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Association of the neuronal nicotinic receptor 2 subunit gene (CHRNB2) with subjective responses to alcohol and nicotine.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 144B:596 –604 (2007)
Association of the Neuronal Nicotinic Receptor
b2 Subunit Gene (CHRNB2) With Subjective
Responses to Alcohol and Nicotine
Marissa A. Ehringer,1,2* Hilary V. Clegg,1 Allan C. Collins,1,3 Robin P. Corley,1 Thomas Crowley,4
John K. Hewitt,1,3 Christian J. Hopfer,4 Kenneth Krauter,5 Jeffrey Lessem,1 Soo Hyun Rhee,1,3
Isabel Schlaepfer,1,2 Andrew Smolen,1 Michael C. Stallings,1,3 Susan E. Young,1 and Joanna S. Zeiger1
1
Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
Department of Integrative Physiology, University of Colorado, Boulder, Colorado
3
Department of Psychology, University of Colorado, Boulder, Colorado
4
Department of Psychiatry, Division of Substance Dependence, University of Colorado School of Medicine, Denver, Colorado
5
Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, Colorado
2
Nicotine addiction and alcohol dependence are
highly comorbid disorders that are likely to
share overlapping genetic components. We have
examined two neuronal nicotinic receptor subunit genes (CHRNA4 and CHRNB2) for possible
associations with nicotine and alcohol phenotypes, including measures of frequency of use
and measures of initial subjective response in
the period shortly after first using the drugs. The
subjects were 1,068 ethnically diverse young
adults participating in ongoing longitudinal studies of adolescent drug behaviors at the University of Colorado, representing both clinical and
community samples. Analysis of six SNPs in the
CHRNA4 gene provided modest support for
an association with past 6 month use of alcohol
in Caucasians (three SNPs with P < 0.08), but
no evidence for an association with tobacco
and CHRNA4 was detected. However, a SNP
(rs2072658) located immediately upstream of
CHRNB2 was associated with the initial subjective response to both alcohol and tobacco. This
study provides the first evidence for association
between the CHRNB2 gene and nicotine- and
alcohol-related phenotypes, and suggests that
polymorphisms in CHRNB2 may be important
in mediating early responses to nicotine and
alcohol.
ß 2007 Wiley-Liss, Inc.
KEY WORDS: CHRNA4; CHRNB2; nicotine;
alcohol; genetic association
Please cite this article as follows: Ehringer MA,
Clegg HV, Collins AC, Corley RP, Crowley T, Hewitt
Grant sponsor: Colorado Tobacco Research Program IDEA;
Grant number: 2I-034; Grant sponsor: NIH; Grant numbers:
DA011015, DA012845, HD010333, EY012562, DA03194,
MH001865, DA13956, DA015522.
Hilary V. Clegg’s present address is University of North
Carolina School of Medicine, 101 Manning Drive, Campus Box
7512, Chapel Hill, NC 27514.
*Correspondence to: Marissa A. Ehringer, University of Colorado, Institute for Behavioral Genetics 447 UCB, Boulder, CO
80309. E-mail: Marissa.Ehringer@colorado.edu
Received 20 December 2005; Accepted 5 October 2006
DOI 10.1002/ajmg.b.30464
ß 2007 Wiley-Liss, Inc.
JK, Hopfer CJ, Krauter K, Lessem J, Rhee SH,
Schlaepfer I, Smolen A, Stallings MC, Young SE,
Zeiger JS. 2007. Association of the Neuronal Nicotinic
Receptor b2 Subunit Gene (CHRNB2) With Subjective
Responses to Alcohol and Nicotine. Am J Med Genet
Part B 144B:596–604.
INTRODUCTION
Nicotine and alcohol are two of the most widely used
addictive drugs in the United States, with 12.8% of American
adults reporting nicotine dependence [Grant et al., 2004b], and
4.65% and 3.81% reporting alcohol abuse and dependence
[Grant et al., 2004a]. Numerous reports have provided
evidence for a strong association between tobacco and alcohol
use as well as dependence. Alcohol-dependent individuals are
more likely than non-alcoholics to use tobacco and to meet
criteria for nicotine dependence [Batel et al., 1995; Marks et al.,
1997]. More recently, data analysis from the Collaborative
Study on Genetics of Alcoholism (COGA) found that current
smokers as well as former nicotine-dependent smokers
displayed greater severity of alcohol dependence, even after
controlling for gender, other drug dependence diagnoses, and
antisocial personality disorder [Daeppen et al., 2000]. An
experiment by Mitchell et al. [1995] demonstrated that regular
smokers smoke more cigarettes following alcohol consumption
compared with drinking a placebo; similarly, a separate
investigation found nicotine to be a more potent reinforcer in
smokers with a past history of alcohol dependence compared to
those with no such history [Hughes et al., 2000]. Furthermore,
behavioral genetics studies strongly suggest that problem use
of both alcohol and tobacco may be due, in part, to genetic
factors common to the etiology of use of both substances [Swan
et al., 1996, 1997; Madden et al., 1997; Hettema et al., 1999;
Hopfer et al., 2001]. In a recent study of adolescent twins,
biological siblings, and adoptive (biologically unrelated)
siblings drawn from community samples, Young et al. demonstrated a genetic correlation of 0.60 between tobacco and
alcohol problem use, with problem use defined as one or more
symptoms of abuse (alcohol only) or dependence [Young et al.,
2006].
In this study, we examined two genes (CHRNA4 and
CHRNB2) that code for the a4 and b2 subunits of neuronal
nicotinic acetylcholine receptors (nAChRs). Nicotinic receptors
are transmembrane protein complexes consisting of five
subunits that surround a central ion channel [Lindstrom,
2003]. Many different receptor subtypes might exist given that
11 neuronal nicotinic receptor subunit genes (a2-7, a9-10, and
b2-4) have been identified in mammalian brain [Lindstrom,
2003]. Subunit composition influences a wide range of receptor
CHRNB2 and Response to Alcohol and Nicotine
properties [Luetje and Patrick, 1991]; however, it is readily
evident that a4b2* (* indicates another subunit may be
included in native receptors) receptors are the most frequently
encountered nicotinic receptor subtype. Studies done with
b2 null mutant mice indicate that virtually all a4-containing
receptors also include the b2 subunit [Picciotto et al., 1995;
Marubio et al., 1999]. However, there are some native
b2-containing receptors that do not include a4 subunits [Zoli
et al., 1998; Marubio et al., 1999]. The a4b2* receptors are
expressed throughout the brain and spinal cord, principally on
presynaptic nerve terminals where they modulate the release
of dopamine [Salminen et al., 2004] and gamma-aminobutyric
acid (GABA) [Lu et al., 1998].
The a4b2* receptors are promising candidates for association studies with nicotine and alcohol use because (1) alcohol
enhances a4b2 receptor function as shown in studies using
receptors expressed in Xenopus oocytes [Cardoso et al., 1999],
rat brain slices [Aistrup et al., 1999], and mouse brain
synaptosomes [Butt et al., 2003]; (2) alcohol enhancement of
receptor function is influenced by a polymorphism (A529T) in
the coding region of the mouse a4 gene (Chrna4) [Butt
et al., 2003], and the A529T polymorphism is associated
with variability in a variety of nicotine- and alcohol-related
phenotypes [Stitzel et al., 2001; Tritto et al., 2001, 2002; Butt
et al., 2003, 2004, 2005; Owens et al., 2003].
The gene for the a4 nAChR subunit (CHRNA4) has been
evaluated as a potential contributor to variance in several
disorders in human populations. The most conclusive studies
have evaluated CHRNA4 as a candidate gene that influences
autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) [Sutor and Zolles, 2001]. Several polymorphisms,
ranging from missense mutations and insertions to silent
SNPs, have been identified in CHRNA4. Those that cause a
change in receptor function, when measured in vitro, are
significantly associated with ADNFLE [Steinlein et al., 1995,
1997; Kent et al., 2001; Todd et al., 2003]. Evidence that
supports the assertion that CHRNA4, rather than a closely
linked gene, is responsible for ADNFLE comes from the recent
finding that a polymorphism in CHRNB2 is also associated
with this seizure disorder [Aridon et al., 2006]. These findings
provide compelling evidence that supports the assertion that
a4b2* receptors modulate ADNFLE.
In addition to their role in epilepsy, the effects of CHRNA4
polymorphisms on panic disorder, and attention-deficit/
hyperactivity disorder have recently been explored [Steinlein
et al., 1995, 1997; Kent et al., 2001; Todd et al., 2003]. In
addition, two studies have reported associations between
CHRNA4 haplotypes and smoking-related phenotypes
[Feng et al., 2004; Li et al., 2005]. Feng et al. [2004] identified
a haplotype surrounding exon 5 that was associated
with nicotine addiction (measured by the Fagerstrom Test
for Nicotine Dependence [Heatherton et al., 1991]) and
tolerance (measured by a revised version of the Fagerstrom
Tolerance Questionnaire [Tate and Schmitz, 1993]). Likewise,
Li et al. [2005] detected an association in a similar CHRNA4
haplotype and nicotine dependence using the Fagerstrom
test. Both of these groups also examined the CHRNB2 gene,
but did not find an association between these smoking
phenotypes that were measured and CHRNB2. There have
been two additional studies of the CHRNB2 gene related to
smoking behavior and nicotine dependence, but neither
found any evidence for association with any smoking-related
phenotypes [Silverman et al., 2000; Lueders et al., 2002].
In summary, previous studies in animals and humans
suggest that the CHRNA4 and CHRNB2 genes are strong
candidates for modulating nicotine and alcohol addiction.
The strongest evidence in humans supports an important
role for CHRNA4, but no evidence for CHRNB2 has been
reported.
597
In this study of an ethnically diverse combined clinical and
community sample, we have examined six individual SNPs in
the CHRNA4 gene and two SNPs in the CHRNB2 gene and
completed a haplotype analysis of these SNPs. We tested
individual SNPs and haplotypes for association with a series of
nicotine and alcohol phenotypes. In addition to measures of
quantity and frequency of use and measures of abuse and
dependence, we also examine a subjective measure of response
to each drug in the period shortly after initiation. This period
may represent a critical step early in the development of
addictive behaviors where specific genes might be important in
contributing to continued use of the drug.
MATERIALS AND METHODS
Subjects
We selected 1,068 young adults who were participants in
ongoing studies of the genetics of adolescent substance abuse in
Colorado, funded by the National Institute of Drug Abuse,
for this study. From the entire pool of potential subjects
encompassing over 5,000 youths, we selected for inclusion
those who were assessed between ages 17 and 21 so they were
old enough to be past the typical age of initiation. Additionally,
we limited subject selection to one subject per family.
Participants were recruited from treatment settings for youth
with substance use disorders, criminal justice settings, and
community-based twin, adoption, and family studies of
adolescent substance use disorders. Twenty-six percent of this
sample was drawn from clinical treatment and criminal
justice settings and their siblings, while the remainder was
ascertained from community-based samples. The consents for
the original studies asked whether the information gathered
could be used for analyses of ‘‘genetic tests related to substance
use disorders.’’ Subjects 18 or older provided consent to be a
part of the study. For subjects who were less than 18, only those
who assented and whose parents consented to this statement
were included in these analyses.
The sample consisted mainly of self-identified Caucasians
(72.1%, 770 subjects), Hispanics (16.2%, 168 subjects), and
African-Americans (4.0%, 43 subjects). The remaining subjects
were self-identified as Asian, Native American, Pacific Islander, or Unknown. The mean age of the subjects was
18.21 1.50% and 58.1% of the subjects were male.
Assessment of Exposure, Use, Abuse,
and Dependence
Substance use patterns (e.g., onset and frequency)
were assessed using the Composite International Diagnostic
Interview—Substance Abuse Module (CIDI-SAM), a structured, face-to-face diagnostic assessment designed to be
administered by trained, lay interviewers [Cottler and Keating, 1990]. Using the CIDI-SAM in adolescents to measure
substance abuse has been validated by Crowley et al. [2001].
Participants were asked questions to determine the number of
times they had used alcohol or tobacco and only those meeting a
frequency threshold were asked follow-up questions about use,
abuse (alcohol, but not tobacco), and dependence. For alcohol,
subjects had to respond ‘‘yes’’ to having consumed at least six
alcoholic beverages in their lifetime, and for tobacco, they
had to respond ‘‘yes’’ to having used tobacco daily for at least a
month. Using DSM-IV criteria for abuse or dependence, a
binary variable for ‘‘problem use’’ was created for alcohol and
tobacco. If subjects were diagnosed with alcohol abuse, or
dependence with or without physiological symptoms, they
were coded as affected for alcohol problem use. Likewise, if
subjects were diagnosed with tobacco dependence with or
without physiological symptoms, they were coded as affected
for tobacco problem use.
598
Ehringer et al.
Assessment of Subjective Effects
of Tobacco and Alcohol
Subjects meeting the frequency of use criteria (described
above) were asked a series of 23 questions aimed at assessing
the early physical and psychological responses to each drug
[Lyons et al., 1997]. These questions included, ‘‘In the period
shortly after you used tobacco/alcohol, did it make you feel
{subjective effect}?’’, to which subjects answered yes or no, as
described in a recent article by Grant et al. [2005].
The list of subjective effects is shown in Table I and a
summary of all phenotypic measures examined are listed in
Table III. There were a total of seven alcohol phenotypes and
six nicotine phenotypes explored in addition to three factor
scores generated from the subjective response questionnaire.
These included age of initiation, number of days used in the
past 6 months, typical pattern of use, peak alcohol use (number
of drinks in 24 hr at time when drinking the most), peak tobacco
use (maximum number of cigarettes, cigars, pipefuls, or chews
per day), frequency of alcohol use (on a scale from 1, almost
every day to 5, less than once a month), and the binary variable
‘‘sensitive,’’ indicating if they answered ‘‘yes’’ to either
‘‘nauseous’’ or ‘‘dizzy’’ on the subjective questionnaire. These
two items were selected on the basis of preliminary findings by
Hutchison et al. [in preparation], who have also looked at the
CHRNA4 gene and its possible association with responses to
smoking.
Selection of SNPs and Genotyping
Candidate polymorphisms for the CHRNA4 and CHRNB2
genes were identified using the Celera Discovery System
database and the public database, dbSNP. Located on chromosome 20 at position 20q13.2-q13.3, CHRNA4 (GeneID: 1137)
spans 34.08 kb (Aceview annotation, NCBI Build 35), and has
at least six alternatively spliced transcripts. CHRNB2 (GeneID: 1141) is located on chromosome 1 at position 1q21.3 and is
shorter in length, spanning 12.25 kb. Six exons comprise the
only known transcript (NM_000748). Criteria for selection
included validation status of the SNP based on the public
dbSNP database and from the Celera Discovery System,
minor allele frequencies (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 CHRNA4 and CHRNB2 genes, and the SNPs
selected, are shown in Figure 1.
Genomic DNA was isolated from buccal cell swabs and
preamplified using the method of Zhang et al. [1992]. Data
obtained using this DNA are high-quality; these methods have
been shown to be reliable for genotyping [Anchordoquy et al.,
2003]. TaqMan1 assays for allelic discrimination (Applied
Biosystems, Foster City, CA) were used to determine SNP
genotypes, per instructions of the manufacturer under standard conditions using ABI PRISM1 7000 and 7900 instruments. We genotyped 1068 subjects for eight SNPs (rs6122429,
rs2273506, rs2273500, rs2229959, rs1044397, rs2236196,
rs2072658, rs2072660). We also genotyped approximately
200 subjects for CHRNB2 SNPs rs3926124, rs2975131,
rs3008434, and the A10160C intron5 SNP reported by Feng
et al. [2004], but did not detect any variation in these SNPs in
our sample.
Analysis of Phenotypic Measures
All phenotypic measures were age- and sex-corrected
based on the distribution of the community sample data.
Scores for clinical subjects were standardized on the community sample (i.e, expressed as deviation scores using the
community sample means and using standard deviations).
The 23 subjective response items were subjected to initial
principal components factor analysis using Statistical Analysis
Software (SAS, v. 9.1) PROC FACTOR command.
Factor Analysis of Subjective Effects
Items for Tobacco and Alcohol
Seven of the tobacco items were endorsed at very low levels
(<2%), and therefore excluded from the analysis, similar to the
TABLE I. Results of Factor Analyses of the Subjective Responses to Tobacco and Alcohol Questionnaire [Lyons et al., 1997]
Tobacco
Subjective effects
Adverse
Depressed
Paranoid
Confused
Anxious
Irritable
Overactive
Laughing/crying
Jumpy
Overconfident
Mellow
Top of world
Energetic
Creative
Sociable
Dizzy
Nauseous
Lazy
Drowsy
Unable to concentrate
Out of control
Hallucinate
More sex drive
Guilty
0.58
0.49
0.56
0.39
0.37
*
*
*
*
0.06
0.21
0.15
0.11
0.07
0.07
0.10
0.16
0.11
0.25
*
*
*
0.07
Negative physical
0.01
0.07
0.05
0.03
0.07
*
*
*
*
0.20
0.07
0.07
0.07
0.06
0.54
0.55
0.50
0.55
0.28
*
*
*
0.18
Alcohol
Positive
Adverse
0.02
0.05
0.07
0.10
0.03
*
*
*
*
0.32
0.29
0.54
0.47
0.43
0.11
0.12
0.05
0.10
0.04
*
*
*
0.22
0.45
0.55
0.46
0.54
0.46
0.40
0.28
0.55
0.14
0.26
0.00
0.15
0.06
0.12
0.16
0.17
0.07
0.11
0.21
0.39
0.36
0.06
0.26
Negative physical
0.21
0.05
0.22
0.05
0.17
0.09
0.07
0.09
0.14
0.34
0.09
0.25
0.05
0.13
0.51
0.51
0.58
0.71
0.49
0.19
0.02
0.20
0.05
Positive
0.08
0.06
0.01
0.09
0.03
0.04
0.28
0.04
0.45
0.36
0.60
0.59
0.50
0.55
0.00
0.03
0.08
0.02
0.04
0.10
0.02
0.33
0.02
Specific items are shown in the first column and the three factor loadings for each item for each substance (tobacco and alcohol) are presented in the following
six columns.
CHRNB2 and Response to Alcohol and Nicotine
599
Fig. 1. Schematic of CHRNA4 (a) and CHRNB2 (b). Boxes represent exons separated by intronic regions in each gene. Six SNPs were genotyped in
CHRNA4, with their rs numbers and gene locations indicated, where bp provides the number of nucleotide base pairs between each SNP. Two SNPs were
genotyped in CHRNB2.
approach taken by Grant et al. [2005], who analyzed the same
factors for cocaine and marijuana. Each item was age- and
sex-corrected using logistic regression based on the prevalence
in the community sample and z-scores were calculated for
all subjects. Factor analysis was then performed using a
PROMAX and a VARIMAX rotation, for the age and sexcorrected scores, and for the raw binary scores. The correlations between factor scores obtained using PROMAX or
VARIMAX rotations were high (0.97 for the raw scores and
0.98 for the z-scores, P < 0.0001). The correlations between
factor scores obtained using raw scores and z-scores were
0.37 (PROMAX rotation, P < 0.0001) and 0.42 (VARIMAX
rotation, P < 0.0001). When we examined the factor loadings
and clustering of subjective effects for all four factor analysis
approaches, a three-factor score using a PROMAX rotation and
the raw binary variables for tobacco emerged as the best model.
This model provided the best separation of factor loadings, so
that each item loaded primarily onto a single factor and so
that the types of items which clustered together in each of the
three factors were similar. The validity of these factors
was evaluated using logistic regression to predict tobacco
dependence or alcohol dependence and abuse, while adjusting
for clinical status in STATA. All of the subjective effects items
were endorsed at higher rates for alcohol (>2%), so all were
included in the factor analysis. We used the same approach
(PROMAX rotation of the raw binary scores) for the alcohol
items and a similar three-factor model emerged.
Haplotype Analyses
Pairwise linkage disequilibrium (D0 ) was calculated using
GOLD [Abecasis and Cookson, 2000] and Haploview [Barrett
et al., 2005]. A linear regression-based test of association
predicting quantitative phenotype from the individual SNPs
was conducted using WHAP (http://www.broad.mit.edu/personal/shaun/whap/). Previous work has shown that linear
regression modeling is robust to possible non-normality of
the phenotypic measures in large samples (>500 subjects)
[Lumley et al., 2002], although an alternative approach would
have been to use a package that allows for non-normal data,
such as Haplostats (http://mayoresearch.mayo.edu/mayo/
research.%20/biostat/schaid.cfm). The binary variable of sensitivity was coded as such in WHAP, which is flexible to analyze
binary and quantitative traits. A dominance model was
assumed for those SNPs that were quite rare (MAF < 0.05),
whereby the rare homozygous subjects were collapsed with the
heterozygotes. A similar regression-based test of association
for haplotypes of the six CHRNA4 SNPs or two CHRNB2 SNPs
was conducted using WHAP.
WHAP uses a weighted regression-based method based on
estimated haplotypes to test for association with a phenotype.
The haplotypes are estimated using SNPHAP (http://
www-gene.cimr.cam.ac.uk/clayton/software/), which assigns
weighted haplotypes to each individual (i.e., haplotypes are not
known with certainty but must be estimated from the data). In
order to confirm the accuracy of the haplotype assignments, we
compared the haplotypes estimated by WHAP (SNPHAP)
to those estimated by PHASE, v.2.0.2, which incorporates
relative position and distance between SNPs in its algorithms
[Stephens et al., 2001; Stephens and Donnelly, 2003]. Less
than 1% of the haplotype assignments were different between
PHASE and SNPHAP for both the CHRNA4 and CHRNB2
gene data, and these were for those individuals where multiple
haplotype assignments were possible.
Four ethnicity groups (Caucasian, Hispanic, AfricanAmerican, and other) were dummy-coded as three covariates
that were included in the regression model, so all reported
statistics include this correction for ethnicity. There was
strong evidence for differences in individual SNP frequencies
between ethnic groups, as well as differences in haplotype
frequencies. Given this correlation between ethnicity and
haplotype, it is more appropriate to stratify our analyses on
ethnicity rather than include it as a covariate. Therefore, we
present results from a primary analysis of the full sample,
followed by results of analyses of the two largest ethnic groups,
Caucasians and Hispanics. All reported P-values are empirical
600
Ehringer et al.
values obtained from completing 500 permutations, but have
not been corrected for multiple testing.
TABLE II. Odds Ratios Calculated Using Logistic Regression to
Predict Alcohol Dependence or Abuse and Tobacco Abuse/
Dependence From the Three Subjective Response Factor Scores
RESULTS
Factor Analysis of Subjective Measures
The three factors retained with individual factor loadings for
each subjective response are shown in Table I. The Eigenvalues
for the three tobacco factors were 2.54, 0.81, and 0.73.
These have been labeled ‘‘adverse,’’ ‘‘negative physical,’’ and
‘‘positive,’’ respectively, which are similar to the labels used by
Grant et al. [2005]. The three alcohol factors had Eigenvalues
of 5.32, 0.98, and 0.91 (‘‘adverse,’’ ‘‘negative physical,’’ and
‘‘positive’’). These factors were highly predictive of alcohol
abuse or dependence and of tobacco dependence using logistic
regression. Odds ratios for each diagnosis for each factor are
shown in Table II and all were significant at the P < 0.01. These
ORs represent the predicted increase in the probability of
diagnosis for an increase of one standard deviate in the factor
score.
Alcohol dependence
Adverse
Negative physical
Positive
Alcohol abuse
Adverse
Negative physical
Positive
Tobacco abuse/dependenceb
Adverse
Negative physical
Positive
ORa
95% CI
1.27
1.28
1.28
1.20–1.35
1.21–1.36
1.21–1.36
1.23
1.24
1.23
1.15–1.33
1.16–1.34
1.14–1.32
1.39
1.39
1.39
1.33–1.45
1.33–1.45
1.33–1.45
a
Adjusted for clinical status and sex.
Tobacco abuse and dependence were combined due to small numbers of
abusers (n ¼ 3).
b
Descriptives of Individual SNPs in this Sample
Descriptive data for each of the SNPs examined, including
SNP ID, location in the genes, and MAF in the whole sample
and separate ethnic groups are provided in Table IV. Three of
the SNPs (rs2273506, rs2273500 in CHRNA4, and rs2072658
in CHRNB2) are relatively rare (<0.10 MAF), but were
originally selected to obtain better coverage of the genes,
and the MAF for rs2273506 is still unavailable at dbSNP
(http://www.ncbi.nlm.nih.gov/SNP/). Minor allele frequency
estimates are available for only some of these SNPs at dbSNP
and Applied Biosystems (http://myscience.appliedbiosystems.com/). In cases where information is provided, the frequencies
obtained in this study match closely with the databases. All
SNPs are in Hardy–Weinberg equilibrium, within the full
sample and within the separate ethnic samples. Two SNPs
were found to differ significantly in their allele frequencies by
ethnic group: rs6122429 (w2 ¼ 15.3, df ¼ 2, P ¼ 0.0005) and
rs2072658 (w2 ¼ 14.0, df ¼ 2, P ¼ 0.0009), and are indicated
with an asterisk in Table IV.
Single Marker Analyses
Results for association tests with the eight individual SNPs
are presented in Table V. The seven alcohol phenotypes and six
tobacco phenotypes shown in Table III were tested with all
SNPs. In addition, the three factor scores for subjective
responses to each drug were explored. Only those results that
reached nominal significance at P < 0.05 are presented and Pvalues have not been corrected for multiple testing.
There were a few SNPs in CHRNA4 for which suggestive
evidence of association was found. Three SNPs (rs2273506,
rs2273500, rs2229959; exon2, intron4, exon5) were associated
with alcohol use in the past 6 months [Likelihood ratio test
(LRT) ¼ 8.98, P ¼ 0.032; LRT ¼ 8.64, P ¼ 0.022; LRT ¼ 11.5,
P ¼ 0.008]. These findings were supported in the Caucasian
sample, but not replicated in the Hispanic sample, as shown in
Table V.
A dominance model was used to test the upstream SNP in
CHRNB2 because of its low MAF, but the direction of
associations presented here remained consistent even when
the rare homozygous subjects were analyzed as a separate
group. There was evidence for an association between
the upstream SNP in CHRNB2 (rs2072658) and four phenotypes: tobacco negative physical (LRT ¼ 10.19, P ¼ 0.002),
tobacco positive (LRT ¼ 5.250, P ¼ 0.022), alcohol adverse
(LRT ¼ 5.026, P ¼ 0.025), and alcohol negative physical
(LRT ¼ 6.088, P ¼ 0.014). The association with tobacco
negative physical was replicated in each of ethnic subsamples,
while the association with alcohol adverse was supported only
TABLE III. Summary of Phenotypes That Were Tested for Association With CHRNA4 and CHRNB2 Genes
Alcohol
Age of initiation
Number days used in past 6 months
Typical pattern of use
0—none
1—<1 time per month
2—1 time per month
3—2 or more times per month
4—1 time per week
5—2 or more times per week
6—1 time per day
7—2 or more times per day
Peak alcohol use
Frequency of alcohol use
Alcohol sensitive
Subjective effects questionnaire responded ‘‘yes’’ to either ‘‘nauseous’’
or ‘‘dizzy’’
Alcohol problems (diagnosis of abuse or dependence)
Tobacco
Age of initiation
Number days used in past 6 months
Typical Pattern of use
0—none
1—<1 time per month
2—1 time per month
3—2 or more times per month
4—1 time per week
5—2 or more times per week
6—1 time per day
7—2 or more times per day
Peak tobacco use
Tobacco sensitive
Subjective effects questionnaire responded ‘‘yes’’ to either
‘‘nauseous’’ or ‘‘dizzy’’
Tobacco problems (diagnosis of dependence)
CHRNB2 and Response to Alcohol and Nicotine
601
TABLE IV. Summary of SNPs Genotyped
dbSNP ID
CHRNA4
rs6122429
rs2273506
rs2273500
rs2229959
rs1044397
rs2236196
CHRNB2
rs2072658
rs2072660
Celera ID
Location
Position from ATG
SNP
MAF All
MAF Caucasians
MAF Hispanics
n.a.
hCV16178935
hCV27164137
hCV25600802
hCV25765467
hCV15953820
Upstream
Exon 2
Intron 4
Exon 5
Exon 5
Intron 6
664
1,578
5,568
10,988
11,378
14,985
T/C
G/A
G/A
C/A
T/C
G/A
0.136*
0.064
0.073
0.131
0.496
0.267
0.124
0.06
0.071
0.121
0.478
0.269
0.205
0.07
0.069
0.109
0.461
0.238
hCV15861927
hCV15949664
Upstream
Exon 6
285
8212
A/G
T/C
0.056*
0.228
0.046
0.218
0.097
0.225
MAF, minor allele frequency; SNP rs6122429 is not listed in the Celera database.
*Indicates a significant difference in minor allele frequency between Caucasians and Hispanics.
in the Hispanic sample. The associations with tobacco positive
and alcohol negative physical were not supported in
either subsample. There was also evidence for association
between the exon6 SNP (rs2072660) and tobacco sensitivity
(LRT ¼ 5.82, P ¼ 0.014), which appeared to be driven primarily
by the Caucasian sample.
Haplotype Structure
Pairwise linkage disequilibrium (LD) estimates (D0 )
obtained from GOLD [Abecasis and Cookson, 2000] and
Haploview [Barrett et al., 2005] are shown in Figure 2 for
CHRNA4. A single haplotype block consisting of ‘‘strong LD’’
between SNPs rs2273506 and rs2273500 in CHRNA4 was
determined using the Haploview default algorithm based on
Gabriel et al. [2002], using the 95% confidence D0 rule in the full
sample and in the Caucasian and Hispanic subsamples. The
estimated D0 between these two markers was 0.98. In general,
there was less LD between the first SNP (rs6122429) and all
the other SNPs, the five of which were in relatively high
pairwise D0 ranging from 0.83 to 0.98. In the Hispanic sample,
the block structure extended from SNP rs2273506 to SNP
rs2236196, with high pairwise D0 in that region, but some
measures of D0 could not be estimated with the same confidence
(as indicated by the lack of numbers in certain dark diamonds
in Fig. 2c). There was very low LD observed between the two
SNPs in CHRNB2 (D0 ¼ 0.29–0.31 for all three groups).
An omnibus test, which tests each estimated haplotype while
controlling for all others, was used to test for an overall effect of
haplotype on the dependent measures. The regression weights
(b coefficients) indicate the relative contribution of each
haplotype; the most common haplotype is fixed to 0 and the
effects of all others are estimated relative to it. All haplotypes
with a frequency of less than 1% were excluded. Based on the
block structure in CHRNA4, only SNPs rs2273506 and
rs2273500 were included in the full sample and Caucasian
sample analyses, and in the Hispanic sample, SNPs rs2229959,
rs1044397, and rs2236196 were also included. Modest
evidence for association using the omnibus test with CHRNA4
was found for only one phenotype, past 6 month use of alcohol
TABLE V. Association Test Results for Individual SNPs
Gene
CHRNA4
Phenotype
Alcohol Past Six Month Use
SNPs
Exon2
Intron4
Exon5
Group
All
Caucasians
Hispanics
CHRNB2
Tobacco Sensitive
Exon6
Tobacco Negative Physical
Upstreama
Tobacco Positive
Upstreama
Alcohol Adverse
Upstreama
Alcohol Negative Physical
Upstreama
All
Caucasians
Hispanics
All
Caucasians
Hispanics
All
Caucasians
Hispanics
All
Caucasians
Hispanics
All
Caucasians
Hispanics
b
0.292
0.249
0.230
0.279
0.255
0.142
0.156
0.176
4.794
0.30
0.482
0.297
0.386
0.342
0.675
0.272
0.188
0.242
0.241
0.228
0.381
0.267
0.229
0.269
LRT
P
8.98
8.64
11.5
6.74
5.79
3.83
0.38
0.47
1.50
5.82
9.70
0.87
10.19
5.451
5.518
5.250
1.480
2.217
5.026
2.572
3.610
6.088
2.532
1.691
0.032
0.022
0.008
0.009
0.046
0.078
0.491
0.511
0.244
0.014
0.004
0.273
0.0014
0.020
0.019
0.022
0.224
0.137
0.025
0.109
0.057
0.014
0.112
0.193
a
A dominance model was assumed for the CHRNB2 Upstream SNP because of its low MAF (<0.05), which collapsed the rare homozygous individuals with the
heterozygous individuals. For the combined analyses with all subjects, ethnicities were included as a covariate. All phenotypes were age and sex corrected.
LRT, Likelihood ratio test statistic.
602
Ehringer et al.
(full sample LRT ¼ 7.534, P ¼ 0.034). This finding was
modestly supported in each of the ethnic-specific samples
(Table VI).
Based on these findings, haplotype-specific tests were
performed using the hs option in WHAP. This option can be
used to test the effect of each haplotype individually against all
other haplotypes (i.e., constraining all other haplotypes to have
equal b weights). When haplotype specific tests were conducted
in the full sample, there was an apparent protective effect of
the GG haplotype for past 6 month alcohol use (b ¼ 0.268,
LRT ¼ 7.534, P ¼ 0.006). The same haplotype was significant in
Caucasians (b ¼ 0.281, LRT ¼ 7.261, P ¼ 0.007). In Hispanics,
none of the individual haplotypes tests for association were
found to be significant.
DISCUSSION
We found modest evidence for an association between the a4
subunit and use of alcohol in the past 6 months. The individual
SNP results suggest that three SNPs in exon 2, intron 4, and
exon 5 may be associated with past 6 month use of alcohol,
particularly in the Caucasian subsample. However, given the
number of phenotypes examined, and the lack of support by the
haplotype analysis, these findings must be interpreted with
caution.
This study provides stronger support for a role of the
b2 subunit neuronal nicotinic receptors and early subjective
responses to alcohol and nicotine. The most striking
finding was the association between the rare upstream SNP
(rs2072658) and negative physical response to tobacco, which
was significant in the entire sample and in both major ethnic
subgroups (Caucasian and Hispanic). An association between
adverse responses to alcohol was also found in the full sample
and in the Hispanic group, but not in the Caucasian group.
These results suggest that the SNP rs2072658 may be
associated with a general ‘‘strong response’’ to both nicotine
and alcohol. For alcohol, longitudinal studies by Schuckit
[1998, 2000] and Schuckit and Smith [2000] have shown that a
measure of early low level of response to alcohol, including
retrospective measure of subject ratings of level of response,
has been shown to be a good predictor of future alcohol
problems. Based on the results presented here, one might
hypothesize that the b2 nicotinic receptor subunit may be
involved in that response to alcohol, and perhaps a similar
response to tobacco.
The upstream SNP, located only 42 bp upstream of the
transcription initiation, may be important for regulating
expression of the gene. Using the Transcription Element
Search System (TESS; URL: http://www.cbil.upenn.edu/tess),
five putative transcription binding sites that might be affected
by SNP rs2072658 were identified. These include sites for
transcription factors LVb, IL-6 RE-BP, PU.1, LBP-1, and NF1.
TABLE VI. CHRNA4 Haplotype Results for Alcohol Past 6 Month
Use Phenotype
Group
All
Caucasians
Fig. 2. Haploview linkage disquilibrium calculations and predicted
block structures between CHRNA4 SNPs in the full sample (a), the
Caucasian sample (b), and the Hispanic sample (c). Pairwise linkage
disequilibrium values (D0 ) are shown between each SNP and higher LD is
illustrated with darker squares. [Color figure can be viewed in the online
issue, which is available at www.interscience.wiley.com.]
Hispanics
Haplotypes Frequency
AA
GG
AA
GG
AG
AAATG
AAACG
AAACA
GGCCA
AACCA
0.94
0.06
0.93
0.06
0.01
0.50
0.24
0.14
0.06
0.06
b
0.000
0.268
0.000
0.279
0.034
0.000
0.058
0.067
0.268
0.017
LRT
P
7.534 0.034
7.282 0.068
9.513 0.034
CHRNB2 and Response to Alcohol and Nicotine
One might speculate that the rare A allele could lead to reduced
efficiency of binding of one of these factors and therefore affect
the transcription of the CHRNB2 gene.
Evidence for an association between the SNP in exon 6 of
CHRNB2 (rs2072660) and tobacco sensitivity was also
detected. People with the rare T allele were less likely to
report feelings of dizziness or nausea in the period shortly after
they first started using tobacco than those with the more
common C allele. This is interesting because it might mean
that the two different CHRNB2 SNPs behave in an opposing
manner, where the rare allele of the upstream SNP is
associated with increased subjective response, while the rare
allele of the exon 6 SNP is associated with decreased response.
Limitations
There are four important limitations to this study. First, the
analyses did not correct for multiple testing. Eight individual
SNPs and a total of 19 different phenotypes were tested, and so
Type I errors are likely to occur. Because of this, our results
must be considered exploratory. Associations that were found
to be consistent across both ethnic groups may be more likely to
be replicated. Second, this study relied on retrospective reports
of initial subjective responses to tobacco or alcohol among
those who had already met frequency of use criterion. This is a
difficult issue to get around, but might be approached through
repeated assessments during the adolescent years. Third, it is
possible that other substances were being tried or used at the
same time these subjects were experimenting with tobacco
and alcohol. Use of other drugs might have affected how the
subjects responded to the questions about tobacco and alcohol,
so perhaps this should be examined in future studies. Finally,
the use of ethnicity as a covariate in the full sample might
not be appropriate, because it is correlated with allelic and
haplotype frequencies. For this reason, we place more
emphasis on the results that held up in one of the ethnicspecific subsamples.
SUMMARY
These findings are in contrast to Feng et al. [2004] and Li
et al. [2005], where both groups detected associations between
tobacco dependence and CHRNA4, but found no associations
with CHRNB2. Similarly, neither Silverman et al. [2000], nor
Lueders et al. [2002] found evidence for associations with
CHRNB2 and smoking behavior or dependence. The animal
studies suggest roles for a4b2* receptors in modulating
‘‘simpler’’ aspects of addiction such as responsiveness or
sensitivity to drug effects. The main difference between the
other human studies and our findings may be due to differences
in our measures of subjective response in the period after
subjects first started using the drug, a measure not examined
in the four previous reports. In addition, these subjective
measures have been analyzed in latent class analyses for
marijuana and cocaine and were found to be highly associated
with lifetime risk of these drugs [Grant et al., 2005]. Therefore,
this study highlights the potential importance of careful
definition of phenotypes when studying addiction disorders
and candidate genes. There is a large body of evidence in
alcohol research showing that initial level of response to
alcohol is one of the best predictors of future alcohol problems
[Schuckit, 1992, 1998; Schuckit, 2000]. One could hypothesize
that the b2 subunit might be involved in a similar initial
response to nicotine and/or alcohol. Furthermore, this underscores the importance of studying addiction as a developmental
process, whereby different genes may be contributing to
the development of disorder at different stages. The study of
adolescents and young adults, despite the analytical difficulties associated with age-of-onset diagnostic criteria, may
603
therefore be critical in understanding the genetic etiologies of
nicotine and alcohol problem use. Our future studies will be
aimed at replicating the finding with the CHRNB2 upstream
SNP and examining possible gene–gene interaction between
CHRNA4 and CHRNB2 to determine whether epistatic
effects may be important in mediating the effects of these two
genes, since the derived proteins are known to interact in the
brain.
ACKNOWLEDGMENTS
This work was supported by Colorado Tobacco Research
Program IDEA grant 2I-034 and Supplement 4S-003 (M.A.E.),
NIH grants DA011015 (T.J.C.), DA012845 (T.J.C.), HD010333,
EY012562 (J.K.H.), DA03194 (A.C.C.), MH001865 (S.J.Y.),
DA13956 (S.H.R.), and DA015522(C.J.H.).
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