Genome-wide meta-analyses identify multiple loci associated with smoking behavior

Abstract

Consistent but indirect evidence has implicated genetic factors in smoking behavior^1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10⁻⁷³). Two 10q25 SNPs (rs1329650[G], β = 0.367, s.e. = 0.059, P = 5.7 × 10⁻¹⁰; and rs1028936[A], β = 0.446, s.e. = 0.074, P = 1.3 × 10⁻⁹) and one 9q13 SNP in EGLN2 (rs3733829[G], β = 0.333, s.e. = 0.058, P = 1.0 × 10⁻⁸) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04–1.08, P = 1.8 × 10⁻⁸). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08–1.18, P = 3.6 × 10⁻⁸) was significantly associated with smoking cessation.

Previous genome-wide association studies (GWAS) for smoking behavior (Supplementary Table 1) have identified a chromosome-15 nicotinic acetylcholine receptor gene cluster as being associated with smoking quantity³. The Tobacco and Genetics (TAG) Consortium conducted GWAS meta-analyses across 16 studies originally designed to evaluate other phenotypes (for example, cardiovascular disease and type 2 diabetes). We examined four carefully harmonized smoking phenotypes (see Online Methods)—smoking initiation (ever versus never been a regular smoker), age of smoking initiation, smoking quantity (number of cigarettes smoked per day, CPD) and smoking cessation (former versus current smokers)—among people of European ancestry (Table 1). Smoking cessation contrasted former versus current smokers, where current smokers reported at interview that they presently smoked and former smokers had quit smoking at least 1 year before interview. Smokers who had quit smoking for less than 1 year at interview were excluded from the analysis to minimize misclassification, as relapse after initial smoking cessation occurs in 70% to 80% of former smokers within the first year⁴.

Table 1.

Descriptive characteristics of the 16 studies participating in the TAG Consortium

Study	n (% female)	Agea, mean (s.d.)	Ever smokers (%)	CPD, mean (s.d.)b	Age of initiation of smokinga, mean (s.d.)b	Former smokers (%)b
Population-based cohort studies
Atherosclerosis Risk in Communities (ARIC)	8,330 (52.9)	54.3 (5.7)	60.4	21.0 (11.7)	18.6 (5.1)	57.4
Baltimore Longitudinal Study of Aging (BLSA)	856 (46.0)	48.1 (17.8)	54.0	NA	19.3 (5.9)	NA
Cardiovascular Health Study (CHS)	3,236 (60.8)	72.3 (5.4)	52.3	17.8 (11.8)	19.6 (6.6)	77.8
Invecchiare in Chianti (InCHIANTI)	1,200 (55.2)	68.4 (15.5)	43.9	14.8 (9.4)	32.2 (16.7)	57.0
Rotterdam Study	5,610 (60.3)	69.1 (8.9)	59.2	15.8 (11.7)	20.4 (8.2)	62.6
Framingham Heart Study (FHS)	7,257 (53.7)	45.4 (10.9)	54.2	15.5 (10.8)	17.9 (4.2)	61.7
Women’s Genome Health Study (WGHS)	22,037 (100)	54.7 (7.1)	49.2	16.0 (11.0)	NA	75.2
Case-control studies
Atherosclerotic Disease Vascular Function and Genetic Epidemiology (ADVANCE)	585 (58.8)	45.8 (5.9)	47.7	13.1 (14.2)	17.0 (4.6)	65.2
Atherosclerosis, Thrombosis and Vascular Biology Italian Study Group (ATVB)	3,260 (11.6)	39.6 (4.9)	68.1	23.4 (14.7)	17.4 (4.0)	21.3
Diabetes Genetic Initiative (DGI)	2,504 (50.0)	61.6 (10.6)	37.7	NA	19.0 (5.5)	NA
Finland-United States Investigation of NIDDM Genetics (FUSION)	1,055 (52.8)	64.0 (7.5)	46.8	16.3 (12.4)	21.0 (7.0)	65.0
International Agency for Research on Cancer (IARC)	8,381 (24.7)	59.6 (10.1)	75.2	19.3 (12.9)	18.7 (5.6)	31.4
Myocardial Infarction Genetics Consortium (MIGen)	2,647 (38.5)	48.8 (8.2)	64.3	NA	NA	41.1
Nurses’ Health Study (NHS)	2,249 (100)	70.5 (6.4)	53.8	18.5 (10.5)	19.6 (3.6)	88.7
Netherlands Twin Registry-Netherlands Study of Depression and Anxiety (NTR/NESDA)	3,438 (66.9)	43.8 (13.4)	64.9	14.5 (9.8)	16.4 (4.2)	52.6
MGS (GAIN):controls	1,390 (54.1)	51.1 (17)	55.9	19.3 (16.4)	NA	62.9

^aAge in years.

^bCalculated among ever regular smokers.

NA, not available.

The 16 TAG studies performed their own genotyping, quality control and imputation (see Supplementary Tables 2 and 3 and Online Methods). Studies ranged in size from n = 585 to n = 22,037 and were genotyped on six different platforms. Genotype imputation⁵ resulted in a common set of ~2.5 million SNPs (Supplementary Table 3). Imputed allele dosages for each SNP (that is, the number of copies of the minor allele) were tested for association with each smoking phenotype, using an additive model.

We performed a fixed-effect meta-analysis for each smoking phenotype by computing pooled inverse variance–weighted β coefficients, s.e. values and z-scores for each SNP⁶. Fixed-effects analyses are regarded as the most efficient method for discovery in the GWAS setting^7,8. Heterogeneity across studies was investigated using the I² statistic⁹. Random-effects analyses are presented in Supplementary Table 4. We used a significance threshold of P < 5 × 10⁻⁸ (refs. ^10,11).

In the initial TAG meta-analysis, only one locus contained SNPs that exceeded genome-wide significance for one of the four phenotypes (Fig. 1 and Supplementary Table 4). A total of 130 SNPs in the 15q25.1 nicotinic receptor gene cluster were significantly associated with CPD (n = 38,181, minimum P = 4.2 × 10⁻³⁵ at rs12914385 in CHRNA3). One SNP approached significance for smoking cessation (n = 41,278, minimum P = 5.5 × 10⁻⁸ for rs7872903, located ~17 kb 5′ of DBH on chromosome 9). No SNPs were significantly associated with ever versus never regular smokers (n = 74,035, minimum P = 2.2 × 10⁻⁷ at rs16941640 in CDC27) or age of smoking initiation (n = 24,114, minimum P = 1.6 × 10⁻⁶ at rs2806464, located 3′ of DISC1) in the initial TAG meta-analysis.

Figure 1.

Genome-wide association results for the TAG Consortium. Manhattan plots showing significance of association of all SNPs in the TAG Consortium meta-analyses for four smoking phenotypes. (a–d) Manhattan plots show SNPs plotted on the x axis according to their position on each chromosome against, on the y axis (shown as −log₁₀ P value), the association with CPD (a), former versus current smoking (b), ever versus never smoking (c) and age of smoking initiation (d).

To follow up associations identified in the TAG Consortium analyses, we partnered with the ENGAGE and Oxford-GlaxoSmithKline (Ox-GSK) consortia and conducted a reciprocal exchange of summary results for the 15 most significant genetic regions for three smoking phenotypes^12,13. Our regions were defined by clusters of P values < 10⁻⁴ (that is, where the correlations (r²) were >0.5 and/or the SNPs were located <50 kb apart; Supplementary Table 5). Sample sizes across the three consortia were n = 143,023 for smoking initiation, n = 73,853 for CPD and n = 64,924 for smoking cessation (data on age of smoking initiation were not available in ENGAGE or Ox-GSK).

Results of the most significant SNPs for each smoking phenotype across the three consortia are summarized in Table 2. We identified three loci associated with CPD. The synonymous SNP rs1051730 in CHRNA3 showed the strongest association: each copy of the A allele corresponded to an increase in smoking quantity of 1 CPD (β = 1.03, s.e. = 0.056, P = 2.8 × 10⁻⁷³, I² = 0.66; Fig. 2) and accounted for 0.5% of the variance in CPD. The SNP rs16969968[G], which has been proposed as a causal variant in this region¹⁴, was the second most significant SNP associated with CPD (P = 5.57 × 10⁻⁷²; Supplementary Fig. 1). In tests of association for SNPs within the 15q25.1 region conditional on rs1051730, we observed residual associations, with the most significant signals at rs684513[G] (P = 6.3 × 10⁻⁹), in CHRNA5, and rs9788682[G] (P = 1.06 × 10⁻⁸) and rs7163730[G] (P = 1.22 × 10⁻⁸), in LOC123688 (Supplementary Fig. 2 and Supplementary Table 6). Our results suggest that several markers within this region may influence CPD independently. Fine mapping and the use of the 1000 Genomes Project data should help refine these signals. We investigated whether the 15q25.1 region was associated with smoking initiation and smoking cessation as well, but no SNP in that region exceeded genome-wide significance (smoking initiation minimum P = 0.98; smoking cessation minimum P = 1.75 × 10⁻⁵).

Table 2.

Meta-analytic results from three GWAS smoking consortia

		TAG meta-analysis					Ox-GSK meta analysis				ENGAGE meta analysis				Combined results
SNP	Alleles	Coded AF	n	β	s.e.	P value	n	β	s.e.	P value	n	β	s.e.	P value	n	β	s.e.	P value
CPDa: CHRNA3
rs1051730	G/A	0.65	38,181	−1.0207	0.086	8.00 × 10−33	14,952	−1.1593	0.139	8.88 × 10−17	20,720	−0.9648	0.089	2.15 × 10−27	73,853	−1.0209	0.056	2.75 × 10−73
rs16969968	G/A	0.65	38,181	−1.0150	0.085	4.48 × 10−33	14,952	−1.1153	0.137	3.72 × 10−16	20,720	−0.9426	0.089	2.07 × 10−26	73,853	−1.0029	0.056	5.57 × 10−72
CPDa: in LOC100188947
rs1329650	T/G	0.28	38,181	−0.4317	0.091	2.33 × 10−6	14,952	−0.2568	0.145	7.61 × 10−2	20,720	−0.3464	0.092	1.73 × 10−4	73,853	−0.3673	0.059	5.67 × 10−10
rs1028936	C/A	0.18	37,284	−0.5545	0.116	1.57 × 10−6	14,952	−0.2451	0.176	1.65 × 10−1	20,720	−0.4252	0.113	1.77 × 10−4	72,956	−0.4464	0.074	1.29 × 10−9
CPDa: EGLN2, near CYP2A6
rs3733829	G/A	0.36	38,181	0.3538	0.090	7.67 × 10−	14,952	0.0477	0.145	7.43 × 10−1	20,720	0.4204	0.089	2.90 × 10−6	73,853	0.3328	0.058	1.04 × 10−8
Smoking initiation (ever versus never smokers): BDNF
rs6265	T/C	0.21	74,035	−0.0630	0.015	1.72 × 10−5	34,226	−0.0448	0.022	4.48 × 10−2	34,762	−0.0762	0.024	1.39 × 10−3	143,023	−0.0614	0.011	1.84 × 10−8
rs1013442	T/A	0.26	74,035	−0.0568	0.014	3.39 × 10−5	34,226	−0.0386	0.021	6.36 × 10−2	34,762	−0.0674	0.020	9.60 × 10−4	143,023	−0.0551	0.010	3.31 × 10−8
rs4923457	T/A	0.23	74,035	−0.0600	0.014	2.08 × 10−5	34,226	−0.0421	0.022	5.05 × 10−2	34,762	−0.0752	0.024	1.91 × 10−3	143,023	−0.0586	0.011	3.33 × 10−8
rs4923460	T/G	0.23	74,035	−0.0598	0.014	2.22 × 10−5	34,226	−0.0427	0.022	4.81 × 10−2	34,762	−0.0734	0.024	2.51 × 10−3	143,023	−0.0583	0.011	4.08 × 10−8
rs4074134	T/C	0.23	74,035	−0.0603	0.014	1.90 × 10−5	34,226	−0.0421	0.022	5.08 × 10−2	34,762	−0.0725	0.024	2.81 × 10−3	143,023	−0.0582	0.011	4.11 × 10−8
rs1304100	G/A	0.26	74,035	−0.0557	0.014	4.86 × 10−5	34,226	−0.0460	0.021	2.62 × 10−2	34,762	−0.0651	0.022	2.88 × 10−3	143,023	−0.0554	0.010	4.44 × 10−8
rs6484320	T/A	0.24	74,035	−0.0597	0.014	2.04 × 10−5	34,226	−0.0387	0.021	6.78 × 10−2	34,762	−0.0723	0.024	2.13 × 10−3	143,023	−0.0571	0.010	4.91 × 10−8
rs879048	C/A	0.23	74,035	−0.0598	0.014	2.28 × 10−5	34,226	−0.0409	0.022	5.86 × 10−2	34,762	−0.0728	0.024	2.41 × 10−3	143,023	−0.0578	0.011	4.94 × 10−8
Smoking cessation (former versus current smokers): near DBH
rs3025343	G/A	0.84	41,278	0.1177	0.026	5.68 × 10−6	23,646	0.1295	0.041	1.76 × 10−3	NA	NA	NA	NA	64,924	0.1210	0.022	3.56 × 10−8

All SNPs coded to NCBI Build 36/UCSC hg18 forward strand. Coded allele frequency refers to the allele analyzed as the predictor allele; it is not necessarily the minor allele. For CPD, 174 SNPs followed up across three consortia; 130 exceeded genome-wide significance and the two top SNPs are presented. NA, not available.

^aCPD was analyzed as a continuous variable representing the number of cigarettes smoked per day. Smoking initiation and smoking cessation were analyzed as dichotomous variables, contrasting ever versus never and former versus current smokers, respectively.

Figure 2.

Forest and regional plots of significant associations for CPD from meta-analyses of the TAG, Ox-GSK and ENGAGE consortia. (a–f) Regional association plots show SNPs plotted by position on chromosome against −log₁₀ P value with each smoking phenotype. Estimated recombination rates (from HapMap-CEU) are plotted in light blue to reflect the local LD structure on a secondary y axis. The SNPs surrounding the most significant SNP (red diamond) are color coded to reflect their LD with this SNP (using pairwise r ² values from HapMap-CEU): blue, r ² ≥ 0.8–1.0; green, 0.5–0.8, orange, 0.2–0.5; gray, <0.2. The gray bars at the bottom of the plot represent the relative size and location of genes in the region.

In addition, markers within regions on chromosomes 10q23 and 19q13 were significantly associated with CPD. The SNPs rs1329650[G] (β = 0.367, s.e. = 0.059, P = 5.7 × 10⁻¹⁰; Fig. 2) and rs1028936[A] (β = 0.446, s.e. = 0.074, P = 1.3 × 10⁻⁹; Supplementary Fig. 1) are located in a noncoding RNA (LOC100188947), where each additional copy of a risk allele corresponded to an increase in smoking quantity of ~0.5 CPD. Linkage disequilibrium (LD) between these SNPs is moderate (r ² = 0.46), suggesting that they may represent one signal. To our knowledge, this region has not been previously investigated in relation to smoking behavior or other addiction phenotypes.

The third locus identified for CPD lies in the first intron of EGLN2 on chromosome 19q13, 40 kb from the 3′ end of CYP2A6. One SNP, rs3733829, exceeded genome-wide significance, and each copy of the G allele corresponded to an increase in smoking quantity of <0.5 CPD (β = 0.333, s.e. = 0.058, P = 1.0 × 10⁻⁸; Fig. 2). CYP2A6 is an established candidate gene for smoking, as it encodes for an enzyme involved in the metabolic inactivation of nicotine to cotinine¹⁵. Many allelic variants of CYP2A6 result in slower metabolism of nicotine¹⁶ and are associated with lower prevalence of smoking and lower amounts of cigarette use^16,17. We interpret this finding with caution, as only one SNP upstream of CYP2A6 was observed and the strength of its association was moderate. However, the 19q13 region merits continued investigation given its biological plausibility as involved in nicotine metabolism and because several markers within this region were identified in the ENGAGE Consortium¹². The SNP identified in our study (rs3733829) lies directly between, and shows moderate LD with, the two most significant markers identified in ENGAGE.

Eight SNPs around BDNF exceeded genome-wide significance for smoking initiation analyses across the three consortia (Fig. 3). The minimum P value was at the missense variant rs6265 (P = 1.8 × 10⁻⁸) located in the first exon of BDNF on chromosome 11. Each copy of rs6265[C] conferred a 6% increase in the relative risk of regular smoking (OR = 1.06, 95% c.i. 1.04–1.08); rs6265 accounted for 0.03% of the variance. BDNF belongs to a family of neurotrophins that regulate synaptic plasticity and survival of cholinergic and dopaminergic neurons¹⁸. The eight SNPs overlap an antisense transcript (BDNFos). BDNF is expressed at high levels in the prefrontal cortex and hippocampus, which are brain regions implicated in the cognitive-enhancing effects of nicotine¹⁹. Although the molecular mechanisms underlying this association have yet to be elucidated, it is plausible that genetic variation at BDNF could alter the rewarding effects of nicotine through modulation of dopamine reward circuits and could contribute to the salience of nicotine’s effects by altering formation of drug-related memories that promote continued use after initial exposure. The SNP rs6265 has been found to be associated with substance-related disorders, eating disorders and schizophrenia²⁰. Most recently, it was identified in a GWAS for body mass index²¹; the allele associated with a greater body mass index was the same allele associated with regular smoking in our study.

Figure 3.

Forest and regional plots of significant associations for smoking behavior. (a–d) Shown are plots for smoking initiation (a,b) and smoking cessation (c,d) from meta-analyses of the TAG, Ox-GSK and ENGAGE consortia. Regional association plots show SNPs plotted by position on the chromosome against −log₁₀ P value with each smoking phenotype. Estimated recombination rates (from HapMap-CEU) are plotted in light blue to reflect the local LD structure on a secondary y axis. The SNPs surrounding the most significant SNP (red diamond) are color coded to reflect their LD with this SNP (using pairwise r² values from HapMap CEU): blue, r² ≥ 0.8–1.0; green, 0.5–0.8; orange, 0.2–0.5; gray, <0.2. The gray bars at the bottom of the plot represent the relative size and location of genes in the region.

For smoking cessation, one SNP, located 23 kb 5′ of DBH on chromosome 9, achieved genome-wide significance: rs3025343[G] was associated with former smoking status (OR = 1.12, 95% c.i. 1.08–1.18, P = 3.6 × 10⁻⁸; Fig. 3) and accounted for 0.19% of the variance in smoking cessation. Because DBH catalyzes conversion of dopamine to norepinephrine, there has been interest in DBH as a candidate gene for various psychiatric phenotypes, including smoking behavior²². Although the SNP identified in this study does not cause amino acid residue changes in DBH, gene expression may be modified either directly or through other variant(s) in strong LD. This view is supported by evidence that a genetic variant (C1021T or rs1611115), located upstream of the DBH translational start site, accounts for 51% of the variation in plasma-DBH activity in European-Americans²². Alternatively, the SNP identified in our study or a variant in LD may influence expression of other genes nearby (ADAMTSL2, FAM163B or SARDH), which would introduce new pathways to our current understanding of addiction biology.

To our knowledge, the sample sizes for the TAG Consortium alone and combined with the ENGAGE and Ox-GSK consortia are among the largest genetic meta-analyses yet conducted²³. Notably, most of the loci identified in this study reside in or near known candidate genes involved in the neurobiology of smoking, which differs from the results of previous GWAS, in which variants identified have generally not been in regions previously suspected. The lack of findings for smoking initiation and cessation is noteworthy in light of considerable genetic epidemiological data suggesting a role for genetic factors in different aspects of smoking behavior (for example, heritability estimates are often >0.50)¹, and we note that the loci identified do not of themselves account for more than small fractions of the phenotypic heritability. Additional smoking behavior loci may be identified with improved genomic coverage and analysis of gene-gene and gene-environment interaction, copy number variation or epigenetic effects. We acknowledge that imprecision in phenotypic assessment and differences across studies could have added noise sufficient to blur all but the most prominent genetic signals. Smoking behavior obtained by questionnaires may be subject to phenotypic misclassification. Recent work²⁴ has shown that genetic variation at 15q25.1 influences cotinine (the main and long-lived metabolite of nicotine) measurements more strongly than it influences CPD values obtained by means of a questionnaire. Future smoking GWAS that use biomarkers or longitudinal assessments that refine phenotypic assessments by incorporating time to quitting or relapsing to smoking may be required. In addition, inclusion of multiple ethnic groups will enhance the investigation of the genetics of smoking.

Notably, the five significant loci identified in these meta-analyses were each associated with only one specific smoking phenotype. Our findings suggests that separate genetic loci contribute modestly to phenotypic variability in each aspect of smoking behavior, which, in turn, may have implications for the way in which smoking cessation therapies and tobacco control efforts are designed and targeted.

Methods

Methods and any associated references are available in the online version of the paper at http://www.nature.com/naturegenetics/.

Appendix

Helena Furberg^1,2, YunJung Kim¹, Jennifer Dackor¹, Eric Boerwinkle³, Nora Franceschini⁴, Diego Ardissino⁵, Luisa Bernardinelli^6,7, Pier M Mannucci⁸, Francesco Mauri⁹, Piera A Merlini⁹, Devin Absher¹⁰, Themistocles L Assimes¹¹, Stephen P Fortmann¹², Carlos Iribarren¹³, Joshua W Knowles¹¹, Thomas Quertermous¹¹, Luigi Ferrucci¹⁴, Toshiko Tanaka¹⁵, Joshua C Bis^16,17, Curt D Furberg¹⁸, Talin Haritunians¹⁹, Barbara McKnight^16,20, Bruce M Psaty^16,17,21,22, Kent D Taylor¹⁹, Evan L Thacker^16,23, Peter Almgren²⁴, Leif Groop²⁴, Claes Ladenvall²⁴, Michael Boehnke²⁵, Anne U Jackson²⁵, Karen L Mohlke^1,2, Heather M Stringham²⁵, Jaakko Tuomilehto^26–28, Emelia J Benjamin^29,30, Shih-Jen Hwang³¹, Daniel Levy³², Sarah Rosner Preis³¹, Ramachandran S Vasan^29,32, Jubao Duan³³, Pablo V Gejman³³, Douglas F Levinson³⁴, Alan R Sanders³³, Jianxin Shi³⁵, Esther H Lips³⁶, James D McKay³⁶, Antonio Agudo³⁷, Luigi Barzan³⁸, Vladimir Bencko³⁹, Simone Benhamou^40,41, Xavier Castellsagué³⁷, Cristina Canova⁴², David I Conway⁴³, Eleonora Fabianova⁴⁴, Lenka Foretova⁴⁵, Vladimir Janout⁴⁶, Claire M Healy⁴⁷, Ivana Holcátová³⁹, Kristina Kjaerheim⁴⁸, Pagona Lagiou⁴⁹, Jolanta Lissowska⁵⁰, Ray Lowry⁵¹, Tatiana V Macfarlane⁵², Dana Mates⁵³, Lorenzo Richiardi⁵⁴, Peter Rudnai⁵⁵, Neonilia Szeszenia-Dabrowska⁵⁶, David Zaridze⁵⁷, Ariana Znaor⁵⁸, Mark Lathrop^59,60, Paul Brennan³⁶, Stefania Bandinelli⁶¹, Timothy M Frayling⁶², Jack M Guralnik⁶³, Yuri Milaneschi⁶⁴, John R B Perry⁶², David Altshuler^65–70, Roberto Elosua⁷¹, Sek Kathiresan^65,68,72, Gavin Lucas⁷¹, Olle Melander⁷³, Christopher J O’Donnell⁷⁴, Veikko Salomaa⁷⁵, Stephen M Schwartz¹⁶, Benjamin F Voight⁷⁶, Brenda W Penninx^77,78, Johannes H Smit^77,78, Nicole Vogelzangs^77,78, Dorret I Boomsma⁷⁹, Eco J C de Geus⁷⁹, Jacqueline M Vink⁷⁹, Gonneke Willemsen⁷⁹, Stephen J Chanock⁸⁰, Fangyi Gu⁸¹, Susan E Hankinson⁸², David J Hunter⁸¹, Albert Hofman⁸³, Henning Tiemeier^83,84, Andre G Uitterlinden^83,85, Cornelia M van Duijn^83,86, Stefan Walter^83,87, Daniel I Chasman⁸⁸, Brendan M Everett^88,89, Guillaume Paré⁸⁸, Paul M Ridker^88,89, Ming D Li⁹⁰, Hermine H Maes^91,92, Janet Audrain-McGovern⁹³, Danielle Posthuma^94,95, Laura M Thornton⁹⁶, Caryn Lerman^93,97, Jaakko Kaprio^26,75,98, Jed E Rose⁹⁹, John P A Ioannidis^100–102, Peter Kraft⁸¹, Dan-Yu Lin¹⁰³ & Patrick F Sullivan^1,2

¹Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. ²University of North Carolina Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA. ³Human Genetics Center and Institute for Molecular Medicine, University of Texas Health Science Center, Houston, Texas, USA. ⁴Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA. ⁵Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy. ⁶Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK. ⁷Department of Applied Health Sciences, University of Pavia, Pavia, Italy. ⁸Department of Internal Medicine and Medical Specialties, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Ospedale Maggiore, Mangiagalli e Regina Elena, University of Milan, Milan, Italy. ⁹Department of Cardiology, Azienda Ospedaliera Niguarda Ca’ Granda, Milan, Italy. ¹⁰HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. ¹¹Cardiovascular Medicine, Stanford University, Stanford, California, USA. ¹²Stanford Prevention Research Center, Stanford University, Stanford, California, USA. ¹³Kaiser Permanente Northern California Division of Research, Oakland, California, USA. ¹⁴National Institute on Aging, Baltimore, Maryland, USA. ¹⁵Medstart Research Institute, National Institute on Aging, Baltimore, Maryland, USA. ¹⁶Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA. ¹⁷Department of Medicine, University of Washington, Seattle, Washington, USA. ¹⁸Division of Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA. ¹⁹Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. ²⁰Department of Biostatistics, University of Washington, Seattle, Washington, USA. ²¹Department of Epidemiology and Health Services, University of Washington, Seattle, Washington, USA. ²²Group Health Research Institute, Seattle, Washington, USA. ²³Department of Epidemiology, University of Washington, Seattle, Washington, USA. ²⁴Department of Clinical Sciences, Diabetes and Endocrinology Unit, Lund University, Malmö, Sweden. ²⁵Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA. ²⁶Hjelt Institute, Department of Public Health, University of Helsinki, Helsinki, Finland. ²⁷Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland. ²⁸Finland South Ostrobothnia Central Hospital, Seinäjoki, Finland. ²⁹Boston University School of Medicine, Boston, Massachusetts, USA. ³⁰Boston University School of Public Health, Boston, Massachusetts, USA. ³¹Center for Population Studies, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. ³²Department of Medicine, Sections of Preventive Medicine and Cardiology, Boston University School of Medicine, Boston, Massachusetts, USA. ³³Center for Psychiatric Genetics, NorthShore University HealthSystem Research Institute, Evanston, Illinois, USA. ³⁴Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA. ³⁵Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA. ³⁶International Agency for Research on Cancer (IARC), Lyon, France. ³⁷Institut Català d’Oncologia, Barcelona, Spain. ³⁸General Hospital, Pordenone, Italy. ³⁹Institute of Hygiene and Epidemiology, First Faculty of Medicine, Charles University, Prague, Czech Republic. ⁴⁰Institut National de la santé et de la Recherche Medicalé (INSERM) U794, Paris, France. ⁴¹Institut Gustave Roussy, Villejuif, France. ⁴²Department of Environmental Medicine and Public Health, University of Padua, Padua, Italy. ⁴³University of Glasgow Medical Faculty Dental School, Glasgow, UK. ⁴⁴Specialized Institute of Hygiene and Epidemiology, Banska Bystrica, Slovakia. ⁴⁵Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic. ⁴⁶Palacky University, Olomouc, Czech Republic. ⁴⁷Trinity College School of Dental Science, Dublin, Ireland. ⁴⁸Cancer Registry of Norway, Oslo, Norway. ⁴⁹University of Athens School of Medicine, Athens, Greece. ⁵⁰Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Cancer Center and Institute of Oncology, Warsaw, Poland. ⁵¹University of Newcastle Dental School, Newcastle, UK. ⁵²University of Aberdeen School of Medicine, Aberdeen, UK. ⁵³Institute of Public Health, Bucharest, Romania. ⁵⁴Center for Experimental Research and Medical Studies, University of Turin, Turin, Italy. ⁵⁵National Institute of Environmental Health, Budapest, Hungary. ⁵⁶Department of Epidemiology, Institute of Occupational Medicine, Lodz, Poland. ⁵⁷Institute of Carcinogenesis, Cancer Research Centre, Moscow, Russia. ⁵⁸Croatian National Cancer Registry, Zagreb, Croatia. ⁵⁹Centre National de Genotypage, Institut Genomique, Comissariat à l’énergie Atomique, Evry, France. ⁶⁰Fondation Jean Dausset-Centre d‘Étude du Polymorphisme Humain (CEPH), Paris, France. ⁶¹Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy. ⁶²Genetics of Complex Traits, Peninsula Medical School, The University of Exeter, Exeter, UK. ⁶³Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland, USA. ⁶⁴Tuscany Health Regional Agency, Florence, Italy. ⁶⁵Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. ⁶⁶Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. ⁶⁷Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, USA. ⁶⁸Center for Human Genetics Research, Massachusetts General Hospital, Boston, Massachusetts, USA. ⁶⁹Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. ⁷⁰Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. ⁷¹Cardiovascular Epidemiology and Genetics, Institut Municipal d’Investigacio Medica, Barcelona, Spain. ⁷²Harvard Medical School, Boston, Massachusetts, USA. ⁷³Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, University Hospital Malmö, Lund University, Malmö, Sweden. ⁷⁴National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA. ⁷⁵National Institute for Health and Welfare (THL), Helsinki, Finland. ⁷⁶Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. ⁷⁷EMGO Institute, Vrije Universiteit (VU) Medical Center, Amsterdam, The Netherlands. ⁷⁸Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands. ⁷⁹Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands. ⁸⁰Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA. ⁸¹Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard University, Boston, Massachusetts, USA. ⁸²Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA. ⁸³Department of Epidemiology, Erasmus Medical Center, Member of the Netherlands Consortium on Healthy Aging, Rotterdam, The Netherlands. ⁸⁴Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands. ⁸⁵Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands. ⁸⁶Centre for Medical Systems Biology, Erasmus Medical Center, Rotterdam, The Netherlands. ⁸⁷Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands. ⁸⁸Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA. ⁸⁹Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA. ⁹⁰Department of Psychiatry and Neurobehavioural Sciences, University of Virginia, Charlottesville, Virginia, USA. ⁹¹Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA. ⁹²Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia, USA. ⁹³Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA. ⁹⁴Department of Functional Genomics, VU Amsterdam, Amsterdam, The Netherlands. ⁹⁵Department of Medical Genomics, VU University Medical Center Amsterdam, Amsterdam, The Netherlands. ⁹⁶Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA. ⁹⁷Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA. ⁹⁸Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland. ⁹⁹Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA. ¹⁰⁰Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. ¹⁰¹Tufts Clinical and Translational Science Institute, Tufts University School of Medicine, Boston, Massachusetts, USA. ¹⁰²Center for Genetic Epidemiology and Modeling, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA. ¹⁰³Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. Correspondence should be addressed to H.F. (helena_furberg@med.unc.edu) or P.F.S. (pfsulliv@med.unc.edu).