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Human Molecular Genetics Advance Access originally published online on November 30, 2007
Human Molecular Genetics 2008 17(5):724-734; doi:10.1093/hmg/ddm344
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

The neuronal nicotinic receptor subunit genes (CHRNA6 and CHRNB3) are associated with subjective responses to tobacco

Joanna S. Zeiger1,{dagger}, Brett C. Haberstick1,4,{dagger}, Isabel Schlaepfer1,2, Allan C. Collins1,3, Robin P. Corley1, Thomas J. Crowley4, John K. Hewitt1,3, Christian J. Hopfer4, Jeffrey Lessem1, Matthew B. McQueen1,3, Soo Hyun Rhee1,3 and Marissa A. Ehringer1,2,*

1 Institute for Behavioral Genetics 2 Department of Integrative Physiology and 3 Department of Psychology, University of Colorado, Boulder, CO, USA and 4 Division of Substance Dependence, Department of Psychiatry, University of Colorado School of Medicine, Denver, CO, USA

* To whom correspondence should be addressed at: Institute for Behavioral Genetics 447 UCB, University of Colorado, Boulder, CO 80309, USA. Tel: +1 303 492 1464; Fax: +1 303 492 8063; Email: marissa.ehringer{at}colorado.edu

Received October 9, 2007; Accepted November 22, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Neuronal nicotinic acetylcholine receptors have been implicated in various measures of nicotine dependence. In this paper, we present findings from an exploratory study of single nucleotide polymorphisms (SNPs) in the CHRNB3 and CHRNA6 genes with tobacco and alcohol phenotypes, including frequency of use and three subjective response factors occurring shortly after initiation of use. Subjects were 1056 ethnically diverse adolescents ascertained from clinical and community settings. The most significant associations were found between two CHRNB3 SNPs (rs4950 and rs13280604) and the three subjective response factors to initial tobacco use. These findings were replicated in a separate community sample of 1524 families participating in the National Longitudinal Study of Adolescent Health. Both CHRNB3 SNPs were found to be associated with similar measures of subjective response to tobacco. These results indicate that early subjective response to nicotine may be a valuable endophenotype for genetic studies aimed at uncovering genes contributing to nicotine use and addiction.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Tobacco and alcohol use among adolescents and young adults is a pervasive public health issue, with serious economic, medical and social consequences. Tobacco use is responsible for ~20% of preventable deaths in the United States (Centers for Disease Control, 2002) and alcohol is the leading cause of injury related deaths in individuals under 21 (National Institute of Alcohol Abuse and Alcoholism, 2005).

Genetics of alcohol and tobacco use
Alcohol and tobacco use, abuse and dependence are complex traits caused by a combination of genetic and environmental factors (1–4). Both genetic and shared environmental influences contributed to smoking status in a cohort of Australian twins, and monozygotic twin correlations for age of onset of smoking were higher than dizygotic twin correlations (5). Heritability estimates of 80% for regular tobacco use and 60% for nicotine dependence (6) and 64% for alcohol dependence (5) have been obtained from twin studies. In addition, it has been shown that alcohol dependence and smoking were transmitted within families and that common and drug-specific influences were factors in this transmission, suggesting that there may be a common factor in nicotine and alcohol dependence (7). Likewise, twin studies suggest evidence for common genetic influences on the use and problem use of alcohol and tobacco (8–13).

Nicotinic receptors
Neuronal nicotinic acetylcholine receptors (nAChR) are the principle target of nicotine. The genes encoding nAChR mRNAs have been categorized into two subfamilies composed of nine alpha ({alpha}2–{alpha}10) and three beta (β2–β4) subunits. In combination, both subunit types contribute to unique pharmacological and physiological responses involved in drug exposure, neurotransmission, and the integration of sensory information. There are two main classes of nAChR subtypes: those that are sensitive to {alpha}Bungarotoxin ({alpha}Btx), consisting of the {alpha}7–{alpha}10 subunits, and those that are insensitive to {alpha}Btx, made up of {alpha}2–6 and β2–4. The receptor binding site is located between two identical subunits in the homomeric {alpha}7 receptors, or between {alpha} and β subunit in the heteromeric receptors. Different combinations of {alpha} and β subunits in heteromeric receptors contribute to differences in the pharmacological properties of the receptor binding site. A variety of different combinations of {alpha} and β subunits have been observed, with differing stoichiometries (i.e. two {alpha} and three β or three {alpha} and two β), which have been shown to be differentially expressed in different brain regions under a variety of conditions (such as low temperature or exposure to nicotine). The {alpha}5 and β3 subunits carry neither type of binding site and are thought to contribute to receptor targeting, localization and receptor permeability (14).

Some of the known nAChRs are expressed on cell bodies, but most are expressed on nerve terminals where they modulate the release of neurotransmitters including glutamate, gamma-amino butyric acid (GABA), serotonin, norepinephrine and dopamine (15). Identifying the subunit compositions of the nAChRs that influence dopamine release has been of marked interest given that dopamine seems to play an important role in modulating the reinforcing effects of virtually all drugs of abuse, including nicotine and alcohol (16,17). Recent studies using {alpha}4, {alpha}5, β2 and β3 null mutant mice coupled with immunological (18) and functional (19) assays indicate that dopaminergic nerve terminals may express as many as four distinct nAChR subtypes: {alpha}4β2, {alpha}4{alpha}5β2, {alpha}4{alpha}6β2β3 and {alpha}6β2β3.

Several studies have examined the nAChR genes and smoking and nicotine dependence. SNPs in the CHRNA4 gene have been associated with tobacco related behaviors (20–22). Most studies of the CHRNB2 have not found evidence for an association (21–24), but two recent reports have shown a possible role for this gene in age of initiation of smoking in women (25), and in smoking and alcohol subjective response phenotypes (20). Other individual SNPs in several nAChR genes have been explored for possible interaction affects with a variety of background factors, psychological characteristics and neurocognitive measures, but these findings must be considered exploratory because they were identified after multiple tests and have not been replicated (25,26). The CHRNB3 and CHRNA5 genes have emerged as strong candidates for nicotine dependence in two recent reports of a genome wide association and a high-throughput candidate gene survey (27,28).

In any genetic study, determining an appropriate phenotype is crucial to the analyses, but it can be difficult to determine in advance which phenotype might be more likely to be associated with a particular gene or specific SNP. Here we have taken the approach of using one sample of subjects to explore multiple alcohol and tobacco phenotypes for possible associations with SNPs in the CHRNB3 and CHRNA6 genes, then using a second sample to replicate our findings. In the exploratory study, we tested conventional phenotypes of alcohol and tobacco behaviors (i.e. age of initiation, number of days used in past 6 months, typical pattern of use, peak use, frequency of use, sensitivity and dependence/abuse) and antecedent phenotypes (subjective effects) for associations with four SNPs in the CHRNB3 gene and three SNPs in the CHRNA6 in a population of ethnically diverse young adults. Based on findings in this first sample, we conducted a replication study of specific SNPs and a single phenotype using a family-based, independently ascertained community sample.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Demographics (CADD)
One thousand and fifty six individuals met the inclusion criteria for this study. Seventy four percent are Caucasian (n = 760), 16% are Hispanic (n = 169), 4% are African American (n = 43), and the remainder are ‘other’ (n = 58). The mean age of the subjects was 18.21 ± 1.50 years and 58.1% of the subjects were male.

Demographics (Add Health)
One thousand five hundred and twenty four families (1445 sib-pairs and 79 sib-trios) were included with a mean age of 22.4 ± 1.7 years and 48% were male. Sixty nine percent of the families were Caucasian, 20% were African American, 7.7% were Asian, 1.7% were Native American and the remaining 1.9% were not identified.

Single marker analyses (CADD)
Allele frequencies for SNPs genotyped in the Colorado Center on Antisocial Drug Dependence (CADD) are presented in Table 1. All SNPs were in Hardy–Weinberg equilibrium across all ethnic groups. The three CHRNA6 SNPs had significantly different allele frequencies compared to Caucasians: rs2304297 ({chi}2 = 15.8, df = 2, P < 0.001), rs892413 ({chi}2 = 20.4, df = 2, P < 0.0001), rs1072003 ({chi}2 = 22.2, df = 2, P < 0.0001). There were no ethnic differences in allele frequencies for any of the CHRNB3 SNPs.


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  		Table 1. Summary of SNPs genotyped

 
CHRNB3
Table 2 summarizes the association results in CHRNB3 for all of the tobacco phenotypes for the entire sample adjusted for race. There were no significant associations with any of the alcohol phenotypes (data not shown). Several SNPs were associated with the three subjective factor scores and more detailed analysis of these is presented in Table 3.


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  		Table 2. SNP results for tobacco phenotypes in the CADD sample

 


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  		Table 3. Association test results for individual SNPs (tobacco phenotypes)

 
Table 3 shows the statistically significant association results for individual SNPs and tobacco subjective responses. The sixth column presents the β estimates from the linear regression analysis in WHAP. Evidence of association was found between the CHRNB3 SNPs and the three subjective factors for tobacco. All four SNPs (rs4950, rs13280604, hCV25772398 and rs4953) were associated with the adverse factor for tobacco in the full sample, the most highly significant association being with hCV25772398. All four SNPs (rs4950, rs1328064, hCV25772398 and rs4953) were also associated with the positive factor for tobacco, with rs4950 and rs13280604 being the most significant across the full sample and two ethnic subgroups. An association was observed between rs4950, rs13280604 and hCV25772398 and the tobacco negative physical factor, again with rs4950 and rs13280604 being the most significant. Several of these associations were also present in the Caucasian and Hispanic subgroups (Table 3).

CHRNA6
Table 4 shows the results for all of the tobacco measures in CHRNA6 for the entire sample adjusted for race. The tobacco positive factor showed an association with rs2304297 in the full sample (P = 0.003) (Table 4). This association was also seen in the Caucasian group (P = 0.05) but not in the Hispanic sample (Table 3, bottom). There were no significant associations with any of the alcohol measures (data not shown).


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  		Table 4. SNP results for tobacco phenotypes in the CADD sample

 
Haplotype structure in CADD
The r2 values for the four SNPs within CHRNB3 and three SNPs within CHRNA6 were calculated using the Haploview program (29). Figure 1 shows the block structure for both CHRNB3 and CHRNA6 for the full sample, Caucasians and Hispanics, using the Gabriel 95% confidence interval and a minor allele frequency (MAF) of 0.04. In the full sample, a haplotype block was assigned to all four of the CHRNB3 SNPs, with r2 estimates ranging from 0.95 to 1.0. A second block was assigned to two of the CHRNA6 SNPs, with r2 estimates ranging from 0.91 to 1.0. These block structures are similar to those available at HapMap for Caucasians. In CHRNB3, there is a large block across the 5' end of the gene, but no LD structure is given for the 3' end, likely because most of the SNPs in that region are quite rare so it may not be able to be calculated with reliability. For CHRNA6, there are a few different putative blocks depending on the algorithm used, as seen in Figure 1.


Figure 1
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  		Figure 1. Haploview r2 calculations and predicted block structures between CHRNB3 and CHRNA6 SNPs in the full sample, the Caucasian sample, and the Hispanic sample. Color scheme: black (r2 = 1); shades of gray (0 < r2 < 1) and white (r2 = 0).

 
Haplotype analysis in CADD
CHRNB3
Haplotypes with a frequency less than 1% were excluded. The β-coefficients for the quantitative traits are linear regression estimates, while for binary traits, an odds ratio can be obtained by taking the exponent of the β-coefficients. The omnibus test compares each estimated haplotype while controlling all others.

Table 5 shows the omnibus results of the separate haplotype analysis of the four SNPs in CHRNB3 and the three SNPs in CHRNA6. In the full sample, the omnibus test was significant for all three factors and tobacco: adverse (P = 0.02), negative physical (P = 0.003) and positive (P < 0.01). The Caucasian subsample was also significant for the tobacco negative physical factor (P = 0.01) and tobacco positive factor (P = 0.03). In the Hispanic subsample, the tobacco positive factor showed a trend toward significance (P = 0.08). There was also evidence for association between CHRNB3 and co-occurrence of alcohol and tobacco dependence/abuse in males (P = 0.046).


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  		Table 5. Haplotype results for CHRNB3 and CHRNA6 tobacco phenotypes

 
Haplotype specific analyses were performed using the –hs option in WHAP, which tests each specific haplotype individually against all other haplotypes. β-Coefficients and significance levels for each haplotype were derived from the –hs option and are shown in Table 5. Significant β-coefficients are indicated with *P < 0.05, **P < 0.01 and ***P < 0.001.

Haplotype analysis of rs4950–rs13280604–hCV25772398–rs4953 showed three major haplotypes in the full sample, Caucasian sample and Hispanic sample (Table 5). There was an association between the most frequent haplotype (74.6%), A–A–T–G, and the tobacco adverse factor in the full sample (β = –0.154, P = 0.04). The G–G–C–C haplotype (3.8%) was associated with higher factor scores for the tobacco adverse factor (β = 0.364, P= 0.02). Estimates in the Caucasian sample were in the same direction and similar magnitude as the full sample, but did not reach statistical significance. The A–A–T–G haplotype was associated with the tobacco negative physical factor in both the full sample (β = –0.225, P < 0.001) and the Caucasian sample (β = –0.185, P < 0.001). The G–G–T–G haplotype (21.6%) was also associated with the tobacco negative physical factor in the full sample (β = 0.215, P < 0.001) and in the Caucasian sample (β = 0.212, P < 0.001).

All three haplotypes were associated with the tobacco positive factor. The A–A–T–G haplotype (β = –0.184, P< 0.01) was associated with lower factor scores of the tobacco positive factor, while the G–G–T–G (β = 0.132, P< 0.05) and G–G–C–C haplotypes (β = 0.294, P< 0.05) were associated with higher factor scores. In the Caucasian sample, the same directional association was observed with the A–A–T–G haplotype (β = –0.178, P< 0.01) and the G–G–T–G haplotype (β = 0.150, P< 0.05). Likewise, the G–G–C–C haplotype was associated with higher factor scores in the Hispanic sample (β = 0.507, P< 0.05).

CHRNA6
In the CHRNA6 gene, the omnibus test was significant in the full sample and the Hispanic sample for the tobacco positive factor (P= 0.003; P= 0.03, respectively). Haplotype analysis of rs2304297–rs892413–rs1072003 revealed five haplotypes in the full sample and four in the Caucasian and Hispanic samples (Table 5). In the full sample, the most frequent C–A–C haplotype (71.6%) was associated with lower factor scores (β = –0.172, P< 0.001) and the G–A–C haplotype (frequency: 3.4%) associated with higher factor scores (β = 0.482, P< 0.001) for the tobacco positive factor. The most frequent C–A–C haplotype in Caucasians was also associated with lower factor scores (74%) (β = –0.130, P < 0.05) (Table 5).

Single marker analyses (Add Health)
Allele frequencies for the four SNPs genotyped in the Add Health subjects are presented in Table 1. All SNPs were in Hardy Weinberg Equilibrium (HWE) within each ethnic group, although there were significant differences in allele frequencies between the two major subgroups (Caucasians and African Americans), where in fact the minor allele is different (Table 1). These differences are consistent with allele frequencies available in the public SNP databases. All results presented included the full sample of 1524 families, because the family based approach used by Family Based Association Test-Principal Components (FBAT-PC) to test for association inherently controls for population stratification. However, when all analyses were restricted to Caucasians, a similar pattern of results emerged.

FBAT-PC was used to reduce the dimensionality of the data to a single maximally heritable principal component (PC) for each SNP and test for association. Our a priori hypothesis, based on the CADD results, was that SNPs rs4950 and rs13280604 would be significantly associated with the subjective effects PC. We had also detected modest evidence for an association with rs2304297, but no evidence for rs892413. Results from the FBAT-PC analyses are presented in Table 6. The most highly significant SNP was rs13280604 (P= 0.011), followed by rs4950 (P= 0.043), and rs2304297 (P= 0.053). There was no evidence for association with rs892413 (P= 0.870). Note that this is a total of only four individual tests; one specific hypothesis was tested for each SNP.


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  		Table 6. Single marker analysis results for Add Health

 
Figure 2A and B show the correlation of each subjective effect item with the PC derived for SNPs rs13280604 and rs2304297. In both cases, the top three items are ‘dizziness,’ ‘pleasurable buzz or rush,’ and ‘relaxed.’ However, ‘coughing’ and ‘unpleasant sensations’ drop from correlations of 0.5 and 0.4 for the PC phenotype associated with rs13280604 to nearly 0 for the rs2304297-associated phenotype.


Figure 2
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  		Figure 2. Correlation of individual subjective effects items with PC for rs13280604 (A) and rs2304297 (B) derived using FBAT-PC in the Add Health sample.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Given the overwhelming evidence that shared genetic factors play a role in nicotine and alcohol abuse and dependence, we examined several tobacco and alcohol phenotypes for associations with SNPs in the {alpha}6 and β3 nAChR subunit genes in an effort to examine potential common pathways. The results of our exploratory study in the CADD sample suggested that SNPs in the {alpha}6/β3 gene cluster are associated with early subjective reactions to tobacco.

The four CHRNB3 SNPs tested (rs4950, rs13280604, hCV25772398 and rs4953) showed an association with the tobacco adverse and positive subjective factors and three SNPs (rs4950, rs13280604 and hCV25772398) with the tobacco negative physical factor in the total sample. There was suggestive evidence for an association with the CHRNA6 SNP rs2304297 and the positive subjective effects factor. The most robust results appeared to be with the two SNPs in the 5' end of CHRNB3 (rs4950 and rs13280604), which consistently showed significance in the Caucasian subgroup. This may be related to the fact that the other two CHRNB3 SNPs are relatively rare, providing less power to detect an effect and that SNPs genotyped for the 3' end of the gene have not completely saturated LD in that region. It appears that the A–A alleles in these two SNPs act overall to reduce subjective responses, while the G–G alleles are associated with increasing response (i.e. answering ‘yes’ to one of the items on the questionnaire).

There were no significant associations between the patterns of use phenotypes for either tobacco or alcohol and the CHRNB3 or CHRNA6 SNPs. However, since the three subjective factors themselves are highly associated with tobacco dependence, it is possible that subjective effects may act as a mediator between the genotype and dependence.

In the Add Health sample, only four SNPs, the two CHRNB3 SNPs with the most robust results in the CADD sample (rs4950 and rs13280604), a CHRNA6 SNP with suggestive evidence (rs2304297), and a CHRNA6 SNP with no evidence of association (rs892413), were examined. Solid evidence for an association with all three of the putatively associated SNPs was detected using a PC approach incorporating nine subjective effects items. Secondary analyses examining the correlation of individual items suggest that ‘dizziness,’ ‘pleasurable buzz or rush,’ and ‘relaxed’ are subjective effects that contribute significantly to the association of all three SNPs. However, there may be differences in how some of the other items (coughing and unpleasant sensations) are ‘loading’ onto the two SNPs in CHRNB3 compared to the SNP in CHRNA6. This is in agreement with the pattern of results detected in the CADD sample, suggesting that perhaps both genes are important in mediating subjective response, and that multiple SNPs or combinations of these SNPs may interact to modulate behavior.

Animal model reward systems
Studies conducted using mice (30), rats (31) and monkeys (32) indicate that {alpha}6 and β3-containing nicotinic receptors ({alpha}4{alpha}6β2β3 and {alpha}6β2β3) are expressed almost exclusively in dopaminergic nerve terminals. It is clear that dopaminergic neurons in the medial forebrain bundle (MFB), particularly those that originate in the ventral tegmental area and terminate in the nucleus accumbens play vital roles in regulating reward systems in the brain (33,34). Rats and monkeys will actively press a lever or perform an operant response to obtain stimulation from electrodes implanted in the MFB. The rewarding effects of MFB stimulation are enhanced by virtually all drugs of abuse, including nicotine, and are attenuated by doses of dopamine receptor antagonists that do not disrupt behavior (35). These findings are consistent with the observations that pretreatment with dopamine receptor antagonists will decrease intravenous self-administration of nicotine in rats (36). It may be that the significant associations that were detected between the CHRNB3 and CHRNA6 genetic markers and subjective effects reflect influences of {alpha}6/β3-containing receptors on the major brain reward pathways.

Potential function of the CHRNB3 and CHRNA6 studied SNPs
Using the publicly available program Transcription Element Search System (TESS; URL: http://www.cbil.upenn.edu/tess), we investigated the putative transcription factors that could bind to the genomic regions of the two CHRNB3 SNPs implicated in this study. The 5'-UTR region of the CHRNB3 (exon 1) displayed a rich variety of putative transcription factor binding sites, and the rs4950 SNP located in this region appears to be in close proximity to the binding site of the ubiquitous trans-activator AP1 (ID: T00029). In view of these potential transcriptional activation-binding sites surrounding the variations examined in the CHRNB3 locus, it is possible to speculate that these SNPs may have an effect on the expression of the β3 receptor subunits. For SNP rs2304297 in CHRNA6, TESS identified a considerable match to a COUP-TF1 factor, a retinoic acid response factor (id; T00149), located about 50 bp downstream of this SNP. The significance of these results is unknown, but one possibility is that nAChR subunit gene expression may be modulated by retinoic acid-mediated actions, for which there is some support in the literature (37,38).

Strengths and limitations
There are several strengths and limitations to this study. Both samples included large samples. A wide range of information about smoking and alcohol behaviors were collected, allowing comprehensive analyses in the original sample. However, seven individual SNPs and a total of 19 different phenotypes were tested, and so Type I errors are likely to have occurred in the first analysis. While we did not correct for multiple testing, all significant P-values were derived from empirical tests based on 500 permutations. However, given the fact that we were able to replicate the association with three specific SNPs and early subjective response to nicotine in a completely separate population-based sample, the issue of multiple testing in the first sample is addressed.

In addition, this study relied on retrospective reports of initial subjective responses to tobacco or alcohol among those who had already met frequency of use criterion. However, many studies have shown that adolescent self-reports of substance use are reliable and valid (39,40).

Summary
We found evidence for association between allelic variants in the CHRNB3 and CHRNA6 genes and early subjective reactions to tobacco use in a selected sample and in an independently ascertained population-based sample. The PC analysis used in the Add Health study suggests that three individual items (dizziness, pleasurable buzz or rush and relaxed) are major contributors to the association with these genes. However, since the three subjective effects factors identified in the first sample are highly correlated with each other (P< 0.001), it suggests that strong reactions from tobacco use in general are likely to be associated with these genes. This is consistent with our previous finding of an association between an SNP in the CHRNB2 gene and alcohol subjective response in this same population (20).

The results from this study add to the growing literature examining the role of nicotinic receptors and nicotine dependence phenotypes. There have been previous reports of associations between CHRNA4 haplotypes and smoking-related phenotypes (20–22), and limited evidence for an association with CHRNB2 (20,25). However, the recent findings from a large genome wide association (27) and candidate gene study (28) have provided additional support for the importance of these receptors and nicotine dependence. Future studies should include a more in-depth analysis of these genes by re-sequencing to identify novel SNPs and provide better coverage of the full variation in the region. Ultimately, molecular approaches aimed at understanding how these variants may (or may not) affect gene function will be necessary to improve our understanding of the complex genetic interactions contributing to tobacco use behaviors.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
FUNDING
 REFERENCES
 
Subjects (Center for Antisocial Drug Dependence – CADD)
One thousand and fifty six individuals were drawn from the CADD family, twin and adoption samples. Adolescents in treatment for substance abuse and delinquency were recruited from the Colorado Adolescent Substance Abuse family study, a study of the familial transmission of substance abuse and associated psychopathology. Unselected adolescents were recruited from the Colorado Twin Registry, a community-based sample of twins living in Colorado, and the Colorado Adoption Project, an ongoing, longitudinal adoption study of the genetic and environmental influences on behavioral, cognitive and emotional development (41).

Twenty-six percent of the sample was drawn from clinical treatment centers, whereas the remainder was ascertained from community based centers. All subjects were between the ages of 17 and 21, and only one subject per family was included in this study.

Subjects (National Longitudinal Study of Adolescent Health – Add Health)
All subjects were selected from the National Longitudinal Study of Adolescent Health (Add Health). Add Health is an ongoing nationally representative probability based sample of adolescents in the United States. The current analyses were conducted among respondents in the sibling-pairs subsample, for which phenotypic data and DNA were available. A total of 1564 families (1445 sib-pairs and 79 sib-trios), ages 18.2–27.4 years at the Wave 3 assessment were included in the final analyses. Information regarding the study design, sampling strategy and collection of DNA within this sample is available elsewhere (42). Study protocols were approved by Intuitional Review Boards (NC and CO, USA), and subjects provided written informed consent.

Assessments
All CADD subjects completed the Composite International Diagnostic Interview-Substance Abuse Module (43), an assessment valid for adolescents (39). It asks the number of times subjects had used alcohol or tobacco. Those who reported meeting specified thresholds (six or more alcoholic drinks in your lifetime and/or used tobacco almost every day for a month) were asked follow-up questions about abuse and dependence symptoms. Scoring algorithms based on lifetime substance-related problems were used to derive the number of Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV) abuse and dependence symptoms for tobacco and alcohol. Subjects meeting use criteria also were asked a series of twenty three questions aimed at assessing the early subjective responses to each drug (44). These questions were asked as: In the period shortly after you used tobacco/alcohol, did it make you feel (subjective effect)?, to which subjects answered yes or no to items such as ‘depressed,’ ‘irritable,’ ‘mellow,’ ‘energetic,’ ‘sociable,’ ‘anxious,’ etc (20). Binary variables called ‘tobacco sensitivity’ and ‘alcohol sensitivity’ were created from two items ‘nauseous’ and ‘dizzy’ such that if the response is ‘yes’ to either of these items it is coded as 1, otherwise it is coded 0. In addition, a complete description of three factors (adverse, negative physical and positive) that were identified using factor analysis of these items is described in a previous study (20). These three factors, along with the other phenotypes examined for each drug, are presented in Table 7.


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  		Table 7. Summary of phenotypes that were tested for association with CHRNB3 and CHRNA6 genes in the CADD sample

 
In the Add Health study, subjective experiences to nicotine exposure were collected from those subjects who reported ever having smoked a cigarette by young adulthood. Subjects’ retrospectively reported whether they had positive and negative experiences following their first few cigarettes when they started smoking. A total of nine items (pleasant sensations, relaxation, a pleasurable rush or buzz, nausea, unpleasant sensations, difficulty inhaling, heart pounding, dizziness and coughing) were assessed.

Selection of SNPs and genotyping
Candidate polymorphisms for the CHRNA6 and CHRNB3 genes were identified using the Celera Discovery System database and the public database, dbSNP (Table 1). Located on chromosome 8 at position 8p11.21, CHRNA6 (GeneID: 8973) spans 16.01 kb (Aceview annotation, NCBI Build 35) and has at least two alternatively spliced transcripts (Fig. 3). CHRNB3 (GeneID: 1142) is located on chromosome 8 at position 8p11.2, spanning 39.99 kb. Six exons can be alternatively spliced into at least two different transcripts.


Figure 3
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  		Figure 3. Schematic of CHRNA6 and CHRNB3. Boxes represent exons separated by intronic regions in each gene. In the CADD sample, four SNPs were genotyped in CHRNB3, with their rs numbers and gene locations indicated, where bp provides the number of nucleotide base pairs between each SNP. Three SNPs were genotyped in CHRNA6.

 
Criteria for selection included validation status of the SNP based on the public dbSNP database and from the Celera Discovery System, MAF greater than 0.10 (if known), and location in the gene such that the SNPs would be approximately evenly distributed throughout the gene. The structures of the CHRNA6 and CHRNB3 genes, and the SNPs selected, are shown in Figure 3.

Genomic DNA was isolated from buccal cell swabs and preamplified using standard methods (45). Data obtained using this DNA are of high quality; these methods have been shown to be reliable for genotyping (46). TaqMan® assays for allelic discrimination (Applied Biosystems) were used to determine SNP genotypes, per instructions of the manufacturer under standard conditions using ABI PRISM® 7000 and 7900 instruments. We genotyped the 1056 subjects in the CADD sample for seven SNPs (rs4950, rs13280604, hCV25772398, rs4953, rs2304297, rs892413 and rs1072003) and 2612 subjects in the Add Health sample for four SNPs (rs4950, rs13280604, rs2304297 and rs892413).

Statistical analysis (CADD)
The twenty-three items assessing the subjective responses to alcohol and tobacco were subjected to initial PC factor analysis using the Statistical Analysis Software (SAS, v. 9.1) PROC FACTOR command as described previously (20). Three factors were retained and labeled on the basis of similar symptoms: adverse (e.g. depressed and paranoid), negative physical (e.g. dizzy and nauseous), and positive (e.g. energetic and sociable) based on the labels used by others (47).

The r2 values between all SNP markers, MAFs and tests for Hardy–Weinberg equilibrium were calculated using Haploview (29). WHAP was utilized to test the effects of individual SNPs in a linear regression-based test of association with quantitative phenotypes (http://www.broad.mit.edu/personal/shaun/whap/). Within WHAP haplotypes were also estimated using SNPHAP (http://www-gene.cimr.cam.ac.uk/clayton/software/) and simultaneously tested for association (48). We found that haplotypes assigned by WHAP (SNPHAP) and those assigned by PHASE (20,49) have ~99% correspondence. All analyses were corrected for ethnicity. In WHAP, the three largest ethnicity groups, Caucasian, Hispanic and African American, were dummy-coded as three covariates that were included in the regression model. Since there was strong evidence for differences in individual SNP frequencies between ethnic groups, as well as differences in haplotype frequencies, results from a primary analysis of the full sample, followed by results of analyses of the two largest ethnic groups, Caucasians and Hispanics, are shown. All reported significant P-values are empirical values obtained from conducting 500 permutations, but have not been corrected for multiple testing, although all of the phenotypes are highly correlated.

Statistical analysis (Add Health)
The statistical package FBAT-PC (50) was used to analyze the four SNPs genotyped in the Add Health sample with the nine subjective response items. This approach is based on the combined linkage-association analysis originally described by Fulker et al. (51) and extends it to incorporate a traditional PC approach whereby a multivariate series of correlated phenotypes are reduced to one, univariate phenotype for testing. In the context of genetic applications, a lesser-known PC approach involves maximizing the heritability (52). Using quantitative traits, this translates to maximizing the variance of the phenotype that is explained by a particular marker (i.e. the marker-specific heritability). Extensions of this approach were developed for family based samples (49) in the genetic association framework in FBAT-PC where a phenotype (a maximally heritable PC) is constructed first using the between-family information and the phenotype is subsequently tested using the within-family information. In this study of the Add Health sample, a specific a priori hypothesis about which SNPs would be most significant with a subjective effects phenotype was predefined based on the results from the exploratory study in the CADD.


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
REFERENCES
 
This work was supported by Colorado Tobacco Research Program IDEA grant 2I-034 and Supplement 4S-003 (M.A.E.); NIH grant AA015336 (M.A.E.); NIH grants DA011015 (T.J.C.), DA012845 (T.J.C.), HD010333, EY012562 (J.K.H.), DA03194 (A.C.C.), DA13956 (S.H.R.), MH15442 (B.C.H.), DA015522 (C.J.H.), and HD031921.

Conflict of Interest statement. None of the authors have any financial interests or connections, direct or indirect, or other situations that pose a conflict of interest which may introduce bias in the work presented in this article.


   FOOTNOTES
 
{dagger} The authors wish it to be known that, in their opinion, the first 2 authors should be regarded as joint First Authors. Back


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
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