Human Genetics

, Volume 128, Issue 5, pp 491–499

Nicotinic acetylcholine receptor genes on chromosome 15q25.1 are associated with nicotine and opioid dependence severity


  • Porat M. Erlich
    • Center for Health ResearchGeisinger Health System
    • Department of MedicineTemple School of Medicine
  • Stuart N. Hoffman
    • Department of NeurologyGeisinger Health System
  • Margaret Rukstalis
    • Center for Health ResearchGeisinger Health System
  • John J. Han
    • Department of Pain MedicineGeisinger Health System
  • Xin Chu
    • Weis CenterGeisinger Health System
  • W. H. Linda Kao
    • Department of EpidemiologyJohns Hopkins Bloomberg School of Public Health
  • Glenn S. Gerhard
    • Weis CenterGeisinger Health System
  • Walter F. Stewart
    • Center for Health ResearchGeisinger Health System
    • Department of EpidemiologyJohns Hopkins Bloomberg School of Public Health
    • Center for Health ResearchGeisinger Health System
    • Department of Medicine and PediatricsMt Sinai School of Medicine
    • Department of PsychiatryTemple School of Medicine
Original Investigation

DOI: 10.1007/s00439-010-0876-6

Cite this article as:
Erlich, P.M., Hoffman, S.N., Rukstalis, M. et al. Hum Genet (2010) 128: 491. doi:10.1007/s00439-010-0876-6


A locus on chromosome 15q25.1 previously implicated in nicotine, alcohol, and cocaine dependence, smoking, and lung cancer encodes subunits of the nicotinic acetylcholine receptor (nAChR) expressed in the mesolimbic system and thought to mediate substance dependence. Opioid dependence severity (ODS), nicotine dependence severity (NDS), smoking status and quantity, and the number of attempts to quit were assessed using questionnaire instruments in 505 subjects who were prescribed opioid medications for chronic pain in outpatient practice sites. Multivariate regression was used to test for genetic association of these phenotypes with 5 SNPs in the nAChR gene cluster on chromosome 15q25.1, adjusting for background variables. A coding variant in CHRNA5 (rs16969968[A]) was significantly associated with 1.4-unit higher ODS (p < 0.00017). A variant in the 3′ untranslated region of CHRNA3 (rs660652[G]) was significantly associated with 1.7-fold higher odds of lifetime smoking (p < 0.0092), 1.1-unit higher NDS (p < 0.0007), 0.7 more pack-years of cigarette smoking (p < 0.0038), and 0.8 more lifetime attempts to quit (p < 0.0084). Our data suggest an association of DNA variants in the nAChR gene cluster on chromosome 15q25.1 with ODS, as well as NDS and related smoking phenotypes. While the association of this locus with NDS and smoking phenotypes is well known, the association with ODS, a dimension of opioid substance dependence, is novel and requires verification in independent studies.



Linkage disequilibrium


Polymerase chain reaction


Minor allele frequency


Opioid dependence severity


Severity of dependence scale


Nicotine dependence severity


Fagerstrom tolerance scale


Electronic health record


Nicotinic acetylcholine receptor


Cholinergic receptor nicotinic alpha


Single nucleotide polymorphism


Smoking continues to be a major cause of preventable death, disability, illness, and healthcare costs worldwide (Hays and Ebbert 2008). Despite evidence that treatment improves smoking cessation rates, an estimated 20–25% of the adult population in the US still smoke (Centers for Disease Control and Prevention 2009; The Clinical Practice Guideline Treating Tobacco Use and Dependence 2008 Update Panel, Liaisons, and Staff 2008), resulting in 440,000 smoking-related deaths per year and over $85 billion in annual healthcare expenditures (Heitjan et al. 2008). Many of those who report a desire to quit smoking do not seek treatment and are unable to quit smoking on their own. Advances in the effort to reduce the prevalence of smoking may depend, in part, on a better understanding of the molecular components involved in the development and reinforcement of nicotine dependence and personalizing treatment protocols (Johnstone et al. 2002; Li 2006).

The heritability of nicotine dependence estimated from twin studies is 40 to 60% (Sullivan and Kendler 1999; Lessov et al. 2004). Recent genome-wide association and comprehensive candidate-gene studies consistently identified variation on chromosome 15q25.1 as the most significant genome-wide location for lung cancer, nicotine dependence, and smoking (Amos et al. 2008; Hung et al. 2008; Spitz et al. 2008; Thorgeirsson et al. 2008; Wang et al. 2008; Portugal and Gould 2008; Berrettini et al. 2008; Saccone et al. 2007; Li and Burmeister 2009; Lips et al. 2010; Bierut et al. 2008), suggesting a biologically plausible link between this locus, which encodes components of the nicotinic acetylcholine receptor (nAChR), and increased susceptibility to nicotine dependence and consequent lung cancer risk.

Fine-mapping studies found two distinct association signals for smoking located on separate LD clades within the 15q25.1 locus, one of which included rs16969968, a coding polymorphism in CHRNA5 with a functional effect on nAChR activity in vitro. Berattini et al. (2008) found a haplotype encompassing CHRNA5 and CHRNA3 that conferred predisposition to nicotine dependence. However, the exact locations of underlying functional polymorphism(s) in 15q25.1 affecting nicotine dependence and smoking are not fully mapped.

Twin studies suggest common genetic predisposition factors for multiple substance dependence phenotypes (Xian et al. 2008) and this concept has been reflected in current discussions related to addiction onset and course (Robinson and Berridge 2008; Li et al. 2007; Hogarth and Duka 2006). In addition to nicotine dependence, polymorphisms in 15q25.1 are associated with alcohol and cocaine dependence (Grucza et al. 2008; Wang et al. 2009); however, their association with phenotypes of opioid dependence has not been conclusively shown. Given these prior findings and knowledge gaps, we hypothesized that genetic variants of 15q25.1 are associated with NDS, smoking phenotypes, and ODS. We tested these hypotheses in a sample of 505 ambulatory care patients with a history of long-term prescription opioid use.

Subjects and methods

Source population and recruitment

The study’s recruitment and data collection procedures have been described elsewhere (Boscarino et al. 2010). Briefly, the Geisinger Institutional Review Board (IRB) approved the study. Following IRB approval, the electronic health record (EHR) database of Geisinger Clinic was searched to identify individuals with a history of four or more opioid drug prescriptions electronically ordered within a 12-month period. Geisinger Clinic, the ambulatory care division of the Geisinger Health System, is a Pennsylvania not-for-profit corporation operating a multi-specialty group medical practice treating outpatients at primary care clinics, specialty care clinics, community practice sites, and ambulatory surgery centers. All clinics and surgery centers in this healthcare system, which are located in 31 of Pennsylvania’s 67 counties, have used Epic System’s (Epic System Corporation, Verona, WI) EHR since 2001. The clinical directors of 22 primary care and specialty care clinics having the highest number of potential study subjects were contacted for study participation. The directors of nine primary care and three specialty care clinics (including an orthopedics, pain and rheumatoid clinic) agreed to allow study investigators to contact study-eligible subjects for research participation.

Individuals were eligible for the study if they were 18+ years old as of 1 July 2007, had electronic prescriptions for opioid medications four or more times for non-malignant cancer pain at any time from 30 June 2006 through to 1 July 2007, and the majority of opioid drug orders were placed at one of the participating clinics. Individuals were excluded if they had a diagnosis of cancer associated with their medication orders during the study index period, if they were deceased, or if they had previously declined participation in research studies. Because the proportion of non-whites in the sample was <2%, while representative of the area served by the Geisinger System, these individuals were excluded from the analyses to prevent admixture artifacts. We did not restrict eligibility to filled prescriptions in this initial eligibility query because medication use was verified later in the telephone interview.

Interviews were conducted from August 2007 to November 2008. We first mailed an introductory letter to 2,459 eligible individuals to explain the purpose of the research and the study’s confidentiality protocols. Individuals were also notified that they could call to be excluded from further contact. Trained telephone interviewers subsequently attempted to contact 2,373 eligible individuals by telephone; 86 individuals were not contacted because the study quota was filled before they were called. Up to 15 call attempts were made to complete telephone interviews with the study subjects. Telephone contact was made with 1,390 individuals. The remaining 983 were not reachable or not qualified for the following reasons: deceased, institutionalized, not proficient in English, incapable of answering questions, or denied taking pain medications. In the beginning of each interview following an introduction, identification, and explanation of the study by the interviewer, each contacted person was asked if he/she is willing to be interviewed. Those who agreed and completed the interview were sent a consent form accompanied by a buccal swab kit and pre-paid return envelope. Altogether, 685 persons declined to participate. Finally, out of the 705 individuals who completed the interview, 505 (72%) returned the adequately signed consent form and buccal swab. An additional 17 buccal swabs did not produce adequate DNA for genotyping. Up to 5 attempts (by mail and telephone) were made to remind each interviewed participant to return the consent form and DNA sample. Details on data collection follow.

Study interviews

Following verbal consent, a structured diagnostic telephone interview was administered using a computer-assisted telephone interview (CATI) system (WinCati, version 4.2 [Sawtooth Technologies, Northbrook, IL, USA]). For our study, we used an existing diagnostic interview (Kessler and Ustun 2004), modified to assess prescription opioid misuse. The instrument also collected data on other substance use disorders, as well as demographic and socio-economic information. In addition, other questionnaire instruments were used to assess cigarette smoking and tobacco dependence (Fagerstrom 1978; Heatherton et al. 1991).

Interviews required 50 to 60 min to complete and were administered by staff with experience and training in the use of the survey instrument and in emergency mental health referral protocols. A referral list of local drug and alcohol counseling services was available and provided during the telephone interview and by mail, if requested. Onsite phone room managers and investigative staff supervised and monitored the interviewers. The buccal swab kit was mailed to consenting individuals, along with a postage-paid return envelope. Participants were offered an incentive for their interview time and effort and for retuning a buccal swab sample by mail.

Phenotypic measures and potential confounders

Smoking status was defined according to the question “did you smoke 100 cigarettes or more in your life?” administered in the telephone interview.

Pack-years of cigarette smoking was calculated as the product of the reported number of cigarettes smoked per day times the total number of years smoked.

Number of attempts to quit smoking was ascertained from the response to the question: “did you ever try to quit smoking?” and if yes “how many times did you try to quit smoking?”

Nicotine Dependence Severity (NDS): we used the Fagerstrom Tolerance Scale (FTS) to assess NDS in the telephone interview (Fagerstrom 1978). FTS measures smoking behaviors associated with NDS and is scored on a scale of 0–11, where higher scores are associated with greater nicotine dependence and withdrawal symptoms. This score was analyzed both as a continuous variable and a dichotomized variable. For the latter, a score of 7+ was used to define nicotine dependence based on prior studies (Heatherton et al. 1991).

Opioid dependence severity (ODS): we used the Severity of Dependence Scale (SDS) for opioids to assess ODS (Gossop et al. 1995). The SDS is based on 5 items, each scored on a 4-point scale (0–3). The total score is obtained by summing the 5-item ratings. The higher the score on the SDS, the higher the severity of drug dependence as validated in previous studies (Gossop et al. 1995; Kaye and Darke 2002; World Health Organization 2009). The questions comprising the SDS instrument were: (i) Do you think your use of prescription opioids was out of control? (ii) Did the prospect of missing a dose make you anxious or worried? (iii) Did you worry about your use of prescription opioids? (iv) Did you wish you could stop? (v) How difficult did you find it to stop or go without prescription opioids?

Brief pain inventory (BPI): to assess the level of pain among study participants we used the Brief Pain Inventory (BPI) (Cleeland and Ryan 1994; Tan et al. 2004), a widely used pain assessment scale. The BPI was used here to assess the current level of overall pain, the level of pain over the past week, and pain-related functional impairment, i.e., to what extent pain has interfered with the participant’s work or lifestyle over the past week.

Other measures ascertained from EHR: clinic type was defined as the category of clinic in which a participant received the majority of his/her opiate prescriptions (primary care vs. specialty care); and number of opioid prescriptions was the count of all EHR drug orders for opioids received over the past 3 years.

Other measures ascertained in telephone survey: low household income (i.e., classified as low if the total income of the household was ≤$30,000), marital status (i.e., married, separated, divorced, single, or widowed), education level (i.e., K to 8th grade, 9 to 11th grade, high school graduate, some college, or college graduate or higher), and employment status (i.e., employed, not employed or retired).

Analytic methods

All statistical tests were performed using SAS version 9.2. (SAS Institute Inc., Cary, NC, USA) except when specified. Multivariate testing for the association of each phenotype with each marker was performed using linear (for continuous traits) or logistic (for binary traits) regression implemented in SAS Proc. GLM or Proc. Logistic, respectively. For each SNP, we first tested an additive intra-locus coding scheme (i.e., subjects assigned 0, 1 or 2 according to the number of copies of the minor allele), followed by dominant/recessive coding schemes (i.e., carriers and homozygotes for the tested allele assigned a value of 1, vs. homozygotes for the alternate allele assigned a value of 0), where the results of the additive analysis were statistically significant. Estimation of linkage disequilibrium and selection of tag-SNPs were performed using the software HaploView and the Tagger subroutine therein (Barrett et al. 2005; de Bakker et al. 2005). Haplotype analysis was performed using the Haplo.GLM subroutine of Haplo.Stats version 1.2 (Schaid et al. 2002) in the R statistical suite version 2.8.1 (R Development Core Team 2008). In this analysis, association tests for 5-SNP haplotypes with NDS and ODS were performed. Rare haplotypes occurring with ≤5 counts in the sample (corresponding to approximately 0.5%) were collapsed.

In models fitted for NDS, smoking phenotypes and ODS, we adjusted for potential confounding by age, sex, clinic type, household income, marital status, education status, and employment status. In models fitted for ODS, we also adjusted for pain level and opioid prescriptions received, given that the ODS in this sample of pain patients is likely to be influenced by the degree of pain and opioid usage. ODS, pack-years, and the number of attempts to quit smoking were log transformed to reduce variable skewness; for ODS, the results of un-transformed analysis are shown, which did not substantially differ from those of the transformed analysis.

For NDS and the various smoking phenotypes (Fig. 2), we show results for the saturated model (adjusted for age, sex, clinic type, low household income, marital status, education status, and employment status) per each phenotype. For ODS (Fig. 3), we show results of five models with stepwise addition of covariates in order to demonstrate that statistical significance of the genetic association increases as more residual confounding is accounted for.

DNA marker selection and genotyping

The approach to marker selection was a combination of agnostic LD tagging with consideration of prior evidence and functional annotation. First, we identified all association signals related to smoking, nicotine dependence, and lung cancer within 15q25.1 in the published literature. Next, we examined the LD structure of the region in the HapMap Caucasian sample (release 23a; and, using the algorithm of Gabriel et al. (2002), estimated the boundaries of the LD block in which the majority of these association signals were located. The following settings were used in this estimation: confidence interval for strong LD set to 0.7–0.98; minimum for strong recombination set to 0.9; and minimum fraction of strong LD in informative comparisons set to 0.95. Finally, we searched dbSNP for non-synonymous polymorphisms occurring within this LD block and found one such polymorphism.

We then used the algorithm of de Bakker et al. implemented in the software HaploView v.4.0 ( (Barrett et al. 2005; de Bakker et al. 2005) to select five tag SNPs for the target block. Of these five markers, two were force-included (the non-synonymous marker in CHRNA5 [rs16969968] and the most significant signal for lung cancer located in CHRNA3 [rs1051730]). The software selected the remaining three SNPs freely. The resulting set of tag-SNPs spanned 25 kb, included rs16969968, rs660652, rs1051730, rs6495308, and rs12443170 and captured 90% of the common variation (MAF ≥ 10%) in the target block with r2 ≥ 0.75 in HapMap Caucasians.

SNP genotyping was performed on an Applied BioSystems 7500 real-time PCR platform using TaqMan kits following the manufacturer’s protocols. Laboratory personnel blinded to phenotype and covariate data assembled the genotyping plates and performed the genotyping. Quality control measures included visual inspection of the allelic discrimination plots, monitoring of concordance of cross-plated duplicate pairs, monitoring of the overall call rate, and monitoring of agreement with Hardy–Weinberg expectation using Fisher’s exact tests. Minor allele frequencies were between 13 and 36% (Table 1). The overall call rate and duplicate concordance rates were >99%. All markers met Hardy–Weinberg expectation. The pairwise LD structure of the target region in HapMap Caucasians as well as in our sample is shown in Fig. 1.
Table 1

Marker specifications (NCBI build 36.3)



Physical location (bp)

MAF (minor/common)

Functional annotation




35% (A/G)

Missense (D⇔N)




36% (A/G)





35% (A/G)





23% (C/T)





13% (A/G)


Basic specifications of markers genotyped in this study including the rs# identifier, physical location, MAF, and functional annotation

MAF minor allele frequency
Fig. 1

Linkage disequilibrium in the target region. The LD structure of the locus in HapMap Caucasians and in our sample. Panel A depicts pairwise r2 estimated in HapMap Caucasians (release 21) for the target haplotype block using HaploView 4.0. Panel B shows pairwise LD in the sample of this study. The boundaries of the target block were set using the algorithm of Gabriel et al. (settings: CI for strong LD = (0.7–0.98); maximum for strong recombination = 0.9; and minimum fraction of high LD in informative comparison = 0.95). SNPs with MAF ≤ 10% were excluded from tagging and from this figure. Five tag-SNPs (highlighted in panel A) were selected using the algorithm of DeBakker et al. These tag-SNPs captured ≥90% of un-typed variation in the target block with r2 ≥ 0.75. The 10-level color scheme for r2 is shown in the legend


The lifetime prevalence of smoking (defined by the question “did you smoke 100 cigarettes or more in your life?”) was 63% overall and significantly (α ≤ 0.01) lower among females compared to males and in older individuals (Table 2). Female smokers had significantly lower mean NDS score and pack-years of cigarettes and fewer attempts at quitting. A status of “no employment” was significantly associated with a higher smoking quantity, higher number of quit attempts, and a higher ODS.
Table 2

Sample characteristics


N (%)

% (OR)

Mean (SE)

Smoking status

Nicotine dependence severity

Number of attempts to quit

Smoking quantity (pack-years)

Opioid dependence severity


505 (100)

63 (NA)

4.3 (0.2)

3.2 (2.8)

15.5 (9.6)

3.8 (0.6)



45 (9)

77 (3.0)*

4.8 (0.5)

3.6 (2.9)

6.0 (3.6)

3.5 (1.0)*


248 (49)

69 (2.1)

4.6 (0.2)

3.8 (3.0)

15.6 (8.3)

4.3 (0.7)


186 (37)

52 (1.0)

3.7 (0.3)

2.4 (2.4)

17.4 (11.9)

3.4 (0.5)


26 (5)

52 (ref)

3.6 (0.8)

2.5 (2.9)

16.9 (12.1)

1.5 (0.3)



153 (30)

77 (ref)*

5.4 (0.3)*

4.3 (3.2)*

20.3 (10.1)*

4.3 (0.6)


352 (70)

57 (0.4)

3.8 (0.2)

2.8 (2.5)

13.4 (9.3)

3.6 (0.6)

Clinic type


104 (21)

69 (ref)

4.9 (0.3)

4.3 (3.3)

16.4 (7.9)

4.2 (0.7)


401 (79)

61 (0.7)

4.1 (0.2)

2.9 (2.6)

15.2 (10.2)

3.7 (0.6)



137 (27)

63 (ref)

4.2 (0.3)

3.1 (2.8)

9.9 (6.4)

3.7 (0.8)


220 (44)

69 (1.3)

4.6 (0.2)

4.0 (3.0)*

18.3 (9.6)*

4.4 (0.6)*


146 (29)

54 (0.7)

3.7 (0.3)

2.2 (2.4)

16.6 (11.8)

2.9 (0.5)

HH income

 <30 K

215 (43)

63 (ref)

4.3 (0.3)

3.3 (2.8)

15.7 (9.3)

3.8 (0.6)

 ≥30 K

233 (46)

62 (1.0)

4.1 (0.2)

3.0 (2.8)

14.1 (9.7)

3.7 (0.6)


57 (11)

66 (1.1)

4.6 (0.5)

3.7 (3.1)

20.0 (10.2)

3.6 (0.6)


 K to 8th

5 (1)

80 (ref)

8.4 (0.5)

2.8 (2.5)

48.9 (13.6)

1.2 (0.3)

 9 to 11th

51 (10)

69 (0.6)

4.4 (0.5)

2.5 (2.4)

20.6 (10.5)

4.3 (0.6)


33 (6)

82 (1.1)

5.3 (0.6)

4.8 (3.7)

21.1 (9.8)

3.5 (0.5)


163 (32)

55 (0.3)

4.0 (0.3)

3.2 (2.8)

16.4 (10.7)

3.8 (0.6)

 Some Col./Tech.

147 (29)

68 (0.4)

4.5 (0.3)

3.7 (3.0)

14.8 (8.2)

3.7 (0.6)


72 (14)

62 (0.4)

3.9 (0.4)

2.6 (2.5)

8.2 (6.9)

3.6 (0.8)

 Graduate school

33 (6)

45 (0.2)

3.4 (0.6)

2.1 (2.1)

11.4 (10.6)

3.6 (0.6)

Marital status


62 (12)

69 (ref)

5.0 (0.4)

4.2 (3.0)

19.3 (9.5)

3.8 (0.7)


325 (64)

61 (0.7)

4.2 (0.2)

3.2 (2.8)

16.4 (9.8)

3.9 (0.6)


13 (3)

75 (1.3)

4.8 (0.8)

5.5 (4.2)

7.5 (3.7)

3.6 (0.7)


61 (12)

64 (0.8)

4.2 (0.5)

2.6 (2.3)

9.5 (8.6)

3.8 (0.8)


44 (9)

58 (0.6)

3.5 (0.5)

2.0 (2.0)

14.1 (11.1)

3.0 (0.6)

The breakdown by background variables of each phenotype analyzed in this study is shown. An asterisk indicates statistical significance at p < 0.01

Nicotine phenotypes

We used multivariate regression to test for association of the five 15q25.1 SNPs with tobacco-related phenotypes. The homozygous state of allele rs660652[G] was associated with smoking status and, among smokers, with higher NDS, higher smoking quantity (pack-years) and more attempts to quit. Specifically (see Fig. 2 for confidence intervals), individuals with the rs660652[G/G] genotype had 1.7-fold higher lifetime odds of smoking (100 cigarettes or more over lifetime), a 1.1-unit higher NDS score, 0.7 more pack-years of smoking, and 0.8 more attempts to quit than [A/G] + [A/A] counterparts. Surprisingly, the coding SNP rs16969968 was not significantly associated with tobacco-related phenotypes in our sample, but was associated with ODS as described below. As we elaborate in the "Discussion", our failure to replicate the well-validated association of rs16969968 with tobacco phenotypes may be due to a selection issue.
Fig. 2

Association of 15q25.1 SNPs with nicotine dependence and smoking phenotypes. For each phenotype a multivariate model was fitted to test for association with each SNP, adjusting for demographic and socioeconomic variables. Results shown are for a dominant/recessive model. The risk allele is indicated where significant. Graph the negative log (base 10) of the p value is plotted for each test. Thresholds for statistical significance are shown. Tablep values, effect estimates, units of measure, and the liability allele are specified per each phenotype for rs660652

Opioid phenotype

We used stepwise multivariate regression to test for association of the five 15q25.1 SNPs with ODS (see Fig. 3 for confidence intervals). The homozygous states of either allele rs16969968[A] or rs1051730[A] were significantly associated with ODS. These two markers were in strong pairwise LD (r2 = 0.99) with each other and likely represent the same association signal. In model 5, rs16969968[A/A] homozygotes had a 1.4-unit higher SDS score and rs1051730[A/A] homozygotes had a 1.5-unit higher SDS score than [A/G] + [G/G] counterparts. rs16969968 is a missense variation in CHRNA5 that results in an aspartate to asparagine amino acid change with functional consequences in vitro (Bierut et al. 2008). These results remained significant after addition of NDS to the models and there was no substantial change (<10% change) to the effect estimates.
Fig. 3

Association of 15q25.1 SNPs with ODS. Five hierarchical models were fitted with stepwise addition of covariates from crude to saturated (model 1: SNP only; model 2: added age, sex and clinic type; model 3: added low household income, marital status, education status, and employment status; model 4: added pain scores; model 5: added the number of opioid prescriptions received. Graph the negative log (base 10) of the p values is plotted for each test. Thresholds for statistical significance are shown. Table the p value and effect estimate for the two associated SNPs are shown per each model. The effect size estimates are beta coefficients (slopes) expressed in units of the ODS scale

Five haplotypes were identified in the sample with frequencies greater than 0.5% (Fig. 4). Haplotypes #1–4 were significantly associated with a higher NDS score, with comparable effect to rs660652[G] analyzed separately. Similarly, haplotype #1 was associated with a higher ODS score, with comparable effect to rs16969968[A] analyzed separately. Surprisingly, however, haplotype #2 had an unexpectedly large effect (8.7 ODS units) that was reversed in direction and larger in magnitude compared to rs16969968[A] analyzed separately. This may suggest that the effect of rs16969968 on ODS is modified by nearby SNPs not typed in this study and that additional re-sequencing and fine-mapping studies are needed to resolve and appropriately model the overall effect of the 15q25.1 locus on genetic susceptibility to substance dependence.
Fig. 4

The results of haplotype analysis for 15q25.1 SNPs on NDS and ODS. Effect estimates and corresponding p values are shown for each haplotype compared to the reference (haplotype 5). A schematic depiction of the structural organization of the locus and the location of the SNPs genotyped in this study is also shown


Previous studies have suggested a role for 15q25.1 in nicotine, alcohol, and cocaine dependence; the results of this study expand the gamut of substances associated with this locus to include prescription opioids.

With few exceptions, studies that reported an association of 15q25.1 with nicotine dependence and/or smoking placed the main association signal on the CHRNA5 coding SNP rs16969968. Our results for ODS were in line with these studies and placed the main effect on rs16969968[A] as expected. However, our results with NDS and smoking failed to replicate this location and instead placed the association signal on rs660652[G]. We note that our study was designed to detect association with ODS and the sample was selected based on opioid use, which we now know is associated with rs16969968[A]. It is therefore, likely that the main effect of rs16969968[A] on NDS and smoking was masked in our sample, giving rise to an overestimation of a minor effect of rs660652[G], which might not be true in the general population.

In addition to ODS measured using the severity of dependence scale (SDS), we also tested for association of 15q25.1 with lifetime and current opioid dependence (OD) as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (American Psychiatric Association 2000), and failed to detect such an association. This lack of detected association with DSM-IV OD may be due to lack of power, since with 185 (36.6%) individuals positive for DSM-IV OD the study had a type 2 error of 17%. More specifically, assuming a minor allele frequency of 0.35, an additive mode of inheritance, a response probability of 0.37 and an effect size of 1.5, our study had 83% power to detect an effect of 15q25.1 on DSM-IV OD if such an effect was truly present. However, the actual type 2 error may have been even greater than 17% if individuals with high opioid dependence were more likely not to participate in the study or to underreport OD symptoms because of stigmatization concerns. However, as we have reported elsewhere (Boscarino et al. 2010), a detailed analysis of study responders and non-responders using the electronic health record found no difference in the number of prescription opioid orders received over a 3-year period between these groups.

Another limitation has to do with the criticism that using the DSM-IV criteria for prescription OD may be inappropriate, since most patients taking these pain medicines experience tolerance and withdrawal as a side effect of this treatment. Thus, the DSM-IV OD criteria for medical use of these medicines may be biased. Due to these and other limitations with DSM-IV (Wu et al. 2010), the American Psychiatric Association is currently revising the criteria for prescription opioid dependence in DSM-V. In any case, our failure to detect association with DSM-IV OD in spite of having detected an association with an endophenotype thereof was somewhat surprising.

Sherva et al. (2010) have examined association of 15q25.1 with multiple substance dependence phenotypes in a sample of illicit drug users. They found nominally significant association of rs16969968 and other 15q25.1 SNPs with opioid dependence defined according to DSM-IV criteria; however, none of these association signals was significant after correction for multiple testing. In addition, as noted above, the focus of our study was on OD among “licit” prescription opioid users, not illicit users. As noted, DSM-IV is currently being revised to address the potential disparity between these phenotypes (Wu 2010). This measurement limitation may account for the lack of significance for DSM-IV OD in our study. Joslyn et al. (2008) described similar findings related to the association of 15q25.1 with alcohol use disorder and its endophenotype—the level of response to alcohol. They suggested that level-of-response phenotypes might be better correlated with biology than the DSM-IV construct of alcohol dependence, and therefore more likely to be useful for detecting genetic associations. DSM-IV criteria are designed to capture clinical endpoints, whereas endophenotypic traits typically capture underlying dimensions that are more etiologically homogeneous and possibly more useful for genetic epidemiological investigation.

Stimulation of neurons in the mesolimbic system by acetylcholine and its exogenous analogs via nicotinic acetylcholine receptors may be central to the development and reinforcement of substance dependence (Janhunen and Ahtee 2007). Multiple genetic association studies have previously demonstrated an association of DNA variation in the nAChR gene cluster on chromosome 15q25.1 with nicotine, alcohol, and cocaine dependence. This study is among the first to show an association of this locus with prescription opioid dependence. Additional studies are needed to evaluate the role of this and other nAChR gene loci in addiction neurobiology, treatment responses, and in public health.


This work was supported by a grant from the Administrative Committee for Research (ACR), Geisinger Clinic, Grant No. TRA-015 to Dr. Boscarino. Preliminary results for this study were presented at the 15th Annual HMO Research Network Conference, Danville, PA, April, 2009.

Conflict of interest

The authors have no conflicts of interest related to this research.

Copyright information

© Springer-Verlag 2010