Genetic susceptibility to heroin addiction: a candidate gene association study

Genetic susceptibility to heroin addiction; a candidate-geneassociation study

O. Levran†,*, D. Londono‡, K. O’Hara†, D. A. Nielsen†, E. Peles§, J. Rotrosenf, P.Casadontef, S. Linzy¥, M. Randesi†, J. Ott‡,Ψ, M. Adelson†,§,¥, and M. J. Kreek†

† The Laboratory of the Biology of Addictive Diseases, The Rockefeller University, 1230 YorkAvenue, New York, NY, USA‡ The Laboratory of Statistical Genetics, The Rockefeller University, 1230 York Avenue, NewYork, NY, USA§ Dr. Miriam and Sheldon G. Adelson Clinic for Drug Abuse, Treatment and Research, Tel AvivElias Sourasky Medical Center, Tel Aviv, Israelf VA New York Harbor Healthcare System and NYU School of Medicine New York, NY, USA¥ Dr. Miriam and Sheldon G. Adelson Clinic for Drug Abuse, Treatment and Research, LasVegas, NV, USAΨ Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

AbstractHeroin addiction is a chronic complex disease with a substantial genetic contribution. This studywas designed to identify genetic variants that are associated with susceptibility to develop heroinaddiction, by analyzing 1350 variants in 130 candidate genes. All subjects had Caucasian ancestry.The sample consisted of 412 former severe heroin addicts in methadone treatment, and 184healthy controls with no history of drug abuse. Nine variants, in six genes, showed the lowestnominal P values in the association tests (P < 0.01). These variants were in non-coding regions ofthe genes encoding the mu (OPRM1; rs510769, rs3778151), kappa (OPRK1; rs6473797), anddelta opioid receptors, (OPRD1; rs2236861, rs2236857 and rs3766951), the neuropeptide galanin(GAL; rs694066), the serotonin receptor subtype 3B (HTR3B; rs3758987) and the casein kinase 1isoform epsilon (CSNK1E; rs1534891). Several haplotypes and multi-locus genotype patternsshowed nominally significant associations (e.g. OPRM1; P = 0.0006 and CSNK1E; P = 0.0007).Analysis of a combined effect of OPRM1 and OPRD1 showed that rs510769 and rs2236861increase the risk of heroin addiction (P = 0.0005). None of these associations remained significantafter adjustment for multiple testing. This study suggests the involvement of several genes andvariants in heroin addiction that is worthy of future study.

Heroin addiction is a chronic relapsing disease characterized by compulsive drug seeking,drug abuse, tolerance and physical dependence. It is treated by methadone, buprenorphineand behavioral therapy. Heroin addiction is part of group of addictions (e.g. cocaine, alcoholand nicotine) that constitutes a worldwide public-health crisis. The genetic contribution tovulnerability to develop heroin addiction is 40–60%, suggesting a complex inheritance modein which multiple genes exert a small effect, along with the environment (Kendler et al.2003; Tsuang et al., 1996, 1998).

*Corresponding author: O. Levran, the Laboratory of the Biology of Addictive Diseases, 1230 York Avenue, box 171, The RockefellerUniversity, New York, NY 10065, USA. Tel: 212 3278282; Fax: 212 3277023; [email protected].

NIH Public AccessAuthor ManuscriptGenes Brain Behav. Author manuscript; available in PMC 2010 June 15.

Published in final edited form as:Genes Brain Behav. 2008 October ; 7(7): 720–729. doi:10.1111/j.1601-183X.2008.00410.x.

NIH

-PA Author Manuscript

NIH


NIH


Several genetic variants have been shown to be associated with heroin addiction by familybased linkage studies and association studies (for review see Kreek et al., 2005a, b, Kreek &LaForge, 2007 and also Cheng et al., 2005, Loh et al., 2007; Nielsen et al., 2008;Proudnikov et al., 2006; Szilagyi et al., 2005; Xu et al., 2004; Zou et al., 2007). Theseinclude variants in the genes encoding the mu and kappa opioid receptors, dopaminereceptors D2 and D4, serotonin receptor 1B, GABA receptor subunit gamma 2, catechol-O-methyltransferase (COMT), period circadian protein (PER3), proenkephalin (PENK),proopiomelanocortin (POMC), tryptophan hydroxylase 2 (TPH2) and brain-derivedneurotrophic factor (BDNF).

To identify genetic variants that underlie heroin addiction, we performed a candidate gene,case-control association study using a SNP array that was designed by the group of D.Goldman at the National Institute of Alcohol Abuse and Alcoholism (NIAAA). Thisapproach is based on physiological hypotheses and the genes were selected based on theirfunction (e.g. drug receptors, neurotransmitters, transporters and drug metabolism enzymes)and related pathways (e.g. reward modulation, behavioral control, cognitive function, signaltransduction, and stress response). In order to maximize the power of the study, the caseswere selected from the extreme margin of the specific phenotype range (e.g. severe heroinaddicts in methadone maintenance treatment), and the controls were healthy volunteers thatwere selected by detailed personal interview and stringent criteria. To reduce the possibleeffect of population stratification, only subjects with Caucasian ancestry were included.

Materials and MethodsSubjects

Six hundred and twenty subjects participated in the study with the majority (90%) fromUSA (NYC and Las Vegas) and the minority from Israel with Jewish ancestry (cases only)(Table 1). Twenty-two DNA samples were too diluted or of low quality for genotyping andtwo samples were excluded from analysis based on low call rate or possible contamination.The patients (cases) were former severe heroin addicts treated at a methadone maintenancetreatment program at the time of recruitment. Subjects were recruited at either theRockefeller University Hospital or opiate substitution programs (e.g. Manhattan Campus ofVA NY Harbor Health Care System, Weill Medical College of Cornell University, and theDr. Miriam and Sheldon G. Adelson Clinics for Drug Abuse Treatment and Research, in LasVegas and Israel). Ascertainment was made by personal interview, using severalinstruments: the Addiction Severity Index (ASI) (Mclellan et al., 1992), KMSK (Kellogg etal., 2003) and DSM-IV. All cases had a history of at least one year of daily multiple uses ofheroin. The 184 healthy control subjects were recruited by posting of notices or referral byphysicians. Each of the following was used as exclusion criteria from this category: a) Atleast one instance of drinking to intoxication, or any illicit drug use in the previous 30 days.b) A past history of alcohol drinking to intoxication, or illicit drug use, more than twice aweek, for more than 6 consecutive months. c) Cannabis use for more than 12 days in theprior 30 days or past use for more than twice a week for more than 4 years. All subjectscompleted a family history questionnaire and were self-identified as Caucasians for threegenerations. Participants were excluded from the study if they had a relative in the study orif they had a mixed ancestry. The Institutional Review Boards of The Rockefeller UniversityHospital, the VA New York Harbor Healthcare System and the Tel-Aviv Sourasky MedicalCenter (Helsinki Committee), approved the study. All subjects signed informed consent forgenetic studies.

Levran et al. Page 2

Genes Brain Behav. Author manuscript; available in PMC 2010 June 15.

NIH


NIH


NIH


DNA and plates preparationBlood samples were taken and DNA was extracted using the standard salting-out method(Miller et al., 1988). DNA was quantified using PicoGreen (Invitrogen, Carlsbad, CA).700ng DNA (45 μL) was precipitated with ethanol by the following procedure: a 120 μl“ethanol mix” (4.5 μl of 3M sodium acetate, pH 4.6; 105 μl of ethanol, 100%; 10.5 μl ofH2O and 0.044 μl of glycogen, 5 mg/ml) was dispensed into each well. The plate wassealed, vortexed and incubated at room temperature for 15 min. The plate was then spun at3700 rpm (2400 g) for 30 min. The plate was inverted onto paper towels, followed by a shortspin with the plate inverted, for 1 minute at 530 rpm (50 g). DNA pellets were washed with150 μl 70% ethanol, followed by re-sealing and inverting the plate a few times. A spin at3700 rpm for 10 min was followed by the inverting procedure (as described above), and theDNA was air dried for 15 min and re-suspended in 6–7 μl Tris-EDTA (10 mM Tris-HCl, 1mM EDTA, pH 8.0). It was then stored at 4°C for up to 2 days, or at −20°C, for a longerperiod.

Genotyping and quality assessmentGenotyping was performed on a 1,536-plex GoldenGate Custom Panel (GS0007064-OPA,Illumina, San Diego, CA). The micro-array was designed by Dr. D. Goldman’s group,NIAAA). 1350 SNPs were selected from 130 genes implicated in addiction (SupplementTable 1). In addition, the array includes 186 ancestry informative markers (AIMs) that wereselected based on allele frequencies in the European, African and Chinese population of theHapMap project (Enoch et al., 2006, Supplement Table 1). Genotyping was performed at theRockefeller University Genomics Resource Center according to the manufacture’s protocol(Illumina). Analysis was performed using BeadStudio genotyping software (Illumina) thatprovides automated genotype calling, cluster separation score (0–1) and quality check.Questionable SNPs were visually inspected and the genotype was determined. Genotypedata was filtered based on SNP call rates (> 99.5%), MAF > 0.01, and deviation fromHardy-Weinberg Equilibrium (HWE) (P = 0.00005). Seventy-seven random samples weregenotyped in duplicates and two identical samples were genotyped on each of the arrays forreproducibility control purposes.

Statistical analysisPopulation stratification—Ancestry informative markers were employed to test forpopulation stratification using the Structure software v2.2 (Pritchard et al., 2000). Thissoftware places populations in K clusters that have distinct marker frequencies. A no-admixture ancestry model with K set to 1 was used to infer the Dirichlet parameter, lambda,of the distribution of allele frequencies. The value of lambda thus obtained was later usedunder an admixture model with allele frequencies correlated using a burn-in length of50,000 followed by 50,000 Markov Chain Monte Carlo iterations, which was sufficient toyield stable results. The genomic control method (Devlin et al., 2001) was also used toexamine the potential impact of population structure in this study. In this method, markersare used to estimate a background association that can be used to infer the variance inflationfactor, lambda, which is introduced when there is subdivision.

Association Analysis—Fisher’s exact test, as implemented in R v2.6.1 (The RDevelopment Core Team 2007, http://www.R-project.org), was used to compare thedistribution of marker alleles and genotypes in cases and controls as well as to estimate oddsratios (OR) with their corresponding 95% confidence intervals. Analyses were carried outusing both US and Israel cases combined and US cases alone against the US controls. Anexact test of difference in allelic proportions between the Israel and US cases was performed



NIH


NIH


NIH


http://www.R-project.org

to account for any underlying allele frequency differences between these two samples. Anuncorrected P value of 0.01 was considered significant for the association test.

Allelic correlations at the locus level were estimated using the exact test of deviation fromthe Hardy-Weinberg (DHW) proportions as implemented in the R package GeneticsBasev1.2.0 from the R genetics project (www.rgenetics.org). A P value of 0.05/1000 = 0.00005was considered significant for the test of DHW.

Linkage Disequilibrium (LD) Blocks, Haplotype Analysis and GenotypePatterns—The pattern of pair-wise LD between SNPs was measured by the standardizeddisequilibrium value (D′), as implemented in Haploview v3.32 (Barrett et al., 2005). LDblocks were identified using the Gabriel’s method (Gabriel et al., 2002). The Score test wasused to test for association between the LD blocks and disease state as implemented in theprogram haplo.score (using simulated p-values) from the R package haplo.stats v1.3.1(Schaid et al., 2002). Differences in genotype pattern frequencies between cases andcontrols were evaluated by using Fisher’s exact test. Genotype patterns with frequencies lessthan 5% were pooled into a single class.

Correction for Multiple Testing—Results were adjusted for multiple testing by usingthe R package QVALUE v1.1 (Storey, 2003; Storey & Tibshirani, 2003, Storey et al., 2004).Instead of controlling the probability of one or more false positives in a family of tests (thefamily-wise error rate), QVALUE controls the expected proportion of false positives amongall rejected hypotheses (the false discovery rate - FDR) (Benjamini & Hochberg, 1995).QVALUE takes a given set of P values and, for each test, estimates the minimum FDR thatis incurred when calling a particular test significant (the q-value of the test). The q-valuemeasures the significance of each of a family of tests performed simultaneously and holdsunder different forms of dependence.

The smallest nominal P value, of all tests performed (Pmin) was obtained from the multi-locus genotype pattern tests. The QVALUE program was run on the list of P values createdby adding Pmin to the set of P values obtained from the single-locus tests. This estimated theexperiment-wise significance of Pmin. An FDR of 0.05 was used as the significant level.

ResultsOne thousand three hundred fifty SNPs, from 130 candidate genes, were genotyped in 412former severe heroin addicts, currently treated at a methadone maintenance treatmentprogram (cases), and 184 healthy volunteers, with no history of drug abuse (controls, seemethods). All individuals self reported Caucasian ancestry. The majority of cases (90%)were from the US (NYC and LV) and the minority from Israel (Table 1). Two hundredseventy five SNPs were excluded because of inadequate quality or low variability in oursample (MAF < 0.01) (Table 2). Genotyping reproducibility was 99.9%. The mean markercall rate for the analyzed markers was 99.8%. Nine SNPs showed significant deviation fromHWE in controls (rs10809907, rs12473028, rs1426223, rs2014663, rs3779084, rs483021,rs7103679, rs6432224 and rs7785096) (P < 0.00005).

Using data from 178 ancestry-informative markers (AIMs), no evidence of populationstratification was found within whole dataset (Fig. 1a), or within the Israeli and the US casesdataset (Fig. 1b), using the Structure program and the genomic control method(Lambda=1.00). To account for any subtle underlying difference that may not be detected bythe AIMs used in this study, allele frequencies of all analyzed SNPs were compared betweenthe US and the Israeli cases. The frequencies of three SNPs were significantly different



NIH


NIH


NIH


using the Bonferroni correction (P < 0.00005), and of 18 SNPs (1.8%) using the q-valueapproach (q < 0.05).

Genotypes and allele frequencies of 1083 SNPs were analyzed for association with heroinaddiction. The analysis was performed in two steps: 1) 350 US cases and 184 US controls 2)412 cases (350 US and 62 Israeli) and 184 US controls. The SNPs with significant differentfrequency between the US and the Israeli cases were excluded from this step. Listed inTable 3 are the SNPs that gave the lowest P values in the combined group (P < 0.01). The Pvalues are given for the allele and genotype tests for the US only and the combined cases.Six SNPs gave nominal significant values in both analyses (Table 3, group 1). Three SNPsgave nominal significant values in the combined cohort and borderline values for the UScases only (group 2). Six SNPs gave nominal significant values in the US only group(Supplement Table 2). One of these SNPs (rs1229984) also showed the most significantdifference in allelic proportions between the Israeli and the US cases (P = 7.2E-10). It is anonsynonymous variant (Arg47His) in the alcohol dehydrogenase 1B gene (ADH1B) that isreported to be common in Asian and Jewish populations and rare in Caucasians (Shibuya &Yoshida, 1988,Thomasson et al., 1991,Neumark et al., 1998,Carr et al., 2002).

OR were calculated for the minor allele. SNPs rs6473797 and rs1534891, showed aprotective effect (Table 3a). Listed in Supplement Table 3 are the alleles and genotypecounts and frequencies in cases and controls. No SNP on chromosome X was significantlyassociated with heroin addiction. Six association tests for haplotype and three tests for Multilocus genotype patterns (MLGPs) that include SNPs with the top signals, gave significantpositive values (Table 4a,b).

The top signals were from SNPs in the following genes: the opioid receptors mu, kappa anddelta, galanin, the 3B subtype of the serotonin receptor, and the casein kinase epsilon. Theinformation on the position of these SNPs is shown in Table 3b.

Mu opioid receptorTwo OPRM1 variants (rs510769, rs3778151) accounted for the two strongest signals in theassociation test (P = 0.0008, 0.003, respectively, Table 3). These SNPs are in strong LD (D′= 0.92) and belong to a haplotype block of 32 kb (block 1, Fig. 2a). The two SNPs arelocated in intron 1, adjacent to rs1799971 (118A>G) on exon 1. SNP rs1799971 belongs tothe same haplotype block (D′ = 0.91 with rs510769, D′ = 1 with rs3778151, Fig. 2a). Noevidence for association was found with SNP rs1799971 (118A>G) in this cohort (P = 0.16,MAF = 0.12).

Haplotype analysis, of OPRM1 SNPs rs510769 and rs3778151, suggested association ofhaplotypes CT (protection) and TC (risk) (P = 0.0006, 0.0033, respectively, Table 4a). Multilocus genotype patterns (MLGPs) analysis, of OPRM1 SNPs rs510769 and rs3778151,revealed significant differences in the genotypes distribution between cases and controls.Particularly, the CC-TT genotype pattern (rs510769-rs3778151, respectively) appears to beprotective (P = 0.005, Table 4b).

Delta opioid receptorThree OPRD1 SNPs (rs2236861, rs2236857 and rs3766951) showed suggestive associationswith heroin addiction (P = 0.002, 0.008 and 0.009, respectively, Table 3). These SNPs arecommon and are located within a 30 kb region of intron 1. SNP rs2236857 is in strong LDwith SNPs rs3766951 and rs678849 (Fig. 2b). An additional SNP in this region, rs2298896,that showed a trend for association (P = 0.015), is in strong LD with SNPs rs2236857 andrs3766951 (Fig. 2b). A 6 kb haplotype block, that includes SNPs rs204055, rs2236857 andrs2298896, showed suggestive association with heroin addiction (P = 0.0067, Table 4a). The



NIH


NIH


NIH


genotype pattern distribution of the two loci rs2236857-rs3766951 was significantlydifferent between cases and controls (P = 0.01, Table 4b).

Combined effect of the Mu and delta opioid receptorsIn order to evaluate whether there is a combined effect of OPRM1 and OPRD1 variants, thegenotypes pattern of rs510769 (OPRM1) and rs2236861 (OPRD1) was compared betweencases and controls. The analysis revealed significant difference of the genotypes patternbetween cases and controls (P = 0.0005, Table 4b).

Kappa opioid receptorOne common SNP (rs6473797) in OPRK1 showed suggestive association (P = 0.004, OR =1.5 for the major C allele, Table 3a, Supplement Table 3). LD analysis with other OPRK1SNPs, in our cohort, revealed that this SNP does not belong to any haplotype block.

Casein kinase 1 epsilonA tentative association was also detected with SNP rs1534891 in CSNK1E with protectionfrom heroin addiction (P = 0.001, Table 3a, Supplement Table 3). Two additional SNPs(rs6001093 and rs135757) showed a trend for association (P = 0.016, 0.019, respectively).Haplotype analysis revealed association with protection from heroin addiction of an 8 kbhaplotype block (P = 0.0007, Table 4a, Fig. 2c).

GalaninAn association was detected for the SNP rs694066 in the galanin gene. (P = 0.001, Table 3).Haplotype analysis revealed suggestive association of a 2 kb haplotype block with heroinaddiction (P = 0.0027, Table 3a, Fig. 2d).

Serotonin receptor 3BA SNP (rs3758987) in the 5′ region (−381) near the serotonin receptor gene (HTR3B) gave anominally significant signal for association (P = 0.007, Table 3). One additional SNP in thisgene (rs11606194, in intron 2) showed a trend toward significance (P = 0.017). SNPrs3758987 is in complete LD with rs11606194 (Fig. 2e). A 5 kb haplotype block, spanning aregion from 5′ upstream of the gene to intron 2, showed association with protection fromheroin addiction (P = 0.0064, Table 4a).

DiscussionOur primary goal in this study was to identify variations, in candidate genes, contributing tovulnerability to heroin addiction. Our top signals are from genes encoding opioid receptors,a neuropeptide (galanin), a ligand-gated ion channel serotonin receptor, and a kinase. All thevariants identified are from non-coding regions. As expected for a complex genetic disorder,each variant shows a small effect on the risk to (or protection from) develop heroinaddiction.

The opioid system plays a central role in reward, drug craving and relapse, in part byaltering stress physiology (for review see Koob and Kreek, 2007). Genes encoding opioidreceptors are prime candidates for opioid addiction. The mu-opioid receptor is a G proteincoupled receptor for beta-endorphin, and the major target for heroin and opioid analgesia. Itmay be a common component of several types of addictions by mediating drug reward. TheOPRM1 gene variant 118A>G (Asn40Asp, rs1799971), was associated with increasedaffinity to beta-endorphin (Bond et al., 1998), increased basal levels of cortisol (Bart et al.,2006), exaggerated hypothalamic-pituitary-adrenal axis response to a mu-opioid receptor



NIH


NIH


NIH


antagonist, naloxone, in healthy individuals (e.g. Wand et al., 2002) and enhanced opiateabuse vulnerability (Drakenberg et al., 2006), among other functions. 118A>G wasassociated with heroin addiction and alcohol dependence in certain populations, in somestudies, but not in others (for review see Glatt et al., 2007; Kreek et al., 2005a, b).Association was also found between two OPRM1 haplotype blocks and drug or alcoholdependence in Caucasians, and between 3 tag SNPs and the response to heroin, in the firstuse, in Han Chinese (Zhang et al., 2006, 2007a). Hoehe et al., (2000) identified a haplotypepattern in the 5′ regulatory region that is associated with poly-substance dependence.

We did not detect association of 118A>G with heroin addiction in this diverse cohort ofEuropean origin. However, an association was suggested with two OPRM1 SNPs (rs510769,rs3778151) in intron 1, that are part of a haplotype block, that spans the 118A>G region.This block is similar to the block previously described (Zhang et al., 2007a). There is noevidence that these SNPs are causative, and they might be in LD with a causative variant.

The delta opioid receptor has been shown, in animal studies, to be involved in addictiveprocesses, emotional response and antinociception (Quock et al., 1999; Filliol et al., 2000;Roberts et al., 2001; Nieto et al., 2005). The OPRD1 gene has only two coding sequencepolymorphisms, the non-synonymous rs1042114 and the synonymous rs2234918. Anassociation between rs2234918 and heroin dependence was reported in German Caucasians,but was not replicated by other studies (Franke et al., 1999; Mayer et al., 1997; Xu et al.,2002). This variant did not give a significant signal in our study (P > 0.5). In a recent study,significant association with opioid dependence was obtained for several OPRD1 markers(Zhang et al., 2007b). Our results imply association of three OPRD1 SNPs (rs2236861,rs2236857, and rs3766951) with heroin addiction. Further analysis is required to verify therole of these SNPs and potential additional SNPs on the risk haplotype.

The combined effect of OPRM1 and OPRD1 indicated in this study is intriguing consideringthe biological interaction of these receptors which are known to form heterodimers as wellas homodimers (Gomes et al., 2000, Rozenfeld & Devi, 2007). It also fits the hypothesis ofcomplex inheritance mode in which multiple genes exert a small effect.

The kappa opioid receptor (OPRK1) is activated by opioid neuropeptides to modulatedopaminergic tone. OPRK1 was also shown to be involved in pain sensitivity and responseto stress, in mice (Simonin et al. 1998, Mclaughlin et al., 2003). The OPRK1 gene iscomprised of four exons, with only two rare non-synonymous variants. In a report from ourlaboratory, Yuferov et al., found a point-wise significant association of the synonymousSNP rs1051660 (36G>T) with opioid addiction (Yuferov et al., 2004). Similar results wereobtained in an Italian population (Gerra et al., 2007). Two studies of European-Americansfound significant association of multiple SNPs (including rs6473797) with alcoholdependence (Xuei et al., 2006), but these results were not replicated in a Taiwanesepopulation (Loh el et al., 2004). SNP rs1051660 (36G>T) was not included in our panel.

Galanin is a 30-amino acid peptide widely distributed in the peripheral and central nervoussystems. Galanin and its receptors were shown to be involved in behavioral processes,morphine withdrawal, behavioral and neurochemical effects of opiates and high stressresponse, among others (Belfer et al., 2006, Hawes et al., 2007, Holmes & Picciotto, 2006,Picciotto et al., 2005, Unschuld et al., 2007, Zachariou et al., 2003). Galanin is considered acandidate for a protective factor against opiate addiction and galanin receptor agonists havebeen suggested as therapeutic targets. A haplotype association with alcoholism, in Finnishand Native American males, was reported, (Belfer et al., 2006). The variant rs694066 wasshown to be associated with weight loss (Ruano et al., 2006). This is the first report



NIH


NIH


NIH


indicating association between a GAL variant and opioid addiction. SNP rs694066 is locatedat intron 2, with relatively low MAF in our control population (0.06).

Serotonergic neurotransmission plays a major role in the regulation of affective states, painperception, reward, anxiety and addiction (Soubrie et al., 1986; Lucki et al., 1998).Serotonin receptors affect the release of other neurotransmitters such as glutamate,dopamine and GABA. The serotonin receptor 3, subunit B (HTR3B) belongs to the super-family of ligand-gated ion channels and is co-expressed with subunit HTR3A in theamygdala, caudate and the hippocampus. A recent study showed association between aHTR3B haplotype block and major depression in Japanese women (Yamada et al., 2006).The variant rs3758987, that gave a nominally significant signal for association in ouranalysis, is located at the regulatory region (−381C>T) and may impact gene expression.

Casein kinase 1 epsilon (CSNK1E) is the human homolog of the Drosophila circadian-associated protein DOUBLETIME (Kloss et al., 1998). Observations in animal modelssuggest interactions between the brain circadian and reward systems (see commentary byYuferov et al., 2005). For example, it was shown in mice that another circadian-associatedgene (clock) regulates the brain rewarding response to cocaine (McClung et al., 2005).CSNK1E participates in important signaling pathways: a) Phosphorylation of DARPP-32(the dopamine-and cyclic AMP regulated phosphoprotein) that mediates the behavioraleffects of serotonin (Svenningsson et al., 2002); b) Modulation of the activation of glycogensynthase kinase-3 (GSK-3), a downstream target of DARPP-32 that is important in stimulantdrug response (Beaulieu et al., 2004); c) Regulation of the circadian clock gene PER1,whose expression has been linked to drug dependence and reward (Yuferov et al., 2003, Liuet al., 2005, 2007). A recent study found a different expression level of Csnk1e in mice withlow- and high- sensitivity to locomotor stimulation that is a characteristic response to drugsof abuse (Palmer et al., 2005). A follow up study showed association between CSNK1E SNPrs135745 and sensitivity to the euphoric effects of amphetamine, in healthy humanvolunteers, suggesting that this variant might facilitate the development of drug abuse(Veenstra-Vanderweele et al., 2006). Interestingly, SNP rs1534891 that shows associationwith protection from heroin addiction, in our study, is in close proximity (8 kb) to rs135757and in almost complete LD. The two SNPs are part of the same haplotype block, suggestingan interesting similarity between the findings of the two studies.

Evaluation of previous studies in relation to this study is complex for a few reasons; somevariants from previous reports were not genotyped in this study, the phenotype was oftendifferent (e.g. multiple dependencies, less stringent criteria for defining specific addiction ordifferent criteria for controls definition), or the population was different. On the other hand,alcohol, cocaine and heroin dependence may share genetic influences, and some populationsmay share similar risk variants, that justify comparison between studies. We cannotdetermine if the negative findings in this study are true negatives because the SNP coverageof some of the genes may be insufficient to exclude real associations, and due to therelatively small number of subjects.

The candidate gene approach has advantages and disadvantages. It is hypothesis-driven,based on genes, with a known or inferred biological function. As such, it is limited by thecurrent knowledge. In contrast, genome-wide association study is an exploratory approach inwhich the whole genome is scanned in an unbiased fashion and as such, it has the potentialto identify novel susceptibility factors. The candidate gene approach allows a scan of alimited number of SNPs thus it is more economical and requires less testing.

This study suggests an extension to the list of susceptibility genes and variants underlyingheroin addiction. These results warrant further exploration to confirm the tentative



NIH


NIH


NIH


associations and to elucidate the involved mechanisms. Although the variants identified inthis study are suggested to produce low relative risk for the vulnerability to develop heroinaddiction, they may uncover novel mechanisms of addictive behavior.

Supplementary MaterialRefer to Web version on PubMed Central for supplementary material.

AcknowledgmentsWe thank all the clinical staff that enrolled and assessed subjects for this study, including Elizabeth Ducat, N.P.,Brenda Ray, N.P., Dorothy Melia, R.N., Lisa Borg, M.D., and Scott Kellogg, M.D. We are grateful to DavidGoldman M.D. and his group from the NIH/NIAAA, for the design of the micro-array and their support; ConnieZhao, Ph.D. and Bin Zhang from the Rockefeller Genomic Resource Center, for their excellent assistance ingenotyping. We thank Ann Ho, Ph.D. and Vadim Yuferov, Ph.D. from our laboratory for discussion and commentson the manuscript. We would like to express our profound gratitude to the late K. Steven LaForge, Ph.D. for hisinvaluable role in setting the foundation for this study. This work was supported in part by NIDA-P60-05130(MJK), NIDA-K05-00049 (MJK), NIH/NCRR-CTSA UL1-RR024143 (The Rockefeller University Center forClinical and Translational Science), NIMH-R01-44292 (JO) and NSFC grant 30730057 from the ChineseGovernment (JO).

ReferencesBarrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype

maps. Bioinformatics. 2005; 21:263–265. [PubMed: 15297300]Bart G, LaForge KS, Borg L, Lilly C, Ho A, Kreek MJ. Altered levels of basal cortisol in healthy

subjects with a 118G allele in exon 1 of the Mu opioid receptor gene. Neuropsychopharmacol.2006; 31:2313–2317.

Beaulieu JM, Sotnikova TD, Yao WD, Kockeritz L, Woodgett JR, Gainetdinov RR, Caron MG.Lithium antagonizes dopamine-dependent behaviors mediated by an AKT/glycogen synthase kinase3 signaling cascade. Proc Natl Acad Sci U S A. 2004; 101:5099–5104. [PubMed: 15044694]

Belfer I, Hipp H, McKnight C, Evans C, Buzas B, Bollettino A, Albaugh B, Virkkunen M, Yuan Q,Max MB, Goldman D, Enoch MA. Association of galanin haplotypes with alcoholism and anxietyin two ethnically distinct populations. Mol Psychiatry. 2006; 11:301–311. [PubMed: 16314872]

Bond C, LaForge KS, Tian M, Melia D, Zhang S, Borg L, Gong J, Schluger J, Strong JA, Leal SM,Tischfield JA, Kreek MJ, Yu L. Single-nucleotide polymorphism in the human mu opioid receptorgene alters beta-endorphin binding and activity: possible implications for opiate addiction. ProcNatl Acad Sci U S A. 1998; 95:9608–9613. [PubMed: 9689128]

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approachto Multiple Testing. J of the Roy Statist Soc Ser B. 1995; 57(1):289–300.

Carr LG, Foroud T, Stewart T, Castelluccio P, Edenberg HJ, Li TK. Influence of ADH1Bpolymorphism on alcohol use and its subjective effects in a Jewish population. Am J Med Genet.2002; 112:138–143. [PubMed: 12244546]

Devlin B, Roeder K, Wasserman L. Genomic control, a new approach to genetic-based associationstudies. Theor Popul Biol. 2001; 60:155–166. [PubMed: 11855950]

Drakenberg K, Nikoshkov A, Horvath MC, Fagergren P, Gharibyan A, Saarelainen K, Rahman S,Nylander I, Bakalkin G, Rajs J, Keller E, Hurd YL. Mu opioid receptor A118G polymorphism inassociation with striatal opioid neuropeptide gene expression in heroin abusers. Proc Natl Acad SciU S A. 2006; 103:7883–7888. [PubMed: 16682632]

Enoch MA, Shen PH, Xu K, Hodgkinson C, Goldman D. Using ancestry-informative markers todefine populations and detect population stratification. J Psychopharmacol. 2006; 20:19–26.[PubMed: 16785266]

Filliol D, Ghozland S, Chluba J, Martin M, Matthes HW, Simonin F, Befort K, Gaveriaux-Ruff C,Dierich A, LeMeur M, et al. Mice deficient for delta- and mu-opioid receptors exhibit opposingalterations of emotional responses. Nat Genet. 2000; 25:195–200. [PubMed: 10835636]



NIH


NIH


NIH


Franke P, Nothen MM, Wang T, Neidt H, Knapp M, Lichtermann D, Weiffenbach O, Mayer P, HolltV, Propping P, Maier W. Human delta-opioid receptor gene and susceptibility to heroin andalcohol dependence. Am J Med Genet. 1999; 88:462–464. [PubMed: 10490698]

Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M,Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES,Daly MJ, Altshuler D. The structure of haplotype blocks in the human genome. Science. 2002;296:2225–2229. [PubMed: 12029063]

Gerra G, Leonardi C, Cortese E, D′Amore A, Lucchini A, Strepparola G, Serio G, Farina G, MagnelliF, Zaimovic A, Mancini A, Turci M, Manfredini M, Donnini C. Human kappa opioid receptorgene (OPRK1) polymorphism is associated with opiate addiction. Am J Med Genet BNeuropsychiatr Genet. 2007; 144:771–775. [PubMed: 17373729]

Glatt SJ, Bousman C, Wang RS, Murthy KK, Rana BK, Lasky-Su JA, Zhu SC, Zhang R, Li J, ZhangB, Li J, Lyons MJ, Faraone SV, Tsuang MT. Evaluation of OPRM1 variants in heroin dependenceby family-based association testing and meta-analysis. Drug Alcohol Depend. 2007; 90:159–165.[PubMed: 17416470]

Gomes I, Jordan BA, Gupta A, Trapaidze N, Nagy V, Devi LA. Heterodimerization of mu and deltaopioid receptors: A role in opiate synergy. J Neurosci. 2000; 20:RC110. [PubMed: 11069979]

Hawes JJ, Brunzell DH, Narasimhaiah R, Langel U, Wynick D, Picciotto MR. Galanin ProtectsAgainst Behavioral and Neurochemical Correlates of Opiate Reward. Neuropsychopharmacol.2007 In press.

Hoehe MR, Kopke K, Wendel B, Rohde K, Flachmeier C, Kidd KK, Berrettini WH, Church GM.Sequence variability and candidate gene analysis in complex disease: association of mu opioidreceptor gene variation with substance dependence. Hum Mol Genet. 2000; 9:2895–2908.[PubMed: 11092766]

Holmes A, Picciotto MR. Galanin: a novel therapeutic target for depression, anxiety disorders anddrug addiction? CNS Neurol Disord Drug Targets. 2006; 5:225–232. [PubMed: 16611095]

Kellogg SH, McHugh PF, Bell K, Schluger JH, Schluger RP, LaForge KS, Ho A, Kreek MJ. TheKreek-McHugh-Schluger-Kellogg scale: a new, rapid method for quantifying substance abuse andits possible applications. Drug Alcohol Depend. 2003; 69:137–150. [PubMed: 12609695]

Kendler KS, Jacobson KC, Prescott CA, Neale MC. Specificity of genetic and environmental riskfactors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants,and opiates in male twins. Am J Psychiatry. 2003; 160:687–695. [PubMed: 12668357]

Kloss B, Price JL, Saez L, Blau J, Rothenfluh A, Wesley CS, Young MW. The Drosophila clock genedouble-time encodes a protein closely related to human casein kinase Iepsilon. Cell. 1998; 94:97–107. [PubMed: 9674431]

Koob G, Kreek MJ. Stress, dysregulation of drug reward pathways, and the transition to drugdependence. Am J Psychiatry. 2007; 164:1149–59. [PubMed: 17671276]

Kreek MJ, Bart G, Lilly C, LaForge KS, Nielsen DA. Pharmacogenetics and human moleculargenetics of opiate and cocaine addictions and their treatments. Pharmacol Rev. 2005a; 57:1–26.[PubMed: 15734726]

Kreek MJ, Nielsen DA, Butelman ER, LaForge KS. Genetic influences on impulsivity, risk taking,stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci. 2005b; 8:1450–1457. [PubMed: 16251987]

Kreek MJ, LaForge KS. Stress responsivity, addiction, and a functional variant of the human mu-opioid receptor gene. Mol Interv. 2007; 7:74–78. [PubMed: 17468387]

Liu Y, Wang Y, Wan C, Zhou W, Peng T, Liu Y, Wang Z, Li G, Cornelisson G, Halberg F. The roleof mPer1 in morphine dependence in mice. Neurosci. 2005; 130:383–388.

Liu Y, Wang Y, Jiang Z, Wan C, Zhou W, Wang Z. The extracellular signal-regulated kinase signalingpathway is involved in the modulation of morphine-induced reward by mPer1. Neurosci. 2007;146:265–271.

Loh el W, Fann CS, Chang YT, Chang CJ, Cheng AT. Endogenous opioid receptor genes and alcoholdependence among Taiwanese Han. Alcohol Clin Exp Res. 2004; 28:15–19. [PubMed: 14745298]



NIH


NIH


NIH


Loh EW, Tang NL, Lee DT, Liu SI, Stadlin A. Association analysis of GABA receptor subunit geneson 5q33 with heroin dependence in a Chinese male population. Am J Med Genet B NeuropsychiatrGenet. 2007; 144:439–443. [PubMed: 17440936]

Lucki I. The spectrum of behaviors influenced by serotonin. Biol Psychiatry. 1998; 44:151–162.[PubMed: 9693387]

Mayer P, Rochlitz H, Rauch E, Rommelspacher H, Hasse HE, Schmidt S, Hollt V. Associationbetween a delta opioid receptor gene polymorphism and heroin dependence in man. Neuroreport.1997; 8:2547–2550. [PubMed: 9261824]

McClung CA, Sidiropoulou K, Vitaterna M, Takahashi JS, White FJ, Cooper DC, Nestler EJ.Regulation of dopaminergic transmission and cocaine reward by the Clock gene. Proc Natl AcadSci U S A. 2005; 102:9377–81. [PubMed: 15967985]

McLaughlin JP, Marton-Popovici M, Chavkin C. κ Opioid Receptor Antagonism and ProdynorphinGene Disruption Block Stress-Induced Behavioral Responses. J Neurosci. 2003; 23:5674–5683.[PubMed: 12843270]

McLellan AT, Kushner H, Metzger D, Peters R, Smith I, Grissom G, Pettinati H, Argeriou M. TheFifth Edition of the Addiction Severity Index. J Subst Abuse Treat. 1992; 9:199–213. [PubMed:1334156]

Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from humannucleated cells. Nucleic Acids Res. 1988; 16:1215. [PubMed: 3344216]

Neumark YD, Friedlander Y, Thomasson HR, Li TK. Association of the ADH2*2 allele with reducedethanol consumption in Jewish men in Israel: a pilot study. J Stud Alcohol. 1998; 59:133–139.[PubMed: 9500299]

Nielsen DA, Barral S, Proudnikov D, Kellogg S, Ho A, Ott J, Kreek MJ. TPH2 and TPH1: Associationof Variants and Interactions with Heroin Addiction. Behav Genet. 2008; 38:133–50. [PubMed:18181017]

Nieto MM, Guen SL, Kieffer BL, Roques BP, Noble F. Physiological control of emotion-relatedbehaviors by endogenous enkephalins involves essentially the delta opioid receptors.Neuroscience. 2005; 135:305–13. [PubMed: 16112476]

Palmer AA, Verbitsky M, Suresh R, Kamens HM, Reed CL, Li N, Burkhart-Kasch S, McKinnon CS,Belknap JK, Gilliam TC, et al. Gene expression differences in mice divergently selected formethamphetamine sensitivity. Mamm Genome. 2005; 16:291–305. [PubMed: 16104378]

Picciotto MR, Hawes JJ, Brunzell DH, Zachariou V. Galanin can attenuate opiate reinforcement andwithdrawal. Neuropeptides. 2005; 39:313–315. [PubMed: 15944028]

Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotypedata. Genetics. 2000; 155:945–959. [PubMed: 10835412]

Quock RM, Burkey TH, Varga E, Hosohata Y, Hosohata K, Cowell SM, Slate CA, Ehlert FJ, RoeskeWR, Yamamura HI. The delta-opioid receptor: molecular pharmacology, signal transduction, andthe determination of drug efficacy. Pharmacol Rev. 1999; 51:503–32. [PubMed: 10471416]

R Development Core Team. R: A language and environment for statistical computing. Vienna,Austria: 2007.

Roberts AJ, Gold LH, Polis I, McDonald JS, Filliol D, Kieffer BL, Koob GF. Increased ethanol self-administration in delta-opioid receptor knockout mice. Alcohol Clin Exp Res. 2001; 25:1249–1256. [PubMed: 11584142]

Rozenfeld R, Devi LA. Receptor heterodimerization leads to a switch in signaling: beta-arrestin2-mediated ERK activation by mu-delta opioid receptor heterodimers. Faseb J. 2007; 21:2455–2465.[PubMed: 17384143]

Ruano G, Windemuth A, Kocherla M, Holford T, Fernandez ML, Forsythe CE, Wood RJ, KraemerWJ, Volek JS. Physiogenomic analysis of weight loss induced by dietary carbohydrate restriction.Nutr Metab (Lond). 2006; 3:20. [PubMed: 16700901]

Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association betweentraits and haplotypes when linkage phase is ambiguous. Am J Hum Genet. 2002; 70:425–434.[PubMed: 11791212]



NIH


NIH


NIH


Shibuya A, Yoshida A. Genotypes of alcohol-metabolizing enzymes in Japanese with alcohol liverdiseases: a strong association of the usual Caucasian-type aldehyde dehydrogenase gene(ALDH1(2)) with the disease. Am J Hum Genet. 1988; 43:744–748. [PubMed: 3189338]

Simonin F, Valverde O, Smadja C, Slowe S, Kitchen I, Dierich A, Le Meur M, Roques BP,Maldonado R, Kieffer BL. Disruption of the kappa-opioid receptor gene in mice enhancessensitivity to chemical visceral pain, impairs pharmacological actions of the selective kappa-agonist U-50,488H and attenuates morphine withdrawal. Embo J. 1998; 17:886–97. [PubMed:9463367]

Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Statist.2003; 31:2013–2035.

Storey JD, Taylor JE, Siegmund D. Strong control, conservative point estimation and simultaneousconservative consistency of false discovery rates: a unified approach. J of the Royal StatistSociety: Ser B (Statist Method). 2004; 66:187–205.

Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A.2003; 100:9440–9445. [PubMed: 12883005]

Soubrie P. Reconciling the role of central serotonin neurons in human and animal behavior. BehavBrain Sci. 1986; 9:319–364.

Svenningsson P, Tzavara ET, Liu F, Fienberg AA, Nomikos GG, Greengard P. DARPP-32 mediatesserotonergic neurotransmission in the forebrain. Proc Natl Acad Sci U S A. 2002; 99:3188–93.[PubMed: 11880652]

Thomasson HR, Edenberg HJ, Crabb DW, Mai XL, Jerome RE, Li TK, Wang SP, Lin YT, Lu RB, YinSJ. Alcohol and aldehyde dehydrogenase genotypes and alcoholism in Chinese men. Am J HumGenet. 1991; 48:677–681. [PubMed: 2014795]

Tsuang MT, Lyons MJ, Eisen SA, Goldberg J, True W, Lin N, Meyer JM, Toomey R, Faraone SV,Eaves L. Genetic influences on DSM-III-R drug abuse and dependence: a study of 3,372 twinpairs. Am J Med Genet. 1996; 67:473–477. [PubMed: 8886164]

Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, EavesL. Co-occurrence of abuse of different drugs in men: the role of drug-specific and sharedvulnerabilities. Arch Gen Psychiatry. 1998; 55:967–972. [PubMed: 9819064]

Unschuld PG, Ising M, Erhardt A, Lucae S, Kohli M, Kloiber S, Salyakina D, Thoeringer CK, Kern N,Lieb R, Uhr M, Binder EB, Muller-Myhsok B, Holsboer F, Keck ME. Polymorphisms in thegalanin gene are associated with symptom-severity in female patients suffering from panicdisorder. J Affect Disord. 2007; 105:177–84. [PubMed: 17573119]

Veenstra-VanderWeele J, Qaadir A, Palmer AA, Cook EH Jr, de Wit H. Association between thecasein kinase 1 epsilon gene region and subjective response to D-amphetamine.Neuropsychopharmacol. 2006; 31:1056–1063.

Wand GS, McCaul M, Yang X, Reynolds J, Gotjen D, Lee S, Ali A. The mu-opioid receptor genepolymorphism (A118G) alters HPA axis activation induced by opioid receptor blockade.Neuropsychopharmacol. 2002; 26:106–114.

Xu K, Liu XH, Nagarajan S, Gu XY, Goldman D. Relationship of the delta-opioid receptor gene toheroin abuse in a large Chinese case/control sample. Am J Med Genet. 2002; 110:45–50.[PubMed: 12116270]

Xu K, Lichtermann D, Lipsky RH, Franke P, Liu X, Hu Y, Cao L, Schwab SG, Wildenauer DB, BauCH, Ferro E, Astor W, Finch T, Terry J, Taubman J, Maier W, Goldman D. Association ofspecific haplotypes of D2 dopamine receptor gene with vulnerability to heroin dependence in 2distinct populations. Arch Gen Psychiatry. 2004; 61:597–606. [PubMed: 15184239]

Xuei X, Dick D, Flury-Wetherill L, Tian HJ, Agrawal A, Bierut L, Goate A, Bucholz K, Schuckit M,Nurnberger J Jr, Tischfield J, Kuperman S, Porjesz B, Begleiter H, Foroud T, Edenberg HJ.Association of the kappa-opioid system with alcohol dependence. Mol Psychiatry. 2006; 11:1016–1024. [PubMed: 16924269]

Yamada K, Hattori E, Iwayama Y, Ohnishi T, Ohba H, Toyota T, Takao H, Minabe Y, Nakatani N,Higuchi T, Detera-Wadleigh SD, Yoshikawa T. Distinguishable haplotype blocks in the HTR3Aand HTR3B region in the Japanese reveal evidence of association of HTR3B with female majordepression. Biol Psychiatry. 2006; 60:192–201. [PubMed: 16487942]



NIH


NIH


NIH


Yuferov V, Fussell D, LaForge KS, Nielsen DA, Gordon D, Ho A, Leal SM, Ott J, Kreek MJ.Redefinition of the human kappa opioid receptor gene (OPRK1) structure and association ofhaplotypes with opiate addiction. Pharmacogenet. 2004; 14:793–804.

Yuferov V, Kroslak T, LaForge KS, Zhou Y, Ho A, Kreek MJ. Differential gene expression in the ratcaudate putamen after “binge” cocaine administration: advantage of triplicate micro array analysis.Synapse. 2003; 48:157–169. [PubMed: 12687634]

Yuferov V, Butelman ER, Kreek MJ. Biological clock: biological clocks may modulate drugaddiction. Eur J Hum Genet. 2005; 13:1101–3. [PubMed: 16094306]

Zachariou V, Brunzell DH, Hawes J, Stedman DR, Bartfai T, Steiner RA, Wynick D, Langel U,Picciotto MR. The neuropeptide galanin modulates behavioral and neurochemical signs of opiatewithdrawal. Proc Natl Acad Sci U S A. 2003; 100:9028–9033. [PubMed: 12853567]

Zhang H, Luo X, Kranzler HR, Lappalainen J, Yang BZ, Krupitsky E, Zvartau E, Gelernter J.Association between two mu-opioid receptor gene (OPRM1) haplotype blocks and drug or alcoholdependence. Hum Mol Genet. 2006; 15:807–819. [PubMed: 16476706]

Zhang D, Shao C, Shao M, Yan P, Wang Y, Liu Y, Liu W, Lin T, Xie Y, Zhao Y, Lu D, Li Y, Jin L.Effect of mu-opioid receptor gene polymorphisms on heroin-induced subjective responses in aChinese population. Biol Psychiatry. 2007a; 61:1244–1251. [PubMed: 17157823]

Zhang H, Kranzler HR, Yang BZ, Luo X, Gelernter J. The OPRD1 and OPRK1 loci in alcohol or drugdependence: OPRD1 variation modulates substance dependence risk. Mol Psychiatry. 2007b InPress.

Zou Y, Liao G, Liu Y, Wang Y, Yang Z, Lin Y, Shen Y, Li S, Xiao J, Guo H, Wan C, Wang Z.Association of the 54-nucleotide repeat polymorphism of hPer3 with heroin dependence in HanChinese population. Genes Brain Behav. 2007; 7:26–30. [PubMed: 17451453]



NIH


NIH


NIH


Figure 1. Population stratification analysisa. Cases (US and Israel) (in red) and controls (in green).b. Cases from the US (in green) and Israel (in red).



NIH


NIH


NIH




NIH


NIH


NIH


Figure 2.Pair-wise LD Analysis. LD between SNPs in seven genes was derived from genotypes ofcontrols. The pair-wise correlation between SNPs was measured as D′ and is shown (x100)in each box. The color scheme indicates the magnitude of D′. Dark red indicated D′ >0.80with D′ =1.0 when no number is given. Haplotypes were generated using the Gabriel ruleand haplotype blocks are marked. a, OPRM1. b, OPRD1. c, CSNK1E. d, GAL. e, HTR3B.



NIH


NIH


NIH


NIH


NIH


NIH



Tabl

e 1

Popu

latio

n de

mog

raph

ic

Cas

esC

ontr

ols U

ST

otal

US

Isra

elT

otal

Mal

es22

137

258

8634

4

Fem

ales

129

2515

498

252

Tota

l35

062

412

184

596


NIH


NIH


NIH



Tabl

e 2

Sum

mar

y of

gen

otyp

ed S

NPs

SNPs

Gen

otyp

edM

AF

< 0.

01Po

or Q

ualit

yA

naly

zed

Aut

o-so

mal

1261

197

6110

03

X-C

hr.

893

680

AIM

s18

61

717

8

Tota

l15

3620

174

1261


NIH


NIH


NIH



Tabl

e 3

The

mos

t sig

nific

ant a

ssoc

iatio

ns o

f sin

gle

SNPs

with

her

oin

addi

ctio

n

a. SNP

Gen

eA

llele

test

Gen

otyp

e te

sta

Effe

ct o

f the

min

or a

llele

P - V

alue

OR

(95%

CI)

P- V

alue

US

only

Com

bine

dC

ombi

ned

US

only

Com

bine

d

Gro

up 1

rs51

0769

OPR

M1

0.00

030.

0008

1.7

(1.2

–2.2

)0.

001

0.00

29ris

k

rs37

7815

1O

PRM

10.

0007

0.00

31.

7 (1

.2–2

.4)

0.00

30.

015

risk

rs64

7379

7O

PRK

10.

0009

0.00

40.

7 (0

.5–0

.9)

0.00

30.

011

prot

ectio

n

rs15

3489

1C

SNK

1E0.

0016

0.00

20.

6 (0

.4–0

.8)

0.00

20.

001

prot

ectio

n

rs69

4066

GAL

0.00

190.

001

2.1

(1.3

–3.6

)0.

006

0.00

4ris

k

rs22

3686

1O

PRD

10.

0029

0.00

21.

6 (1

.7–2

.2)

0.00

30.

003

risk

Gro

up 2

rs37

6695

1O

PRD

10.

0125

0.00

91.

4 (1

.1–1

.9)

0.04

00.

030

risk

rs22

3685

7O

PRD

10.

0165

0.00

81.

5 (1

.1–1

.9)

0.01

90.

005

risk

rs37

5898

7H

TR3B

0.01

740.

007

1.5

(1.1

–2.0

)0.

049

0.01

9ris

k

b. S

NP

loca

tion

SNP

Gen

e Sy

mbo

lG

ene

Gen

e L

ocat

ion

Chr

.Po

sitio

n B

uild

36.

2

rs51

0769

OPR

M1

mu

opio

id re

cept

orin

tron

16

1544

0371

2

rs37

7815

1O

PRM

1m

u op

ioid

rece

ptor

intro

n 1

615

4435

373

rs64

7379

7O

PRK

1ka

ppa

opio

id re

cept

orin

tron

28

5431

5535

rs15

3489

1C

SNK

1Eca

sein

kin

ase

1, e

psilo

nin

tron

722

3702

5045

rs69

4066

GAL

gala

nin

intro

n 2

1168

2095

61

rs22

3686

1O

PRD

1de

lta o

pioi

d re

cept

orin

tron

11

2901

2343

rs22

3685

7O

PRD

1de

lta o

pioi

d re

cept

orin

tron

11

2903

4196

rs37

6695

1O

PRD

1de

lta o

pioi

d re

cept

orin

tron

11

2904

2146

rs37

5898

7H

TR3B

sero

toni

n re

cept

or 3

B−381

1111

3280

485

a addi

tive

mod

e of

inhe

ritan

ce.


NIH


NIH


NIH



Tabl

e 4

Hap

loty

pe a

nd g

enot

ype

patte

rns a

ssoc

iatio

n w

ith h

eroi

n ad

dict

ion

a. H

aplo

type

s

SNPs

Gen

eH

aplo

type

aP-

valu

eE

ffect

rs51

0769

-rs3

7781

51O

PRM

1C

T0.

0006

prot

ectio

n

rs15

3489

1-rs

6001

093-

rs13

5757

CSN

K1E

AG

T0.

0007

prot

ectio

n

rs18

9367

9-rs

6940

66G

ALG

A0.

0012

risk

rs51

0769

-rs3

7781

51O

PRM

1TC

0.00

33ris

k

rs37

5898

7-rs

1160

6194

HTR

3BTT

0.00

64pr

otec

tion

rs20

4055

-rs2

2368

57-r

s229

8896

OPR

D1

TCG

0.00

67ris

k

b. M

ulti

locu

s gen

otyp

e pa

ttern

s

SNPs

Gen

eP-

valu

e

rs51

0769

-rs3

7781

51O

PRM

10.

005

rs22

3685

7-rs

3766

951

OPR

D1

0.01

3

rs22

3686

1-rs

5107

69O

PRD

1/O

PRM

10.

0005

a Alle

les a

re in

the

sens

e or

ient

atio

n of

the

rele

vant

gen

e.

SNPs

that

gav

e si

gnifi

cant

P-v

alue

s on

sing

le S

NP

anal

ysis

(Tab

le 3

) are

in b

old.


Genetic susceptibility to heroin addiction: a candidate gene association study

Documents