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Keywords:

  • Association study;
  • DSM-IV nicotine dependence;
  • nicotine;
  • nicotinic acetylcholine receptors

Abstract

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Nicotine binds to nicotinic acetylcholine receptors and studies in animal models have shown that α4β2 receptors mediate many behavioral effects of nicotine. Human genetics studies have provided support that variation in the gene that codes for the α4 subunit influences nicotine dependence (ND), but the evidence for the involvement of the β2 subunit gene is less convincing. In this study, we examined the genetic association between variation in the genes that code for the α4 (CHRNA4) and β2 (CHRNB2) subunits of the nicotinic acetylcholine receptor and a quantitative measure of lifetime DSM-IV ND symptom counts. We performed this analysis in two longitudinal family-based studies focused on adolescent antisocial drug abuse: the Center on Antisocial Drug Dependence (CADD, N = 313 families) and Genetics of Antisocial Drug Dependence (GADD, N = 111 families). Family-based association tests were used to examine associations between 14 single nucleotide polymorphisms (SNPs) in CHRNA4 and CHRNB2 and ND symptoms. Symptom counts were corrected for age, sex and clinical status prior to the association analysis. Results, when the samples were combined, provided modest evidence that SNPs in CHRNA4 are associated with ND. The minor allele at both rs1044394 (A; Z = 1.988, P = 0.047, unadjusted P-value) and rs1044396 (G; Z = 2.398, P = 0.017, unadjusted P-value) was associated with increased risk of ND symptoms. These data provide suggestive evidence that variation in the α4 subunit of the nicotinic acetylcholine receptor may influence ND liability.

Smoking is the leading cause of self-induced morbidity and premature mortality, and 12.8% of the U.S. adult population meets criteria for nicotine dependence (ND; Grant et al. 2004). In the brain, nicotinic acetylcholine receptors (nAChRs) are the primary target for nicotine. Nicotinic acetylcholine receptors are ligand-gated ion channels that respond to the endogenous neurotransmitter acetylcholine as well as nicotine (Arias et al. 2006; McGehee 1999).

The most abundant nAChR found in the brain is the high-affinity α4β2* receptor (where * indicates that it can combine with other subunits; Picciotto et al. 1995). These receptors have been implicated in modulating the behavioral effects of nicotine in animal models. Pharmacological blockade of α4β2 nAChRs modulates nicotine self-administration and conditioned place aversion (Corrigall et al. 1994; Jackson et al. 2009; Liu et al. 2007). Furthermore, mice lacking the β2 subunit fail to self-administer nicotine (Picciotto et al. 1998; Pons et al. 2008) and do not exhibit nicotine-induced conditioned place preference (Walters et al. 2006). Moreover, use of genetically modified animals has shown that the α4 subunit modulates nicotine-induced hypothermia, reward, sensitization and tolerance (Pons et al. 2008; Tapper et al. 2004, 2007). These studies provide evidence for the involvement of α4β2* receptors in nicotine behaviors.

Given this literature, early human genetic studies of the genes coding for nAChR subunits focused on CHRNA4 and CHRNB2. For CHRNB2, results of association studies have been mixed. Early studies that were limited in sample size did not find an association between CHRNB2 variants and ND (Etter et al. 2009; Feng et al. 2004a; Li et al. 2005; Lueders et al. 2002; Silverman et al. 2000). However, more recent work examining other nicotine-related phenotypes (such as smoking initiation, subjective effects and abstinence rates) has detected evidence for association (Conti et al. 2008; Ehringer et al. 2007; Greenbaum et al. 2006; Hoft et al. 2011; King et al. 2012; Perkins et al. 2009). Furthermore, common and rare genetic variants in CHRNB2 have been associated with ND in individuals seeking smoking cessation treatment (Wessel et al. 2010).

In contrast to the human genetics studies examining an association between CHRNB2 and ND, the work with CHRNA4 has been more consistent. Feng et al. initially reported an association between rs1044396 and rs1044397 and ND in Chinese males (Feng et al. 2004a). Since this report, additional studies have reported associations between ND and CHRNA4 variation (Breitling et al. 2009; Chu et al. 2011; Han et al. 2011; Li et al. 2005; Saccone et al. 2009, 2010; Xie et al. 2011).

The goal of this study was to investigate the role of CHRNA4 and CHRNB2 in lifetime DSM-IV ND symptom counts in two large young adult samples ascertained through clinical referrals for drug treatment. These samples are unique because subjects were recruited as young adults and have been assessed longitudinally. Single nucleotide polymorphisms (SNPs) in each gene were selected to capture most common genetic variation and Family-Based Association Tests (FBAT) were employed to test for association with DSM-IV ND symptoms.

Materials and methods

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

We examined the association between genetic variation in the CHRNA4 and CHRNB2 genes and ND symptoms in two sample populations: Center on Antisocial Drug Dependence (CADD) and Genetics of Antisocial Drug Dependence (GADD), described in detail below. These samples are unique because they initially represented heavy drug using adolescents who have been followed longitudinally. Our primary analysis with SNPs in CHRNA4 and CHRNB2 was performed in the CADD sample. Initial analyses were performed in non-Hispanic European Americans (EA) from each population. To increase the sample size we then included the second largest ethnic subset in our populations, individuals who self-identified as Hispanic. As we are using family-based methods, false-positive associations due to population stratification are unlikely. Following analysis in the CADD sample, we examined the GADD population as a replicate sample using only the two top SNPs found within CHRNA4.

Samples

Center on Antisocial Drug Dependence

The CADD sample was recruited as a longitudinal study designed to study the genetics of adolescent drug abuse. For this analysis, we focused on subjects recruited through the family portion of this study who were 17 years or older at the time of assessment. Details regarding sample ascertainment and exclusion criteria have been described previously (Stallings et al. 2003, 2005). Briefly, clinical probands were recruited from residential and outpatient treatment facilities in the Denver metropolitan area operated by the Department of Psychiatry, University of Colorado School of Medicine. Clinical probands were drawn from individuals with consecutive admissions to the treatment facilities between February 1993 and June 2001. Community-based probands were recruited to be matched to clinical probands based on age, gender, ethnicity and zip code. All individuals living in the same household as the proband were asked to participate in the study. The CADD is currently in its third wave of data collection. For this study, data collected during wave 2 were included in the analysis. If the subjects were not assessed at wave 2, data from wave 1 were included. Substance use was assessed with the Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM; Cottler & Keating 1990). Individuals were also assessed for psychopathologies (e.g. depression, anxiety and antisocial personality disorder) using the Diagnostic Interview Schedule for DSM-IV (DIS; Robins et al. 2000). At wave 2, 27% of the control population met criteria for lifetime major depression, 8% for generalized anxiety and 29% for antisocial personality disorder compared to 33%, 15% and 67% in the clinical population, respectively. At the time of assessment, buccal cell DNA was collected from subjects on a voluntary basis. All recruiting, assessment and DNA collection procedures were approved by the University of Colorado's IRB. For analysis, genotypic data from all available family members were included, but the phenotypic data were limited to the offspring generation (probands, siblings, half-siblings, cousins and adopted siblings who were less than 35 years old at the time of assessment). Table 1 provides descriptive information about the sample used in this analysis.

Table 1. Descriptive statistics of the test populations
 CADD

European American

CADD

Hispanic

GADD European AmericanGADD

Hispanic

CommunityClinicalCommunityClinical
Genotyped subjects788944397614662290
Subjects in the offspring generation341469193346445245
Males (%)244 (71.6)339 (72.3)129 (66.8)238 (68.8)296 (66.5)131 (53.5)
Age (years ± SD)21.33 ± 3.7523.0 ± 4.4321.62 ± 4.6022.93 ± 4.3018.36 ± 1.8518.73 ± 2.10
Tolerance (%)77 (22.58)256 (54.58)26 (13.47)118 (34.10)198 (44.49)64 (26.12)
Withdrawal (%)65 (19.06)245 (52.24)20 (10.36)79 (22.83)177 (39.78)55 (22.45)
Taken in larger amounts or longer than intended (%)62 (18.18)230 (49.04)24 (12.44)114 (32.95)169 (37.98)73 (29.80)
Persistent desire or unsuccessful attempts to decrease (%)83 (24.34)293 (62.47)37 (19.17)162 (46.82)247 (55.51)114 (46.53)
A great deal of time spent obtaining or using nicotine (%)67 (19.65)284 (60.55)23 (11.92)108 (31.21)210 (47.19)78 (31.84)
Important activities forgone (%)10 (2.93)73 (15.57)5 (2.59)15 (4.34)42 (9.44)8 (3.27)
Continued use despite problems (%)42 (12.32)180 (38.38)14 (7.25)58 (16.76)122 (27.42)37 (15.10)
DSM-IV nicotine dependence symptoms (mean ± SD)1.19 ± 1.883.28 ± 2.170.77 ± 1.461.89 ± 1.942.62 ± 2.231.75 ± 1.98
Z_score tobacco symptoms0 ± 11.09 ± 1.190 ± 10.77 ± 1.430.99 ± 1.220.89 ± 1.53
DSM-IV diagnosis of nicotine dependence (%)80 (23.5)306 (65.2)28 (14.5)121 (35.0)233 (52.4)84 (34.3)
One or more substance use disorder (%)131 (38.4)371 (79.1)75 (38.9)247 (71.4)301 (67.6)165 (67.3)
Number of substance use disorders (mean ± SD)0.66 (1.08)2.17 (1.79)0.73 (1.18)1.51 (1.39)1.63 (1.60)1.39 (1.39)
Genetics of Antisocial Drug Dependence

The GADD sample is a multisite research project that began in 2000 to focus on antisocial drug dependence. Probands in Denver and San Diego were identified via residential or outpatient treatment programs, involvement with the criminal justice system or special schools with the presumption of having a substance use disorder or conduct disorder. A sibling and one or both biological parents who agreed to participate were also tested. At the time of recruitment probands were 14–19 years old, had an IQ of >80, had one or more lifetime substance dependence symptom (excluding ND symptoms) and had at least one conduct disorder symptom. The GADD is a longitudinal study currently in the second wave of data collection. Data from wave 1 were included in the analysis. Psychiatric diagnoses were evaluated using the DIS or Diagnostic Interview Schedule for Children (DISC; Shaffer et al. 2000) for those under 17 years of age. At wave 1, 23% of the sample met the criteria for lifetime major depression, 11% for generalized anxiety and 57% for antisocial personality disorder (only measured in the adult portion of the sample). Subjects were assessed for lifetime ND symptoms with the CIDI-SAM, and DNA was voluntary obtained through either buccal cells or blood. All recruitment, assessment and DNA collection procedures were approved by the IRBs at the University of Colorado and University of California. A similar analytical approach was taken with the GADD sample as the CADD. Briefly, only participants who were 17 or older at the time of assessment were included in the analysis. Genotypic data available for all participants were included in the analysis, but phenotypic data were included only for the offspring generation.

SNP selection and genotyping

Twenty SNPs in CHRNA4 and CHRNB2 were originally genotyped for this study. Polymorphisms were selected on a number of criteria. Single nucleotide polymorphisms were chosen based on the work of Conti et al. (2008) who examined a number of SNPs in these genes and reported low linkage disequilibrium (LD) in their sample to test statistically independent loci (HapMap data were also examined, where available). Second, SNPs were selected based on previous evidence for association with smoking behaviors in other studies (Breitling et al. 2009; Conti et al. 2008; Ehringer et al. 2007, 2010; Feng et al. 2004b; Greenbaum et al. 2006; Han et al. 2011; Hoft et al. 2011; Hutchison et al. 2007; Li et al. 2005; Saccone et al. 2009, 2010). Finally, only common SNPs [minor allele frequency of >0.01 in dbSNP HapMap CEU population (http://www.ncbi.nlm.nih.gov/SNP/) or had a minor allele frequency of >0.01 in the CADD sample in our previous work (Ehringer et al. 2007)] were included. Genomic DNA was extracted from either buccal or blood cells and amplified with primer extension preamplification (Anchordoquy et al. 2003) or using the REPLI-g kit according to the manufacturer's protocol (Qiagen, Valencia, CA, USA). TaqMan assays were used for SNP genotyping according to the manufacturer's instructions (Applied Biosystems, Foster City, CA, USA). Polymerase chain reactions (PCRs) were set up with a Biomek® 3000 Laboratory Automation Workstation (Beckman Coulter Inc, Brea, CA, USA) and cycled in an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems) or ABI PRISM® 7900 (Applied Biosystems). The ABI PRISM® 7900 was used to analyze PCR products. Initially, the genotype clusters were autocalled by the Applied Biosystems Sequence Detection System v. 2.3 (Applied Biosystems) and then visually examined by two independent lab personnel. DNA samples with overall call rates <90% were excluded. All SNPs were genotyped in a subset of samples in replicate reactions to determine the genotyping error rate. Five SNPs (rs1127313, rs7543174, rs2072660, rs3787140 and rs3787138) had an error rate above 1% and thus were excluded from further analysis; the remaining SNPs had an average error rate of 0.4%. In total, 15 SNPs (Fig. 1) were included in the analysis. Mendelian errors were detected using PLINK's (http://pngu.mgh.harvard.edu/purcell/plink/; Purcell et al. 2007) Mendel option. If there was a Mendelian error detected in only one SNP within a family, this was considered to be sporadic and the genotypes for that family at the single SNP were removed. If there were two or more Mendelian errors detected within a family, the genotypes of the entire family were removed at all loci.

image

Figure 1. Diagram of the SNPs tested in relationship to (a) CHRNA4 and (b) CHRNB2 genes. Figure was created in the UCSC genome browser (http://genome.ucsc.edu) using the 37.2 genome build.

Download figure to PowerPoint

Statistical methods

SNP assessment

SNPs were assessed for LD and conformity to Hardy–Weinberg equilibrium (HWE) with Haploview (Barrett et al. 2005). As both the CADD and GADD are family-based samples, one individual per family was randomly selected for the HWE analysis.

Statistical analyses

All seven DSM-IV ND symptoms were included. Briefly, an individual who endorsed one of the dependence criteria was coded as ‘1’, whereas those who did not endorse the symptom were coded as ‘0’. A sum of all seven symptoms was constructed, such that each individual scored between 0 and 7. Non-smokers were coded as having zero ND symptoms. Nicotine dependence symptom counts were standardized in the CADD community sample with residuals from sex, age and age2 based on a linear regression run in SAS (SAS Institute Inc., Cary, NC, USA). The β-coefficients established from the CADD community sample were then applied to the CADD clinical and GADD samples. The correlation between sum number of ND symptom counts and the final standardized score was 0.98 in CADD and 0.99 in GADD (both P-values < 0.001). This score was used as the primary dependent variable in a family-based association test carried out in the FBAT software package v 2.0.2c using an additive model (Laird et al. 2000). To correct for the testing of multiple SNPs, the Min-p option in FBAT was used (Rakovski et al. 2007); this performs a permutation test to indicate the significance of the most significant statistic when corrected for multiple testing. The Min-p correction was performed on the total number of SNPs tested across both genes.

Results

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

HWE and LD of the SNPs

Using the threshold of P < 0.001 (Barrett et al. 2005), all SNPs conformed to HWE (Tables 2 and 3). To determine if SNPs represented statistically independent loci, LD was examined. Figures S1–S3, Supporting Information present the LD found among the SNPs tested in both samples in the non-Hispanic EA and Hispanic subsets. Single nucleotide polymorphisms rs1127314 and rs3766927 in CHRNB2 were highly correlated in both the CADD non-Hispanic EA and Hispanic sample (r2 = 0.99–1), but all other SNPs represent independent loci (r2 ≤ 0.70). As rs1127314 and rs3766927 represent a single genetic signal, rs3766927 was not tested for association with ND.

Table 2. Haploview results for SNPs utilized in the CADD sample
SNPLocationGeneAlleles Major/MinorMinor allele frequencyHardy–Weinberg P-value
CADD

European

American

CADD

Hispanic

CADD

European

American

CADD

Hispanic

  1. Location from dbSNP in reference to the CHRNA4 or CHRNB2 gene. Major and minor allele is defined by the European American sample; alleles for rs755203 designated by a * are reversed in the Hispanic sample.

rs755203UpstreamCHRNA4A/G*0.4390.4830.6820.136
rs2093107UpstreamCHRNA4G/A0.0770.1380.5171
rs1044394Exon 5 – synonymousCHRNA4G/A0.0730.0790.9810.995
rs1044396Exon 5 – synonymousCHRNA4A/G0.4560.4640.4590.418
rs2236196UTR-3′CHRNA4A/G0.2650.2420.9840.469
rs6090378UTR-3′CHRNA4A/G0.0540.0491.0000.945
rs4522666DownstreamCHRNA4A/G0.3510.4660.7840.312
rs4809538DownstreamCHRNA4G/A0.2310.4050.2590.483
rs4845652UpstreamCHRNB2C/T0.0940.12710.727
rs2072658UpstreamCHRNB2G/A0.0130.06610.593
rs2072659UTR-3′CHRNB2C/G0.0990.07210.476
rs3811450UTR-3′CHRNB2C/T0.0730.0510.3511
rs9616DownstreamCHRNB2/ADARA/T0.2850.3190.9600.780
rs1127314DownstreamCHRNB2/ADARA/G0.3060.2770.6200.243
rs3766927DownstreamCHRNB2/ADARC/T0.2970.2780.7790.258
Table 3. Haploview results for SNPs utilized in the GADD sample
SNPLocationGeneAllelesMajor/MinorMinor allele frequencyHardy–Weinberg P-value
GADD

European

American

GADD

Hispanic

GADD

European

American

GADD

Hispanic

  1. Location from dbSNP in reference to the CHRNA4 gene. Major and minor allele is defined by the European American sample; alleles for rs1044396 designated by a * are reversed in the Hispanic sample.

rs1044394Exon 5 – synonymousCHRNA4G/A0.0640.0660.121
rs1044396Exon 5 – synonymousCHRNA4A/G*0.4750.4830.7430.588

SNPs in CHRNA4 are associated with ND

Genetic variation in the CHRNA4 gene, but not in the CHRNB2 gene, showed nominal association with DSM-IV nicotinic dependence symptoms (Table 4). Analysis was performed on both genes using FBAT (Laird et al. 2000). When CHRNA4 was analyzed in the non-Hispanic EA CADD sample, two SNPs (rs1044394 and rs1044396) were significant at an uncorrected P < 0.05. Permutation testing carried out using the Min-p option in FBAT (Rakovski et al. 2007) indicated that the most significant statistic reached a suggestive P-value of 0.12 when corrected for multiple testing. When individuals who self-identified as Hispanic were included in the analysis, rs1044394 remained at a suggestive P = 0.063, whereas the P-value associated with rs1044396 increased to 0.171.

Table 4. Results of association studies in the CADD sample with selected SNPs in the CHRNA4 andCHRNB2 and lifetime DSM-IV nicotine dependence symptoms
SNPGeneCADD

European American

CADD

European American and Hispanic

Risk alleleInformative familiesZP-valueRisk alleleInformative familiesZP-value
  1. Family-based tests of association were carried out with FBAT (Laird et al. 2000). The SNP rs3766927 was not tested because it is in high LD with rs1127314, see Fig. S2.

  2. a

    Min-p result = 0.12.

rs755203CHRNA4G1101.0920.275G1820.5900.555
rs2093107CHRNA4A321.0130.311G780.5270.598
rs1044394CHRNA4A382.5730.010aA621.8600.063
rs1044396CHRNA4G1162.2170.027G1841.3700.171
rs2236196CHRNA4G1011.7770.076G1651.0660.287
rs6090378CHRNA4G310.1900.849A530.0750.941
rs4522666CHRNA4G1030.7550.450G1650.7010.483
rs4809538CHRNA4A850.3540.723A1520.5210.603
rs4845652CHRNB2T451.6260.104T801.0630.288
rs2072658CHRNB2G111.4000.162A320.3320.740
rs2072659CHRNB2G520.6160.538G761.4980.134
rs3811450CHRNB2C460.9130.361C591.2160.224
rs9616CHRNB2/ADART990.0720.942T1580.2460.805
rs1127314CHRNB2/ADARA970.7790.436A1610.3960.692
rs3766927CHRNB2/ADAR

In contrast to the results with CHRNA4, no SNPs in CHRNB2 were associated with DSM-IV ND symptoms (Table 4) in the CADD non-Hispanic EA sample. Increasing the number of informative families with the addition of the Hispanic subsample also did not yield significant associations.

We attempted to replicate the genetic findings in an independent sample population, the GADD. In this sample, we only genotyped the top two nominally significant SNPs in CHRNA4. In the replication sample, we did not detect an association with SNPs in CHRNA4 in either the non-Hispanic EA alone or when the Hispanic sample was included (Table 5).

Table 5. Results of association studies in the GADD sample with selected SNPs in the CHRNA4 and lifetime DSM-IV nicotine dependence symptoms
SNPGeneGADD

European American

GADD

European American and Hispanic

Risk alleleInformative familiesZP-valueRisk alleleInformative familiesZP-value
  1. Family-based tests of association were carried out with FBAT (Laird et al. 2000).

rs1044394CHRNA4G141.0520.293G160.5270.598
rs1044396CHRNA4G430.9730.331G620.6700.503

Finally, we combined the CADD and GADD samples to determine if there was an association signal present in these genes. Both SNPs tested in the combined non-Hispanic EA CADD and GADD sample showed nominal associations with DSM-IV ND symptoms (all P-values < 0.05; Table 6). When taken into account that two SNPs were tested, the significance level associated with rs1044396 exceeds the Min-p threshold (P < 0.05). In the combined EA and Hispanic population both CHRNA4 SNPs were no longer significantly associated with ND.

Table 6. Results of association studies in the combined CADD and GADD sample with selected SNPs in the CHRNA4 and lifetime DSM-IV nicotine dependence symptoms
SNPGeneCombined

European American

Combined

European American and Hispanic

Risk alleleInformative familiesZP-valueRisk alleleInformative familiesZP-value
  1. Family-based tests of association were carried out with FBAT (Laird et al. 2000).

  2. a

    Min-p result < 0.05.

rs1044394CHRNA4A521.9880.047A781.5640.118
rs1044396CHRNA4G1592.3980.017aG2461.5190.129

Discussion

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

In this study, we investigated the role of genetic variation in the genes that code for the α4 and β2 nAChR subunits in a quantitative measure of lifetime DSM-IV ND symptoms in two separate samples. Six and eight SNPs in each gene (CHRNB2 and CHRNA4, respectively) were examined in the CADD sample using methods that account for LD between SNPs. Modest evidence for association with two SNPs was detected in CHRNA4. In CHRNA4, the minor alleles of rs1044394 and rs1044396 were associated with increased risk for ND symptoms (P = 0.010 and 0.027, respectively). This association was strongest in the non-Hispanic EA portion of the sample, and although the number of informative families increased with the addition of the Hispanic population, this did not provide additional support for an association. However, when these SNPs were examined in the GADD sample, the associations did not replicate.

Our findings are consistent with early human genetics studies that provided no evidence of an association with variation in CHRNB2 and ND (Etter et al. 2009; Feng et al. 2004a; Li et al. 2005; Lueders et al. 2002; Silverman et al. 2000). Two distinct genetic loci in CHRNB2 have recently been associated with nicotine behaviors in multiple studies. Two highly correlated SNPs in the 3′ UTR region rs2072660/rs2072661 have been associated with smoking initiation (Greenbaum et al. 2006), smoking cessation (Conti et al. 2008; Perkins et al. 2009) and ND (Wessel et al. 2010). We attempted to genotype rs2072660 in this study, but the error rate in our sample was 1.3%; thus, we did not include this SNP in our analyses.

Similarly, SNP rs2072658 in the 5′ UTR of CHRNB2 has been associated with the subjective effects reported when subjects were asked to retrospectively recall how nicotine made them feel shortly after taking the drug (Ehringer et al. 2007) and reported subjective responses to cigarettes when smoked in a controlled laboratory setting (Hoft et al. 2011). However, there was no evidence for association with rs2072658 in this study of ND symptoms.

The two CHRNA4 SNPs implicated in this study with ND symptoms have been implicated previously in smoking-related phenotypes. The SNP rs1044396 has been nominally associated with nicotine addiction, ND, heavy and regular smoking vs. control, and smoking quantity (Breitling et al. 2009; Feng et al. 2004b; Li et al. 2005), although a recent study found no association between this SNP and smoking status (Tsai et al. 2012). Together with the data presented here, there are now three studies showing evidence that the minor allele of rs1044396 increases the risk of ND (Breitling et al. 2009; Feng et al. 2004b).

For rs1044394, one other study has provided evidence of an association with ND as measured both by DSM-IV ND criteria and the Fagerström Test for ND (Han et al. 2011). In contrast to our results where the minor allele was associated with increased risk of ND symptoms, Han et al. found evidence that the minor allele lowered the risk of ND (Han et al. 2011). Differences do exist between these studies that could account for the difference in effect. The Han et al. study examined both African Americans and EA, whereas we examined non-Hispanic EA and self-identified Hispanics. The association with rs1044394 and ND was driven by the African American sample in their study (although there was suggestive evidence in the EA sample). It is also possible that the differences observed between these studies are due to differences in rare functional variants associated with rs1044294 that differ between our samples. There is evidence that CHRN SNPs show opposite allelic effects with different drugs (Grucza et al. 2008; Sherva et al. 2010; Wang et al. 2008) and in some cases the major allele in EA samples is the minor allele in African American samples, so this is another factor that may contribute to directional differences in effect. Both rs1044394 and rs1044396 are synonymous mutations located in the fifth exon of CHRNA4. However, possible functional effects of these alleles remain unknown.

Our clinical subjects form an extreme sample in terms of drug abuse in line with the ascertainment criteria (having at least one drug dependence symptom, excluding ND criteria). Over 70% of the CADD clinical subjects have at least one substance use disorder other than ND. The GADD sample is similar in that 67% of the sample has one or more substance use disorders. We did not control for the use of other substances in our study, and given the extreme nature of this sample, this may limit the generalizability of these results. Additionally, in this analysis, we have included subjects who have not tried tobacco products. While many groups limit their samples to subjects meeting a certain use threshold (e.g. Saccone et al. 2009, 2010) others have found associations with the CHRNA4 gene among samples including individuals who have never smoked (Han et al. 2011). This is an important limitation to this study and it is possible that this association is driven by tobacco use rather than dependence specifically, but unfortunately we do not have enough subjects to test this hypothesis.

Our results provide evidence that common genetic variation in CHRNA4 is associated with ND. Rare genetic variation in this region has also been associated with this trait (Wessel et al. 2010; Xie et al. 2011). Together, these results show that both common and rare variations in this region of CHRNA4 are likely to influence ND.

Although we found evidence of an association between SNPs in CHRNA4 and ND symptoms in the CADD sample, our results with non-Hispanic EA were stronger than when Hispanics were also included in the analysis. We may have observed this pattern because in our samples the non-Hispanic EA show more severe ND compared with the Hispanics (see Table 1). In our samples, the EA have a greater number of DSM-IV ND criteria as well as a greater number of substance use disorders compared with the corresponding Hispanic sample.

Furthermore, we were unable to replicate our results in the GADD sample, and for rs1044394 the ‘risk’ allele was different. We chose the GADD as a replication sample because it was ascertained in a manner similar to the CADD with recruitment being through drug abuse treatment centers. Overall, the samples were phenotypically similar (see Table 1). However, the entire CADD sample was ascertained in the Denver metro area, whereas the GADD sample was recruited in both Denver and San Diego. Furthermore, there were fewer informative families in the GADD analysis compared with the CADD; thus, there was less power to detect an association in this sample (see Table 5). In the GADD non-Hispanic EA sample there were only 14–43 informative families per SNP (compared with 32–116 for the same SNPs in CADD). This is quite a small number of informative families and would only provide limited power to detect an association. Given this limitation, future studies that have greater power to detect an association are warranted.

Taken together, results from this study follow the pattern of complexity typically observed for psychiatric genetics, which is illustrative of how both genetic diversity (ethnic differences) and phenotypic heterogeneity (comorbidity with other disorders) make it difficult to interpret non-replications. In the CADD sample, we found suggestive evidence of associations between genetic variation in CHRNA4 and lifetime DSM-IV ND symptoms, but this result must be interpreted with caution. As the results did not replicate in the GADD, which is phenotypically quite similar, it is not clear whether this represents a true non-replication or if it is due to reduced sample size and statistical power. It is encouraging that the nominal associations reported here are consistent and replicate prior results with CHRNA4 in separate samples. In conclusion, this work adds to the emerging literature exploring the role of these genes in smoking-related behaviors, suggesting that the common α4β2* nicotinic receptors remain an important possible target for pharmacogenetic therapies.

References

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information
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Acknowledgments

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

We thank the subjects who volunteered to be part of the CADD and GADD studies without which none of this work would have been possible. We acknowledge Ryan Cox for his assistance making the tables and figures herein and Jill Miyamoto for technical assistance. This work was supported by NIH grants K01 AA019447 (H.M.K.), R01 AA017889 (M.A.E.), R01 DA021913 (C.J.H.), R01 DA021905 (S.A.B.), P60 DA011015 (J.K.H.) and R01 DA012845 (J.K.H.). Drs H.M.K. and M.A.E. are current members of IBANGS.

Supporting Information

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information
FilenameFormatSizeDescription
gbb12021-sup-0001-Figure S1.docWord document61KFigure S1: Haploview graphs of the pairwise linkage disequilibrium (LD) between SNPs in CHRNA4 in the CADD (a) European American and (b) Hispanic sample. The numbers inside the squares denote the pairwise correlation (r2) between SNPs.
gbb12021-sup-0002-Figure S2.docWord document53KFigure S2: Haploview graphs of the pairwise linkage disequilibrium (LD) between SNPs in CHRNB2 in the CADD (a) European American and (b) Hispanic sample. The numbers inside the squares denote the pairwise correlation (r2) between SNPs.
gbb12021-sup-0003-Figure S3.docWord document32KFigure S3: Haploview graphs of the pairwise linkage disequilibrium (LD) between SNPs in CHRNA4 in the GADD (a) European American and (b) Hispanic sample. The numbers inside the squares denote the pairwise correlation (r2) between SNPs.

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