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Human Genetics ISSN 0340-6717Volume 128Number 5 Hum Genet (2010)128:491-499DOI 10.1007/s00439-010-0876-6

Nicotinic acetylcholine receptor genes onchromosome 15q25.1 are associated withnicotine and opioid dependence severity

ORIGINAL INVESTIGATION

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

Porat M. Erlich • Stuart N. Hoffman • Margaret Rukstalis •

John J. Han • Xin Chu • W. H. Linda Kao • Glenn S. Gerhard •

Walter F. Stewart • Joseph A. Boscarino

Received: 29 April 2010 / Accepted: 6 August 2010 / Published online: 20 August 2010

� Springer-Verlag 2010

Abstract 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 meso-

limbic system and thought to mediate substance depen-

dence. 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 pre-

scribed 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 30 untranslated

region of CHRNA3 (rs660652[G]) was significantly asso-

ciated 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.

Abbreviations

LD Linkage disequilibrium

PCR Polymerase chain reaction

MAF Minor allele frequency

ODS Opioid dependence severity

SDS Severity of dependence scale

NDS Nicotine dependence severity

FTS Fagerstrom tolerance scale

EHR Electronic health record

nAChR Nicotinic acetylcholine receptor

CHRNA Cholinergic receptor nicotinic alpha

SNP Single nucleotide polymorphism

P. M. Erlich � M. Rukstalis � W. F. Stewart �J. A. Boscarino (&)

Center for Health Research, Geisinger Health System,

100 N. Academy Avenue, Danville, PA 17822-4400, USA

e-mail: jaboscarino@geisinger.edu

P. M. Erlich

Department of Medicine, Temple School of Medicine,

Philadelphia, PA, USA

S. N. Hoffman

Department of Neurology, Geisinger Health System,

Danville, PA, USA

J. J. Han

Department of Pain Medicine, Geisinger Health System,

Danville, PA, USA

X. Chu � G. S. Gerhard

Weis Center, Geisinger Health System, Danville, PA, USA

W. H. Linda Kao � W. F. Stewart

Department of Epidemiology, Johns Hopkins Bloomberg School

of Public Health, Baltimore, MD, USA

J. A. Boscarino

Department of Medicine and Pediatrics, Mt Sinai School

of Medicine, New York, NY, USA

J. A. Boscarino

Department of Psychiatry, Temple School of Medicine,

Philadelphia, PA, USA

123

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DOI 10.1007/s00439-010-0876-6

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Introduction

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 Prac-

tice 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 personali-

zing 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 identi-

fied variation on chromosome 15q25.1 as the most signi-

ficant 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 plau-

sible link between this locus, which encodes components

of the nicotinic acetylcholine receptor (nAChR), and

increased susceptibility to nicotine dependence and con-

sequent 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 polymor-

phism(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, poly-

morphisms 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 Penn-

sylvania not-for-profit corporation operating a multi-spe-

cialty 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 poten-

tial 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 prescrip-

tions in this initial eligibility query because medication use

was verified later in the telephone interview.

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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. Indi-

viduals were also notified that they could call to be

excluded from further contact. Trained telephone inter-

viewers subsequently attempted to contact 2,373 eligible

individuals by telephone; 86 individuals were not con-

tacted 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 inter-

view following an introduction, identification, and expla-

nation 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 ade-

quately signed consent form and buccal swab. An addi-

tional 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 sub-

stance 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?’’ adminis-

tered 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 Sever-

ity 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 vali-

dated in previous studies (Gossop et al. 1995; Kaye and

Darke 2002; World Health Organization 2009). The ques-

tions 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 dif-

ficult 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

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(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 B$30,000), marital status (i.e.,

married, separated, divorced, single, or widowed), educa-

tion 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 contin-

uous traits) or logistic (for binary traits) regression imple-

mented 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 domi-

nant/recessive coding schemes (i.e., carriers and homozy-

gotes 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

B5 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 skew-

ness; 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 pheno-

type. For ODS (Fig. 3), we show results of five models

with stepwise addition of covariates in order to demon-

strate 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 associa-

tion 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; http://www.hapmap.org)

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 set-

tings were used in this estimation: confidence interval for

strong LD set to 0.7–0.98; minimum for strong recombi-

nation 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. imple-

mented in the software HaploView v.4.0 (http://www.broad.

mit.edu./mpg/haploview) (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-syn-

onymous 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 C 10%) in the target block with r2 C 0.75 in HapMap

Caucasians.

SNP genotyping was performed on an Applied Bio-

Systems 7500 real-time PCR platform using TaqMan kits

following the manufacturer’s protocols. Laboratory per-

sonnel blinded to phenotype and covariate data assem-

bled the genotyping plates and performed the genotyping.

Quality control measures included visual inspection of

the allelic discrimination plots, monitoring of concor-

dance 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.

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Results

The lifetime prevalence of smoking (defined by the ques-

tion ‘‘did you smoke 100 cigarettes or more in your life?’’)

was 63% overall and significantly (a B 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.

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 asso-

ciated with smoking status and, among smokers, with

higher NDS, higher smoking quantity (pack-years) and

more attempts to quit. Specifically (see Fig. 2 for con-

fidence 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 sig-

nificantly associated with tobacco-related phenotypes in

our sample, but was associated with ODS as described

below. As we elaborate in the ‘‘Discussion’’, our fail-

ure to replicate the well-validated association of

rs16969968 with tobacco phenotypes may be due to a

selection issue.

Table 1 Marker specifications (NCBI build 36.3)

SNP Gene Physical location (bp) MAF (minor/common) Functional annotation

rs16969968 CHRNA5 76669980 35% (A/G) Missense (D,N)

rs660652 CHRNA3 76674887 36% (A/G) UTR-30

rs1051730 CHRNA3 76681394 35% (A/G) CDS-synonymous

rs6495308 CHRNA3 76694711 23% (C/T) Intron

rs12443170 CHRNA3 76694791 13% (A/G) Intron

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 Adepicts 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 compari-

son = 0.95). SNPs with MAF B 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 C90% of un-typed variation in the target block with

r2 C 0.75. The 10-level color scheme for r2 is shown in the legend

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Opioid phenotype

We used stepwise multivariate regression to test for asso-

ciation 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 repre-

sent 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 conse-

quences 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.

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

All 505 (100) 63 (NA) 4.3 (0.2) 3.2 (2.8) 15.5 (9.6) 3.8 (0.6)

Age

18–37 45 (9) 77 (3.0)* 4.8 (0.5) 3.6 (2.9) 6.0 (3.6) 3.5 (1.0)*

38–57 248 (49) 69 (2.1) 4.6 (0.2) 3.8 (3.0) 15.6 (8.3) 4.3 (0.7)

58–77 186 (37) 52 (1.0) 3.7 (0.3) 2.4 (2.4) 17.4 (11.9) 3.4 (0.5)

78–97 26 (5) 52 (ref) 3.6 (0.8) 2.5 (2.9) 16.9 (12.1) 1.5 (0.3)

Sex

Male 153 (30) 77 (ref)* 5.4 (0.3)* 4.3 (3.2)* 20.3 (10.1)* 4.3 (0.6)

Female 352 (70) 57 (0.4) 3.8 (0.2) 2.8 (2.5) 13.4 (9.3) 3.6 (0.6)

Clinic type

Specialty 104 (21) 69 (ref) 4.9 (0.3) 4.3 (3.3) 16.4 (7.9) 4.2 (0.7)

Primary 401 (79) 61 (0.7) 4.1 (0.2) 2.9 (2.6) 15.2 (10.2) 3.7 (0.6)

Employed

Yes 137 (27) 63 (ref) 4.2 (0.3) 3.1 (2.8) 9.9 (6.4) 3.7 (0.8)

No 220 (44) 69 (1.3) 4.6 (0.2) 4.0 (3.0)* 18.3 (9.6)* 4.4 (0.6)*

Retired 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)

C30 K 233 (46) 62 (1.0) 4.1 (0.2) 3.0 (2.8) 14.1 (9.7) 3.7 (0.6)

Unknown 57 (11) 66 (1.1) 4.6 (0.5) 3.7 (3.1) 20.0 (10.2) 3.6 (0.6)

Education

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)

HS-GED 33 (6) 82 (1.1) 5.3 (0.6) 4.8 (3.7) 21.1 (9.8) 3.5 (0.5)

HS-Grad 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)

Col-Grad 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

Divorced 62 (12) 69 (ref) 5.0 (0.4) 4.2 (3.0) 19.3 (9.5) 3.8 (0.7)

Married 325 (64) 61 (0.7) 4.2 (0.2) 3.2 (2.8) 16.4 (9.8) 3.9 (0.6)

Separated 13 (3) 75 (1.3) 4.8 (0.8) 5.5 (4.2) 7.5 (3.7) 3.6 (0.7)

Single 61 (12) 64 (0.8) 4.2 (0.5) 2.6 (2.3) 9.5 (8.6) 3.8 (0.8)

Widowed 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

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Five haplotypes were identified in the sample with fre-

quencies 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.

Discussion

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 associa-

tion 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

Phenotype P-value Effect size (95%CI)

Unit Liability allele

]G[256066sr stinu STF )7.1 ,4.0( 1.1 56000.0 SDNNDS (dichotomized) 0.00065 2.0 (1.3, 3.1) Odds ratio rs660652[G] Smoking quantity 0.0037 0.7 (0.5, 0.9) Pack-years rs660652[G] The number of quit attempts 0.0083 0.8 (0.7, 0.9) # of attempts rs660652[G] Smoking status 0.0091 1.7 (1.1, 2.6) Odds ratio rs660652[G]

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. Graphthe negative log (base 10) of the p value is plotted for each test.

Thresholds for statistical significance are shown. Table p values,

effect estimates, units of measure, and the liability allele are specified

per each phenotype for rs660652

]A[0371501sr ]A[86996961sr

Model hierarchy Effect size (95%CI) P-value Effect size (95%CI) P-value Model 1 (crude) 1.2 (0.5, 2.0) 0.00104 1.3 (0.6, 2.0) 0.000469

Model 2 1.2 (0.5, 2.0) 0.00095 1.3 (0.6, 2.0) 0.000432

Model 3 1.3 (0.5, 2.0) 0.00052 1.4 (0.6, 2.1) 0.000251

Model 4 1.3 (0.6, 2.0) 0.00039 1.4 (0.6, 2.1) 0.000172

Model 5 (saturated) 1.4 (0.7, 2.1) 0.00016 1.5 (0.7, 2.2) 0.000061

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

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

Hum Genet (2010) 128:491–499 497

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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 Asso-

ciation 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 inappro-

priate, 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 sig-

nificant 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 endopheno-

type—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 endophe-

notypic 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 ace-

tylcholine receptors may be central to the development

and reinforcement of substance dependence (Janhunen and

Ahtee 2007). Multiple genetic association studies have pre-

viously 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 evalu-

ate the role of this and other nAChR gene loci in addiction

neurobiology, treatment responses, and in public health.

Acknowledgments 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.

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