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Human Molecular Genetics Advance Access originally published online on March 24, 2005
Human Molecular Genetics 2005 14(9):1211-1219; doi:10.1093/hmg/ddi132
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Ethnic- and gender-specific association of the nicotinic acetylcholine receptor {alpha}4 subunit gene (CHRNA4) with nicotine dependence

Ming D. Li1,*, Joke Beuten1, Jennie Z. Ma1, Thomas J. Payne2, Xiang-Yang Lou1, Veronica Garcia1, Aristeo S. Duenes1, Karen M. Crews2 and Robert C. Elston3

1Program in Genomics and Bioinformatics on Drug Addiction, Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA, 2The ACT Tobacco Center, University of Mississippi Medical Center, Jackson, MS, USA and 3Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

* To whom correspondence should be addressed at: MSC 7792, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA. Tel: +1 2105670830; Fax: +1 2105670853; Email: lim2{at}uthscsa.edu

Received January 24, 2005; Accepted March 13, 2005


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
We tested six single nucleotide polymorphisms (SNPs) in the {alpha}4 subunit gene (CHRNA4) and four SNPs in the ß2 subunit gene (CHRNB2) of nicotinic acetylcholine receptors (nAChRs) for association with nicotine dependence (ND), which was assessed by smoking quantity (SQ), the heaviness of smoking index (HSI) and the Fagerström test for ND (FTND) in 2037 subjects from 602 nuclear families of either European-American (EA) or African-American (AA) ancestry. Analysis of the six SNPs within CHRNA4 demonstrated that in the EA sample SNPs rs2273504 and rs1044396 are significantly associated with the adjusted SQ and FTND score, respectively. In the AA samples, SNPs rs3787137 and rs2236196 are each significantly associated with at least two adjusted ND measures. Association of rs2236196 with the adjusted HSI and FTND scores in the AA samples remained significant after correction for multiple testing. Furthermore, analysis revealed gender- and ethnic-specific associations for several SNPs with ND measures in both ethnic samples; however, only the association of SNP rs2236196 with the three adjusted ND measures remained significant after correcting for multiple testing in the AA female samples. Haplotype analysis of rs2273505–rs2273504–rs2236196 showed significant association after Bonferroni correction of a C–G–G haplotype (53.4%) with three adjusted ND measures in samples from the AA females. A similar analysis for the four SNPs within CHRNB2 did not reveal significant association with the three ND measures. In summary, our findings provide convincing evidence for the involvement of the nAChR {alpha}4 subunit, but not of the nAChR ß2 subunit, in nicotine addiction.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Despite the well-known adverse health effects, ~1.2 billion people worldwide smoke tobacco daily (1Go). In 2000, ~66 million Americans used tobacco products (2Go), and there were an estimated 435 000 smoking-related deaths, representing 18.1% of the total deaths in the USA (3Go). Cigarette smoking is both psychologically and physiologically addictive. Numerous studies led to the conclusion that nicotine is the primary substance responsible for continued tobacco use and addiction (1Go,2Go,4Go). Like many other drug dependencies, nicotine addiction is a complex behavior with both genetic and environmental determinants. Over the past few decades, many large-sample twin studies have yielded results consistent with an overall conclusion that genetics contribute to the risk of becoming a regular smoker (5Go). Meta-analysis of its polygenic heritability based on 17 published twin studies indicated that the weighted mean heritability for nicotine dependence (ND) is 0.59 in male smokers and 0.46 in female smokers (6Go). A familial aggregation study among siblings of nuclear families also suggested that genetics plays an important role in determining the vulnerability to ND (7Go). Furthermore, biochemical and pharmacological studies using inbred mouse strains showed that genetic factors contribute considerably to the different behavioral and physiological effects of nicotine among different inbred strains (8Go–10Go).

The behavioral and physiological effects of nicotine are mediated largely by neuronal nicotinic acetylcholine receptors (nAChRs). Molecular analyses have identified expression of nine {alpha} subunits ({alpha}2–{alpha}10) and three ß subunits (ß2–ß4) of nAChRs in the central nervous system (11Go–14Go). Immunological studies indicated that receptors consisting of {alpha}4 and ß2 subunits make up the majority of high-affinity nicotine binding sites in the brain (15Go) and that the genes for both subunits are upregulated under chronic nicotine exposure (16Go,17Go). Furthermore, it was recently reported that activation of the {alpha}4 subunit of nAChRs is sufficient for nicotine-induced reward, tolerance and sensitization (18Go). Therefore, the genes (CHRNA4 and CHRNB2) encoding these two subunits represent logical candidates for an association study with ND measures. The CHRNA4 gene, which was mapped to chromosome 20q13.2–13.3 (19Go), is ~17 kb long and contains six exons (20Go). The CHRNB2 gene was mapped to chromosome 1q21.3 (21Go). Its sequence is ~12 kb long and contains six exons (22Go). Genetic polymorphisms within CHRNA4 and CHRNB2 have been investigated for potential association with several psychiatric and brain disorders including nocturnal frontal lobe epilepsy (23Go–26Go), attention-deficient hyperactivity disorder (27Go,28Go), Alzheimer's disease (29Go,30Go) and febrile convulsions (31Go).

Association studies of CHRNA4 and CHRNB2 with respect to ND or other smoking-related behavior have been limited. To date, three independent studies have been reported on CHRNB2, none of which demonstrated a significant association with ND for individual single nucleotide polymorphisms (SNPs) or haplotypes (22Go,32Go,33Go). For CHRNA4, only one study has been reported (33Go), which showed a significant association of the gene with ND in Chinese men. Considering the evidence for ethnic differences in nicotine metabolism (34Go–36Go) and gender differences, both in the response to smoking (37Go) and on the genetic influences on ND (6Go), we were interested in whether a significant association of ND with CHRNA4 or CHRNB2 can be detected in other ethnic groups. Thus, the primary objectives of this study were to determine whether a significant association exists between ND and allelic variants of CHRNA4 or CHRNB2 in European-American (EA) and African-American (AA) smokers, and, if so, whether the association is different between male and female smokers.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Nicotinic acetylcholine receptor {alpha}4 subunit gene (CHRNA4)
Individual SNP analysis using the PBAT–GEE program revealed no significant association of CHRNA4 variants with three ND measures in the pooled samples. To determine whether there exists significant interaction between each SNP and ethnicity or gender in our samples, we used PBAT interaction statistics to determine the presence of heterogeneity. Several SNPs within CHRNA4 had a significant or suggestively significant ethnicity-specific association with the three ND measures (Table 1). Moreover, compared with the allele frequencies of SNPs within CHRNA4 in the AA and EA samples, we noticed differences for several SNPs when we calculated the allele frequency by directly counting the numbers of each allele from the progenitors of our samples (Table 2), suggesting an ethnic-specific allele distribution. We therefore analyzed both the ethnic groups, as well as males and females within each ethnic group, separately to eliminate potential heterogeneity in the pooled samples.


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  		Table 1. P-values for gene–ethnicity and gene–gender interactions of individual CHRNA4 SNPs with three ND measures in the pooled, AA and EA samples
 

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  		Table 2. Observed versus reported allele frequency of six SNPs within CHRNA4 and of four SNPs within CHRNB2
 
Under different genetic models, we found significant associations for SNPs rs3787137 and rs2236196 in the AA samples and rs2273504 and rs1044396 in the EA samples with at least one of the adjusted ND measures (Table 3). However, only the association of SNP rs2236196 with adjusted heaviness of smoking index (HSI) and Fagerström test for ND (FTND) scores in the AA samples remained significant after correction for multiple testing based on the SNP spectral decomposition approach (38Go).


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  		Table 3. P-values for association of individual CHRNA4 SNPs with three ND measures in AA and EA samples
 
Next, we looked for a possible gender- or ethnic-specific association of CHRNA4 with the three ND measures by analyzing the six SNPs in males and females separately for each ethnic-specific sample (Table 4). For the male samples, SNP rs3787137 showed a significant association with age-adjusted smoking quantity (SQ) (P=0.03) in the EA group, but no significant association was identified in the AA group. In contrast, SNPs rs2273504 and rs2236196 or SNP rs2273504 were significantly associated with at least one adjusted ND measure in the AA or EA female samples, respectively. After correcting for multiple testing, only the association of rs2236196 with all three adjusted ND measures remained significant in the AA female samples.


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  		Table 4. P-values for association of individual CHRNA4 SNPs with three ND measures in male and female samples
 
The pair-wise |D'| values for the six SNPs within CHRNA4 were calculated using the Haploview program (39Go) and were generally high (range 0.50–0.92) in the pooled as well as each ethnic-specific sample group (Fig. 1). The high linkage disequilibrium (LD) between the SNPs within CHRNA4 suggests that these six SNPs are likely part of a single haplotype block in the pooled and AA samples. In the EA samples, however, SNPs rs2236196 and rs3787137 could not be assigned into a haplotype block with the other four SNPs according to the criteria of Gabriel et al. (40Go).



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  		Figure 1. LD structure of six SNPs within CHRNA4 in the pooled (top) and AA (bottom left) and EA (bottom right) samples. Haplotype block in each sample group was defined according to the criteria of Grabiel et al. (40Go).

 
Haplotype-based association analysis was performed for different combinations of SNPs within CHRNA4, including SNPs that were significant for single SNP-based analysis as well as for non-significant SNPs. In the AA samples, we found a major haplotype C–G–A (with a frequency of 53.4%) showing a significant inverse association with the adjusted SQ (Z=–2.22, P=0.025), HSI score (Z=–1.86, P=0.05) and FTND score (Z=–2.04, P=0.043). However, these associations were no longer significant after Bonferroni correction. We found no major haplotype in the EA samples that showed a significant association with ND.

Similar to the analytical strategy used for individual SNPs, we next investigated the association of major haplotypes (defined as >5.0%) with ND measures in males and females separately in both ethnic sample groups. In the AA females, a common haplotype C–G–G formed by rs2273505, rs2273504 and rs2236196 (in the direction 5'->3') with a frequency of 53.4% showed a significant inverse association with all three adjusted ND measures. These associations remained significant after Bonferroni correction for testing of three major haplotypes (Table 5). Additionally, we found that two other major haplotypes C–G–A (18.2%) and C–A–A (14.2%) formed by the three SNPs showed significant association with at least one adjusted ND measure in the AA female samples. But none of these associations remained significant after correction for multiple testing. As for the samples of AA males and EA males and females, we found that no major haplotype formed by these three SNP combinations showed significant association with any ND measures used in the present study (Table 5).


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  		Table 5. Z and permutation P-values for association of major CHRNA4 haplotypes for rs2273505–rs2273504–rs2236196 with ND phenotypes in males and females of each sample group
 
Nicotinic acetylcholine receptor ß2 subunit gene (CHRNB2)
We performed a similar association analysis for both single and multiple SNPs within CHRNB2. The pair-wise |D'| values for the four SNPs were high with a complete LD between rs2072658 and rs3811450 in the pooled samples and both ethnic sample groups. Overall, the pair-wise |D'| values were higher in the EA samples (range 0.64–1.00) than in the AA samples (range 0.08–1.00), suggesting that the four SNPs are likely part of one haplotype block.

Of the four SNPs examined within CHRNB2, SNP rs2072660 showed a significant association (P=0.04) in the pooled sample with age-, sex- and ethnicity-adjusted HSI and FTND scores under both the dominant and recessive models. Subsequent association analysis revealed no significant association with the three adjusted ND measures in the male or female samples of either ethnic group. Haplotype analyses using all possible SNP combinations yielded non-significant results for the pooled and the ethnic- and gender-specific samples (data not shown).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
In this study, we confirmed a potential biological role for CHRNA4 in susceptibility to ND. Because we have a relatively large number of samples drawn from two ethnic groups, including male and female smokers, we were able to perform association analysis on several subgroups, thereby demonstrating important ethnic and gender differences within the broader population. For CHRNA4, this was true at the single- and multiple-SNP levels in the pooled and ethnic-specific samples. Furthermore, the ethnic-specific association results became more pronounced when we examined gender-specific effects. In contrast, we found no evidence for association of CHRNB2 with any of the three adjusted ND measures in our dataset. Thus, we conclude that the genetic influence of CHRNB2 on ND is relatively small, if there is any effect at all. This finding is consistent with the results of three other independent studies (22Go,32Go,33Go) that reported no significant association between polymorphisms of CHRNB2 and ND.

Our findings indicate an important role for the CHRNA4 gene in ND; however, the exact nature of its effect(s) remains to be determined. Nicotine exerts its pharmacological and physiological effects by binding to nAChRs (41Go). Biochemical and physiological studies have shown that {alpha}4ß2-nAChRs make up the majority of the high-affinity nicotine-binding sites in the brain (15Go,41Go). Furthermore, knockout mice for the {alpha}4- or ß2-subunit of nAChRs show no high-affinity binding sites in their brains and fail to self-administer nicotine or show the usual nicotine-induced dopamine release in the ventral tegmental area (42Go–45Go). Together, these studies provide direct evidence for the role of {alpha}4ß2-nAChRs in nicotine reinforcement in mice.

However, experimental evidence for the association of CHRNA4 with ND in humans has been very limited. Recently, Feng et al. (33Go) reported a significant association of CHRNA4 with ND in Chinese men, but the number of female smokers they studied was too small to test for this association. Our association analysis using six SNPs within CHRNA4 in a total of 2037 males and females of EA or AA ancestry confirmed a significant association of the gene with ND in each ethnic group. At the individual SNP level, we found that SNPs rs3787137 and rs2236196 in the AA samples and SNPs rs2273504 and rs1044396 in the EA samples were significantly associated with at least one of the three adjusted ND measures. On the other hand, we only identified a common haplotype (C–G–G; 53.4%) for SNPs rs2273505–rs2273504–rs2236196 in the AA female samples showing a significant protective effect against ND even after Bonferroni correction. Moreover, we found two other haplotypes (C–A–A, 14.2%; C–G–A, 18.2%) in the AA female samples showing a significant positive association with ND. However, only the association for the haplotype C–G–G remained significant with ND after Bonferroni correction. To our knowledge, this report represents the first study confirming the genetic role of the CHRNA4 gene in ND in AA and EA populations.

When compared with the study reported by Feng et al. (33Go), three of the six SNPs used in their study were also included in the present study, whereas other three SNPs were excluded because they were either less informative (i.e. with a very low frequency) or too close to other SNPs under investigation in the study. Of the three SNPs (i.e. rs2273504, rs1044396, rs2236196) used in both studies, rs2273504 showed no significant association in both studies, whereas the other two SNPs showed different association results. For example, SNP rs1044396 showed a significant association with ND in Chinese men sample groups but showed no association in our samples. In contrast, SNP rs2236196 showed significant association in our AA samples but showed no association in Chinese and our EA sample group. Furthermore, comparing the protective haplotypes against ND identified in these two studies, two of the three SNPs contained in our haplotype (C–G–G) were also included in the haplotype (G–C–T–A–T–A; the italicized nucleotides indicate the common alleles between the two haplotypes) reported by Feng et al. (33Go), in which the G allele at rs2274504 is the same, but the allele at rs2236196 differs between the two identified haplotypes. Such a discrepancy between these two studies is likely due to different ethnic samples used in the two studies. However, although different SNPs and haplotypes were found to be associated with ND between these two studies, we all agree on the involvement of CHRNA4 in the etiology of ND.

This study has several strengths. First, the number of subjects is significantly larger than in most reported studies on ND (reviewed in 46Go,47Go). Relatively small sample size is commonly cited as a reason for failure to replicate reported associations across studies (48Go,49Go). Secondly, our large number of subjects encompasses both EA and AA subgroups, again representing an improvement over most previous work. Although it remains to be proved (50Go), evidence suggests that there are genetic differences among ethnic groups (51Go,52Go). Recruiting a relatively large number of subjects from each subgroup reduces the potential impact of population heterogeneity on the final genetic association results with regard to lifestyle, social and cultural norms and socioeconomic status. This, in turn, leads to greater power to detect potential associations of genetic variants with ND. Thirdly, we adjusted all three ND measures for age, gender and ethnicity in the pooled samples and for age and gender in each ethnic-specific sample. This reduces the influence of confounding factors on the association results. Such adjustments are essential, as both animal and human studies of nicotine administration or smoking behavior have documented the significant roles of these factors in ND (6Go,36Go,53Go).

In conclusion, the present study is the first to provide evidence for the association of allelic variants of the nAChR {alpha}4 subunit gene with ND in AA and EA populations. Furthermore, we identified a common haplotype (C–G–G; 53.4%) in the AA females that showed a protective effect against ND. In addition, we found that another haplotype (C–A–A; 14.2%) formed by the same SNPs showed a positive association with two of three adjusted ND measures in the AA females. These findings support the hypothesis regarding the existence of ethnic- and gender-specificity in the association of CHRNA4 with ND. Finally, although CHRNA4 is highly involved in the etiology of ND in human smokers, especially in the AA samples, there appears to be much less, if any, involvement for CHRNB2.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Subjects and smoking phenotypes
The subjects are of EA or AA ancestry recruited in the USA from the states of Tennessee, Mississippi and Arkansas during 1999–2004. Extensive clinical data are available on each participant, including demographics (e.g. sex, age, race, relationships, weight, height, years of education and marital status), medical history, smoking history and current behavior, ND and personality traits. Of the families recruited, 75.7% (74.2% of EAs, 76.7% of AAs) have at least two siblings whose FTND score was ≥5. All participants provided informed consent. The study protocol and forms/procedures have been approved by the participating Institutional Review Boards.

Three measures were used to assess ND: the SQ, the HSI and the FTND (54Go). Our primary reasons for examining all three measures were: (a) the current lack of consensus as to the best approach to assess ND as a phenotype and (b) to permit maximum backward compatibility with previous studies of ND. SQ provides a simple, quantified index of amount of consumption, i.e. number of cigarettes smoked per day. In contrast, the HSI (0–6 point scale) includes one item addressing quantity (using a 0–3 point compressed format) plus another item assessing smoking urgency, i.e. ‘How soon after you wake up do you smoke your first cigarette?’ The FTND (0–10 point scale) includes the HSI plus other indicators of behavioral propensity to smoke in various situations. The FTND has been accepted as a standard in both clinical and research settings (55Go,56Go), although recent evidence suggests ND is a broader and more complex construct than previously considered (55Go,57Go). Thus, although it is premature to endorse other measures that have received interesting but limited support, we believe it is prudent to examine the three measures mentioned earlier.

Of the 2037 participants, the average age was 39.4±14.4 (SD) for the AA group and 40.5±15.5 for the EA group. The average nuclear family size was 3.14±0.75 for AAs and 3.17±0.69 for EAs. The average HSI and FTND scores were 3.7±1.4 and 6.26±2.15 for AAs and 3.9±1.4 and 6.33±2.22 for EAs, respectively. The average number of cigarettes smoked per day was 19.4±13.3 for AA participants and 19.5±13.4 for EA participants. A detailed description of demographical and clinical characteristics is presented in Table 6.


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  		Table 6. Clinical characteristics for pooled, AA and EA samples
 
DNA samples, SNP selection and genotyping
DNA was extracted from peripheral blood samples using kits from Qiagen Inc. (Valencia, CA, USA). The 10 SNPs for CHRNA4 and CHRNB2 were selected from reported work (33Go) or from the NCBI SNP database to obtain as much uniform coverage of these genes as possible. Data on these SNPs, including location within the gene, chromosomal position and primer/probe sequences, are summarized in Table 7.


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  		Table 7. Position, nucleotide variation, minor allele frequency and primer/probe sequences of six SNPs within CHRNA4 and of four SNPs within CHRNB2
 
All SNPs were genotyped using the TaqMan assay in a 384-well microplate format (Applied Biosystems Inc., Foster City, CA, USA). Briefly, 15 ng of DNA was amplified in a total volume of 7 µl containing an MGB probe and 2.5 µl of TaqMan universal PCR master mix. Amplification reaction conditions were 2 min at 50°C and 10 min at 95°C, followed by 40 cycles of 95°C for 25 s and 60°C for 1 min. Allelic discrimination analysis was performed on the Prism 9700 Sequence Detection System (Applied Biosystems Inc.). To ensure the quality of the genotyping, SNP-specific control samples were added to each 384-well reaction plate.

Statistical analysis
Genotyping consistency for Mendelian inheritance was determined using the PedCheck program (58Go). A total of 48 genotyping inconsistencies (30 in the AA samples and 18 in the EA samples) were identified out of ~21 000 assays for 10 SNPs across all DNA samples and were excluded from all subsequent analyses. Pair-wise LD between all SNP markers was assessed using the Haploview program (39Go). Initially, the pooled samples were used to test for possible ethnic- or gender-specific interactions by the family based association test (FBAT) interaction statistics adjusting for age, ethnicity and gender (http://www.biostat.harvard.edu/~clange/pbat3/default.htm). Associations between individual SNPs and smoking phenotypes were determined by the PBAT program using generalized estimating equations (59Go), which constitutes a multivariate extension of the (FBAT) (60Go,61Go). Associations between each ND measure and haplotypes containing different combinations of SNPs were examined using the haplotype FBAT program (60Go,61Go), with the option of computing P-values of the Z-statistics with the use of Monte Carlo sampling under the null distribution of no linkage and no association.

Three genetic models (i.e. additive, dominant and recessive) were tested for individual and multi-locus SNPs (i.e. haplotype analysis) with sex, age and ethnicity as covariates in the pooled sample set and with sex and age as covariates in the AA and EA samples. Three ND measures were individually analyzed: SQ, HSI and FTND scores. All associations found to be significant were corrected for multiple testing according to the SNP spectral decomposition (SNPSpD) approach (38Go) for individual SNP analysis, and using Bonferroni correction by dividing the significance level by the number of major haplotypes (frequency >5.0%) for haplotype-based association analysis.


   ACKNOWLEDGEMENTS
 
We acknowledge the invaluable contributions of personal information and blood samples by all participants in the study. Clinical and research staff at The University of Texas Health Science Center at San Antonio, the University of Tennessee Health Science Center and the ACT Center of the University of Mississippi Medical Center have been involved in clinical recruitment and genetic analysis, and we thank them for their dedicated work. This project is funded by a grant from the National Institute on Drug Abuse to M.D.L. (DA-12844), grants from the National Center for Research Resources (RR03655) and the National Institute of General Medical Sciences (GM28356) to R.C.E. and general support for T.J.P. and K.M.C. via a grant from The Partnership for a Healthy Mississippi to the University of Mississippi School of Dentistry.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

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