Genome-wide meta-analyses of smoking behaviors in african americans

ABSTRACT The identification and exploration of genetic loci that influence smoking behaviors have been conducted primarily in populations of the European ancestry. Here we report results of

the first genome-wide association study meta-analysis of smoking behavior in African Americans in the Study of Tobacco in Minority Populations Genetics Consortium (_n_=32 389). We identified

one non-coding single-nucleotide polymorphism (SNP; rs2036527[A]) on chromosome 15q25.1 associated with smoking quantity (cigarettes per day), which exceeded genome-wide significance

(_β_=0.040, s.e.=0.007, _P_=1.84 × 10−8). This variant is present in the 5′-distal enhancer region of the _CHRNA5_ gene and defines the primary index signal reported in studies of the

European ancestry. No other SNP reached genome-wide significance for smoking initiation (SI, ever vs never smoking), age of SI, or smoking cessation (SC, former vs current smoking).

Informative associations that approached genome-wide significance included three modestly correlated variants, at 15q25.1 within _PSMA4_, _CHRNA5_ and _CHRNA3_ for smoking quantity, which

are associated with a second signal previously reported in studies in European ancestry populations, and a signal represented by three SNPs in the _SPOCK2_ gene on chr10q22.1. The

association at 15q25.1 confirms this region as an important susceptibility locus for smoking quantity in men and women of African ancestry. Larger studies will be needed to validate the

suggestive loci that did not reach genome-wide significance and further elucidate the contribution of genetic variation to disparities in cigarette consumption, SC and smoking-attributable

disease between African Americans and European Americans. SIMILAR CONTENT BEING VIEWED BY OTHERS GENETIC INSIGHTS INTO SMOKING BEHAVIOURS IN 10,558 MEN OF AFRICAN ANCESTRY FROM CONTINENTAL

AFRICA AND THE UK Article Open access 05 November 2022 GENOME-WIDE ASSOCIATION STUDY OF SMOKING TRAJECTORY AND META-ANALYSIS OF SMOKING STATUS IN 842,000 INDIVIDUALS Article Open access 20

October 2020 RARE GENETIC VARIANTS EXPLAIN MISSING HERITABILITY IN SMOKING Article 04 August 2022 INTRODUCTION Smoking is influenced by genetic and environmental factors.1, 2 Genome-wide

association studies (GWAS) in populations of European ancestry have identified genetic variation associated with smoking behaviors, including smoking initiation (SI), smoking quantity and

smoking cessation (SC). An initial, large (_n_=10 995) GWAS of smoking quantity identified associations with genetic variants in the nicotinic acetylcholine receptor α5, α3 and β4 subunit

cluster on chromosome 15q25.1.3 Genome-wide meta-analyses in three large consortia (_n_=74 053, 31 226 and 41 150) of smoking behaviors confirmed the finding at 15q25.1 and refined the

association signal within the locus.4, 5, 6 Additional studies in diverse populations also have revealed independent signals in this region, suggesting multiple biologically functional

variants.7, 8 This locus has also been reported as a susceptibility locus for lung cancer; however, whether this effect is independent of smoking behavior is unclear.9, 10 Additional regions

have been identified for smoking quantity (_CHRNB3_/_CHRNA6_) on 8p11,4 _CYP2A6_ on 19q134, 6 and _LOC100188947_ on 10q256), SI (_BDNF_ on 11p13)6 and SC (_DBH_ on 9q34).6 To date, all

published GWAS for smoking behaviors have been conducted in populations of European descent.11 Conducting GWAS in non-European populations, such as African ancestry populations is important

because of their greater genetic diversity and population differences in disease allele frequency, linkage disequilibrium patterns and phenotype prevalence.12 For smoking behaviors, the need

for GWAS in African American populations is particularly clear; African Americans, on average, initiate smoking later, smoke fewer cigarettes per day, yet are less likely to successfully

quit smoking. Further, they have a higher risk of smoking-related lung cancer than many other populations.13 Ethnic differences in the clearance of nicotine, cotinine and other metabolites

have been shown to contribute to the observed differences in cigarette consumption across populations, mediated in part by genetic variants in the cytochrome _p450 2A6_ gene.14, 15, 16 The

genetic architecture of smoking-related traits is not well described in non-European ancestral groups, but there is evidence that genetic determinants have important implications for

multiple addictive behaviors in populations globally.17 We established the Study of Tobacco in Minority Populations (STOMP) Genetics Consortium, which represents 13 GWAS studies of men and

women of African ancestry, to search for risk loci for smoking behaviors in this population. MATERIALS AND METHODS STUDY DESCRIPTION The STOMP Genetics Consortium is comprised of the

following studies: the Women's Health Initiative SNP Health Association Resource (_n_=8208), the African American GWAS consortia of Breast Cancer (_n_=5061) and Prostate Cancer

(_n_=5556), the Candidate Gene Association Resource Consortium (including the Atherosclerosis Risk in Communities (_n_=2916) study, the Cleveland Family Study (_n_=632), the Coronary Artery

Risk Development in Young Adults (_n_=953) study, the Jackson Heart Study (_n_=2145) and the Multi-Ethnic Study of Atherosclerosis (_n_=1646)), the Cardiovascular Health Study (_n_=801), the

Healthy Aging in Neighborhoods across the Life Span Study (_n_=918), the Health ABC Study (_n_=1137), the Genetic Study of Atherosclerosis Risk (_n_=1175) and the Hypertension Genetic

Epidemiology Network (_n_=1241). A description of each participating study as well as details regarding the measurement and collection of smoking data for each study are provided in

Supplementary Materials. All studies had local Institutional Review Board approval for the present study and all participants provided written informed consent. SMOKING PHENOTYPES We

examined four smoking phenotypes previously shown to be heritable in the African and European ancestry samples18, 19, 20, 21 and used in prior GWAS of smoking behavior.4, 5, 6 SI contrasted

individuals who reported having smoked 100 cigarettes during their lifetime (ever smokers) with those who reported having smoked between 0 and 99 cigarettes during their lifetime (never

smokers), consistent with the Centers for Disease Control classification.22 Among smokers, the age of SI (AOI) represented the age individuals began smoking. Some studies captured the age

they first tried smoking, whereas others collected the age they began smoking regularly. As prior research suggests similar heritabilities and high genetic correlation between these

phenotypes, we justified using either value in a general assessment of AOI. Similarly, for cigarettes smoked per day (CPD), some studies collected maximum CPD, whereas others collected

average CPD. Longitudinal twin data suggests a high correlation between these variables over time, which supported using either value in our analyses. For studies that collected CPD as

ranges, the mid-point of the interval was used as the data point; for example, individuals who reported the CPD category 0–4 were assigned a CPD value of 2. SC contrasted individuals who had

quit smoking at interview (former smokers) with those who were current smokers. As relapse to smoking is highest within the first year after quitting,23 we tried to reduce misclassification

by excluding smokers who quit within 1 year of interview within studies with available data. Table 1 presents distributions of smoking phenotypes across participating studies. GENOTYPING

AND QUALITY CONTROL Each study performed its own genotyping using Illumina (San Diego, CA, USA) or Affymetrix GWAS arrays (Santa Clara, CA, USA). Supplementary Tables 1 and 2 present the

details of the arrays, genotyping quality control procedures and sample exclusions (i.e., sex mismatch, call rate failure, relatedness, missing smoking and ancestry outliers) for each study.

The quality control filters applied by each study were comparable; single-nucleotide polymorphisms (SNPs) with call rates <95% (except the Genetic Study of Atherosclerosis Risk,

<90%), <1% minor allele frequency or significant (_P_<10−6) departure from Hardy–Weinberg equilibrium were excluded, as were individuals with excess autosomal heterozygosity,

mismatch between reported and genetically determined sex, or first- or second-degree relatedness. Genome-wide imputation24 was carried out in each study using the software MACH, IMPUTE,

BEAGLE or BIMBAM v0.99,25, 26, 27, 28, 29, 30, 31, 32 to infer genotypes for SNPs that were not genotyped directly on the platforms, but were genotyped on the HapMap phase 2 CEU and YRI

samples.33 SNPs with imputation quality scores <0.5 were excluded. DATA ANALYSES _Study-specific GWAS analysis_. Each study conducted uniform cross-sectional analyses for each smoking

phenotype using an additive genetic model. Logistic regression was used for discrete traits (SI and SC) and linear regression was used for quantitative traits (CPD and AOI). Continuous,

quantitative traits were normalized by transformation to _Z_ scores, owing to heavy tails and non-normality. Outliers were removed within each study, where abs (_Z_)>2. Link (_Y_)=_Z_

scores were fit using ordinary least squares regression. To investigate potential sources of heterogeneity across studies, we examined the distribution of African ancestry in each cohort

(Supplementary Figure 1). To account for population stratification and admixture, all studies adjusted for an appropriate number of eigenvectors3, 4, 5, 6, 7, 8, 9, 10 from a study-specific

principal components analysis.34 In addition, study-specific analyses included adjustment for age and case status or study site, when appropriate. Genomic control inflation factors were

computed using standard methods.35, 36 _Meta-analyses of GWAS results_. We performed fixed-effect meta-analysis for each smoking phenotype by computing pooled inverse-variance-weighted

_β_-coefficients, s.e. and _Z_ scores for each SNP.37 All GWAS results were corrected via genomic control before the meta-analysis. The study-specific lambda values utilized in this step

ranged from 1.01 to 1.08 for SI (Supplementary Table 1). Heterogeneity across studies was investigated using the _I_2 statistic.38 The results presented herein are corrected by a second GC

correction based on _λ_ of the meta-analyses (_λ_<1.02). A significance threshold of _P_<5 × 10−8 was considered to indicate genome-wide significance. Linkage disequilibrium statistics

for the largest of the STOMP cohorts (Women's Health Initiative, _n_=8208) were calculated using DPRIME (http://www.phs.wfubmc.edu/public/bios/gene/downloads.cfm). Linkage

disequilibrium statistics for CEU and YRI were obtained from HapMap phase 2 33. Statistical power analysis was performed using QUANTO.39 RESULTS The meta-analysis included 32 389 genotyped

men and women of African ancestry from 13 studies with sample sizes ranging from _n_=632 to _n_=8208 (Table 1). Our meta-analysis sample was 66.1% female, the mean age when smoking

information was collected ranged from 35.5 to 73.4 years, and 52.7% were ever smokers. Among smokers, mean CPD ranged from 11.5 to 15.7, the mean AOI ranged from 17.3 to 23.3 years, and

44.8% were former smokers. Sample sizes for the four smoking phenotype analyses (i.e., with complete genotype and phenotype data) were _n_=32 389 for SI, _n_=16 877 for AOI, _n_=15 547 for

CPD and _n_=16 215 for SC. Manhattan plots for the four smoking phenotypes after double-GC scaling are shown in Figure 1. In the entire analysis, only one SNP, rs2036527, achieved

genome-wide significance for one trait, CPD (_β_=0.04, s.e.=0.007, _P_=1.84 × 10−8, _I_2=41.6%, Table 2; study-specific results are show in Supplementary Table 3). This variant is located

6246 bp 5′ of the _CHRNA5_ gene on chromosome 15q25.1. We observed multiple SNPs with _P_-values of 10−7 associated with CPD: rs3101457, located in intron 2 (IVS2) of _C1orf100_ on 1q44, and

rs547843, located 63 kb 5′ of a non-coding RNA sequence (_LOC503519_) on 15q12. Three highly correlated SNPs (_r_2>0.95, YRI) in the _SPOCK2_ gene on 10q22.1 exhibited a _P_-value of

10−7 with AOI (Table 2). The most significant associations for SI and SC were observed at rs566973 (∼20 kb 3′ of _CRCT1_ on 1q21.3) and rs3813637 (in the 3′-untranslated region of _C1orf49_

on 1q25.2), respectively (data not shown). Four top SNPs associated with CPD span approximately 100 kb (76.6–76.7 Mb) at 15q25.1; from rs3813570, located in the 5′-untranslated region

(c.-72T>C) of _PSMA4_, to rs938682, located in IVS4 (c.378-1941C>T) of _CHRNA3_ (Table 2 and Figure 2). The most significant SNP, rs2036527, is located between _PSMA4_ and _CHRNA5_,

and is correlated with the index signals (rs1051730, rs16969968) for CPD reported in previous European ancestry studies. In CEU, the _r_2 is 0.84 between rs2036527 and rs1051730, and 0.93

between rs2036527 and rs16969968. The _r_2 between rs2036527 and 1051730 is 0.44 in YRI, and 0.502 in STOMP, whereas rs16969968 is non-polymorphic. Rs2036527 is also correlated with SNPs in

the European Americans that tag a haplotype associated with increased expression of _CHRNA5_ in prefrontal cortex brain samples from European Americans and African Americans,40 but is not

correlated with this haplotype in African ancestry samples (_r_2 between rs2036527 and rs1979905=0.443 in CEU, 0.045 in YRI and 0.064 in STOMP). The additional signals at 15q25.1 with near

genome-wide significance in our study are represented by rs667282, rs938682 and rs3813570, which are weakly correlated with rs2036527 (_r_20.2 in CEU, 0.12 in YRI and 0.084 in STOMP). These

three SNPs are correlated with each other (_r_20.60 in CEU and 0.32 in YRI) as well as with rs578776 and other SNPs at 15q25.1 that define a signal for smoking intensity in the European

ancestry populations that is independent of rs2036527.8 However, when conditioning on rs2036527 in the four largest study populations in our sample (the African American GWAS consortia of

Prostate Cancer, African American GWAS consortia of Breast Cancer, Candidate Gene Association Resource and Women's Health Initiative; _n_=13 113), the association between these three

SNPs and CPD diminished (_P_-values of 10−3 after conditioning on rs2036527; Supplementary Figure 2). Assuming the GWAS arrays utilized in this study provide adequate coverage of common

alleles at 15q25.1, this suggests there are not multiple independent signals for CPD in this region in African Americans or the frequencies of the functional alleles and/or their effect

sizes are much smaller than the signal defined by rs2036527. Supplementary Table 4 presents how the variants associated with smoking behaviors in European ancestry populations performed in

STOMP (rs1051730 in _CHRNA3_; rs16969968 in _CHRNA5_; rs1329650 and rs1028936 in _LOC100188947_; rs3733829 in _EGLN2_, near _CYP2A6_; rs6265, rs1013443, rs4923457, rs4923460, rs4074134,

rs1304100, rs6484320 and rs879048 in _BDNF_; and rs3025343, near _DBH_). We observed modest nominally statistically significant associations for CPD with rs1051730 (_P_=0.0079) and

rs16969968 (_P_=0.027), and for SC with rs3025343 (_P_=0.03). DISCUSSION Investigating whether there are genetic variants associated with smoking behavior among African Americans is

important, given that smoking prevalence and smoking-attributable mortality differ by race/ethnicity. Smoking prevalence and smoking intensity are lower for African Americans than European

Americans, yet African Americans are less likely to successfully quit smoking.41 To our knowledge, this is the first meta-analysis of GWAS data for smoking behaviors in African Americans.

The single genome-wide significant association we observed between rs2036527 and CPD is the same signal that was reported previously at 15q25.1 for nicotine dependence, smoking intensity and

lung cancer in European ancestry samples.4, 5, 6, 42, 43 The strong association that we found for this SNP supports studies suggesting that it is highly correlated with the functional

allele(s) in populations of African ancestry. The fact that we did not observe a strong second association signal in this region after conditioning on rs2036527 suggests that rs2036527 and

correlated SNPs in the African ancestry populations may define a single common haplotype at chr15q25.1 with sufficient effect size to be detected in our sample. After back transformation of

the beta estimate, mean CPD values for each rs2036527 genotype were 14.6 for AA, 13.5 for AG and 12.8 for GG, suggesting that there is an increase of less than one cigarette smoked per day

for each copy of the A allele. This SNP accounted for approximately 0.20% of the phenotypic variance of CPD in our sample. This effect is similar to that reported for rs1051730, which is

correlated with rs2036527, where each copy of the rs1051730 A allele corresponds to a approximately one CPD increase and accounts for 0.5% of the phenotypic variance in smoking quantity in

populations of European ancestry. A study of _CHRNA5_ knock-out mice showed that re-expressing this gene in the medial habenula, which extends projections to a brain region shown to mediate

nicotine withdrawal,44 abolished the inhibitory effects of nicotine while maintaining the reinforcing effects of nicotine.45 In a functional magnetic resonance study of smokers, genetic

variation in _CHRNA5_ appeared to also affect reactivity to smoking cues in the insula, hippocampus and dorsal striatum, regions implicated in addictive behavior and memory.46 Thus, it is

biologically plausible that rs2036527, as a correlate of increased expression of the _CHRNA5_ gene, could be associated with smoking quantity as a consequence of neuro-adaptations resulting

from complex interactions between genes and environment that alter positive and negative reinforcement.47 To our knowledge, no SNPs in the _SPOCK2_ gene, which encodes a protein that forms

part of the extracellular matrix, have been reported previously in association with smoking behaviors or smoking-related cancer phenotypes. Variants at the _SPOCK2_ locus have been linked to

bronchopulmonary dysplasia, a respiratory condition observed in premature infants48 that has been linked to intrauterine smoke exposure.49 These variants are weakly correlated with the SNPs

identified at this locus for AOI in Europeans (_r_2<0.25 in CEU), but are not correlated in the African ancestry populations (_r_2=0). The top SNP associated with SC (rs3813637) is

located at 1q25 in the _C1orf49_ gene. This locus has been linked to late-onset Alzheimer's disease, but genetic variation at this locus has not been reported in association with

smoking behavior.50 We are not aware of any smoking-related, other behavioral or pathological phenotypes associated with the variants we detected at 1q44 (_C1orf100)_ and 15q12 (_LOC503519_)

or _CTCT1_ for CPD. Although this is the largest GWAS meta-analysis of smoking phenotypes conducted to date in men and women of African ancestry, statistical power was a significant

limitation. We had 80% power (for a mean allele frequency of 0.15 and _α_ of 5 × 10−8) to detect effect sizes of 1.25 for SI, AOI and SC, and a _β_ of 0.15 for CPD. Notably, effect sizes for

variants reported with many of these smoking phenotypes reported in the larger GWAS of the European ancestry were much smaller. For example, TAG, ENGAGE and Ox-GSK consortia reported _β_

for SI of 0.015 for SNPs in _BDNF_ and 0.026 for rs3025343 in _DBH_. Thus, we cannot rule out the possibility of additional loci that influence smoking behavior among African Americans that

may be detected with larger sample sizes. This analysis was limited by the fact that we were not able to adjust for local admixture, and the chip coverage of common variants (>5%) is less

complete compared with the European populations,51 which applies to most GWAS of African American populations. However, the use of a global adjustment for population genetic variation in

the regression analysis using the principal components approach provided some measure of control for potential confounding because of population admixture.34, 52 Additionally, we acknowledge

the limited precision of the smoking phenotypes. Smoking quantity is a highly heritable trait: estimates for CPD, heavy versus light smoking and/or pack-years range from 40 to 70%

heritability in the European, African and Asian ancestry twin and family studies. Other studies have estimated that shared environmental factors account for 50% or more of the observed

variation in SI, AOI and SC.1, 18, 20, 53, 54, 55, 56, 57 We were unable to directly assess more refined phenotypes and highly heritable traits such as nicotine metabolism,58 given our

reliance on existing data originally collected for other purposes. Moreover, we were unable to examine gene × environment interactions using meta-GWAS analytic approach. Our analyses did not

incorporate environmental covariate analyses, such as type of cigarettes smoked, mentholated or non-mentholated, dietary factors, socioeconomic status and other factors that might influence

one or more of the phenotypes analyzed—data were not uniformly available and beyond the scope of the planned analyses we undertook in this discovery investigation. Future prospective

studies with more detailed characterizations of smoking phenotypes and relevant environmental covariates are needed to identify additional variants that may be associated with smoking

behaviors. In summary, collective findings from GWAS among the African and European ancestry populations implicate chromosome 15q25 region as the most significant for smoking quantity.

However, for both populations, SNPs in this region are associated with very small changes in smoking quantity and explain a small proportion of the variance, which suggests that conventional

GWAS approaches may not be adequate to discover the likely hundreds of variants contributing small increments in risks of the additive genetic effects for heritable traits or so-called

‘missing heritability’ of complex diseases.59 The use of more refined, specific and harmonized phenotypes capturing the complex behavior of SI, trajectories of progression and cessation, and

environmental effect-modifiers are also needed to detect the genetic architecture of smoking behavior in different ancestral populations. Larger studies utilizing next-generation SNP

arrays, whole-exome or whole-genome sequencing will be required to investigate lower-frequency variation, which may contribute to unexplained heritability for common traits.60 REFERENCES *

Lessov CN, Martin NG, Statham DJ, Todorov AA, Slutske WS, Bucholz KK _et al_. Defining nicotine dependence for genetic research: evidence from Australian twins. _Psychol Med_ 2004; 34:

865–879. Article  PubMed  Google Scholar  * Broms U, Silventoinen K, Madden PA, Heath AC, Kaprio J . Genetic architecture of smoking behavior: a study of Finnish adult twins. _Twin Res Hum

Genet_ 2006; 9: 64–72. Article  PubMed  Google Scholar  * Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP _et al_. A variant associated with nicotine dependence, lung

cancer and peripheral arterial disease. _Nature_ 2008; 452: 638–642. Article  CAS  PubMed  PubMed Central  Google Scholar  * Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N,

Geller F _et al_. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. _Nat Genet_ 2010; 42: 448–453. Article  CAS  PubMed  PubMed Central  Google Scholar  * Liu JZ, Tozzi

F, Waterworth DM, Pillai SG, Muglia P, Middleton L _et al_. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. _Nat Genet_ 2010; 42: 436–440. Article  CAS 

PubMed  PubMed Central  Google Scholar  * Furberg H, Kim Y, Dackor J, Boerwinkle E, Franceschini N, Ardissino D _et al_. Genome-wide meta-analyses identify multiple loci associated with

smoking behavior. _Nat Genet_ 2010; 42: 441–447. Article  CAS  Google Scholar  * Saccone NL, Schwantes-An TH, Wang JC, Grucza RA, Breslau N, Hatsukami D _et al_. Multiple cholinergic

nicotinic receptor genes affect nicotine dependence risk in African and European Americans. _Genes Brain Behav_ 2010; 9: 741–750. Article  CAS  PubMed  PubMed Central  Google Scholar  *

Saccone NL, Wang JC, Breslau N, Johnson EO, Hatsukami D, Saccone SF _et al_. The CHRNA5-CHRNA3-CHRNB4 nicotinic receptor subunit gene cluster affects risk for nicotine dependence in

African-Americans and in European-Americans. _Cancer Res_ 2009; 69: 6848–6856. Article  CAS  PubMed  PubMed Central  Google Scholar  * Bierut LJ . Convergence of genetic findings for

nicotine dependence and smoking related diseases with chromosome 15q24-25. _Trends Pharmacol Sci_ 2010; 31: 46–51. Article  CAS  PubMed  Google Scholar  * Thorgeirsson TE, Stefansson K .

Commentary: gene-environment interactions and smoking-related cancers. _Int J Epidemiol_ 2010; 39: 577–579. Article  PubMed  Google Scholar  * Hindorff LA, Junkins HA, Hall PN, Mehta JP,

Manolio TA . _A Catalog of Published Genome-Wide Association Studies_, Available at:##www.genome.gov/gwastudies##Accessed 25 July 2011. 2011. * Rosenberg NA, Huang L, Jewett EM, Szpiech ZA,

Jankovic I, Boehnke M . Genome-wide association studies in diverse populations. _Nat Rev Genet_ 2010; 11: 356–366. Article  CAS  PubMed  PubMed Central  Google Scholar  * Haiman CA, Stram

DO, Wilkens LR, Pike MC, Kolonel LN, Henderson BE _et al_. Ethnic and racial differences in the smoking-related risk of lung cancer. _N Engl J Med_ 2006; 354: 333–342. Article  CAS  PubMed 

Google Scholar  * Benowitz NL, Dains KM, Dempsey D, Wilson M, Jacob P . Racial differences in the relationship between number of cigarettes smoked and nicotine and carcinogen exposure.

_Nicotine Tob Res_ 2011; 13: 772–783. Article  CAS  PubMed  PubMed Central  Google Scholar  * Mwenifumbo JC, Sellers EM, Tyndale RF . Nicotine metabolism and CYP2A6 activity in a population

of black African descent: impact of gender and light smoking. _Drug Alcohol Depend_ 2007; 89: 24–33. Article  CAS  PubMed  Google Scholar  * Moolchan ET, Berlin I, Robinson ML, Cadet JL .

Characteristics of African American teenage smokers who request cessation treatment: implications for addressing health disparities. _Arch Pediatr Adolesc Med_ 2003; 157: 533–538. Article 

PubMed  Google Scholar  * Bierut LJ . Genetic vulnerability and susceptibility to substance dependence. _Neuron_ 2011; 69: 618–627. Article  CAS  PubMed  PubMed Central  Google Scholar  *

Whitfield KE, King G, Moller S, Edwards CL, Nelson T, Vandenbergh D . Concordance rates for smoking among African-American twins. _J Natl Med Assoc_ 2007; 99: 213–217. PubMed  PubMed Central

  Google Scholar  * Li MD, Payne TJ, Ma JZ, Lou XY, Zhang D, Dupont RT _et al_. A genomewide search finds major susceptibility loci for nicotine dependence on chromosome 10 in African

Americans. _Am J Hum Genet_ 2006; 79: 745–751. Article  CAS  PubMed  PubMed Central  Google Scholar  * Li MD, Cheng R, Ma JZ, Swan GE . A meta-analysis of estimated genetic and environmental

effects on smoking behavior in male and female adult twins. _Addiction (Abingdon, England)_ 2003; 98: 23–31. Article  Google Scholar  * True WR, Heath AC, Scherrer JF, Waterman B, Goldberg

J, Lin N _et al_. Genetic and environmental contributions to smoking. _Addiction (Abingdon, England)_ 1997; 92: 1277–1287. Article  CAS  Google Scholar  * CDC. Cigarette smoking among

adults--United States, 2007. _Morb Mortal Wkly Rep_ 2008; 57: 1221–1226. Google Scholar  * Hughes JR, Keely J, Naud S . Shape of the relapse curve and long-term abstinence among untreated

smokers. _Addiction (Abingdon, England)_ 2004; 99: 29–38. Article  Google Scholar  * Li Y, Willer C, Sanna S, Abecasis G . Genotype imputation. _Annu Rev Genomics Hum Genet_ 2009; 10:

387–406. Article  CAS  PubMed  PubMed Central  Google Scholar  * Li N, Stephens M . Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide

polymorphism data. _Genetics_ 2003; 165: 2213–2233. CAS  PubMed  PubMed Central  Google Scholar  * Howie BN, Donnelly P, Marchini J . A flexible and accurate genotype imputation method for

the next generation of genome-wide association studies. _PLoS Genet_ 2009; 5: e1000529. Article  PubMed  PubMed Central  Google Scholar  * Scheet P, Stephens M . A fast and flexible

statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. _Am J Hum Genet_ 2006; 78: 629–644. Article  CAS  PubMed  PubMed

Central  Google Scholar  * Browning BL, Browning SR . A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. _Am J

Hum Genet_ 2009; 84: 210–223. Article  CAS  PubMed  PubMed Central  Google Scholar  * Browning SR . Multilocus association mapping using variable-length Markov chains. _Am J Hum Genet_ 2006;

78: 903–913. Article  CAS  PubMed  PubMed Central  Google Scholar  * Browning SR . Missing data imputation and haplotype phase inference for genome-wide association studies. _Human Genet_

2008; 124: 439–450. Article  CAS  Google Scholar  * Browning SR, Browning BL . Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of

localized haplotype clustering. _Am J Hum Genet_ 2007; 81: 1084–1097. Article  CAS  PubMed  PubMed Central  Google Scholar  * Marchini J, Howie B . Genotype imputation for genome-wide

association studies. _Nat Rev Genet_ 2010; 11: 499–511. Article  CAS  PubMed  Google Scholar  * Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA _et al_. A second generation

human haplotype map of over 3.1 million SNPs. _Nature_ 2007; 449: 851–861. Article  CAS  PubMed  Google Scholar  * Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D .

Principal components analysis corrects for stratification in genome-wide association studies. _Nat Genet_ 2006; 38: 904–909. Article  CAS  PubMed  Google Scholar  * Clayton DG, Walker NM,

Smyth DJ, Pask R, Cooper JD, Maier LM _et al_. Population structure, differential bias and genomic control in a large-scale, case-control association study. _Nat Genet_ 2005; 37: 1243–1246.

Article  CAS  PubMed  Google Scholar  * Devlin B, Bennett P, Dawson G, Figlewicz DA, Grigorenko EL, McMahon W _et al_. Alleles of a reelin CGG repeat do not convey liability to autism in a

sample from the CPEA network. _Am J Med Genet B Neuropsychiatr Genet_ 2004; 126B: 46–50. Article  PubMed  Google Scholar  * de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight

BF . Practical aspects of imputation-driven meta-analysis of genome-wide association studies. _Hum Mol Gen_ 2008; 17 (R2): R122–R128. Article  CAS  PubMed  PubMed Central  Google Scholar  *

Ioannidis JP, Patsopoulos NA, Evangelou E . Heterogeneity in meta-analyses of genome-wide association investigations. _PLoS One_ 2007; 2: e841. Article  PubMed  PubMed Central  Google

Scholar  * Gauderman WJ, Morrison JM . _QUANTO 1.1: A Computer Program for Statistical Power and Sample Size Calculations for Genetic-Epidemiology Studies_ 2006. * Smith RM, Alachkar H, Papp

AC, Wang D, Mash DC, Wang JC _et al_. Nicotinic alpha5 receptor subunit mRNA expression is associated with distant 5′ upstream polymorphisms. _Eur J Hum Genet_ 2011; 19: 76–83. Article  CAS

  PubMed  Google Scholar  * Trinidad DR, Perez-Stable EJ, White MM, Emery SL, Messer K . A nationwide analysis of US racial/ethnic disparities in smoking behaviors, smoking cessation, and

cessation-related factors. _Am J Public Health_ 2011; 101: 699–706. Article  PubMed  PubMed Central  Google Scholar  * Amos CI, Wu X, Broderick P, Gorlov IP, Gu J, Eisen T _et al_.

Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. _Nat Genet_ 2008; 40: 616–622. Article  CAS  PubMed  PubMed Central  Google Scholar  *

Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D _et al_. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. _Nature_

2008; 452: 633–637. Article  CAS  PubMed  Google Scholar  * Salas R, Sturm R, Boulter J, De Biasi M . Nicotinic receptors in the habenulo-interpeduncular system are necessary for nicotine

withdrawal in mice. _J Neurosci_ 2009; 29: 3014–3018. Article  CAS  PubMed  PubMed Central  Google Scholar  * Fowler CD, Lu Q, Johnson PM, Marks MJ, Kenny PJ . Habenular alpha5 nicotinic

receptor subunit signalling controls nicotine intake. _Nature_ 2011; 471: 597–601. Article  CAS  PubMed  PubMed Central  Google Scholar  * Janes AC, Smoller JW, David SP, Frederick BD,

Haddad S, Basu A _et al_. Association between CHRNA5 genetic variation at rs16969968 and brain reactivity to smoking images in nicotine dependent women. _Drug Alcohol Depend_ 2012; 120:

7–13. Article  PubMed  Google Scholar  * Robinson TE, Berridge KC . The psychology and neurobiology of addiction: an incentive-sensitization view. _Addiction (Abingdon, England)_ 2000; 95

(Suppl 2): S91–S117. Google Scholar  * Hadchouel A, Durrmeyer X, Bouzigon E, Incitti R, Huusko J, Jarreau PH _et al_. Identification of SPOCK2 as a Susceptibility Gene for Bronchopulmonary

Dysplasia. _Am J Respir Crit Care Med_ 2011; 184: 1164–1170. Article  CAS  PubMed  Google Scholar  * Antonucci R, Contu P, Porcella A, Atzeni C, Chiappe S . Intrauterine smoke exposure: a

new risk factor for bronchopulmonary dysplasia? _J Perinat Med_ 2004; 32: 272–277. Article  PubMed  Google Scholar  * Liu F, Arias-Vasquez A, Sleegers K, Aulchenko YS, Kayser M, Sanchez-Juan

P _et al_. A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population. _Am J Hum Genet_ 2007; 81: 17–31. Article  CAS  PubMed  PubMed Central  Google

Scholar  * Jorgenson E, Witte JS . A gene-centric approach to genome-wide association studies. _Nat Rev Genet_ 2006; 7: 885–891. Article  CAS  PubMed  Google Scholar  * Patterson N, Price

AL, Reich D . Population structure and eigenanalysis. _PLoS Genet_ 2006; 2: e190. Article  PubMed  PubMed Central  Google Scholar  * Kaprio J, Koskenvuo M, Langinvainio H . [Finnish twins

reared apart. IV: smoking and drinking habits. A preliminary analysis of the effect of heredity and environment]. _Acta Genet Med Gemellol_ 1984; 33: 425–433. Article  CAS  PubMed  Google

Scholar  * Kaprio J, Koskenvuo M, Sarna S . Cigarette smoking, use of alcohol, and leisure-time physical activity among same-sexed adult male twins. _Prog Clin Biol Res_ 1981; 69 (Pt C):

37–46. PubMed  Google Scholar  * Lessov-Schlaggar CN, Pang Z, Swan GE, Guo Q, Wang S, Cao W _et al_. Heritability of cigarette smoking and alcohol use in Chinese male twins: the Qingdao twin

registry. _Int J Epidemiol_ 2006; 35: 1278–1285. Article  PubMed  Google Scholar  * Carmelli D, Swan GE, Robinette D, Fabsitz RR . [Heritability of substance use in the NAS-NRC Twin

Registry]. _Acta Genet Med Gemellol (Roma)_ 1990; 39: 91–98. Article  CAS  Google Scholar  * Hettema JM, Corey LA, Kendler KS . A multivariate genetic analysis of the use of tobacco,

alcohol, and caffeine in a population based sample of male and female twins. _Drug Alcohol Depend_ 1999; 57: 69–78. Article  CAS  PubMed  Google Scholar  * Swan GE, Lessov-Schlaggar CN,

Bergen AW, He Y, Tyndale RF, Benowitz NL . Genetic and environmental influences on the ratio of 3′hydroxycotinine to cotinine in plasma and urine. _Pharmacogenet Genomics_ 2009; 19: 388–398.

Article  CAS  PubMed  PubMed Central  Google Scholar  * Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ _et al_. Finding the missing heritability of complex diseases.

_Nature_ 2009; 461: 747–753. Article  CAS  PubMed  PubMed Central  Google Scholar  * Goldstein DB . Growth of genome screening needs debate. _Nature_ 2011; 476: 27–28. Article  CAS  PubMed 

Google Scholar  Download references ACKNOWLEDGEMENTS We wish to acknowledge the many contributors from multiple institutions and funders who contributed to this project. Detailed

acknowledgements are described in the supplementary information available at _Translational Psychiatry_'s website. AUTHOR INFORMATION Author notes * A Hamidovic and G K Chen: Joint

first authors. * E Jorgenson, C A Haiman and H Furberg: Joint senior authors. AUTHORS AND AFFILIATIONS * Policy Division, Center for Health Sciences, SRI International, Menlo Park, CA, USA S

P David, A W Bergen, J Wessel & G E Swan * Division of General Medical Disciplines, Center for Education and Research in Family and Community Medicine, Stanford University School of

Medicine, Stanford, CA, USA S P David * Department of Family Medicine, Center for Primary Care and Prevention, Brown Alpert Medical School, Pawtucket, RI, USA S P David & C B Eaton *

Department of Preventative Medicine, Northwestern University, Chicago, IL, USA A Hamidovic, B Hitsman & B Spring * Department of Preventive Medicine, Keck School of Medicine, University

of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA G K Chen, G Casey, B E Henderson, S A Ingles, M Press & C A Haiman * Department of Public Health, Division

of Epidemiology and Environmental Health, Indiana University School of Medicine, Indianapolis, IN, USA J Wessel * Department of Medicine, Division of Cardiology, Indiana University School

of Medicine, Indianapolis, IN, USA J Wessel * Department of Neurology, Ernest Gallo Clinic and Research Center, University of California, San Francisco, CA, USA J L Kasberger & E

Jorgenson * Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA W M Brown, K K Lohman & B Snively * Department of Epidemiology and

Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA S Petruzella & H Furberg * Department of Epidemiology, University of Washington, Seattle, WA, USA E L Thacker *

Genometrics Section, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, USA Y Kim & A F Wilson * Laboratory of Neurogenetics, National Institute on

Aging, National Institutes of Health, Baltimore, MD, USA M A Nalls, D G Hernandez & A B Singleton * California Pacific Medical Center Research Institute, San Francisco, CA, USA G J

Tranah & D S Evans * Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA Y J Sung & D C Rao * Department of Cancer Prevention and Control, Roswell

Park Cancer Institute, Buffalo, NY, USA C B Ambrosone * Department of Epidemiology, University of Alabama, Birmingham, AL, USA D Arnett * The Cancer Institute of New Jersey, New Brunswick,

NJ, USA E V Bandera * Department of Medicine, The Johns Hopkins GeneSTAR Research Program, The Johns Hopkins University School of Medicine, Baltimore, MD, USA D M Becker, L Becker & L R

Yanek * Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA S I Berndt, N Caporaso, S J Chanock, K Yu & R G Ziegler

* Department of Population Science, Division of Cancer Etiology, Beckman Research Institute, City of Hope, Duarte, CA, USA L Bernstein * International Epidemiology Institute, Rockville, MD,

USA W J Blot & L B Signorello * Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University and the Vanderbilt-Ingram Cancer Center,

Nashville, TN, USA W J Blot, S L Deming, L B Signorello & W Zheng * Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA U Broeckel * Jackson Heart Study, Jackson

State University, Jackson, MS, USA S G Buxbaum * Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA W R Diver & M J Thun * Health Disparities Research Section,

Clinical Research Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA M K Evans * Division of Epidemiology, Brown Foundation Institute of Molecular

Medicine, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA M Fornage * Department of Epidemiology, University of North Carolina at Chapel Hill,

Chapel Hill, NC, USA N Franceschini * Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, MD, USA T B Harris * Department of Epidemiology and Public

Health, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA J J Hu & J L Rodriguez-Gil * Department of Internal Medicine, University of

Utah, Salt Lake City, UT, USA S C Hunt * Cancer Prevention Institute of California, Fremont, CA, USA E M John * Stanford University School of Medicine, Stanford Cancer Institute, Stanford,

CA, USA E M John * Department of Medicine, Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA R Kittles * Division of

Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA S Kolb & J L Stanford * Epidemiology Program, Cancer Research Center, University of Hawaii, Honolulu, HI,

USA L N Kolonel & L Le Marchand * Sticht Center on Aging, Wake Forest University School of Medicine, Winston-Salem, NC, USA Y Liu * Department of Biostatistics, University of

Washington, Seattle, WA, USA B McKnight * Department of Epidemiology, Gillings School of Global Public Health, and Lineberger Comprehensive Cancer Center, University of North Carolina,

Chapel Hill, NC, USA R C Millikan & S Nyante * Department of Urology, Northwestern University, Chicago, IL, USA A Murphy * Department of Public Health Sciences, Henry Ford Hospital,

Detroit, MI, USA C Neslund-Dudas & B A Rybicki * Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, USA B M Psaty * Group Health Research

Institute, Group Health Cooperative, Seattle, WA, USA B M Psaty * Department of Medicine and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA S Redline * Department of

Psychiatry, Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA J Smoller * Department of Epidemiology, The University of Texas MD,

Anderson Cancer Center, Houston, TX, USA S S Strom & Y Yamamura * Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA K D Taylor * Departments of Epidemiology

and Biostatistics, and Urology, Institute for Human Genetics, University of California, San Francisco, CA, USA J S Witte * Laboratory of Personality and Cognition, National Institute on

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DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. ADDITIONAL INFORMATION Supplementary Information accompanies the paper on the Translational Psychiatry website

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION (DOC 719 KB) RIGHTS AND PERMISSIONS This work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported

License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE David, S., Hamidovic, A., Chen,

G. _et al._ Genome-wide meta-analyses of smoking behaviors in African Americans. _Transl Psychiatry_ 2, e119 (2012). https://doi.org/10.1038/tp.2012.41 Download citation * Received: 13

March 2012 * Accepted: 10 April 2012 * Published: 22 May 2012 * Issue Date: May 2012 * DOI: https://doi.org/10.1038/tp.2012.41 SHARE THIS ARTICLE Anyone you share the following link with

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content-sharing initiative KEYWORDS * African American * genome-wide association * health disparities * nicotine * smoking * tobacco