Dopamine Genes and Nicotine Dependence in
Treatment-Seeking and Community Smokers
Andrew W Bergen*,1, David V Conti2, David Van Den Berg2, Wonho Lee2, Jinghua Liu2, Dalin Li2, Nan Guo3,
Huaiyu Mi3, Paul D Thomas3, Christina N Lessov-Schlaggar4, Ruth Krasnow1, Yungang He1, Denise Nishita1,
Ruhong Jiang1, Jennifer B McClure5, Elizabeth Tildesley6, Hyman Hops6, Rachel F Tyndale7,
Neal L Benowitz8, Caryn Lerman9and Gary E Swan1
1Center for Health Sciences, SRI International, Menlo Park, CA, USA;2Department of Preventative Medicine, University of Southern California,
Pasadena, CA, USA;3Artificial Intelligence Center, SRI International Menlo Park, CA;4Department of Psychiatry, Washington University, St Louis,
MO, USA;5Center for Health Studies, Group Health Cooperative, Seattle, WA, USA;6Oregon Research Institute, Eugene, OR, USA;7Department
of Neuroscience, Centre for Addiction and Mental Health and Department of Pharmacology, University of Toronto, Toronto, ON, USA;
8Departments of Medicine, Psychiatry and BioPharmaceutical Sciences, University of California at San Francisco, San Francisco, CA, USA;
9Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
We utilized a cohort of 828 treatment-seeking self-identified white cigarette smokers (50% female) to rank candidate gene single
nucleotide polymorphisms (SNPs) associated with the Fagerstro ¨m Test for Nicotine Dependence (FTND), a measure of nicotine
dependence which assesses quantity of cigarettes smoked and time- and place-dependent characteristics of the respondent’s smoking
behavior. A total of 1123 SNPs at 55 autosomal candidate genes, nicotinic acetylcholine receptors and genes involved in dopaminergic
function, were tested for association to baseline FTND scores adjusted for age, depression, education, sex, and study site. SNP P-values
were adjusted for the number of transmission models, the number of SNPs tested per candidate gene, and their intragenic correlation.
DRD2, SLC6A3, and NR4A2 SNPs with adjusted P-values o0.10 were considered sufficiently noteworthy to justify further genetic,
bioinformatic, and literature analyses. Each independent signal among the top-ranked SNPs accounted for B1% of the FTND variance in
this sample. The DRD2 SNP appears to represent a novel association with nicotine dependence. The SLC6A3 SNPs have previously been
shown to be associated with SLC6A3 transcription or dopamine transporter density in vitro, in vivo, and ex vivo. Analysis of SLC6A3 and
NR4A2 SNPs identified a statistically significant gene–gene interaction (P¼0.001), consistent with in vitro evidence that the NR4A2 protein
product (NURR1) regulates SLC6A3 transcription. A community cohort of N¼175 multiplex ever-smoking pedigrees (N¼423 ever
smokers) provided nominal evidence for association with the FTND at these top ranked SNPs, uncorrected for multiple comparisons.
Neuropsychopharmacology (2009) 34, 2252–2264; doi:10.1038/npp.2009.52; published online 3 June 2009
Keywords: dopamine transporter; Fagerstro ¨m Test for Nicotine Dependence; single nucleotide polymorphism; candidate gene;
Cigarette smoking remains prevalent (Kilmer et al, 2008), a
risk factor for more than two dozen diseases (CDC, 2003),
and is the largest cause of preventable mortality worldwide
(Ezzati and Lopez, 2003). Identifying factors influencing
smoking will help us understand the nature of this behavior
and potentially guide the development of effective inter-
ventions for behavior change.
Tobacco dependence is considered to be a multidimen-
sional construct (Hudmon et al, 2003; Russell et al, 1974)
and is measured using a variety of questionnaires, including
smoking quantity (cigarettes per day), the Fagerstro ¨m
Tolerance Questionnaire (FTQ) (Fagerstro ¨m, 1978), the
Heaviness Smoking Index (HSI), consisting of two items
from the FTQ (Heatherton et al, 1989), and the Fagerstro ¨m
Test for Nicotine Dependence (FTND), a revision of the
FTQ (Heatherton et al, 1991), among other scales. The
FTND is one of the most commonly employed measures of
nicotine dependence in smoking intervention trials, epide-
miological studies, and genetic studies. Scale items assess
current smoking level, as well as other time- and place-
dependent characteristics of the respondent’s smoking beha-
vior. Scores on this six-item scale range from 0 to 10, with
higher scores considered indicative of greater nicotine depen-
dence. The additive genetic component of nicotine dependence
defined by the FTND has been estimated at 67, 75, and 40%
among American, Dutch, and Finnish twins, respectively
(Broms et al, 2007; Maes et al, 2004; Vink et al, 2005).
Received 11 December 2008; revised 22 April 2009; accepted 24 April
*Correspondence: Dr AW Bergen, Molecular Genetics Program,
Center for Health Sciences, SRI International, 333 Ravenswood
Avenue, Menlo Park, CA 94025, USA,
Tel: +1 650 859 4618, Fax: +1 650 859 5099,
Neuropsychopharmacology (2009) 34, 2252–2264
& 2009 Nature Publishing GroupAll rights reserved 0893-133X/09 $32.00
We chose to use the FTND in this study due to the scale’s
established estimates of heritability and its broad and continued
use in the field (Piper et al, 2006).
A decade of linkage analyses of smoking behaviors
continues to inform and motivate candidate gene studies
in the same family-based samples (Li, 2008). Through a
combination of genome-wide association scans (GWAS)
and candidate gene studies, nicotinic receptor candidate
genes currently are the leading candidates for influencing
responses to cigarette smoking, cigarette smoking quantity,
and nicotine dependence (Li and Burmeister, 2009). Other
candidate gene families, eg, GABAergic (Agrawal et al, 2008;
Beuten et al, 2005; Lou et al, 2007b; Saccone et al, 2007), cell
adhesion molecule (Bierut et al, 2007; Gelernter et al, 2006;
Uhl et al, 2007; Vink et al, 2009), dopaminergic (Caporaso
et al, 2009; Li and Burmeister, 2009; McKinney et al, 2000;
Morton et al, 2006), transient receptor potential (Bierut
et al, 2007; Caporaso et al, 2009; Saccone et al, 2007; Vink
et al, 2009), and nicotine metabolic (Caporaso et al, 2009;
Malaiyandi et al, 2006; Saccone et al, 2007) candidate gene
families, have also been identified in GWAS and candidate
gene studies of smoking behaviors.
A candidate gene SNP genotyping panel focused primarily
on neuronal presynaptic nicotinic receptors and the
dopaminergic pathway was developed to test candidate
gene association to nicotine addiction and treatment
outcomes by the Pharmacogenetics of Nicotine Addiction
and Treatment consortium (Conti et al, 2008), a member of
the Pharmacogenetics Research Network (Giacomini et al,
2007). We utilized SNP genotypes from this panel and
baseline FTND scores from treatment-seeking smokers
from two randomized clinical trials (RCTs) of smoking
cessation pharmacotherapy, one RCT that randomized
participants to bupropion and placebo and one RCT that
randomized participants to two forms of nicotine replace-
ment therapy (Lerman et al, 2006), to rank SNPs associated
with nicotine dependence. Association analyses of SNPs to
smoking cessation outcomes have been performed in these
two RCTs previously, eg, significant association of an
insertion deletion SNP at the dopamine D2 receptor gene
(DRD2) to abstinence has been reported (Lerman et al,
2006). A community-ascertained, multiplex ever smoker
pedigree cohort was genotyped using the same SNP panel
and these genotype data were used to evaluate the
significance of association of the top-ranked SNP associa-
tion findings from the treatment-seeking cohort in a second
sample. The multiplex ever smoker pedigree cohort
consisted of pedigrees with at least three first-degree ever-
smoking relatives with ever smoking defined as X100
cigarettes smoked lifetime (Swan et al, 2003). Herein we
report the ranking of candidate gene SNP associations to
baseline FTND score in a cohort of self-identified white
treatment-seeking smokers, and the significance of associa-
tion of top-ranked SNPs to FTND in a second cohort of
smokers from a cohort of multiplex ever smoker pedigrees.
PARTICIPANTS AND METHODS
Treatment-seeking smoker cohort
Current daily smokers were recruited for participation into
two RCTs of smoking cessation, one evaluating bupropion
vs placebo and another evaluating two modalities of
administration of nicotine replacement therapy, as pre-
viously described (Lerman et al, 2006). Study sites include
Washington, DC; Buffalo, NY; and Philadelphia, PA.
Inclusion criteria included age X18 and a recent smoking
history of X10 cigarettes per day, whereas exclusion criteria
included pregnancy or lactation, uncontrolled hypertension,
unstable angina, heart attack or stroke within the past
6 months, current treatment or recent diagnosis of cancer,
drug or alcohol dependence, current diagnosis or history of
a psychiatric disorder, seizure disorder, and current use of
bupropion or nicotine-containing products other than
cigarettes. Participants were assessed for eligibility, com-
pleted a baseline assessment, provided a venous blood
sample, were randomized to treatment, and attended at least
one clinical visit. In conjunction with the baseline assess-
ment, participants provided demographic information
including the Center for Epidemiologic Studies scale for
depression symptoms (Radloff, 1977), and completed the
FTND. Both genomic DNA and whole-genome-amplified
DNA samples were available from participants in the
treatment-seeking smoker cohort, and were genotyped
using the candidate gene panel as described below and by
Conti et al (2008).
Multiplex Ever Smoker Pedigree Cohort
A community-ascertained multiplex ever smoker pedigree
cohort was selected to evaluate SNPs that were top-ranked
in the treatment-seeking smoking cohort. These pedigrees
were recruited from a longitudinal study that originally
recruited N¼734 probands of 11–15 years of age, their
parents and siblings, and assessed longitudinal phenotypes
of tobacco use (Hops et al, 2000). Pedigrees with three first-
degree relatives with a smoking history of X100 cigarettes
smoked were selected for recruitment for an integrated
study of the genetics of smoking behavior and nicotine
metabolism after administration of a family history of
tobacco use questionnaire (Swan et al, 2003). After
identification and recruitment of probands and family
members, a detailed baseline assessment was performed
that included sociodemographics, cigarette smoking his-
tory, and the FTND at the period of greatest smoking
intensity. This analysis utilized smoking history question-
naire data and genomic DNA from N¼175 pedigrees.
Genomic DNA was extracted from venous whole blood
using a standard salt-based precipitation method (Miller
et al, 1988), and DNA was genotyped using the same SNP
panel as described (Conti et al, 2008).
Candidate Gene and SNP Selection
Candidate genes for the association analysis of nicotine
dependence and for the pharmacogenetic analysis of
smoking cessation trials were selected from the presynaptic
neuronal nicotinic acetylcholine receptor genes and dopa-
minergic receptor genes whose proteins mediate the
nicotine-stimulated release of dopamine within the meso-
limbic system, corpus striatum and prefrontal cortex of the
brain, and self-administration in animal models of nicotine
addiction (Benowitz, 2008). SNP coverage of candidate
genes, ie, the mean (SD) proportion of HapMap Phase II
Dopaminergic SNPs and the FTND
AW Bergen et al
SNPs tagged at an r2of 40.80 by the candidate gene
genotyping panel SNPs, is 0.92 (0.10). Additional details on
the candidate gene and SNP selection process are provided
by Conti et al (2008).
Analysis of the Treatment-Seeking Smoker Cohort
As described in Conti et al (2008), genotyping of DNA
samples from the RCT participants was performed using the
GoldenGate assay (Illumina, San Diego, CA), with quality
control procedures that included automated sample hand-
ling protocols and the inclusion of replicate DNA samples to
aid in identifying genotyping errors. The mean genotype
concordance rate across 14 replicate DNA samples was
99.0%. As described in Conti et al, 41 SNPs with a genotype
call rate of 0 and 57 SNPs with a minor allele frequency
o1% from 1528 SNPs (232 ancestry informative markers on
the panel) were removed from the analysis dataset. Of the
remaining 1430 SNPs, 95% had genotype completion rates
of 497%. Individual call rates were calculated using the
entire multiethnic sample (N¼1216) and after stratifying
by DNA type, ie, genomic (gDNA) and whole genome
amplified DNA (wgaDNA). Individuals with a call rate less
than 90% were removed from the analysis (N¼48) for a
total of 1091 individuals with gDNA and 90 individuals with
wgaDNA. Although there was a substantial difference
between the call rate distributions for the gDNA samples
(99.6% of individuals with a call rate X95%) in comparison
to the wgaDNA samples (85.3% of individuals with a call
rate X95%), inspection of Illumina data for SNPs with
drastically discordant SNP call rates between the two DNA
types revealed reliable genotyping calls for SNPs with a SNP
call rate calculated using only wgaDNA samples of 480%
(Conti et al, 2008). Thus, for 51 SNPs in which the SNP call
rate was o80% for wgaDNA samples, we only report
analyses using the individuals with gDNA samples (Conti
et al, 2008). Using ancestry informative markers included in
the genotyping panel, Conti et al (2008) demonstrated little
individual admixture within each self-identified ethnic
group in the treatment-seeking smoker cohort. We report
the results of association analysis to covariate-adjusted
FTND with 1123 SNPs at 55 autosomal candidate genes (51
contiguous candidate gene regions) in N¼821 self-identi-
fied white treatment-seeking smokers. We choose to limit
our reporting to results from autosomal genotypes to model
SNP association to FTND using a single multivariate model,
removing 30 SNPs at three X chromosome candidate genes
(FLNA, MAOA, and MAOB) from the available genotype
dataset. We choose to limit our reporting of analyses to self-
identified white participants of the RCTs because of the
potential for differential linkage disequilibrium across
ethnic groups that might lead to heterogeneity of effect
estimates (Conti et al, 2008).
FTND SNP association analysis was based on a general-
ized linear model (GLM), treating FTND as a quantitative
variable, with additive or dominant genetic models, and
covariates age, self-reported depression symptoms, educa-
tion, sex, and site. Covariates included in the GLM were
significantly associated (univariate Po0.05) with baseline
FTND scores. A GLM including the FTND and covariates
was estimated as the base model, and then two GLMs for
each SNP using dominant and additive models were
estimated. A 1 degree of freedom likelihood ratio test was
used to rank SNP GLM results where the P-value (PLRT) of
the more significant GLM (additive or dominant) is
reported. We performed multiple test correction to account
for the correlated tests from the estimation of two GLMs
(additive and dominant) for each SNP and for the multiple
correlated tests from the SNPs within each gene (Conneely
and Boehnke, 2007). We report this adjusted P-value for
correlated tests as PACT, and selected SNPs with PACTo0.10
as noteworthy SNPs for further genetic, bioinformatic, and
literature-based analyses. We evaluated the distribution of
FTND scores by SNP genotype via analysis of variance in
post hoc analyses of noteworthy SNPs. Genetic interaction
was modeled using GMDR (Lou et al, 2007a). Power
analyses were performed using Quanto (Gauderman,
2002). Statistical analyses were performed using SAS (SAS
for Windows, version 9.1, SAS Institute, Cary, NC) and R
(Team RDC, 2003).
Analysis of the Multiplex Ever Smoker Pedigree Cohort
We evaluated association between SNPs and the FTND in
the multiplex ever smoker pedigree cohort previously
identified as associated with the FTND in the treatment-
seeking smoker cohort. FTND data were available on
N¼425 individual ever smokers (X100 cigarettes lifetime)
from N¼175 nuclear pedigrees. We treated the FTND as an
ordinal variable and calculated a covariate-adjusted FTND
score using logistic regression (Wang et al, 2006), and
utilized this score as the quantitative trait for family-based
association testing (FBAT) (Schneiter et al, 2005). 16
genotypes inconsistent with Mendelian transmission were
deleted before FBAT. We performed FBATs using the
genetic models suggested by the post hoc ANOVA analyses
of SNP effects on FTND in the treatment-seeking smoker
cohort, as well as alternative genetic models, ie, dominant,
additive, and genotype models, but not recessive models.
We did not perform multiple test correction for multiple
models or multiple SNPs tested in the FBAT analyses.
Genetic interaction of SLC6A3 and NR4A2 SNPs in
pedigrees was modeled using PGMDR (Lou et al, 2008).
Genomic Annotation and Visualization
We evaluated the physical location of SNPs in relationship
to NCBI Build 35 genomic annotation with Genewindow
(Staats et al, 2005), with the UCSC Genome Browser
(Hinrichs et al, 2006) and linkage disequilibrium (LD)
among SNPs with Haploview 4.0 (Barrett et al, 2005), using
genotype data from the treatment-seeking smoker cohort or
from the CEU HapMap sample (International HapMap
FTND, Covariates, and SNPs in the Treatment-Seeking
The treatment-seeking smoker cohort consists of 828 self-
identified white treatment-seeking smokers, with a mean
(SD) baseline FTND score of 5.3 (2.1) (Table 1). Participants
are about equally divided between college graduates and
Dopaminergic SNPs and the FTND
AW Bergen et al
nongraduates, women and men, and recruitment site.
Older age, a higher depression symptom score, lower
educational achievement, male sex, and two of three sites
are significantly associated with higher FTND scores in this
cohort (Table 1). The multivariate model indicates that
these covariates account for 10% of the variance of FTND
in the treatment-seeking smoker cohort (F827¼18.01,
A total of 73 SNPs at 22 candidate genes were identified
in the treatment-seeking smoker cohort with a PLRTvalue
o0.05 (see Supplementary Table S1 for all SNP GLM
results). Five SNPs at three genes (DRD2, SLC6A3 and
NR4A2) were identified with a PACTo0.10 (Table 2). These
SNPs were evaluated in detail using genomic annotation,
literature review and genotyping in a second cohort of
multiplex ever smoker pedigrees.
The top-ranked DRD2 SNP rs10891552 is associated with
a B0.9U increase of FTND score and with B1.3% of FTND
variance in an additive or dominant model, as, in this
sample, the minor allele homozygote is not observed
(Table 3). rs10891552 is located in a LD block within the
large first intervening sequence (IVS1), but exhibits only
weak LD (r2o0.23) with SNPs in the local LD block in this
sample (Supplementary Figure S1). There are three addi-
tional DRD2 SNPs (rs2440390, rs4586205, and rs1076562)
with PLRTvalues o0.05 (Supplementary Table S1), located
an average of 37 kilo base pairs (kbp) 30of rs10891552
within IVS1 of DRD2, and within a LD block that spans
IVS1 through the coding region of DRD2, but these SNPs
have PACT values B0.6 (Supplementary Table S1), and
rs10891552 has an r2o0.01 with these SNPs (Supplementary
Thetop-rankedthree SLC6A3SNPs (rs2975226,
rs2652510, and rs2652511) identified as associated with
FTND (Table 2) are highly correlated with each other (mean
r2B0.97) in a LD block that extends in this sample from
rs12652860, 61kbp 50of SLC6A3 to rs6350 in exon 2 of
SLC6A3, and are only modestly correlated (r2B0.2) with
SNPs in two 30LD blocks extending from IVS1 through
IVS7 (Figure 1). SNP FTND association model character-
istics are similar for the three SLC6A3 SNPs, ie, for each of
these SNPs, the presence of the minor allele is associated
with a B0.5U increase of FTND score and with B1% of
FTND variance in the additive model (Table 3). Inspection
of all SLC6A3 SNP FTND association results (Supplemen-
tary Table S1) identifies rs12652860, also in the 50linkage
block, with a PLRTo0.01 and a PACT¼0.20, and with similar
effects on FTND score and variance (data not shown),
whereas the remaining SLC6A3 SNPs exhibit PLRTvalues
40.15 and PACTvalues 40.95 (Supplementary Table S1).
Estimation of haplotypes in the 50linkage block results in
five haplotypes with frequency 40.01, with the minor allele
at each of the three top-ranked SLC6A3 SNPs associated
with increased FTND found in three haplotypes with
combined frequency 39% (Figure 1).
The top-ranked NR4A2 SNP rs834829 was identified in
the initial association analysis as most significantly
associated with a dominant model (Table 2), and although
both additive and dominant models are significantly
associated with increased FTND score, inspection of FTND
distributions by genotype in post hoc analysis identifies the
heterozygote genotype as significantly associated with
increased FTND scores, accounting for a B0.4U increase
of mean FTND score and B0.8% of FTND variance
(Table 3). The NR4A2 SNP associated with FTND is found
B12kbp 50of the 50UTR of the NR4A2 gene, within a LD
block that extends from 50of the gene, across the entire gene
to the 30flanking region in this sample (Supplementary
Figure S2). Inspection of all NR4A2 SNP FTND association
results (Supplementary Table S1) identifies rs1150144 and
rs1150143, B2.5kbp 30of the 30UTR, with PLRT values
o0.05, strong r2with rs834829 (0.79 and 0.94, respectively),
and modest PACTvalues (0.12 and 0.21, respectively).
In post hoc power analyses, the sample size of 821
treatment-seeking smokers has power of 82% to detect
b-coefficients of 78, 42, and 43% for minor allele
frequencies of 4% (DRD2), 29% (NR4A2), and 39%
(SLC6A3), respectively, for genetic effects in a dominant
model with an r2of 0.01 and the observed FTND mean and
standard deviation (Table 1) at an a-value of 0.05 with a
SLC6A3 NR4A2 Interaction Analysis
We evaluated gene–gene interaction between rs2652511 at
SLC6A3 and rs834829 at NR4A2, using a continuous model
of FTND score, adjusted for age, depressive symptoms,
education, sex, and site in a sample of N¼794 individuals
with complete FTND, covariate, and genotype data. We
chose these two SNPs to perform the interaction analysis
because the gene product of NR4A2, NURR1, is known to be
required for SLC6A3 gene transcription (see Discussion),
and because these two SNPs are highly ranked in our GLM
results (Table 2). We evaluated training (prediction)
Table 1 Treatment-Seeking Smoker Cohort Covariate
Associations with the FTND
(SD)r, t, or Fa
o0.0001 5.84 o0.0001
0.0082.67 5.3 (2.1)0.090.0077
410 4.9 (2.2)5.58
o0.0001 4.37 o0.0001
Female 4205.1 (2.2)3.03 0.003 3.470.0006
Buffalo2605.5 (2.1)8.51 0.0002 2.890.0039
Philadelphia 2585.6 (2.1)
Washington 3104.9 (2.2)
aUnivariate association estimate.
cMultivariate association estimate.
eMean (SD) age is 45.5 (11.5) years.
fMean (SD) CESD score is 11.8 (8.5).
Dopaminergic SNPs and the FTND
AW Bergen et al
accuracy (TA) of three GMDR analyses to predict the
distribution of covariate-adjusted normalized FTND score
within genotype classes for rs2652511, for rs834829, and for
both rs2652511 and rs834829 (Supplementary Figure S3).
Although each SNP independently predicted FTND score
(for rs2652511, TA¼0.56, TA Odds Ratio (OR)¼1.71, 95CI
1.37–2.14, Po0.0001, cross validation sign test P¼0.02; for
rs834829, TA¼0.56, TA OR¼1.64, 95 CI 1.31–2.04,
Po0.0001, cross validation sign test P¼0.83), only the
analysis with both rs2652511 and rs834829 resulted in a
highly significant cross validation sign test, in addition to
increased training accuracy and odds ratio (TA¼0.59, TA
OR¼2.15, 95CI 1.72–2.70, Po0.0001, cross validation sign
Multiplex Smoking Pedigree Cohort Analysis
FTND score, age, and the proportion of individuals with 16
or more years of education were significantly lower in the
multiplex smoking pedigree cohort than in the treatment-
seeking smoker cohort (all Po0.0001). Age, depressive
symptoms, education, and sex exhibited significant associa-
tion with FTND in the multiplex smoking pedigree cohort in
the same direction as in the treatment-seeking smoker
cohort (Table 4). The multivariate model of covariate
association with FTND indicates that these covariates
account for 15% of the variance of FTND in the multiplex
r2¼0.1537). With one exception (rs834829), each of the
five top-ranked SNPs in the treatment-seeking smoker
cohort (Table 2) exhibited nominally significant (Po0.05)
association with FTND in the multiplex smoking pedigree
cohort in FBAT analyses uncorrected for multiple testing
(Table 5). For rs10891552 (DRD2), the minor allele (in
additive, dominant, or genotype models) was associated
with increased FTND. For the SLC6A3 SNPs, the minor
allele (in the genotype model) was associated with increased
FTND. For rs834829, the FBAT with the smallest P-value
was the heterozygote genotype model (P¼0.0511). We
evaluated statistical interaction between rs2652511 at
SLC6A3 and rs834829 at NR4A2 in 411 individuals with
FTND scores from N¼175 pedigrees using PGMDR
(Supplementary Figure S4). The PGMDR analysis with
both rs2652511 and rs834829 had the greatest training
(prediction) accuracy, but did not exhibit a significant cross
validation sign test P-value (TA¼0.56, TA OR¼1.65, 95 CI
1.24, 2.21, P¼0.0006, cross validation sign test P¼0.82).
The increase in TA over the PGMDR analysis of rs2652511
(TA¼0.54, TA OR¼1.66, 95 CI 1.15, 2.40, P¼0.0063, cross
validation sign test P¼0.38) was modest. The PGMDR
analysis of rs834829 had a nonsignificant training accuracy
estimate and OR (data not shown).
This analysis ranked 1123 SNPs at 55 autosomal candidate
genes for significance of association to baseline FTND score
Table 2 Top-Ranked SNPs Associated with the FTND in the Treatment-Seeking Smoker Cohort
rs10891552DRD2 IVS1+12127AT 0.04Add 0.870.27 0.0014 0.037
TA0.39 Add0.310.10 0.0029 0.057
rs2652510 SLC6A3TC 0.40Dom 0.450.150.00260.081
rs834829NR4A2GA 0.29Dom0.37 0.150.01170.091
aGenomic, location of SNP relative to gene.
bMajor or referent allele.
cMinor or variant allele.
dMAF, minor allele frequency.
eModels are additive (Add) and dominant (Dom).
fCoefficient of genotype term of GLM model. A positive value implies association of the minor allele with increased FTND.
gSE, standard error of Beta.
hLikelihood ratio test P-value.
iP-value adjusted for correlated tests.
Table 3 Unadjusted FTND Distributions by Genotype in the Treatment-Seeking Smoker Cohort
SNPMajor Mean (SD)HeterozygoteMean (SD)MinorMean (SD)PFa
rs108915527545.27 (2.18)646.20 (2.10)
rs2975226 2895.02 (2.20)3455.49 (2.15) 1235.65 (2.22)0.0061.32
rs2652510 2965.19 (2.21)377 5.47 (2.15)1325.59 (2.19)0.0300.87
rs8348294055.18 (2.12) 334 5.58 (2.23)69 5.12 (2.29)0.0100.82
rs2652511300 5.06 (2.21)3775.49 (2.13)1245.59 (2.20)0.0161.04
aGenetic model of ANOVA is dominant or additive for rs10891552, heterotic for rs834829, and additive for SLC6A3 SNPs. The genotype(s) associated with increased
FTND scores can be inferred by inspection.
Dopaminergic SNPs and the FTND
AW Bergen et al
in 821 self-identified white treatment-seeking smokers.
We observed five SNPs assigned to three candidate genes
with P-values adjusted for correlated tests of o0.10. We
focused our subsequent analyses on these five SNPs to avoid
the post hoc exercise of going deeper into the list of ranked
SNPs in search of biological coherence. These top-ranked
SNPs were found within dopamine candidate genes, with a
single association finding each at a dopamine receptor
involved in intracellular signaling through multiple path-
ways (Bonci and Hopf, 2005), at the dopamine transporter,
which regulates extracellular dopamine concentration by
reuptake of dopamine (Sotnikova et al, 2006), and at an
essential regulator of dopaminergic gene transcription and
dopaminergic neuron development (Jankovic et al, 2005).
The panel of candidate genes was developed using nicotinic
acetylcholine receptor and dopaminergic candidate genes,
and thus observing top-ranked SNPs at dopaminergic
candidate genes is not unexpected.
However, our analysis results do raise the question of why
a nicotinic acetylcholine receptor (nACHR) SNP was not
identified within the small group of SNPs that met our
threshold for follow-up analyses. In our analyses of the
treatment-seeking smoker cohort, we tested 121 SNPs at 9
nACHR genes for association with FTND (Supplementary
Table S1). Three of these nACHR SNPs (CHRNB4 rs3971872
and CHRNA2 rs748283 and rs4733065, all several kbp 30of
their assigned gene) exhibited unadjusted P-values o0.05,
but adjusted P-values were 40.10. We did not observe
significant association of two CHRNB2 SNPs with FTND in
this analysis that were previously identified by Conti et al
(2008) in an age, gender, and FTND score adjusted analysis
of abstinence in approximately half of the treatment-seeking
smoker cohort investigated here. FTND is associated with
abstinence in the cohort of treatment-seeking smokers
analyzed here (data not shown), however, this analysis is
focused on baseline nicotine dependence as measured by
the FTND in treatment-seeking smokers of two RCTs
(Lerman et al, 2006).
Based on recent association studies of nicotinic receptor
SNPs and nicotine dependence, the lack of nACHR SNPs
among the top-ranked SNPs in this study may be assumed
to be the result of a lack of statistical power and/or
differences between the treatment-seeking smokers in our
study and the smokers in other recent population based and
clinically based studies. Our treatment-seeking smoker
cohort was B19- and B13-fold smaller than two GWAS
studies that identified the chr15q25.1 nACHR locus as
associated with heavy smoking (X25 cigarettes per day
(CPD) vs o5 CPD) in population based and clinically based
samples originally recruited for other genetic studies
Figure 1SLC6A3 SNPs, LD and haplotypes, treatment-seeking smoker cohort.
Dopaminergic SNPs and the FTND
AW Bergen et al
(Berrettini et al, 2008), and with smoking quantity in
smokers originally recruited for other genetic studies
(Thorgeirsson et al, 2008). Our treatment-seeking smoker
cohort was B3.4- and B2.3-fold smaller than two candidate
gene association studies of the chr15q25.1 nACHR locus
and FTND that characterized risk and protective haplotypes
in three cohorts of current and former smokers, one of
which was composed of treatment-seeking smokers (Weiss
et al, 2008), and a study that evaluated association of SNPs
at 348 candidate genes, including 40 of the candidate genes
investigated in this study, to a population based sample of
nondependent and dependent ever smokers (Saccone et al,
2007). Although our treatment-seeking smoker sample was
much smaller than those earlier studies, our noteworthy
SNPs at dopaminergic candidate genes each accounted
for B1% of FTND variance. Thorgeirsson et al (2008)
estimated that B0.7% of the variance of cigarettes smoked
per day is explained by the chr15q25.1 variant (rs1051730)
identified in the Icelandic population sample of 10995
smokers used in their study. rs1051730 was tested in our
treatment-seeking smoker cohort for association with
FTND with nonsignificant unadjusted and adjusted P-values
(0.47 and 0.99, respectively). Finally, our sample of
treatment-seeking smokers exhibited a normal distribution
of FTND scores, rather than a dichotomized distribution
utilized by some investigators, ie, selected for extremes of
the CPD or nicotine dependence distributions from a larger
population or clinically based sample. Our results and the
ascertainment differences between our treatment-seeking
cohort and previously studied cohorts raise the possibility
that dopaminergic candidate genes may exert a greater
influence on nicotine dependence among treatment-seeking
smokers with selected nicotine dependence scores than do
the chr15q25.1 nicotinic receptor candidate genes. Addi-
tional recent GWAS in population based and clinically
ascertained populations have identified a wide variety of
candidate genes as significantly associated with smoking
behaviors in addition to nicotinic receptor candidates
(Bierut et al, 2007; Caporaso et al, 2009; Uhl et al, 2007,
2008; Vink et al, 2009), suggesting that many factors,
including sample ascertainment, sample size, genotyping
platforms, and analyses approaches, may influence the
ranking of specific candidate genes or candidate gene
families in association analyses of smoking behaviors.
We evaluated association of dopaminergic SNPs identi-
fied in the treatment-seeking smoker cohort in an
independent cohort of nontreatment-seeking ever smokers
from multiplex ever-smoking pedigrees that were signifi-
cantly younger, with significantly less education and with
significantly lower FTND scores than the individuals in the
treatment-seeking smoker cohort. We also evaluated the
biological coherence of these findings by performing
interaction analyses among SNPs from two genes with a
priori evidence for biological interaction, which revealed a
significant SLC6A3 NR4A2 interaction in the treatment-
seeking smoker cohort, although we failed to observe a
significant gene–gene interaction in the multiplex smoker
pedigree cohort. The sample size of the multiplex ever
smoker pedigree cohort, about half that of the treatment-
seeking smoker cohort, may have limited our ability to
observe statistically significant interaction in these pedi-
grees. Below, we attempt to place our top-ranked associa-
tion findings of DRD2, SLC6A3, and NR4A2 SNPs and the
FTND in the context of the nicotine dependence suscepti-
bility candidate gene association literature.
This study has identified one DRD2 SNP (rs10891552)
among 52 SNPs tested at the ANKK1 and DRD2 loci as
worthy of follow-up evaluation (SNPs from the two regions
were tested together because SNPs at these genes are in LD).
There are three extant studies that have evaluated associa-
tion with nicotine dependence measures and DRD2 SNPs
(Gelernter et al, 2006; Huang et al, 2008; Saccone et al,
2007). Saccone et al (2007) tested 41 SNPs at the two genes
for association with nicotine dependence and rs4245150,
69kbp 50of DRD2, was identified as significant with a FDR
o0.40. Huang et al (2008) tested 23 SNPs at the two genes
for association with nicotine dependence in the Mid South
Table 5 FBAT of the FTND in the Multiplex Ever Smoker
rs10891552 DRD2A/D and G0.11 & 0.2115 2.301 0.0214
rs2975226 SLC6A3G0.17 423.065 0.0022
rs2652510 SLC6A3G0.11 473.059 0.0022
rs834829 NR4A2G 0.32 671.951 0.0511
rs2652511 SLC6A3G 0.10 422.856 0.0043
aFBAT genetic model: A, additive; D, dominant; G genotype.
bAllele or genotype frequency of minor allele model tested.
cNumber of informative pedigrees.
dNormalized Z score, (S?E(S))/Var(S)), where a positive Z implies association of
the genotype with a higher FTND score.
eP-value of FBAT.
Table 4 Multiplex Ever Smoker Pedigree Cohort Covariate
Associations with the FTND
(SD)r or ta
o0.001 6.87 o0.0001
0.022 3.654103.9 (2.7) 0.110.0003
1103.3 (3.0)3.07 0.002 4.93 o0.0001
Female2193.6 (2.7)2.43 0.015 2.830.0048
Male 2064.3 (2.6)
aUnivariate association estimate.
cMultivariate association estimate.
eMean (SD) is 39.4 (13.6).
fMean (SD) is 11.9 (9.4).
Dopaminergic SNPs and the FTND
AW Bergen et al
Tobacco Family sample and identified one ANKK1 SNP
(rs2734849, Ex8+475, H490R) associated with nicotine
dependence in African-American pedigrees after multiple
test correction. Gelernter et al (2006) tested 43 SNPs at these
two genes and two additional flanking genes (TTC12 and
NCAM1) for association to both DSM-IV and FTND
nicotine dependence and identified one, four, and five
SNPs that exhibited significant association to DSM-IV
nicotine dependence in African-American pedigrees, Eur-
opean-American pedigrees, and both sets of pedigrees
combined; however, no SNPs were considered to be
significantly associated to FTND after multiple test correc-
tion. Inspection of r2in the HapMap CEU sample between
the SNP associated with FTND in this study and SNPs found
associated with nicotine dependence in Saccone et al,
Huang et al, and Gelernter et al, identified no substantial LD
(r2p0.01 for six such SNPs). PACTvalues for SNPs declared
significantly associated with nicotine dependence by Huang
et al (2008) and Gelernter et al (2006) were nonsignificant
(for five such SNPs, PACTvalues 40.98). rs10891552 does
not appear to be in LD with flanking DRD2 SNPs (Figure 1),
or with DRD2 SNPs associated with nicotine dependence in
other studies (Gelernter et al, 2006; Huang et al, 2008;
Saccone et al, 2007), and those other SNPs do not exhibit
association in this study. The association of rs10891552 in
the cohort of treatment-seeking smokers thus represents a
novel finding of association of a DRD2 SNP to nicotine
dependence and the cohort of multiplex smoking pedigrees
provides nominal support for this association in a second
There are three published studies investigating SLC6A3
SNPs for association with nicotine dependence (Ling et al,
2004; Saccone et al, 2007; Segman et al, 2007). Ling et al
(2004) evaluated association of a 30UTR SNP (rs27072) with
nicotine dependence and age of smoking onset in a sample
(668 individuals, 99% male, from 253 sibships) of severely
nicotine-dependent smokers (mean (SD) FTND¼8.1 (2.1)).
Ling et al (2004) did not identify a significant association
with nicotine dependence, but did identify significant
association with smoking onset p18 years of age, in the
subsample of severely dependent smokers with a FTND
score X8. Segman et al (2007) evaluated association of four
SLC6A3 SNPs (located in the promoter (rs2652510), IVS1,
Exon 9, and Exon 15) and the 30VNTR polymorphism,
located in exon 15, to smoking initiation and to dichot-
omized nicotine dependence in a sample of 390 female
undergraduate students, 242 of whom who had smoked
daily for at least 1 year and 148 of whom had never smoked.
The 242 daily smokers were dichotomized into a group with
FTQ scores of X6 (N¼127) and a group with scores p4
(N¼115). After multiple test correction, Segman et al did
not observe significant association between any SNP and
either smoking initiation or nicotine dependence. Segman
et al (2007) did identify significant association to both
phenotypes with a haplotype composed of the 30VNTR and
a SNP in the same (15th) exon. Saccone et al (2007) tested
five SLC6A3 SNPs located in the 30UTR (3 SNPs), exon 12,
and exon 13; none of these SNPs were associated with
In contrast to these two studies evaluating association
between SLC6A3 SNPs and nicotine dependence, there have
been many studies focused on assessing association between
a 30polymorphic variable number of tandem repeat (VNTR)
locus found in exon 15 of the SLC6A3 gene (Vandenbergh
et al, 1992) and smoking behaviors, including smoking
status (Jorm et al, 2000; Lerman et al, 1999; Sabol et al,
1999; Segman et al, 2007; Timberlake et al, 2006;
Vandenbergh et al, 2002), adolescent smoking progression
(Audrain-McGovern et al, 2004; Laucht et al, 2008),
smoking cessation (David et al, 2007; David and Munafo,
2008; Han et al, 2008; Lerman et al, 2003; O’Gara et al, 2007;
Styn et al, 2008; Swan et al, 2007; Ton et al, 2007), and
affective, behavioral, and brain responses to smoking
(Brody et al, 2006; Erblich et al, 2004, 2005; Franklin et al,
2008; Newberg et al, 2007; Perkins et al, 2008; Yang et al,
2008). Only one of these studies has evaluated a sample of
smokers for potential association of the SLC6A3 30VNTR
with nicotine dependence (Lerman et al, 1999). Lerman et al
(1999) evaluated four smoking behaviors in a sample of 289
smokers and 233 controls; association of 30
genotypes with FTND in the sample of 289 smokers was
not observed. Lerman et al (1999) did identify significant
association with smoking status in the entire sample, and
with age of initiation and quitting history in the subsample
The SLC6A3 30VNTR polymorphism has been associated
with dopamine transporter expression differences in vivo
via single photon emission computed tomography of the
pane (b-CIT) for measurement of striatal dopamine
transporter binding potential (Heinz et al, 2000; Martinez
et al, 2001; van Dyck et al, 2005), and with differences in
SLC6A3 gene expression via quantitative polymerase-chain
reaction analysis of RNA ex vivo in lymphocytes (Mill et al,
2002) and in post mortem human brain (Brookes et al, 2007;
Mill et al, 2002). The presence of the nine repeat allele of the
30VNTR has generally been associated with reduced
dopamine transporter binding potential or reduced levels
of SLC6A3 transcripts. However, the SLC6A3 promoter and
50UTR region and the 30VNTR region (exon 15) are in very
modest linkage disequilibrium in the sample investigated in
this study (Figure 1), and in other samples (Greenwood and
Kelsoe, 2003; Kelada et al, 2005).
SLC6A3 promoter SNPs have been associated with
dopamine transporter expression in vivo via [11C] cocaine
positron emission tomography (PET; Drgon et al, 2006),
in vitro via expression constructs (Greenwood and Kelsoe,
2003; Kelada et al, 2005; Sacchetti et al, 1999) and ex vivo in
post mortem human brain via [3H] carboxyfluorotropane
binding studies (Drgon et al, 2006). In studies of promoter
sequence constructs, Sacchetti et al (1999) found that a
proximal promoter construct that includes rs2975226 drives
expression up to 150 times greater than an expression
whereas SLC6A4 promoter sequences further 50serve to
repress transcription. Greenwood and Kelsoe (2003) eval-
uated promoter constructs that differed by promoter
sequence and by allelic content. Promoter expression
constructs that include the very proximal promoter and 50
UTR sequence, including rs2975226, have greater reporter
activity than expression constructs that include more 50
Dopaminergic SNPs and the FTND
AW Bergen et al
promoter and/or more 30sequence, as observed by Sacchetti
et al (1999). However, promoter constructs that included
the minor allele of common promoter SNPs, eg, at
rs2652510, within the core or extended promoter constructs,
exhibited reduced reporter activity in vitro (Greenwood and
Kelsoe, 2003). Another analysis of expression constructs of
the SLC6A3 promoter and more 30sequence spanning
7391bp evaluated transcription levels for six haplotypes
that included rs2975226, rs2652511, and rs2652510, as well
as other SLC6A3 promoter SNPs (Kelada et al, 2005).
Expression constructs containing the minor alleles at these
three SNPs in the promoter haplotype exhibited signifi-
cantly reduced transcription in vitro (Kelada et al, 2005).
In vivo [11C] cocaine PET and ex vivo [3H] carboxyfluor-
otropane binding studies indicate that the minor allele at
rs2975226 is associated with reduced dopamine transporter
binding (Drgon et al, 2006). Thus, four studies of SLC6A3
promoter SNPs including in vitro, in vivo and ex vivo
designs showed significantly reduced expression with the
minor allele of rs2975226, rs2652510, and/or rs2652511
(Drgon et al, 2006; Greenwood and Kelsoe, 2003; Kelada
et al, 2005; Sacchetti et al, 1999).
There are three published reports associating NR4A2
et al, 2002; Nielsen et al, 2008; Saccone et al, 2007).
Ishiguro et al (2002) identified rs34884856 in the promoter
(?2920), and a biallelic CA dinucleotide repeat sequence in
the 30UTR of the NR4A2 gene and identified a significant
haplotypic association with alcohol dependence in a sample
of 171 individuals with a DSM-IV diagnosis of alcohol
dependence and 161 unscreened individuals. Nielsen et al
(2008) identified rs1405735, 45kbp 50of NR4A2, as the
fourth ranked candidate gene SNP from an a priori
candidate gene genotypic association analysis nested within
a case–control GWAS of heroin dependence with 99
individuals meeting Federal criteria for methadone main-
tenance and 99 individuals screened for past and current
history of drug use genotyped for this SNP. Information on
the smoking status of cases and controls was not provided
by Ishiguro et al (2002) and Nielsen et al (2008). Saccone
et al (2007) tested two SNPs at NR4A2 (rs12803 and
rs834835, located in the 30UTR and IVS1, respectively) for
association with dichotomized FTND, however, neither
exhibited significant association with nicotine dependence.
The levels of LD (r2) in the HapMap CEU sample between
rs834829 and rs1405735, rs12803, and rs834835 are 0.12,
0.49, and 1.0, respectively. The NR4A2 SNP (rs34884856)
tested by Ishiguro et al (2002) was not genotyped in the
NURR1, the protein product of the NR4A2 gene, is an
orphan nuclear receptor transcription factor (Law et al,
1992) expressed in dopaminergic neurons (Zetterstrom
et al, 1996), required for the development and maintenance
of dopaminergic neurons (Saucedo-Cardenas et al, 1998;
Zetterstrom et al, 1997), and coexpressed with tyrosine
hydroxlase and the dopamine transporter (DAT) within
striatal neurons (Cossette et al, 2005). Together with DAT,
NURR1 is decreased in expression in dopamine neurons of
cocaine (Bannon et al, 2002) and heroin (Horvath et al,
2007) abusers, suggesting that DAT and NURR1 are
involved in the adaptation of the striatal dopaminergic
system in addiction, as well as in normal development.
NURR1 regulates DAT expression using sites in the
promoter region B1000bp 50of the transcription start site
of SLC6A3 (Sacchetti et al, 1999; Sacchetti et al, 2001). The
finding that NR4A2 variation is associated with nicotine,
heroin, and alcohol dependence is of great interest because
it suggests that genetic variation at NURR1 may influence
the development and course of substance dependence
generally and not just for a single substance (Goldman
and Bergen, 1998). The SLC6A3–NR4A2 interaction identi-
fied in this study is consistent with what is known regarding
the development and maintenance of dopaminergic neurons
by NURR1 (Zetterstrom et al, 1997) and the regulation of
the SLC6A3 gene by NURR1 (Sacchetti et al, 2001). A
challenge now will be to identify the specific biological
interaction between the NURR1 protein and the SLC6A3
promoter that increases the risk for nicotine dependence.
In this study, the DRD2 SNP rs10891552, the SLC6A3 SNPs
rs2975226, rs2652510, and rs2652511, and the NR4A2 SNP
rs834829 are top-ranked in association with increased
FTND scores among both treatment-seeking and commu-
nity smokers. The DRD2 SNP appears to be a novel
association with nicotine dependence. There is abundant
existing information on the three SLC6A3 promoter SNP
SNPs identified in this study demonstrating association
with SLC6A3 expression. One hypothesis generated by this
study and the extant literature is that the minor alleles of
these SLC6A3 promoter SNPs result in increased nicotine
dependence severity among smokers via reduced expression
of the SLC6A3 gene. Hypotheses on whether SLC6A3
promoter variation influences risk for nicotine dependence
among individuals naive to tobacco smoke, or whether the
primary influence is on DAT expression during smoking
initiation or during regular smoking, are not addressed by
this analysis. Finally, evidence from this study and the
existing drug dependence literature suggests that NR4A2 is
a candidate susceptibility gene influencing nicotine, alcohol,
and opioid dependence. Our interaction analysis suggests
that some component of this influence occurs through
interactive effects with variation at SLC6A3.
The primary limitation of this candidate gene association
study with the FTND is the modest sample size of
treatment-seeking smokers. A larger sample, or a similarly
sized sample oversampled for extreme FTND scores from a
larger sample, would have had greater power to detect SNPs
associated with FTND (Schork et al, 2000). To limit the
reporting of false positives, we used adjustment for testing
of additive and dominant models and for testing of multiple
SNPs at a candidate gene to rank SNPs in the treatment-
seeking smoker cohort. This adjustment does not adjust for
the number of genes evaluated. We used a second multiplex
ever smoker pedigree cohort to evaluate the top-ranked
SNPs, although this cohort had substantial demographic
and behavioral differences from the treatment-seeking
smoker cohort, suggesting that its value in this analysis is
supportive in nature. With these limitations, noteworthy
SNPs associated with FTND in the treatment-seeking
Dopaminergic SNPs and the FTND
AW Bergen et al
smoker cohort at three dopaminergic genes were associated
with FTND in a second multiplex ever smoker pedigree
cohort. For SLC6A3 and NR4A2 SNPs, supportive evidence
for association to substance dependence is available in the
genetic epidemiological literature (Li and Burmeister, 2009;
Nielsen et al, 2008), and for the SLC6A3 SNPs, there is
abundant evidence for functional effects of these note-
This study was supported by U01 DA020830-03, R01
CA63562, P50 CA84718, P50 CA084735, R01 GM069890,
R01 DA03706, and 7PT2000-2004 from UC TRDRP. We
thank Dr Faith Allen and Dr Judy Andrews for their
contribution to PNAT data curation and to the collection of
the multiplex ever smoker pedigree cohort, respectively,
and we thank the participants in the treatment-seeking
smoker and multiplex ever smoker pedigree cohorts.
DISCLOSURE/CONFLICT OF INTEREST
Related to THIS WORK: Authors Conti, Van Den Berg, Lee,
Liu, Li, Guo, Mi, Thomas, Krasnow, He, Nishita, Jiang,
McClure, Tildesley, Hops, Tyndale, Benowitz, Lerman, and
Swan declare that all financial and material support for this
work was provided by their primary employer. In the past 3
years, Dr Bergen has been an employee of the National
Cancer Institute. In the past 3 years, Dr Lessov-Schlaggar
has received compensation for research and professional
services from SRI International. Related to ALL OTHER
COMPENSATION: Authors Van Den Berg, Lee, Liu, Li, Guo,
Mi, Thomas, Krasnow, He, Nishita, Jiang, McClure, and
Tildesley declare that except for income provided from their
primary employer no financial support or compensation
has been received from any individual or corporate entity
over the past 3 years for research or professional services
and there are no personal financial holdings that could be
perceived as constituting a potential conflict of interest. In
the past 3 years, Dr. Bergen has been an employee of the
National Cancer Institute and has received compensation
for professional services from the National Institutes of
Health, and from the Price Foundation Ltd. In the past 3
years, Dr Conti has been a paid consultant to Pfizer Inc. In
the past 3 years, Dr Lessov-Schlaggar has received
compensation for research and professional services from
SRI International. In the past 3 years, Dr Hops has received
compensation from the University of Washington for
consulting on federally funded projects in adolescent drug
use and abuse. In the past 3 years, Dr Tyndale has consulted
for Novartis, a company that develops and/or markets
nicotine dependence medications. Dr Tyndale is also a
shareholder and scientific officer in Nicogen, a company
focused on the development of novel smoking cessation
treatments. Over the past 3 years Dr Benowitz has received
compensation from pharmaceutical companies marketing
or developing smoking cessation medications, including
Pfizer, GlaxoSmithKline, Novartis, and Aradigm. He has
also served as a paid expert witness in litigation against
tobacco companies. In the past 3 years, Dr Lerman has
received compensation and/or research support from
GlaxoSmithKline, Pfizer, Astra Zeneca, and Novartis,
companies that develop and/or market nicotine dependence
medications. In the past 3 years, Dr Swan has received
compensation from Pfizer.
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Dopaminergic SNPs and the FTND
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