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Association of a variant in the muscarinic acetylcholine receptor 2 gene (CHRM2) with nicotine addiction.

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Neuropsychiatric Genetics
Association of a Variant in the Muscarinic
Acetylcholine Receptor 2 Gene (CHRM2) With
Nicotine Addiction
A. Mobascher,1,2 D. Rujescu,3,4 K. Mittelstraß,5 I. Giegling,3,4 C. Lamina,5,6 B. Nitz,5 H. Brenner,7
C. Fehr,8 L.P. Breitling,7 J. Gallinat,9 D. Rothenbacher,7 E. Raum,7 H. M€uller,7 A. Ruppert,10
A.M. Hartmann,3,4 H.J. M€oller,4 A. Gal,11 Ch. Gieger,5 H.E. Wichmann,5 T. Illig,5
N. Dahmen,8 and G. Winterer1,2*
Department of Psychiatry, Neuropsychiatric Research Laboratory, Heinrich-Heine University, Duesseldorf, Germany
Institute of Neurosciences and Biophysics, Helmholtz Research Center, Juelich, Germany
Department of Psychiatry, Division of Molecular and Clinical Neurobiology, Ludwig-Maximilians-University, Munich, Germany
Department of Psychiatry, Ludwig-Maximilians-University, Munich, Germany
Helmholtz Zentrum M€unchen, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Neuherberg, Germany
Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology,
Innsbruck Medical University, Innsbruck, Austria
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
Department of Psychiatry, University of Mainz, Mainz, Germany
Department of Psychiatry, Charite University Medicine Berlin, Campus Mitte, Berlin, Germany
Genetics Research Centre GmbH, Munich, Germany
Institute for Human Genetics, University of Hamburg Medical Center Eppendorf, Hamburg, Germany
Received 7 January 2009; Accepted 15 June 2009
Genetic factors contribute to the overall risk of developing
nicotine addiction, which is the major cause of preventable
deaths in western countries. However, knowledge regarding
specific polymorphisms influencing smoking phenotypes remains scarce. In the present study we provide evidence that a
common single nucleotide polymorphism (SNP) in the 50 untranslated region of CHRM2, the gene coding for the muscarinic
acetylcholine receptor 2 is associated with nicotine addiction.
CHRM2 was defined as a candidate gene for nicotine addiction
based on previous evidence that linked variations in CHRM2 to
alcohol and drug dependence. A total of more than 5,500 subjects
representative of the German population were genotyped and
How to Cite this Article:
Mobascher A, Rujescu D, Mittelstraß K,
Giegling I, Lamina C, Nitz B, Brenner H, Fehr
C, Breitling LP, Gallinat J, Rothenbacher D,
Raum E, M€
uller H, Ruppert A, Hartmann
AM, M€
oller HJ, Gal A, Gieger Ch, Wichmann
HE, Illig T, Dahmen N, Winterer G. 2010.
Association of a Variant in the Muscarinic
Acetylcholine Receptor 2 Gene (CHRM2)
With Nicotine Addiction.
Am J Med Genet Part B 153B:684–690.
Additional Supporting Information may be found in the online version of this article.
Grant sponsor: German Federal Ministry of Education and Research (BMBF); Grant sponsor: State of Bavaria and the National Genome Research Network
(NGFN); Grant sponsor: BMBF; Grant number: 01EB0113; Grant sponsor: DFG; Grant number: Wi2997/1-1; Grant sponsor: Project Berlin
Neuroimaging Center; Grant number: 01G00208; Grant sponsor: Baden W€
urttemberg Ministry of Research, Science and Arts; Grant sponsor:
Helmholtz Zentrum M€
unchen (KORA, ESTHER); Grant sponsor: Genetics Research Centre, Munich/Germany (NCOOP).
*Correspondence to:
G. Winterer, M.D., Ph.D., Department of Psychiatry, Laboratory of Neuropsychiatric Research, Heinrich-Heine University, Bergische Landstr. 2, 40629
Duesseldorf, Germany. E-mail:
Published online 30 July 2009 in Wiley InterScience (
DOI 10.1002/ajmg.b.31011
2009 Wiley-Liss, Inc.
assessed regarding their smoking habits. The impact of three
SNPs in CHRM2 on smoking behavior/nicotine addiction was
investigated using logistic regression models or a quasi-Poisson
regression model, respectively. We found the T allele of
SNP rs324650 to be associated with an increased risk of smoking/
nicotine dependence according to three different models,
the recessive models of regular or heavy smokers vs. neversmokers (odds ratio 1.17 in both analyses) and according to the
om index of nicotine addiction. In the analysis stratified
by gender this association was only found in females. Our data
provide further evidence that variations in CHRM2 may be
associated with the genetic risk of addiction in general or
with certain personality traits that predispose to the development of addiction. Alternatively, variations in CHRM2 could
modulate presynaptic auto-regulation in cholinergic systems
and may thereby affect an individual’s response to nicotine more
specifically. 2009 Wiley-Liss, Inc.
Key words: smoking; nicotine addiction; CHRM2
Smoking due to nicotine addiction is the major cause of early,
preventable deaths in western societies [Frieden and Bloomberg,
2007; WHO Report on the global tobacco epidemic, 2008].
Environmental as well as genetic factors play a role in the etiology
of nicotine dependence and twin studies suggest a heritability of at
least 50% [Li, 2006]. A number of studies have found an association
between the CHRNA3-CHRNA5-CHRNB4 nicotinic acetylcholine
receptor subunit gene cluster on chromosome 15q24/25 and
smoking [Bierut et al., 2007, 2008; Saccone et al., 2007; Berrettini
et al., 2008; Chanock and Hunter, 2008; Hung et al., 2008;
Thorgeirsson et al., 2008]. Furthermore, CHRNA4, the gene coding
for the a4-subunit of nicotinic acetylcholine receptors, has been
linked to nicotine addiction and smoking [Feng et al., 2004;
Li et al., 2005; Hutchison et al., 2007; Breitling et al., in press].
However, other genes involved in dopaminergic or GABAergic
neurotransmission as well as genes involved in nicotine metabolism
have also been associated with smoking [for recent reviews see Ho
and Tyndale, 2007; Benowitz, 2008; Ray et al., 2008].
CHRM2, the gene coding for the muscarinic acetylcholine
receptor 2 has previously been identified as a risk gene for alcoholism in the collaborative study on the genetics of alcoholism
[COGA; Wang et al., 2004]. This finding was later replicated
and subsequent research suggested that genetic variations in
CHRM2 may also be associated with the abuse of illicit drugs
[Luo et al., 2005; Dick et al., 2007b]. In fact, the latter study
suggested that the association between CHRM2 and alcoholism in
the COGA sample was driven by a subsample with comorbid drug
The comorbidity of alcoholism, drug addiction and nicotine
abuse is high [e.g. Bien and Burge, 1990], a phenomenon that may
be mediated by an overlapping set of risk genes [Grucza and Bierut,
2006]. Therefore and in view of the notion of a common molecular/
neurobiological pathway for addiction that is shared by different
addictive substances [Nestler, 2005], we sought to address the
question whether polymorphisms in CHRM2 may also be associated with smoking and nicotine dependence.
Thus, we investigated CHRM2 genotype, extent of nicotine
dependence and smoking habits in 5,561 subjects (derived from
three subsamples R1–R3) recruited from the general population in
In the present study, ‘‘smoking status’’ was defined as follows:
people smoking at least one cigarette per day were defined as
regular smokers. People smoking 20 or more cigarettes per
day were defined as heavy smokers. Never-smoking controls had
a life-time history of no more than 100 cigarettes. In the MONICA/
KORA study (R1 sample) and NCOOP study (R2 sample) only
current smokers were considered. However, in the ESTHER
study (R3 sample) former smokers were also considered, if they
had been regular heavy smokers in the past (see also below).
In a subset of participants of the R1 and R2 subsamples, the
om test for nicotine dependence (FTND) was also performed. Detailed characteristics of the three study populations
are shown in Table I.
The three population-representative subsamples have been
described in more detail elsewhere [Low et al., 2004; Holle et al.,
2005; Wichmann et al., 2005; Twardella et al., 2006].
In brief, the first sample (R1) was derived from the S3/F3 (1994/
1995 and 10 years later) and S4 (1999/2001) surveys of the
MONICA/KORA study (Cooperative Health Research in the
Augsburg Region) which is a research platform for population
based health research. Out of 9,117 participants of the S3 and
S4 surveys drawn from the southern German population aged
25–84 years, N ¼ 1,412 subjects were randomly selected for
the present study. See Keil et al. [1998], Holle et al. [2005], or
Wichmann et al. [2005] for a further description of the MONICA/
KORA study procedures and characteristics.
The second sample (R2) was recruited within the framework of
the cooperation study on nicotine addiction (NCOOP) that was
performed in five German cities. Study centers were the LudwigMaximilians-University of Munich, the German Cancer Research
Center Heidelberg, the University of Mainz, the Heinrich-Heine
University Duesseldorf and the University of Berlin. The NCOOP
sample consisted of a total of N ¼ 1,855 subjects aged 18–79 years in
which the smoking phenotype was characterized. Due to missing
values, N ¼ 28 subjects of the R2 sample were dropped from the
present analysis.
The third sample (R3) was derived from the ESTHER cohort
study (epidemiological investigations of the chances of preventing,
recognizing early and optimally treating chronic diseases in an
elderly population), a research platform for studies of risk factors
and risk indicators for common diseases. The total ESTHER study
population comprised 9,953 participants aged 50–74 who were
recruited during health screening examinations by their general
practitioner. See Low et al. [2004] and Twardella et al. [2006] for
further details on that study. For the present analysis, only subjects
who reported a daily consumption of more than 20 cigarettes at
some point in their lives and never-smoking controls were considered. We reckoned that ever having reached regular, heavy smoking
status indicated the development of nicotine dependence. Therefore R3 subjects falling into this category were treated as regular and
heavy smokers, although many had already quit smoking at
study enrollment. Eventually, N ¼ 1,525 ever-smokers and N ¼ 769
TABLE I. Characteristics of the Three Study Populations
Total (N)
Males (N)
Females (N)
Age in years, mean (range)
FTNDa score (N)
Total, mean (range)
Males, mean (range)
Females, mean (range)
Regular smokers
Total, N (%)
Males, N (%)
Females, N (%)
Heavy smokers
Total, N (%)
Males, N (%)
Females, N (%)
Never-smoking controls
Total, N (%)
Males, N (%)
Females, N (%)
46.0 (25–84)
3.18 (0–10)
3.34 (0–10)
3.04 (0–10)
45.7 (18–79)
4.75 (0–10)
4.82 (0–10)
4.67 (0–10)
60.4 (48–75)
786 (55.7)
502 (53.6)
284 (59.7)
823 (44.4)
425 (48.6)
398 (40.6)
1,525 (66.5)
1,194 (66.6)
331 (66.2)
627 (44.4)
425 (45.4)
202 (42.4)
555 (29.9)
319 (36.5)
236 (24.1)
1,525 (66.5)
1,194 (66.6)
331 (66.2)
626 (44.3)
434 (46.4)
192 (40.3)
1004 (54.1)
432 (49.4)
572 (58.4)
769 (33.5)
600 (33.4)
169 (33.8)
FTND, Fagerstr€om test for nicotine dependence.
769 controls who were randomly selected (matched for sex and
age-group) from the over-all sample contributed to this study.
Ethical approval from the appropriate institutions was obtained
in the context of the individual studies that contributed to the
present study. The analyses presented in this report were covered by
the ethical approvals and written informed consent that was given
for study participation throughout.
The three single nucleotide polymorphisms (SNPs) rs1824024,
rs2061174, and rs324650 in the 5’ untranslated region of the
CHRM2 gene, which have been associated with alcoholism in the
original COGA report by Wang et al. [2004] were genotyped. DNA
was extracted from peripheral blood in all studies following standard procedures. Genotyping platforms employed were the Illumina BeadStation 500G Sentrix Array Matrices (Illumina, San
Diego, CA) for the MONICA/KORA study and the iPLEX
MALDI-TOF mass spectrometry (Sequenom, San Diego, CA) for
the NCOOP and ESTHER studies. Genotyping was performed
blind for smoking status. Logistic regression models were employed
to examine associations of individual SNPs with smoking status. A
quasi-Poisson regression model was used for association analysis
with FTND. Using R 2.6.0 (R Foundation of Statistical Computing,
2007), additive and recessive genotype models were calculated, all
adjusted for age and sex. Combined effect estimates were obtained
using a fixed effects model with inverse variance weights. A Q-test of
heterogeneity was performed for each SNP to evaluate the appropriateness of the fixed effects assumption [using SAS 9.1 (SAS
Institute, 2003)]. Bonferroni correction was applied on independent number of tests [P(corr)] for the main analyses. This number
was calculated using the effective number of loci [Li and Ji, 2005],
which accounts for the correlation structure between the SNPs.
Multiple testing correction did not include the multiple phenotype
models as well as the two different transmission models (additive
and recessive), as these are highly correlated. A haplotype analysis of
the three SNPs under investigation was performed using package
haplo.stats in R [Schaid et al., 2002].
Genotype information and quality criteria in the three cohorts
are provided in Supplementary Table S1. No deviation from
Hardy–Weinberg–Equilibrium (HWE) was seen in any of the
three sub-samples (P > 0.05). SNPs were moderately to highly
correlated in each sample (D’ ¼ 0.82–0.87 and r2 ¼ 0.66–0.68
between rs1824024 and rs2061174, D’ ¼ 0.52–0.56 and r2 ¼
0.15–0.18 between rs324650 and rs1824024/ rs2061174).
Figure 1 shows a linkage disequilibrium (LD) plot of the three
SNPs in the NCOOP sample (similar results were obtained in the
other two samples, data not shown). Due to this correlation
structure, the effective number of loci was estimated to be two out
of the three tested loci.
While rs1824024 and rs2061174 were not associated with nicotine
addiction in our study, we found rs324650 (an A/T single nucleotide polymorphism [with a minor (T-) allele frequency between
46% and 49% in the three samples]) to be associated with smoking
status in the NCOOP sample alone as well as in the pooled analysis
according to three different models: the recessive model of regular
smokers vs. nonsmokers, the recessive model of heavy smokers
versus nonsmokers and according to the additive model of nicotine
addiction as quantified by the Fagerstr€
om index. See Table II for
further details. There was no evidence for heterogeneity between
studies. A haplotype analysis of the three SNPs did not provide
additional information, that is, within these SNPs the genetic
liability for nicotine addiction was completely driven by rs324650.
FIG. 1. Linkage disequilibrium (LD plot) plot of CHRNA4 single
nucleotide polymorphisms (SNPs) in the NCOOP sample. [Color
figure can be viewed in the online issue, which is available at]
In a post-hoc analysis stratified by gender the association of
rs324650 with smoking was only found in females (Table II).
We found rs324650, a variant in the 5’ untranslated region
of CHRM2, to be associated with smoking in a large sample
representative of the German population. Smoking behavior
has several—partially associated dimensions—such as smoking
initiation, smoking persistence, cigarettes smoked per day, and
nicotine dependence. Heritability estimates suggest that the genetic
architecture of the different aspects of smoking like smoking
initiation and nicotine dependence only partially overlaps [Maes
et al., 2004; Vink et al., 2005; Ho and Tyndale, 2007]. This means
that some risk genes are most likely specific to certain smoking
phenotypes [Sullivan et al., 2001] while others may have pleiotropic
effects [Caporaso et al., 2009]. Our data on rs324650 suggest that
CHRM2 may be associated with at least two different aspects of
smoking behavior, namely smoking initiation (being a regular or
heavy smoker) and severity of nicotine dependence as assessed by
the FTND.
However, the association between rs324650 and smoking
was restricted to females, a finding that is consistent with
gender-specific aspects in the genetic liability of smoking and
nicotine addiction.
Our Results allow at least three Different Interpretations.
OR# (95% CI)/
b (95% CI)*
0.80 (0.57, 1.10)
1.44 (1.06, 1.96)
0.81 (0.57, 1.15)
1.55 (1.08, 2.22)
OR# (95% CI)
Combined analysis
1.17 (1.02, 1.34)
1.17 (1.01, 1.35)
0.05 (0.01, 0.10)
OR# (95% CI)/
b (95% CI)*
Combined analysis
1.14 (0.89, 1.46) 0.313/0.626 1.00 (0.91–1.10)
0.95 (0.60, 1.49) 0.822/1
1.27 (1.02–1.59) 0.034*/0.068
1.14 (0.89, 1.46) 0.313/0.626 0.993 (0.895–1.10)
0.95 (0.60, 1.49) 0.822/1
1.26 (1.00–1.59) 0.048*/0.096
OR# (95% CI)
1.09 (0.88, 1.36)
1.09 (0.88, 1.36)
OR# (95% CI)/
b (95% CI)*
OR, odds ratio; CI, confidence interval; Pcorr, P-value corrected for number of effective loci (see LD plot); #OR are given for the minor (T) allele; Asterisk (*) for case–control analyses (model (a)) and (b); the OR and its CI is given; for Fagerstr€om analyses (model
(c)); the b-estimator and its CI is given; RS, regular smokers; HS, heavy smokers; NS, never-smokers.
OR# (95% CI)
1.23 (1.04, 1.61)
1.36 (1.06, 1.74)
0.06 (0.01, 0.10)
OR# (95% CI) P/Pcorr
b: Models adjusted for age, stratified by sex
Males: recessive model RS vs. NS 1.03 (0.75, 1.42) 0.842/1
Females: recessive model RS vs. NS 1.30 (0.81, 2.10) 0.275/0.55
Males: recessive model HS vs. NS 0.96 (0.66, 1.41) 0.842/1
Females: recessive model HS vs. NS 1.22 (0.81, 1.85) 0.336/0.672
a: Models adjusted for age and sex
Recessive model RS vs. NS 1.12 (0.86, 1.46)
Recessive model HS vs. NS 1.08 (0.82, 1.43)
Additive model FTND
0.00 (0.12, 0.13)
OR# (95% CI)/
b (95% CI)*
TABLE II. Association of CHRM2 SNP rs324650 With Smoking-Related Phenotypes
Firstly, the association found may be driven by undetected
comorbid alcohol abuse/alcoholism or drug abuse. This possibility
seems least likely because of the structure of the three populationrepresentative samples.
Secondly, rs324650 may be associated with addiction in the
broader sense of an unspecific ‘‘addiction gene.’’ This notion would
not only be in line with previous findings regarding its association
with alcoholism and drug addiction [Wang et al., 2004; Luo et al.,
2005; Dick et al., 2007b], but also with evidence that CHRM2 may
be associated with certain personality traits such as externalizing
behavior which itself is associated with alcohol and drug
dependence/abuse [Dick et al., 2008]. However, in this context it
is interesting to note that in our study the T-allele of rs324650 was
the risk variant while in the original report on the COGA sample
[Wang et al., 2004] the T-allele was part of the T-T-T (rs1824024rs2061174-rs324650) haplotype that was under-transmitted to
subjects with alcoholism.
Thirdly, though not immediately apparent, genetic variations in
CHRM2 could be associated with nicotine addiction in a more
specific way. A direct modulation of the CHRM2 receptor by
nicotine seems unlikely based on the fundamental pharmacological
differences between nicotinic and muscarinic acetylcholine receptors. This is a striking difference between the association of the
variation in CHRM2 and smoking behavior/nicotine addiction
found in our study and numerous previous reports of the contribution of variations in genes coding for nicotinic acetylcholine
receptor subunits to the genetic liability of smoking. However,
CHRM2 is a G-protein coupled receptor that is most abundant in
the brain on presynaptic terminals of cholinergic neurons where it
regulates acetylcholine release [Mash et al., 1985; Zhou et al., 2001].
SNPs in the 50 UTR of CHRM2—like rs324650 which is located in
intron 5 of the 50 UTR—may alter gene expression and receptor
protein levels. It seems therefore plausible that a risk allele in
CHRM2 may shift the cholinergic tone in neuronal networks
involved in cognition or reward—networks that play a role in the
development of nicotine addiction—away from its optimum. This
situation could make an individual more susceptible to the addictive effects of nicotine and may therefore increase the risk for
nicotine dependence. For instance, there is increasing evidence
that subjects with impaired cognitive performance (like patients
suffering from schizophrenia or attention deficit hyperactivity
disorder) are more likely to be smokers than the general population
and that beneficial effects of nicotine on cognition are more
pronounced in these groups [Mobascher and Winterer, 2008].
In this context it is interesting to note, that variations in CHRM2
have also been implicated in the genetics of intelligence [Comings
et al., 2003; Gosso et al., 2006; Dick et al., 2007a]. Moreover it has
been reported that this gene is associated with event-related
electrophysiological brain responses to infrequent target stimuli
(EEG P300 oscillations), a cognitive endophenotype [Jones et al.,
The main purpose of this study was to investigate the role of
CHRM2, a gene that has previously been shown to be associated
with alcoholism and drug addiction, in the genetics of smoking
behavior and nicotine addiction. The significant association between rs324650 and smoking found in the pooled analysis was
mainly driven by one out of three subsamples with a higher
proportion of female participants than the other two samples.
Clearly, these results should be replicated in other, ideally still
larger studies which provide not only the adequate power to assess
potential variation of associations according to sex and possibly
other modifying factors but also feature improved gene coverage.
In summary, we found a common genetic variation (SNP
rs324650) in CHRM2, to be associated with smoking in a large
sample representative of the German population. This finding
provides further evidence that CHRM2 may be associated with
addiction in a broader sense, for instance by affecting certain
personality traits. However, more specific mechanisms that could
be related to the regulation of cholinergic neurotransmission may
also play a role.
Further research is needed to clarify how this and other SNPs in
CHRM2 affect gene function and gene expression and how these
genetic variants ultimately impact on the genetic liability for
nicotine dependence and other addictions.
We are indebted to the study participants and personnel contributing to all aspects of the studies involved. The present work was
funded by grants within the German Research Foundation (DFG)
national priority programme SPP1226 ‘‘Nicotine: Physiological
and Molecular Effect in the CNS’’ (Br1704/11-1, Da370/5-1,
Ga804/1-1, Ru744/4-1, Wi1316/6-1, Wi1316/7-1). KORA was
supported by the German Federal Ministry of Education and
Research (BMBF), the State of Bavaria and the National Genome
Research Network (NGFN). NCOOP was supported in parts by
BMBF (#01EB0113), DFG (Wi2997/1-1), and further BMBF
funding is gratefully acknowledged (Project Berlin Neuroimaging
Center, #01G00208). ESTHER was supported by the Baden
urttemberg Ministry of Research, Science and Arts. Genotyping
was carried out at the Genome Analysis Center (GAC) of the
Helmholtz Zentrum M€
unchen (KORA, ESTHER) and the Genetics
Research Centre, Munich/Germany (NCOOP) a joint venture
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acetylcholine, associations, nicotine, variant, genes, muscarinic, chrm2, receptov, addiction
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