). In secondary meta-analysis of European women-only, we identified an interaction between rs6076699, near PRNP, and SMK on WCadjBMI (Table 4, Supplementary Data 5, Supplementary Fig. 6), a locus also identified in Approach 2 (SNPjoint) for European women. The major allele, A, has a positive effect on current smokers as compared to a weaker and negative effect on WC in nonsmokers (bsmk ¼0.169, bnonsmk ¼0.070), suggesting why this variant remained undetected in previous GWAS of WCadjBMI (Supplementary Data 8). Approach 4 (SNPscreen; Fig. 1, Methods section) evaluated GxSMK interactions after screening SNPadjSMK results (from Testing for Interaction of SNP with Current SMK Testing for SNP accounting for current SMK WHRadjBMI: 45 / 6 WCadjBMI: 76 / 11BMI: 68 / 6 Approach 4.b Test SNP adjusting for SMK P SNPadjSMK <5E –8 Approach 1 Total number of significant loci/number of novel Interaction of SNP with current SMK Effect of SNP accounting for current SMK Approach 1 Approach 2 Approach 3 Approach 4 0 0 Approach 3 Approach 2 Test SNP + interaction with SMK P SNPjoint <5E –8 Test SNP interaction with SMK P SNPint <5E –8 Screen approach 1 results P SNPadjSMK <5E –8 Test selected SNP interaction P SNPint < 0.05/# loci Trait BMI WCadjBMI WHRadjBMI 44 / 5 72 / 9 65 / 4 57 / 2 66 / 5 24 / 2 2 / 2 2 / 1 1 / 0 5 / 0 Total non-overlapping loci Approach 4.a Figure 1 | Summary of study design and results. Approach 1 uses both SNP and SMK in the association model. 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). NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 3 Approach 1) using Bonferroni-correction (Methods section, Tables 3–4, Supplementary Data 1–6). We identified two SNPs, near LYPLAL1 and RSPO3, with significant interaction; both have previously published main effects on anthropometric traits. These loci exhibit effects on WHRadjBMI in nonsmokers, but not in smokers (Fig. 2). In secondary meta-analyses, we identified three known loci with significant GxSMK interaction effects on WHRadjBMI near MAP3K1,HOXC4-HOXC6 and JUND (Table 4, Supplementary Data 3 and 6). We identified rs1809420, near CHRNA5-CHRNA3-CHRNB4, for BMI in the men-only, combined-ancestries meta-analysis (Supplementary Data 1). 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. 3). 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. 3). Our findings for both approaches agree with these power predictions, supporting using both analytical approaches to identify GxSMK interactions. Enrichment of genetic effects by smoking status. 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. (Fig. 2, Supplementary Figs 11 and 12). However, our results for WHRadjBMI were enriched for loci with a stronger effect in nonsmokers as compared to smokers, with 35 of 45 loci displaying numerically larger effects in nonsmokers (P binomial ¼1.2104). We calculated the variance explained by subsets of SNPs selected on 15 significance thresholds for Approach 1 from P SNPadjSMK ¼1108to P SNPadjSMK ¼0.1 (Supplementary Table 9, Fig. 4). Differences in variance explained between smokers and nonsmokers were significant (P RsqDiff o0.003¼0.05/15, Bonferroni-corrected for 15 thresholds) for BMI at each threshold, with more variance explained in smokers. For WCadjBMI, the difference was significant for SNP sets beginning with P SNPadjSMK Z3.16104, and for WHRadjBMI at P SNPadjSMK Z1106. In contrast to BMI, SNPs from Approach 1 explained a greater proportion of the variance in nonsmokers for WHRadjBMI. Differences in variance explained were greatest for BMI (differences ranged from 1.8 to 21% for smokers) and lowest for WHRadjBMI (ranging from 0.3 to 8.8% for nonsmokers). 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 smokers4,6,7. Additionally, smoking increases oxidative stress and general inflammation in the body19 and may exacerbate weight gain20. Many genes implicated in BMI are involved in appetite regulation and feeding behaviour1. 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. Functional or biological role of novel loci. We conducted thorough searches of the literature and publicly available bioinformatics databases to understand the functional role of all genes within 500kb of our lead SNPs. 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 10–12). We found the majority of novel loci are near strong candidate genes with biological functions similar to previously identified adiposity-related loci, including regulation of body fat/weight, angiogenesis/adipogenesis, glucose and lipid homeostasis, general growth and development. (Supplementary Notes 1 and 3). We identified rs17396340 for WCadjBMI (Approaches 1 and 2), an intronic variant in the KIF1B gene. This variant is associated with expression of KIF1B in whole blood with and without accounting for SMK (GTeX and Supplementary Tables 10 and 12) and is highly expressed in the brain21. Knockout and mutant forms of KIF1B in mice resulted in multiple brain abnormalities, including hippocampus morphology22, a region involved in (food) memory and cognition23. Variant rs17396340 is associated with expression levels of ARSA in LCL tissue. Human adipocytes express functional ARSA, which turns dopamine sulfate into active dopamine. Dopamine regulates appetite through leptin INPP4B* CCDC39 CHRNB4* SRRM1P2 SOX11 ADAMTS3 KIF1B LYPLAL1 ¥ RSPO3 ¥ TMEM38B HLA-C HDLBP CDK6 ARFGEF2 DOCK3 GRIN2A* 0.08 0.1 0.06 0.04 0.02 Effect on BMI (SD per allele) 0 –0.02 –0.04 0.08 –0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 0.04 0.02 Effect on WCadjBMI (SD per allele) Effect on WHRadjBMI (SD per allele) 0 –0.02 –0.04 Smokers Non-smokers Smokers Non-smokers Smokers Non-smokers abc Figure 2 | Forest plot for novel and GxSMK loci stratified by smoking status. Estimated effects (b±95% CI) for smokers (Nup to 51,080) and nonsmokers (Nup to 190,178 ) per risk allele for (a) BMI, (b) WCadjBMI and (c) WHRadjBMI for novel loci from Approaches 1 and 2 (SNPadjSMK and SNPjoint, respectively) and all loci from Approaches 3 and 4 (SNPint and SNPscreen) identified in the primary meta-analyses. Loci are ordered by greater magnitude of effect in smokers compared to nonsmokers and labelled with the nearest gene. For the locus near TMEM38B, rs9409082 was used for effect estimates in this plot. (floci identified for Approach 4, *loci identified for Approach 3). ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 4NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications and adiponectin levels, suggesting a role for ARSA in regulating appetite24. Expression of CD47 (CD47 molecule), near rs670752 for WHRadjBMI (Approach 1, women-only), is significantly decreased in obese individuals and negatively correlated with BMI, WC and Hip circumference25. Conversely, in mouse models, CD47-deficient mice show decreased weight gain on high-fat diets, increased energy expenditure, improved glucose profile and decreased inflammation26. 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 10). The CHRNA5-CHRNA3-CHRNB4 cluster is involved in the eNOS signalling pathway (Ingenuity KnowledgeBase, http://www.ingenuity.com) that is key for neutralizing reactive oxygen species introduced by tobacco smoke and obesity27. Disruption of this pathway has been associated with dysregulation of adiponectin in adipocytes of obese mice, implicating this pathway in downstream effects on weight regulation27,28. This finding is especially important due to the compounded stress adiposity places on the body as it increases chronic oxidative stress itself28.INPP4B has been implicated in the regulation of the PI3K/Akt signalling pathway29 that is important for cellular growth and proliferation, but also eNOS signalling, carbohydrate metabolism, and angiogenesis30. GRIN2A, near rs4141488, controls long-term memory and learning through regulation and efficiency of synaptic transmission31 and has been associated with heroin addiction32. Nicotine increases the expression of GRIN2A in the prefrontal cortex in murine models33. There are no established relationships between GRIN2A and obesity-related phenotypes in the literature, yet memantine and ketamine, pharmacological antagonists of 0.00010 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.00005 0.00070 0.0034 0.0017 0.0000 0.0017 0.0034 0.0034 0.0017 0.0000 0.0017 0.0034 0.00035 0.00000 0.00035 0.00070 0.00070 0.00035 0.00000 0.00035 0.00070 Opposite direction Consistent direction 0.00000 0.00005 0.00010 0.00010 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.0 0.2 0.4 0.6 0.8 1.0 Power 0.00005 0.00000 0.00005 0.00010 R2 SMK = 0.01% R2 SMK = 0.07% R2 NONSMK = 0.01% R2 NONSMK = 0.07% R2 SMK = 0.34% R2 NONSMK = 0.34% R2 NONSMK Opposite direction Consistent direction R2 SMK Opposite direction Consistent direction R2 NONSMK Opposite direction Consistent direction R2 SMK Opposite direction Consistent direction R2 NONSMK Opposite direction Consistent direction R2 SMK Approach 1 (PadjSMK < 5 x 10–8) Approach 3 (Pint < 5 x 10–8) Approach 2 (PJoint2df < 5 x 10–8) Approach 4 (PadjSMK < 5 x 10–8 → Pint<0.05 / # loci) ab cd ef Figure 3 | Power comparison across Approaches. 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 SMKand NonSMK-specific effects and assuming 50,000 smokers and 180,000 nonsmokers. For (a,c,e), the effect in smokers was fixed at a small (R2 SMK ¼0.01%, similar to the realistic NUDT3 effect on BMI), medium (R2 SMK ¼0.07%, similar to the realistic BDNF effect on BMI) or large (R2 SMK ¼0.34%, similar to the realistic FTO effect on BMI) genetic effect, respectively, and varied in nonsmokers. For (b,d,f), the effect in nonsmokers was fixed to the small, medium and large BMI effects, respectively, and varied in smokers. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 5 GRIN2A activity34,35, are implicated in treatment for obesityassociated disorders, including binge-eating disorders and morbid obesity (ClinicalTrials.gov identifiers: NCT00330655, NCT02334059, NCT01997515, NCT01724983). Memantine is under clinical investigation for treatment of nicotine dependence (ClinicalTrials.gov identifiers: NCT01535040, NCT00136786 and NCT00136747). While our lead SNP is not within a characterized gene, rs4141488 and variants in high LD (r240.7) are within active enhancer regions for several tissues, including liver, fetal leg muscle, smooth stomach and intestinal muscle, cortex and several embryonic and pluripotent cell types (Supplementary Note 2), and therefore may represent an important regulatory region for nearby genes like GRIN2A. In secondary meta-analysis of European women-only, we identified a significant GxSMK interaction for rs6076699 on WCadjBMI (Table 4, Supplementary Data 4, Supplementary Fig. 6). This SNP is 100kb upstream of PRNP (prion protein), a signalling transducer involved in multiple biological processes related to the nervous system, immune system, and other cellular functions (Supplementary Note 2)36. Alternate forms of the oligomers may form in response to oxidative stress caused by copper exposure37. Copper is present in cigarette smoke and Variance explained for BMI in smokers and nonsmokers The difference in the variance explained for BMI in smokers and nonsmokers 0.30 0.25 0.20 0.15 Total explained variance Total explained variance smokers vs nonsmokers 0.10 0.05 0.00 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1E–08 1E–06 P adjusted for smoking threshold 1E–04 1E–02 1E–08 1E–06 P adjusted for smoking threshold 1E–04 1E–02 1E–08 1E–06 P adjusted for smoking threshold 1E–04 1E–02 1E–08 1E–06 P adjusted for smoking threshold 1E–04 1E–02 1E–08 1E–06 P ad j usted for smokin g threshold 1E–04 1E–02 1E–08 1E–06 P ad j usted for smokin g threshold 1E–04 1E–02 Smokers Nonsmokers Variance explained for WCadjBMI in smokers and nonsmokers The difference in the variance explained for WCadjBMI in smokers and nonsmokers 0.30 0.25 0.20 0.15 Total explained variance Total explained variance nonsmokers vs smokers 0.10 0.05 0.00 0.30 0.25 0.20 0.15 0.10 0.05 0.00 Variance explained for WHRadjBMI in smokers and nonsmokers The difference in the variance explained for WHRadjBMI in smokers and nonsmokers 0.30 0.25 0.20 0.15 Total explained variance Total explained variance smokers vs nonsmokers 0.10 0.05 0.00 0.5 0.4 0.3 0.2 0.1 0.0 ab cd ef Smokers Nonsmokers Smokers Nonsmokers Figure 4 | Stratum specific estimates of variance explained. Total smoking status-specific explained variance (±s.e.) by SNPs meeting varying thresholds of overall association in Approach 1 (SNPadjSMK) and the difference between the proportion of variance explained between smokers and nonsmokers for these same sets of SNPs in BMI (a,b), WCadjBMI (c,d), and for WHRadjBMI (e,f). ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 6NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications elevated in the serum of smokers, but is within safe ranges38,39. Another gene near rs6076699, 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 agents40,41. SLC23A2 is present in the adrenal glands and murine models indicate that it plays an important role in regulating dopamine levels42. This region is associated with success in smoking cessation and is implicated in addictive behaviours in general43,44. 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). Nicotinamide mononucleotide adenylyltransferease (NMNAT1), upstream of WCadjBMI variant rs17396340, is responsible for the synthesis of NAD from ATP and NMN45. 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 hippocampus46. Also for WCadjBMI, both CDK6, near SNP rs10269774, and FAM49B, near SNP rs6470765, are targets of the BACH1 transcription factor, involved in cellular response to oxidative stress and management of the cell cycle47. Influence of novel loci on related traits. In a look-up in existing GWAS of smoking behaviours (Ever/Never, Current/NotCurrent, Smoking Quantity (SQ))48 (Supplementary Data 8), eight of our 26 SNPs were nominally associated with at least one smoking trait. After multiple test correction (P Regression o0.05/ 26 ¼0.0019), only one SNP remains significant: rs12902602, identified for Approaches 2 (SNPjoint) and 3 (SNPint) for BMI, showed association with SQ (P¼1.45 109). We conducted a search in the NHGRI-EBI GWAS Catalog49,50 to determine if any of our newly identified loci are in high LD with variants associated with related cardiometabolic and behavioural traits or diseases. Of the seven novel BMI SNPs, only rs12902602 was in high LD (r240.7) with SNPs previously associated with smoking-related traits (for example, nicotine dependence), lung cancer, and cardiovascular diseases (for example, coronary heart disease; Supplementary Table 13). Of the 12 novel WCadjBMI SNPs, 5 were in high LD with previously reported GWAS variants for mean platelet volume, height, infant length, and melanoma. Of the six novel WHRadjBMI SNPs, three were near several previously associated variants, including cardiometabolic traits (for example, LDL cholesterol, triglycerides and measures of renal function). Given high phenotypic correlation between WC and WHR with height, and established shared genetic associations that overlap our adiposity traits and height1,2,51 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). No novel BMI loci were significantly associated with height (P Regression o0.002(0.05/24) SNPs). However, there are additional variants that may be associated with height, but not previously reported in GWAS examining height, including two for WHRadjBMI near EYA4 and TRIB1, and two for WCadjBMI near KIF1B and HDLBP (P Regression o0.002). 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. Therefore, we expected that smoking-associated SNPs exhibit some interaction with smoking on BMI. We looked up published smoking behaviour SNPs49,50, 10 variants in 6 loci, in our own results. Two variants reached nominal significance (P SNPint o0.05) for GxSMK interaction on BMI (Supplementary Table 14), but only one reached Bonferroni-corrected significance (Po0.005). No smokingassociated SNPs exhibited GxSMK interaction. Therefore, we did not see a strong enrichment for low interaction Pvalues among previously identified smoking loci. Validation of novel loci. We pursued validation of our novel and interaction SNPs in an independent study sample of up to 119,644 European adults from the UK Biobank study (Tables 1–4, Supplementary Table 15, Supplementary Fig. 9). We found consistent directions of effects in smoking strata (for Approaches 2 and 3) and in SNPadjSMK results (Approach 1) for each locus examined (Supplementary Fig. 13). For BMI, three SNPs were not GWS (P SNPadjSMK ,P SNPjoint ,P SNPInt 45E8) following metaanalysis with our GIANT results: rs12629427 near EPAH3 (Approach 1); rs1809420 within a known locus near ADAMTS7 (Approach 4) remained significant for interaction, but not for SNPadjSMK; and rs336396 near INPP4B (Approach 3). For WCadjBMI, 3 SNPs were not GWS (P SNPadjSMK ,P SNPjoint , P SNPInt 45E8) following meta-analysis with our results: Table 1 | Summary of association results for novel loci reaching genome-wide significance in Approach (App) 1 (P SNPadjSMK o5E8) or Approach 2 (P SNPjoint o5E8) for our primary meta-analysis in combined ancestries and combined sexes. App Marker Chr:Pos (hg19) Nearest Gene NEAF Alleles E/O Smokers Non-smokers Main and interaction effects GIANT þUKBB bP-value bP-value b adj P SNPadjSMK P SNPint P SNPjoint P SNPadjSMK P SNPint P SNPjoint BMI 1,2 rs10929925 2:6155557 SOX11 225,067 0.55 C/A 0.019 7.80E03 0.02 8.40E08 0.020 1.1E09 8.2E 01 1.6E08 1.5E 13 4.5E 01 9.8E 13 1 rs6794880 3:84451512 SRRM1P2 186,968 0.85 A/G 0.025 2.30E02 0.027 3.90E06 0.028 4.3E08 8.5E01 1.8E06 4.9E09 4.5E01 9.7E08 2 rs13069244 3:180441172 CCDC39 233,776 0.08 A/G 0.061 1.80E05 0.031 6.60E05 0.035 1.2E07 4.6E02 3.5E 08 6.1E 10 1.1E 02 9.6E 11 WCadjBMI 1,2 rs17396340 1:10286176 KIF1B 206,485 0.14 A/G 0.016 1.40E01 0.035 4.70E10 0.028 3.0E08 9.8E02 9.1E10 1.0E11 2.9E02 1.5E13 1,2 rs6743226 2:242236972 HDLBP 200,666 0.53 C/T 0.018 1.30E02 0.023 2.60E09 0.022 1.2E 10 5.5E01 5.8E10 6.7E12 7.0E01 2.8E11 1 rs4378999 3:51208646 DOCK3 156,566 0.13 T/A 0.035 1.30E02 0.035 1.30E06 0.036 4.1E08 9.7E 01 4.1E 07 7.6E 11 5.3E 01 3.2E 10 1,2 rs7697556 4:73515313 ADAMTS3 206,017 0.49 T/C 0.004 6.30E01 0.025 7.30E11 0.021 5.2E09 6.7E 03 7.6E 10 5.4E 19 1.9E 02 2.7E 19 1 rs10269774 7:92253972 CDK6 157,552 0.34 A/G 0.024 6.60E 03 0.023 1.10E 06 0.023 2.9E 08 8.8E 01 1.6E 07 2.9E 10 7.7E 01 2.1E 09 1 rs6470765 8:130736697 GSDMC 157,450 0.76 A/C 0.032 1.90E03 0.023 1.70E05 0.026 4.8E08 4.3E01 9.5E07 2.5E 12 8.9E 01 9.0E 11 2 rs9408815 9:108890521 TMEM38B 156,427 0.75 C/G 0.012 2.30E01 0.03 4.20E09 0.026 2.3E08 8.5E02 1.7E08 1.2E 11 3.0E01 2.8E11 1 rs9409082 9:108901049 157,785 0.76 C/T 0.017 8.10E 02 0.029 2.60E 08 0.027 1.5E 08 2.7E 01 4.6E 08 9.5E 12 6.6E 01 6.5E 11 1 rs6012558 20:47531286 ARFGEF2 208,004 0.41 A/G 0.026 5.40E04 0.018 6.50E06 0.020 1.9E 08 3.3E01 1.3E07 1.5E 09 7.0E02 3.0E09 WHRadjBMI 1,2 rs1049281 6:31236567 HLA-C149,285 0.66 C/T 0.022 1.30E02 0.027 2.00E08 0.025 2.2E09 5.6E01 5.3E09 1.2E 18 8.3E01 1.8E10 Adj, adjusted for smoking; app, approach; int, interaction; chr, chromosome; EAF, effect allele frequency; E/O, effect/other; Pos, position (bp). Significant P-values that reach genome-wide significance (Po5108) threshold are in bold. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 7 rs1545348 near RAI14 (Approach 1); rs4141488 near GRIN2A (Approach 3); and rs6012558 near PRNP (Approach 3). For WHRadjBMI, only 1 SNP from Approach 4 was not significant following meta-analysis with our results: rs12608504 near JUND remained GWS for SNPadjSMK, but was only nominally significant for interaction (P SNPint ¼0.013). Challenges in accounting for environmental exposures in GWAS. 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 cessation7,13,52. Second, this definition allowed us to maximize sample size, as many participating studies only had current smoking status available. However, WC and WHR may not behave in the same manner as weight and BMI with former smokers retaining excess fat around their waist. 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)53. 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. Our analyses adjusted both WC and WHR for BMI. WHR has a correlation of 0.49 with BMI, while WC has a correlation of 0.85 (ref. 53). Using previously published results for BMI, WCadjBMI and WHRadjBMI, we find three novel loci for WCadjBMI (near DOCK3, ARFGEF2 and TMEM38B) and two for WHRadjBMI (near EHMT2 and HLA-C; Supplementary Data 8) with nominally significant associations with BMI and opposite directions of effect. At these loci, the genetic effect estimates should be interpreted with caution. Additionally, we adjusted for SMK in Approach 1 (SNPadjSMK). However binary smoking status, as we used, has a low correlation to BMI, WC, and WHR, as estimated in the ARIC study’s European descent participants ( 0.13, 0.08 and 0.12, respectively) and in the Framingham Heart Study ( 0.05, 0.08 and 0.16). 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. To assess how much additional information is provided by accounting for SMK and GxSMK in GWAS for obesity traits, we compared genetic risk scores (GRSs) based on various subsets of lead SNP genotypes in various regression models (Methods section). While any GRS was associated with its obesity trait (P GRS o1.6 107, Supplementary Table 16), adding SMK and GxSMK terms to the regression model along with novel variants to the GRSs substantially increased variance explained. For example, variance explained increased by 38% for BMI (from Table 2 | Novel loci showing significant association in Approaches 1 (SNPadjSMK) and/or 2 (SNPjoint) identified in secondary meta-analyses and not significant in primary meta-analyses. Approach: Strata Marker Chr:Pos (hg19) Nearest Gene NEAF Alleles E/O Smokers Non-smokers Main and interaction effects GIANT þUKBB bP-value bP-value b adj P SNPadj P SNPint P SNPjoint P SNPadjSMK P SNPint P SNPjoint BMI 1:EC rs2481665 1:62594677 INADL 209,453 0.56 T/C 0.015 4.60E 02 0.021 8.90E 08 0.019 3.50E 08 4.00E 01 6.70E 08 3.3E 11 7.8E 01 2.0E 08 1:AW rs12629427 3:89145340 EPHA3 137,961 0.26 C/T 0.025 2.10E 02 0.028 3.60E 07 0.027 4.80E 08 8.00E 01 2.00E07 7.7E 08 9.1E01 3.0E 07 1:EW rs2173039 3:89142175 117,942 0.26 C/G 0.024 3.10E 02 0.032 8.90E 08 0.031 7.30E 09 5.70E 01 6.50E 08 2.4E 09 9.3E 01 2.2E 07 WCadjBMI 1:EM rs1545348 5:34718343 RAI14 77,677 0.73 T/G 0.044 3.10E 04 0.03 1.90E 05 0.034 1.80E 08 3.20E 01 1.70E 07 1.2E 07 1.2E 01 4.8E 07 2:EW rs6076699 20:4566688 PRNP 76,930 0.97 A/G 0.169 1.40E 05 0.07 1.20E 04 0.034 3.50E 02 1.40E 08 4.80E 08 4.2E 02 2.3E06 3.4E06 WHRadjBMI 1:AW rs670752 3:107312980 BBX 107,568 0.32 A/G 0.012 5.50E 02 0.009 1.50E 02 0.027 4.90E 08 6.80E 01 7.80E 03 3.1E 10 3.8E 01 9.5E 05 1:EC rs589428 6:31848220 EHMT2 162,918 0.66 G/T 0.006 1.20E 01 0.011 4.10E 04 0.022 2.80E 08 3.50E 01 7.00E 04 1.1E 17 8.4E 02 1.6E 10 2:EC rs1856293 6:133480940 EYA4 127,431 0.52 A/C 0.006 5.30E 01 0.028 9.10E 09 0.019 6.50E 06 5.40E 04 4.70E 08 9.6E 08 1.3E 02 1.5E 08 1:AW rs2001945 8:126477978 TRIB1 103,446 0.4 G/C 0.009 1.20E 01 0.013 1.00E 04 0.025 4.70E 08 5.90E 01 1.30E 04 1.1E 09 3.0E 01 1.4E 06 1:EC rs17065323 13:44627788 SMIM2* 69,968 0.01 T/C 0.154 1.90E 01 0.23 1.20E 10 0.181 9.20E 09 1.40E 03 3.90E 10 9.6E 09 3.6E 03 1.3E 09 A, all ancestries; C, combined sexes; Chr, chromosome; E, European-only; EAF, effect allele frequency; E/O, effect/other; int, interaction; M, men only; Pos, position (bp); Padj, adjusted for smoking; W, women only. All estimates are from the stratum specified in the Approach:Sample column. *This locus was filtered from approaches 2–4 due to low sample size in the SMK strata, and only Pvalues for Approach 1 are considered significant. Significant P-values that reach genome-wide significance (Po5108) threshold are in bold. Table 3 | Summary of association results for loci showing significance for interaction with smoking in Approach (App) 3 (SNPint) and/or Approach 4 (SNPscreen) in our primary meta-analyses of combined ancestries and combined sexes. App Marker Chr:Pos (hg19) Nearest Gene NEAF Alleles E/O Smokers Non-smokers Main and interaction effects GIANT þUKBB bP-value bP-value b adj P SNPadj P SNPint P SNPjoint P SNPadjSMK P SNPint P SNPjoint BMI 3 rs336396 4:143062811 INPP4B 169,646 0.18 T/C 0.063 4.8E08 0.006 3.4E01 0.007 2.3E01 2.1E08 1.9E07 7.4E01 2.7E06 1.3E05 3 rs12902602* 15:78967401 CHRNB4 240,135 0.62 A/G 0.047 1.8E 11 0.002 5.5E01 0.009 8.6E03 4.1E11 1.1E10 1.1E01 6.0E13 1.6E12 WCadjBMI 3 rs4141488 16:9629067 GRIN2A 153,892 0.5 T/C 0.037 2.2E 05 0.015 9.6E04 0.003 4.4E 01 2.7E 08 5.0E 07 9.5E 01 1.8E 06 1.1E 05 WHRadjBMI 4 rs765751* 1:219669226 LYPLAL1 189,028 0.64 C/T 0.003 3.9E 01 0.019 3.1E11 0.029 3.1E16 7.3E 04 2.1E 10 9.1E31 1.4E 04 7.8E22 4 rs7766106* 6:127455138 RSPO3 188,174 0.48 T/C 0.007 7.9E02 0.022 2.2E15 0.037 3.7E27 9.7E04 3.8E15 4.4E51 1.0E 05 3.4E 34 Adj, adjusted for smoking; app, approach; int, interaction; chr, chromosome; EAF, effect allele frequency; E/O, effect/other; Pos, position (bp). *Known locus. Significant P-values after multiple test correction are italicized. Significant P-values that reach genome-wide significance (Po5108) threshold are in bold. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 8NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 1.53 to 2.11%, P GRSDiff ¼4.3105), by 27% for WCadjBMI (from 2.59 to 3.29%, P GRSDiff ¼3.9 106) and by 168% for WHRadjBMI (from 0.82 to 2.20%, P GRSDiff ¼3.21011). Therefore, despite potential limitations, much is gained by accounting for environmental exposures in GWAS studies. 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. We identified 161 loci in total, including 23 novel loci (6 for BMI, 11 for WCadjBMI, and 6 for WHRadjBMI). 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. We were able to replicate the previous GxSMK interaction with BMI within the CHRNA5-CHRNA3-CHRNB4 gene cluster. One novel BMIassociated locus near INPP4B and two novel WCadjBMI-associated loci near GRIN2A and PRNP displayed significant GxSMK interaction. We were also able to identify significant GxSMK interaction for one known BMI-associated locus near ADAMTS7 and for five known WHRadjBMI-associated loci near LYPLAL1, RSPO3,MAP3K1,HOXC4-HOXC6 and JUND. The majority of these loci harbour strong candidate genes for adiposity with a possible role for the modulation of effects through tobacco use. We identified 18 new loci in Approach 1 (P SNPadjSMK )by adjusting for current smoking status. 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 studies1,2 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 analysis2. While sample sizes for both WCadjBMI and WHRadjBMI are comparable with previous GIANT investigations, our Pvalues for variants identified in Approach 1 are at least two orders of magnitude lower than previous findings. 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 studies54. Furthermore, the ‘winner’s curse’ (inflated discovery effects estimates) requires larger sample sizes for adequate power in replication studies55. Despite these challenges, we were able to detect consistent direction of effect in an independent sample for all novel loci. Some results that did not remain GWS in the GIANT þUKBB meta-analysis had results that were just under the threshold for significance, suggesting that a larger sample may be needed to confirm these results, and thus the associations near INPP4B,GRIN2A,RAI14, PRNP and JUND should be interpreted with caution. 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 WCadjBMI, there was no enrichment for stronger effects in one stratum compared to the other for our significant loci; however, there was a greater proportion of explained variance in WCadjBMI for loci identified in Approach 1 (SNPadjSMK) in nonsmokers. For WHRadjBMI, there were significantly more loci that exhibit greater effects in nonsmokers, and this pattern was mirrored in the variance explained analysis. The large difference between effects in smokers and nonsmokers likely explains the sub-GWS levels of our loci in previous GIANT investigations2. For example, the T allele of rs7697556, 81kb from the ADAMTS3 gene, was associated with increased WCadjBMI and exhibits a sixfold greater effect in nonsmokers compared to smokers, although the interaction effect was only nominal; in previous GWAS this variant was nearly GWS. 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 BMI5,9,10. Our results support previous findings that implicate genes involved in transcription and gene expression, appetite regulation, macronutrient metabolism, and glucose homeostasis. Several of our novel loci have candidate genes within 500 kb of our tag Table 4 | Summary of association results for loci showing significance for interaction with smoking in Approach 3 (SNPint) and/or Approach 4 (SNPscreen) in our secondary meta-analyses not identified in primary meta-analyses. Approach: Strata Marker Chr:Pos (hg19) Nearest Gene NEAF Alleles E/O Smokers Non-smokers Main and interaction effects GIANT þUKBB bPbPb adj P SNPadj P SNPint P SNPjoint P SNPadjSMK P SNPint P SNPjoint BMI 4:AM rs1809420* 15:79056769 ADAMTS7 57,081 0.59 T/C 0.074 9.8E 08 0.023 2.0E 03 0.036 4.9E 08 9.4E 04 5.6E09 9.8E 05 3.3E 05 1.9E 07 WCadjBMI 3:EW rs6076699 20:4566688 PRNP 76,930 0.97 A/G 0.169 1.4E 05 0.07 1.2E 04 0.034 3.5E 02 1.4E 08 4.8E 08 4.2E02 2.3E 06 3.4E06 WHRadjBMI 4:EM rs30000* 5:55803533 MAP3K1 71,424 0.27 G/A 0.002 7.8E 01 0.031 3.7E 08 0.04 1.7E 10 1.6E04 2.7E 07 2.7E 17 3.2E 07 3.8E 15 4:AM rs459193* 5:55806751 80,852 0.27 A/G 0.004 5.0E 01 0.034 4.1E10 0.043 2.3E 13 6.8E 05 2.2E 09 3.5E 20 2.5E07 1.6E 17 4:AM rs2071449* 12:54428011 HOXC470,868 0.37 A/C 0.003 6.0E 01 0.026 1.0E06 0.034 9.1E 09 1.1E03 5.7E 06 2.7E 12 8.0E 04 2.8E 09 4:EM rs754133* 12:54418920 HOXC6 71,136 0.36 A/G 0.003 6.2E 01 0.026 8.2E 07 0.034 3.0E 09 1.1E 03 4.0E 06 2.1E 12 9.7E 04 4.0E09 4:AM rs12608504* 19:18389135 JUND 80,087 0.37 A/G 0.006 2.6E01 0.025 5.0E 07 0.032 4.7E09 5.5E 03 1.8E 06 2.9E 11 1.3E 02 1.6E 08 A, all ancestries; Adj, adjusted for smoking; app, approach; C, combined sexes; Chr, chromosome; E, European-only; EAF, effect allele frequency; E/O, effect/other; int, interaction; M, men only; Pos, position (bp); W, women only. All estimates are from the stratum specified in the Approach:Sample column The R2between the ADAMTS7 (rs1809420) and CHRNB4 variant (rs1290362) in Table 3 is 0.72 (HapMap 2, CEU). Additionally, the PRNP variant (rs6076699) is the same as the variant that came up from Approach 2 (Table 2). *Known locus. Significant P-values after multiple test correction are italicized. Significant P-values that reach genome-wide significance (Po5108) threshold are in bold. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 9 Jaakko Tuomilehto187,188,190, Andre G. Uitterlinden32,191, Liesbeth Vandenput34, Marie-Claude Vohl58,192, Henry Vo ¨lzke68, Judith M. Vonk72,Ge ´rard Waeber158, Melanie Waldenberger108,109, R.G.J. Westendorp193, Sarah Wild98, Gonneke Willemsen77, Bruce H.R. Wolffenbuttel73, Andrew Wong194, Alan F. Wright39, Wei Zhao25, M. Carola Zillikens191, Damiano Baldassarre195,196, Beverley Balkau197, Stefania Bandinelli198, Carsten A. Bo ¨ger36, Dorret I. Boomsma77, Claude Bouchard170, Marcel Bruinenberg199, Daniel I. Chasman11,200, Yii-Der Ida Chen201, Peter S. Chines93, Richard S. Cooper186, Francesco Cucca174,202, Daniele Cusi203, Ulf de Faire106, Luigi Ferrucci67, Paul W. Franks19,21,204, Philippe Froguel26,205, Penny Gordon-Larsen83,206, Hans-Jo ¨rgen Grabe207,208, Vilmundur Gudnason61,62, Christopher A. Haiman209, Caroline Hayward39,118, Kristian Hveem145, Andrew D. Johnson13, J. Wouter Jukema69,210,211, Sharon L.R. Kardia25, Mika Kivimaki45, Jaspal S. Kooner81,99,212, Diana Kuh194, Markku Laakso144, Terho Lehtima ¨ki48,49, Loic Le Marchand46, Winfried Ma ¨rz213,214, Mark I. McCarthy37,17,215, Andres Metspalu35, Andrew P. Morris17,216, Claes Ohlsson34, Lyle J. Palmer217, Gerard Pasterkamp71,218, Oluf Pedersen8, Annette Peters109,110, Ulrike Peters82, Ozren Polasek98,142, Bruce M. Psaty219,220,221,LuQi 21,222, Rainer Rauramaa43,223, Blair H. Smith118,224, Thorkild I.A. Sørensen8,225,226, Konstantin Strauch54,157, Henning Tiemeier227, Elena Tremoli195,196, Pim van der Harst76,183,228, Henrik Vestergaard8,9, Peter Vollenweider158, Nicholas J. Wareham47, David R. Weir103, John B. Whitfield53, James F. Wilson39,229, Jessica Tyrrell230,231, Timothy M. Frayling232, Ine ˆs Barroso233,234,235, Michael Boehnke28, Panagiotis Deloukas40,233,236, Caroline S. Fox10, Joel N. Hirschhorn74,75,237, David J. Hunter21,75,238,239, Tim D. Spector50, David P. Strachan5,240, Cornelia M. van Duijn24,241,242, Iris M. Heid2,243, Karen L. Mohlke79, Jonathan Marchini244, Ruth J.F. Loos22,23,47,245,246,**, Tuomas O. Kilpela ¨inen8,47,247,**, Ching-Ti Liu4,**, Ingrid B. Borecki3,**, Kari E. North1,** & L. Adrienne Cupples4,10,** 1Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina 27599, USA. 2Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, D-93053 Regensburg, Germany. 3Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine; St. Louis, Missouri 63108, USA. 4Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA. 5Population Health Research Institute, St. George’s, University of London, London SW17 0RE, UK. 6TransMed Systems, Inc., Cupertino, California 95014, USA. 7Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. 8The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 9Steno Diabetes Center, Gentofte, Denmark. 10 NHLBI Framingham Heart Study, Framingham, Massachusetts 01702, USA. 11 Division of Preventive Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA. 12 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA. 13 Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, The Framingham Heart Study, Framingham, Massachusetts, USA. 14 Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland. 15 Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. 16 Swiss instititute of Bioinformatics, 1015 Lausanne, Switzerland. 17 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 18 Department of Biobank Research, Umeå University, Umeå, Sweden. 19 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, SE-205 02 Malmo¨, Sweden. 20 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA. 21 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA. 22 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA. 23 The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, USA. 24 Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015GE, The Netherlands. 25 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA. 26 University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199 - EGID, Lille, France. 27 Internal Medicine - Nephrology, University of Michigan, Ann Arbor, Michigan, USA. 28 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA. 29 Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley 6009, Australia. 30 Department of Health Sciences, University of Milan,Via A. Di Rudinı ´, 8 20142, Milano, Italy. 31 Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA. 32 Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. 33 Department of Psychiatry, Dokuz Eylul University, Izmir, Turkey. 34 Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden. 35 Estonian Genome Center, University of Tartu, Tartu 51010, Estonia. 36 Department of Nephrology, University Hospital Regensburg, Regensburg, Germany. 37 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK. 38 Epidemiology Domain, Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore. 39 MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland. 40 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. 41 Department of Health, National Institute for Health and Welfare, Helsinki FI-00271, Finland. 42 Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. 43 Kuopio Research Institute of Exercise Medicine, Kuopio, Finland. 44 ISER, University of Essex, Colchester CO43SQ, UK. 45 Department of Epidemiology and Public Health, UCL, London, WC1E 6BT, UK. 46 Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii 96813, USA. 47 MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge CB2 0QQ, UK. 48 Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland. 49 Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 16 NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 50 Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. 51 NIHR Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London, UK. 52 Center for Public Health Genomics and Biostatistics Section, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22903, USA. 53 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane 4029 , Australia. 54 Institute of Genetic Epidemiology, Helmholtz Zentrum Mu ¨nchen - German Research Center for Environmental Health, D-85764 Neuherberg, Germany. 55 Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universita ¨t, D-81377 Munich, Germany. 56 DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany. 57 Department of Kinesiology, Faculty of Medicine, Universite ´Laval, Quebec City, Que ´bec, Canada, G1V 0A6. 58 Institute of Nutrition and Functional Foods, Universite ´Laval, Quebec City, Que ´bec, Canada, G1V 0A6. 59 Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia. 60 Department of Social and Health Care, City of Helsinki, Helsinki, Finland. 61 Icelandic Heart Association, Kopavogur, Iceland. 62 Faculty of Medicine, University of Iceland, Reykjavik, Iceland. 63 Department of Medicine, Institute of Clinical Medicine, University of Eastern Finland, 70210 Kuopio, Finland.. 64 Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden. 65 Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden. 66 Division of Biostatistics, Washington University School of Medicine, St Louis, Missouri, USA. 67 Translational Gerontology Branch, National Institute on Aging, Baltimore, Maryland, USA. 68 Institute for Community Medicine, University Medicine Greifswald, Germany. 69 Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. 70 Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands. 71 Laboratory of Experimental Cardiology, Department of Cardiology, Division Heart & Lungs, UMC Utrecht, Utrecht, The Netherlands. 72 Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands. 73 Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 74 Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, Massachusetts 02115, USA. 75 Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA. 76 Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 77 Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands. 78 Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands. 79 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA. 80 Dept Epidemiology and Biostatistics, School of Public Health, Imperical College London, UK. 81 Cardiology, Ealing Hospital NHS Trust, Middlesex, UK. 82 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. 83 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 84 Department of Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 85 Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98101, USA. 86 Busselton Population Medical Research Institute, Nedlands, Western Australia 6009, Australia. 87 PathWest Laboratory Medicine of WA, Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia. 88 School of Pathology and Laboraty Medicine, The University of Western Australia, 35 Stirling Hwy, Crawley, Western Australia 6009, Australia. 89 Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. 90 Clinic for Prosthetic Dentistry, Gerostomatology and Material Science, University Medicine Greifswald, Greifswald, Germany. 91 South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, Texas, USA. 92 Human Genetics Center, The University of Texas Health Science Center, PO Box 20186, Houston, Texas 77225, USA. 93 Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland 20892, USA. 94 Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA. 95 Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland. 96 Department of Genetics, Rutgers University, Piscataway, New Jersey 08854, USA. 97 Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey 08854, USA. 98 Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Scotland, UK. 99 Imperial College Healthcare NHS Trust, London, UK. 100 Department of Vascular Surgery, Division of Surgical Specialties, UMC Utrecht, Utrecht, The Netherlands. 101 EMGO þInstitute Vrije Universiteit & Vrije Universiteit Medical Center, Amsterdam, the Netherlands. 102 Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece. 103 Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA. 104 Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK. 105 Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona JMAAW15, Jamaica. 106 Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 107 Hypertension and Related Disease Centre, AOU-University of Sassari, Sassari, Italy. 108 Research Unit of Molecular Epidemiology, Helmholtz Zentrum Mu ¨nchen, German Research Center for Environmental Health, D-85764 Neuherberg, Germany. 109 Institute of Epidemiology II, Helmholtz Zentrum Mu ¨nchen - German Research Center for Environmental Health, D-85764 Neuherberg, Germany. 110 German Center for Diabetes Research, D-85764 Neuherberg, Germany. 111 Department of Public Health and Clinical Medicine, Section for Nutritional Research, Umeå University, Umeå, Sweden. 112 Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA. 113 Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands. 114 Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. 115 Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, USA. 116 Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. 117 Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK. 118 Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland. 119 St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway. 120 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. 121 Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. 122 School of Medicine and Pharmacology, The University of Western Australia, 25 Stirling Hwy, Crawley, Western Australia 6009, Australia. 123 Department of Cardiovascular Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia. 124 Department of Pediatrics, Tampere University Hospital, Tampere 33521, Finland. 125 Department of Pediatrics, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland. 126 Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, 751 85, Sweden. 127 Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA. 128 Science for Life Laboratory, Uppsala University, Uppsala 750 85, Sweden. 129 Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia. 130 Department of Physiology, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden. 131 Department of Epidemiology and Biostatistics, MRC–PHE Centre for Environment & Health, School of Public Health, Imperial College London, Norfolk Place, London, UK. 132 Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, P.O.Box 5000, FI-90014, Oulu, Finland. 133 Biocenter Oulu, University of Oulu, Oulu, Finland. 134 Unit of Primary Care, Oulu University Hospital, Kajaanintie 50, P.O.Box 20, FI-90220, 90029 Oulu, Finland. 135 Department of Medicine, University of Turku, Turku 20520, Finland. 136 Division of Medicine, Turku University Hospital, Turku 20521, Finland. 137 Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland. 138 Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland. 139 Clinical and Molecular Osteoporosis Research Unit, Department of Orthopedics and Clinical Sciences, Skåne University Hospital, Lund University, Malmo¨, Sweden. 140 Department of Medicine and Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki FI-00029, Finland. 141 Minerva Foundation Institute for Medical Research, Biomedicum 2U, Helsinki FI-00290, Finland. 142 Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia. 143 Department of Cardiology, Onassis Cardiac Surgery Center, Athens, Greece. 144 Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Kuopio, Finland. 145 HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, 7600 Levanger, Norway. 146 Institute NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 17 of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Finland. 147 USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City 6000, Philippines. 148 Department of Anthropology, Sociology and History, University of San Carlos, Cebu City 6000, Philippines. 149 Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden. 150 Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK. 151 Research Centre for Prevention and Health, the Capital Region of Denmark, Copenhagen, Denmark. 152 Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark. 153 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 154 Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Level 5, Singapore 138648, Singapore. 155 Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. 156 Department of Psychology, University of Notre Dame, Notre Dame, USA. 157 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universita ¨t, D-81377 Munich, Germany. 158 Department of Medicine, Internal Medicine, Lausanne university hospital (CHUV), Lausanne, Switzerland. 159 Program in Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. 160 Molecular Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4029, Australia. 161 School of Population Health, The University of Western Australia, 35 Stirling Hwy, Crawley, Western Australia 6009, Australia. 162 Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia. 163 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. 164 Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland. 165 Research Center for Prevention and Health, Glostrup Hospital, Glostrup, Denmark. 166 Department of Public Health, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark. 167 Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland. 168 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland. 169 Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland. 170 Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA. 171 Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. 172 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 173 Division of Cardiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA. 174 Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale Delle Ricerche (CNR), Cittadella Universitaria di Monserrato, 09042 Monserrato, Italy. 175 BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, UK. 176 Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA. 177 Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. 178 Division of Angiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. 179 Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. 180 Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Science , University of Leuven, Campus Sint Rafael, Kapucijnenvoer 35, Leuven, Belgium. 181 R&D VitaK Group, Maastricht University, Brains Unlimited Building, Oxfordlaan 55, Maastricht, The Netherlands. 182 Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, UK. 183 Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 184 Center for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor/UCLA Medical Center, Torrance, California, USA. 185 Department of Pediatrics, University of California Los Angeles, Los Angeles, California, USA. 186 Department of Public Health Sciences, Stritch School of Medicine, Loyola University of Chicago, Maywood, Illinois 61053, USA. 187 Research Division, Dasman Diabetes Institute, Dasman, Kuwait. 188 Department of Neurosciences and Preventive Medicine, Danube-University Krems, 3500 Krems, Austria. 189 Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland. 190 Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia. 191 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands. 192 School of Nutrition, Universite ´Laval, Laval, Que ´bec, Canada. 193 Department of Public Health and Center for Healthy Aging, University of Copenhagen, 1014 Copenhagen, Denmark. 194 MRC Unit for Lifelong Health and Ageing at UCL, 33 Bedford Place, London WC1B 5JU, UK. 195 Dipartimento di Scienze Farmacologiche e Biomolecolari, Universita `di Milano, Milan, Italy. 196 Centro Cardiologico Monzino, IRCCS, Milan, Italy. 197 Inserm U-1018, CESP, 94807 Villejuif cedex, France. 198 Geriatric Unit, Azienda USL Toscana centro, Florence, Italy. 199 Lifelines Cohort Study, PO Box 30001, 9700 RB Groningen, The Netherlands. 200 Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts, USA. 201 Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute and Department of Pediatrics, Harbor-UCLA, Torrance, California 90502, USA. 202 Dipartimento di Scienze Biomediche, Universita’ degli Studi di Sassari, Sassari, Italy. 203 Sanipedia srl, Bresso (Milano), Italy and Institute of Biomedical Technologies National Centre of Research Segrate, Milano, Italy. 204 Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden. 205 Department of Genomics of Common Disease, Imperial College London, London, UK. 206 Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, USA. 207 Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. 208 German Center for Neurodegenerative Diseases (DZNE), Rostock and Greifswald Site, Greifswald, Germany. 209 Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA. 210 Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands. 211 Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands. 212 Faculty of Med, National Heart & Lung Institute, Cardiovascular Science, Hammersmith Campus, Hammersmith Hospital, Hammersmith Campus, Imperial College London, London, UK. 213 Synlab Academy, Synlab Services GmbH, Mannheim, Germany. 214 Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria. 215 Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, UK. 216 Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK. 217 School of Public Health, University of Adelaide, Adelaide, South Australia 5005, Australia. 218 Laboratory of Clinical Chemistry and Hematology, Division Laboratories & Pharmacy, UMC Utrecht, Utrecht, The Netherlands. 219 Department of Medicine, University of Washington, Seattle, Washington 98195, USA. 220 Department of Epidemiology, University of Washington, Seattle, Washington 98101, USA. 221 Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101, USA. 222 Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA. 223 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland. 224 Division of Population Health Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD2 4RB, Scotland. 225 Department of Clinical Epidemiology (formerly Institute of Preventive Medicine), Bispebjerg and Frederiksberg Hospital (2000 Frederiksberg), The Capital Region, Copenhagen, Denmark. 226 MRC Integrative Epidemiology Unit, Bristol University, Bristol, UK. 227 Department of Psychiatry Erasmus Medical Center, Rotterdam, The Netherlands. 228 Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands. 229 Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Scotland, UK. 230 Genetics of Complex Traits, University of Exeter Medical School, RILD Building University of Exeter, Exeter EX2 5DW, UK. 231 European Centre for Environment and Human Health, University of Exeter Medical School, The Knowledge Spa, Truro TR1 3HD, UK. 232 Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK. 233 Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. 234 NIHR Cambridge Biomedical Research Centre, Level 4, Institute of Metabolic Science Box 289 Addenbrooke’s Hospital, Cambridge CB2 OQQ, UK. 235 University of Cambridge Metabolic Research Laboratories, Level 4, Institute of Metabolic Science Box 289 Addenbrooke’s Hospital, Cambridge CB2 OQQ, UK. 236 Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia. 237 Department of Genetics, Harvard Medical School, Boston Massachusetts 02115, USA. 238 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA. 239 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA. 240 Division of Population Health Sciences and Education, St George’s, University of London, London SW17 0RE, UK. 241 Netherlands Genomics ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 18 NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA). Leiden, The Netherlands. 242 Center for Medical Systems Biology, Leiden, The Netherlands. 243 Institute of Genetic Epidemiology, Helmholtz Zentrum Mu ¨nchen - German Research Center for Environmental Health, Neuherberg 85764, Germany. 244 Department of Statistics, University of Oxford, Oxford, UK. 245 Mount Sinai School of Medicine, New York 10029, USA. 246 The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA. 247 Department of Preventive Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA. * These authors contributed equally to this work. ** These authors jointly supervised this work. zDeceased. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI: 10.1038/ncomms14977 | www.nature.com/naturecommunications 19