Association of a variant in the muscarinic acetylcholine receptor 2 gene (CHRM2) with nicotine addiction.код для вставкиСкачать
BRIEF RESEARCH COMMUNICATION 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* 1 Department of Psychiatry, Neuropsychiatric Research Laboratory, Heinrich-Heine University, Duesseldorf, Germany Institute of Neurosciences and Biophysics, Helmholtz Research Center, Juelich, Germany 2 3 Department of Psychiatry, Division of Molecular and Clinical Neurobiology, Ludwig-Maximilians-University, Munich, Germany 4 Department of Psychiatry, Ludwig-Maximilians-University, Munich, Germany Helmholtz Zentrum M€unchen, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Neuherberg, Germany 5 6 Department of Medical Genetics, Molecular and Clinical Pharmacology, Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria 7 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany Department of Psychiatry, University of Mainz, Mainz, Germany 8 9 Department of Psychiatry, Charite University Medicine Berlin, Campus Mitte, Berlin, Germany 10 Genetics Research Centre GmbH, Munich, Germany Institute for Human Genetics, University of Hamburg Medical Center Eppendorf, Hamburg, Germany 11 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: firstname.lastname@example.org Published online 30 July 2009 in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/ajmg.b.31011 2009 Wiley-Liss, Inc. 684 MOBASCHER ET AL. 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 Fagerstr€ 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 abuse. 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. 685 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 Germany. 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 Fagerstr€ 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. , Holle et al. , or Wichmann et al.  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.  and Twardella et al.  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 769 686 AMERICAN JOURNAL OF MEDICAL GENETICS PART B 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 (%) KORA 1,412 936 476 46.0 (25–84) 308 3.18 (0–10) 3.34 (0–10) 3.04 (0–10) NCOOP 1,855 875 980 45.7 (18–79) 751 4.75 (0–10) 4.82 (0–10) 4.67 (0–10) ESTHER 2,294 1,794 500 60.4 (48–75) 0 — — — 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) a 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.  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 www.interscience.wiley.com.] 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. KORA 0.40/0.80 0.57/1 0.97/1 P/Pcorr OR# (95% CI)/ b (95% CI)* NCOOP P/Pcorr 0.02/0.04 0.015/0.03 0.019/0.038 0.167/0.334 0.019*/0.038* 0.242/0.484 0.017*/0.034* 0.80 (0.57, 1.10) 1.44 (1.06, 1.96) 0.81 (0.57, 1.15) 1.55 (1.08, 2.22) P/Pcorr 0.43/0.86 0.43/0.86 P/Pcorr 0.021/0.042 0.029/0.058 0.028/0.056 P/Pcorr OR# (95% CI) P/Pcorr 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) 0.995/1 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) 0.896/1 0.95 (0.60, 1.49) 0.822/1 1.26 (1.00–1.59) 0.048*/0.096 OR# (95% CI) ESTHER 1.09 (0.88, 1.36) 1.09 (0.88, 1.36) OR# (95% CI)/ b (95% CI)* ESTHER 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. P/Pcorr OR# (95% CI) NCOOP 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)* KORA TABLE II. Association of CHRM2 SNP rs324650 With Smoking-Related Phenotypes MOBASCHER ET AL. 687 688 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., 2004]. 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 AMERICAN JOURNAL OF MEDICAL GENETICS PART B 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. ACKNOWLEDGMENTS 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 W€ 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 between LMU and GlaxoSmithKline Germany. REFERENCES Benowitz NL. 2008. 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