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. Author manuscript; available in PMC: 2008 Oct 8.
Published in final edited form as: Drug Alcohol Depend. 2007 Apr 9;90(2-3):159–165. doi: 10.1016/j.drugalcdep.2007.02.022

Evaluation of OPRM1 Variants in Heroin Dependence by Family-Based Association Testing and Meta-Analysis

Stephen J Glatt 1,*, Chad Bousman 2, Richard S Wang 2, Kenton K Murthy 2, Brinda K Rana 2, Jessica A Lasky-Su 1, Shao C Zhu 3, Ruimin Zhang 4, Jianhua Li 4, Bo Zhang 4, Jixiang Li 4, Michael J Lyons 5, Stephen V Faraone 1, Ming T Tsuang 2,6,7
PMCID: PMC2012941  NIHMSID: NIHMS30207  PMID: 17416470

Abstract

OPRM1, which codes for the μ-opioid receptor, is the most frequently studied candidate gene for opioid dependence. Despite numerous allelic association studies, no definitive conclusion has been reached regarding the role of OPRM1 polymorphisms in determining risk for opioid dependence. We attempted to resolve this by conducting a family-based association study and meta-analysis which may be more robust and powerful, respectively, than traditional case-control analyses. First, we genotyped three single nucleotide polymorphisms (SNPs) of OPRM1 in 1208 individuals from 473 Han Chinese families ascertained on the basis of having two or more siblings with DSM-IV-defined opioid dependence. The Val6Ala and Arg111His SNPs were detected, but with low minor allele frequencies (0.002 and 0.001, respectively). The Asn40Asp SNP was more informative (minor allele frequency: 0.419), but no significant evidence was observed for either a dominant (p=0.810) or additive (p=0.406) effect of this polymorphism on risk for opioid dependence. In addition, a meta-analysis of case-control studies of opioid dependence was performed, and found a similar lack of evidence for an association with the Asn40Asp SNP (p=0.859). Although a role of OPRM1 polymorphisms in determining risk for opioid dependence cannot be entirely discounted, a major contribution of the Asn40Asp polymorphism seems unlikely. Further analysis is warranted in samples from specific ancestral groups. In addition, it is critical that other OPRM1 variants, including all haplotype-tagging and amino-acid-coding SNPs, be tested for an influence on risk for opioid dependence, since the Asn40Asp polymorphism is only one of several hundred known mutations in the gene.

Keywords: allelic association, genetic polymorphism, mu-opioid receptor gene, opiate dependence, opioid dependence

1.0 INTRODUCTION

Opioid dependence has been shown repeatedly to have a heritable component (Kendler et al., 2000; Tsuang et al., 1996; van den Bree et al., 1998), but the genes that mediate familial transmission of the disorder are not known. The results of two genome-wide linkage analyses have been published (Gelernter et al., 2006; Glatt et al., 2006), implicating loci on chromosomes 4q and 17q, among others. These findings should prove useful in guiding future position-directed candidate-gene association analyses. Simultaneously, several groups have been pursuing case-control association studies of functional (rather than positional) candidate genes for opioid dependence. Foremost among these functional candidates has been OPRM1, which codes for the μ-opioid receptor, due to its clear involvement in mediating the physiological effects of endogenous and exogenous opioids (including heroin), as well as other drugs of abuse such as nicotine (Berrettini and Lerman, 2005) and alcohol (Town et al., 2000).

To date, at least a dozen independent case-control association studies have been performed to determine the nature of the relationship between OPRM1 variants and risk for opioid dependence. Although some significant results (of the Asn40Asp polymorphism in particular) have been reported (Bond et al., 1998; Tan et al., 2003), opposite or null effects have been reported even more frequently (Bart et al., 2005; Szeto et al., 2001). As such, an influence of OPRM1 on risk for opioid dependence has still not been confirmed or refuted, despite considerable efforts devoted to the issue.

Most of the previous case-control association studies of OPRM1 in opioid dependence have carefully matched cases and controls on variables such as age (to preclude cohort effects), sex (to mitigate differences in the genetic etiology of the disorder), and race (to avoid the confounding effects of admixture). This approach can theoretically prevent population stratification, which can artificially induce evidence for (or against) an association between gene and phenotype. However, because most of the allelic association studies of OPRM1 in opioid dependence were completed before methods of genomic control (Devlin et al., 2001) were widely available, close matching of cases and controls on factors directly related to the distribution of alleles and genotypes in the two groups has not been implemented uniformly. It is therefore possible that some of the ambiguity in relating OPRM1 variants to opioid dependence is caused by inappropriate matching of cases and controls at the genetic level which, by chance or design, is present in some studied samples but not in others.

While still susceptible to the same shortcomings as individual case-control studies (i.e., population stratification), meta-analysis provides greater power than single studies to detect an allelic association, despite the uneven quality or stringency of the included studies. Recently, Arias et al. (2006) adopted this approach and meta-analyzed the extant data on the role of the OPRM1 Asn40Asp polymorphism in risk for all forms of substance dependence. The results of that meta-analysis found no evidence for the existence of any relationship, with an overall effect size (odds ratio of 1.01) no different from what would be expected by chance. Although the meta-analysis of Arias et al. (2006) found evidence of heterogeneity among the reviewed studies, it was not attributable to differences between the samples in the primary drug choice of dependent subjects. This is consistent with previous work by Tsuang et al. (1998), which has shown that there exists a common genetic liability towards dependence on any one of several classes of drugs (including opioids and other drugs evaluated in the meta-analysis); thus, in theory, the pooled analysis of Arias et al. was testing if OPRM1 mediated a portion of this shared liability. Yet, the work of Tsuang et al. (1998) also demonstrated that opioid dependence, more than any other evaluated substance use disorder, was driven by a specific genetic liability in addition to the general genetic liability shared among all substances. Thus, despite the absence of evidence for an effect of OPRM1 on substance dependence in general, we hypothesized that the gene might have a stronger and/or more robust effect on risk for opioid dependence in particular (i.e., may mediate drug-specific liability), based on both the greater specific genetic factor thought to underlie opioid dependence and the essential role of OPRM1 in responsiveness to opioids relative to the biological actions of other drugs (e.g., cocaine or amphetamines, which predominantly affect dopamine neurotransmission). In addition to performing a meta-analysis of case-control studies, we have also performed a family-based association study of three single nucleotide polymorphisms (SNPs) of OPRM1 (including Asn40Asp) in opioid dependence, the first such study of which we are aware. This family-based study avoids the potential problems of population stratification encountered in case-control studies by focusing not on the distribution of alleles between cases and controls in the population, but rather on the probability of inheriting alternate forms of a single nucleotide polymorphism (SNP) on a family-by-family basis. Capitalizing on the power of meta-analysis and the robustness of family-based association testing, we have attempted to explicate the nature of the relationship between OPRM1 and opioid dependence.

2.0 METHODS

2.1 Family-Based Association Analysis

2.1.1 Ascertainment and Clinical Assessment

The ascertainment and clinical assessment procedures used in this study are fully described elsewhere (Glatt et al., 2006). Briefly, heroin-dependent probands were recruited from the Yunnan Institute of Drug Abuse (YIDA), Yunnan Province, China. An initial screening was performed to determine if the proband had any affected siblings; if so, the proband was enrolled and asked for permission to contact their family members, who were then individually evaluated for inclusion in the study. The final sample for the present analyses included 1208 individuals (including 899 affected individuals) from 473 families.

Each proband and his or her affected sibling underwent a confirmatory diagnostic screen using supplemental medical records and a semi-structured interview that was based on the Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) (American Psychiatric Association, 1994). Following this screen, the Mandarin Chinese version of the Diagnostic Interview for Genetic Studies (DIGS) (Chen et al., 1998; Faraone et al., 1996; Nurnberger et al., 1994) was administered. Interviewers underwent rigorous training on the DIGS to ensure an accurate diagnostic assessment. Test-retest reliabilities of diagnoses based on the English, French, Korean, and Colombian versions of the DIGS have previously been shown to be excellent (Faraone et al., 1996; Joo et al., 2004; Nurnberger et al., 1994; Palacio et al., 2004; Preisig et al., 1999); however, the test-retest reliability of the Chinese version has not yet been empirically determined. We supplemented structured interview data with information extracted from medical records. Best-estimate final diagnoses were made by two board-certified psychiatrists independently based on all the clinical information that was collected. When these psychiatrists disagreed, a third diagnostician was used as the tiebreaker. All procedures used in this study were approved by the Institutional Review Boards of the participating institutions, including YIDA, Harvard Medical School, and the University of California, San Diego.

2.1.2 DNA Handling, Preparation, and Genotyping

Approximately 10 ml of blood was drawn from each subject and immediately shipped to the NIDA Center for Genetic Studies at the Rutgers University Cell and DNA Repository, where cells were immortalized via transformation with Epstein-Barr virus. DNA was extracted from these cell lines and sent to UCSD for genotyping. A BioMEK® FX robot (Beckman-Coulter, Inc.; Fullerton, CA) was used for DNA aliquoting, subsequent PCR, and sequencing reactions to minimize sample cross-contamination.

PCR reactions were performed in Dyad® thermal cyclers (MJ Research; Waltham, MA) in a volume of 25 μL using 25 ng of DNA (or H2O for negative controls), 20 μM of primer, PCR Buffer, 1X Q-Solution, and HotStar® Taq DNA Polymerase according to recommended protocols (Qiagen; Valencia, CA). Taq polymerase was heat-activated by incubation at 95°C for 15 min. The reaction was cycled 40 times with a denature step of 95°C for 30 sec, an annealing step of 65.8°C for 1 min, an elongation step of 72°C for 1 min, and a final elongation step of 72°C for 8 min. Ten μL of PCR product was used to verify amplification (or the absence of amplified product in negative controls) on a 1% agarose gel using electrophoresis. Fifteen μL of PCR products were then purified with Exonuclease I (3 U/reaction) and Shrimp Alkaline Phosphatase (0.8 U/reaction) by incubation at 37°C for 30 min, and then at 85°C for 15 min. Sequencing reactions were then performed according to the BigDye® Terminator v3.1 Cycle Sequencing Kit Protocol (Applied Biosystems; Foster City, CA), with sequencing products purified through Sephadex® G-50 DNA Grade beads (Sigma Scientific; St. Louis, MO). Ten μl of Hi-Di Formamide was added to the purified sequencing reaction, and final genotyping was performed by sequencing on an ABI Prism® 3100 Genetic Sequencer (Applied Biosystems; Foster City, CA).

2.1.3 Data Handling and Analysis

The Phred, Phrap, and Consed suite of sequence analysis software tools was used to automate base-calling, assemble sequence fragments, and visualize sequence data. PolyPhred was used to detect heterozygous sites, and SNPs were re-verified via Consed graphical interface and manually on the ABI chromatogram. To test for genotyping errors, 40 repeat samples were included and genotyped by a blinded technician, after which the genotypes of the original and repeated samples were compared by a second individual. Pedigree inconsistencies, Mendelian inconsistencies, and unlikely genotypes were evaluated and either corrected or eliminated from analysis prior to the linkage analysis previously performed on these samples (Glatt et al., 2006), and thus, these erroneous samples were not included in the present association study. Family-based association analysis was then conducted using the PBAT software package (Lange et al., 2004) as implemented in the HelixTree Genetic Analysis Software suite, version 5.13 (GoldenHelix, Inc.; Bozeman, MT). Because the true mode of inheritance of opioid dependence is unknown, we evaluated four genetic models (additive, dominant, recessive, and heterozygous advantage). The type-I-error rate (α) for all analyses was fixed at 0.05.

2.2 Meta-Analysis

2.2.1 Literature Search

To identify studies eligible for meta-analysis, MEDLINE citations (January, 1966 to May, 2006) were surveyed using the National Library of Medicine's PubMed online search engine with “heroin”, “opiate”, “opioid”, and “OPRM1” as keywords. The retrieved abstracts were reviewed to identify studies that examined the allelic association between a polymorphism within the OPRM1 gene and any form of opioid dependence. Studies of this type were then read in their entirety to assess their appropriateness for inclusion in the meta-analysis. All references cited in these studies were also reviewed to identify additional studies not indexed by MEDLINE.

2.2.2 Inclusion Criteria

Only those studies examining the frequency of the OPRM1 Asn40Asp (A118G) polymorphism in relation to a primary diagnosis of opioid dependence were included in the meta-analysis. Furthermore, studies had to meet all of the following criteria: 1) be published in a peer-reviewed journal; 2) present original data; and 3) provide enough data to calculate an effect size. If the third criterion was not satisfied for a given study, the authors of that study were contacted in an attempt to obtain additional information. The application of these criteria yielded 13 studies eligible for meta-analysis, all of which used case-control designs (Bart et al., 2004; Bergen et al., 1997; Bond et al., 1998; Crowley et al., 2003; Drakenberg et al., 2006; Franke et al., 2001; Gelernter et al., 1999; Li et al., 2000; Luo et al., 2003; Shi et al., 2002; Szeto et al., 2001; Tan et al., 2003; Zhang et al., 2006a). Multiple samples (e.g., samples of different ancestral groups) within the same publication were treated as distinct samples for the purpose of deriving pooled estimates. These procedures resulted in a total of 21 samples, representing 1742 opioid-dependent cases and 2585 control subjects.

2.2.3 Coding of Study Characteristics

To delineate potential moderating, mediating, and/or confounding influences of various sample characteristics on the size of the effects obtained in the case-control studies under consideration, each sample was coded on the following variables: 1) ancestry of the sample; 2) diagnostic screening method; 3) recruitment site; and 4) screening of control subjects for psychiatric and substance-abuse disorders. Ancestry of the samples varied and thus required creation of five dummy variables that represented samples of European (Swedish, German, and European-American), African, Asian (Chinese, Indian, and Malaysian), Hispanic, and Native American descent. To examine effects of screening methodology each study was also dichotomized as standardized (DSM checklist, SCID, SADS-L, C-DIS-R) or unstandardized (physician interview, medical record review, self-report). Additionally, four dummy variables were created to represent the source of subject recruitment, which included physician referral, detoxification program/unit, hospital or other (university treatment program, forensic unit, tribal community). Lastly, the screening of control subjects for psychiatric and substance-abuse disorders was simply coded as a dichotomous variable (present or absent). Age and gender data were not uniformly provided in the selected studies; thus, analyses of these potential moderator variables were underpowered and not performed. Descriptive characteristics of the studies are presented in Table 1.

Table 1.

Descriptive Characteristics of Case-Control Studies of the Association between the OPRM1 Asn40Asp Polymorphism and Opioid Dependence

Study Cases (n) Controls (n) Ancestry Screening Method Recruitment Source Psychiatric and Substance Abuse Screening of Controls? Poly-Drug Screening of Cases?
Bart et al. 2004 139 170 Swedish Standardized Detoxification Facility Yes Yes
Bergen et al. 1997 21 108 Native American Standardized Tribal Community Yes No
Bond et al. 1998 23 8 African Unstandardized Physician Yes Yes
Bond et al. 1998 30 22 European Unstandardized Physician Yes Yes
Bond et al. 1998 58 9 Hispanic Unstandardized Physician Yes Yes
Crowley et al. 2003 129 101 European Unstandardized Treatment Program Yes Yes
Crowley et al. 2003 96 99 African Unstandardized Treatment Program Yes Yes
Drakenberg et al. 2006 39 26 European Standardized Forensic Unit Yes No
Franke et al. 2001 287 365 German Standardized Detoxification Facility Yes Yes
Gelernter et al. 1999 40 146 European Standardized Hospital No No
Gelernter et al. 1999 19 64 African Standardized Hospital No No
Gelernter et al. 1999 20 18 Hispanic Standardized Hospital No No
Li et al. 2000 226 208 Asian Standardized Hospital No No
Luo et al. 2003 10 179 European Standardized Hospital Yes No
Luo et al. 2003 5 55 African Standardized Hospital Yes No
Shi et al. 2002 145 48 Asian Unstandardized Physician Yes No
Szeto et al. 2001 200 97 Asian Standardized Hospital Yes No
Tan et al. 2003 20 117 Indian Standardized Detoxification Facility No Yes
Tan et al. 2003 25 131 Malaysian Standardized Detoxification Facility No Yes
Tan et al. 2003 52 156 Asian Standardized Detoxification Facility No Yes
Zhang et al. 2006 91 338 European Standardized Hospital Yes Yes
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2.2.4 Statistics

Data from each case-control study were used to construct a two-by-two table in which subjects were classified by diagnostic category (case or control) and allele (Asn or Asp). The strength of association in these two-by-two tables was summarized using the odds ratio (OR), in which Asp was assigned as the risk allele based on the initial report of significant association (Bond et al., 1998), and an OR>1.0 indicated a positive association between this allele and opioid dependence. For case-control studies, the OR estimates the relative risk, which represents the increase in the probability of observing the Asp allele in cases relative to controls.

Case-control studies were analyzed by random-effects meta-analysis. The pooled OR was calculated according to the methods of DerSimonian and Laird (1986), and its 95% confidence interval (CI) was constructed using Woolf's method (1955). The heterogeneity of the group of ORs was assessed using a χ2 test of goodness of fit, and the significance of the pooled OR was determined by the z test. The influence of individual studies on the pooled OR was determined by sequentially removing each study and recalculating the pooled OR and 95% CI. Publication bias within the group of ORs was assessed by the method of Egger et al. (Egger et al., 1997).

The moderating influences of sample ancestry, diagnostic screening method, recruitment site, and degree of control screening on the OR derived from each case-control study were assessed by multiple regression. The type-I error rate for all analyses was set at 0.05. All procedures for meta-analysis were conducted using Stata SE, version 9.1 (Stata Corporation; College Station, TX).

3.0 RESULTS

3.1 Family-Based Association Testing

The frequency of the alternate forms of the Asn40Asp polymorphism of OPRM1 were 42.0% (Asp) and 58.0% (Asn), which is consistent with frequencies reported by many other groups (see Table 2). Family-based association analysis of the Asn40Asp polymorphism failed to detect any significant deviation from the expected transmission patterns of the alternate forms of the SNP to opioid-dependent subjects. One-hundred twenty-four families were informative and contributed data to the analysis of an additive mode of inheritance, but the test statistic for this model was not significant (p=0.406). Evidence from 91 informative families did not support either dominant or recessive models of inheritance as well (p=0.810). Finally, there was no evidence in 117 informative families of a heterozygous advantage at this SNP (p=0.599).

Table 2.

Meta-Analysis of Case-Control Studies of the Association between the OPRM1 Asn40Asp Polymorphism and Opioid Dependence

Study Case Asp Frequency Control Asp Frequency OR 95% CI
Bart et al. 2004 0.31 0.15 2.30 1.37 - 3.88
Bergen et al. 2004 0.14 0.31 0.43 0.12 - 1.46
Bond et al. 1998 (African) 0.04 0.00 1.09 0.04 - 28.1
Bond et al. 1998 (European) 0.23 0.23 1.03 0.30 - 3.49
Bond et al. 1998 (Hispanic) 0.21 0.78 0.18 0.06 - 0.56
Crowley et al. 2003 (European) 0.31 0.31 1.01 0.61 - 1.68
Crowley et al. 2003 (African) 0.09 0.10 0.92 0.37 - 2.32
Drakenberg et al. 2006 0.31 0.12 2.97 0.79 - 11.1
Franke et al. 2001 0.26 0.24 1.11 0.80 - 1.55
Gelernter et al. 1999 (European) 0.25 0.31 0.78 0.38 - 1.63
Gelernter et al. 1999 (African) 0.00 0.09 0.24 0.01 - 4.44
Gelernter et al. 1999 (Hispanic) 0.25 0.28 0.89 0.23 - 3.35
Li et al. 2000 0.69 0.60 1.22 0.92 - 1.63
Luo et al. 2003 (European) 0.20 0.27 0.70 0.16 - 3.11
Luo et al. 2003 (African) 0.20 0.05 3.96 0.37 - 42.1
Shi et al. 2002 0.50 0.54 0.91 0.53 - 1.52
Szeto et al. 2001 0.79 0.59 1.57 1.09 - 2.27
Tan et al. 2003 (Indian) 0.50 0.95 0.37 0.17 - 0.79
Tan et al. 2003 (Malaysian) 0.84 0.90 0.88 0.48 - 1.63
Tan et al. 2003 (Asian) 0.60 0.74 0.73 0.45 - 1.17
Zhang et al. 2006 0.26 0.25 1.04 0.64 - 1.69
Pooled 0.33 0.39 0.98 0.78 - 1.21
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In addition to the Asn40Asp polymorphism of OPRM1, we genotyped two other non-synonymous SNPs, including rs1799972 which results in substitution of valine for alanine at codon 68, and a novel SNP detected by direct sequencing which results in substitution of arginine for histidine in codon 111. The rs1799972 polymorphism was nearly monomorphic for the C allele, with only two CT heterozygotes and no homozygotes observed in our sample. The Arg111His locus was even less polymorphic, with only one GA heterozygote and no AA homozygotes observed. Based on these very low minor allele frequencies, family-based association analyses of these polymorphisms were precluded.

3.2 Meta-Analysis

The OR and 95% CI for each case-control study of the OPRM1 Asn40Asp polymorphism and opioid dependence are shown in Table 2. The pooled OR derived from 1742 cases and 2585 controls was not significant (OR=0.98, z=0.18, p=0.859), and there was no evidence of publication bias within this group of studies (t20=-0.42, p=0.68). Sequential omission of individual case-control studies produced pooled ORs ranging from 0.93-1.05 with 95% CIs that always encompassed 1.0, indicating that the pooled OR was not excessively influenced by any single study. However, significant heterogeneity was observed among this group of ORs [χ220 =41.72, p=0.003], suggesting the presence of some moderating variable(s).

Screening method (p=0.932), recruitment source (ps>0.740), and nature of screening methods for control subjects (p=0.352) did not significantly influence the obtained ORs. A significant effect of sample ancestry was observed whereby the samples of European ancestry differed in their observed ORs relative to studies of other ancestral populations (z=-2.62, p=0.009); however, stratification of the group of ORs by sample ancestry (European, African, Asian, Hispanic, or Native American) yielded only slightly different estimates of the association of this OPRM1 polymorphism with opioid dependence in each ancestral group. The pooled ORs and corresponding 95% CIs derived from the eight European, six Asian, four African, two Hispanic, and one Native America samples were 1.20 (0.91-1.58), 0.93 (0.66-1.31), 0.99 (0.44-2.21), 2.60 (0.54-12.47), and 2.34 (0.68-8.03) respectively, and the association with opioid dependence was not significant for any of these specific ancestral groups.

Two different genotype-wise analyses were also conducted, including a regression predicting additive risk for opioid dependence with each additional Asp allele (Asn/Asn vs. Asn/Asp vs. Asp/Asp) and a comparison of risk between homozygotes (Asn/Asn vs. Asp/Asp). The results of these analyses indicated no significant additive risk for opioid dependence with each additional Asp allele, and yielded a non-significant pooled OR when comparing homozygotes (data available upon request).

4.0 DISCUSSION

Prior evidence in the literature for an association between OPRM1 polymorphisms and risk for opioid dependence was not substantiated in either analysis presented here. In our family-based study of OPRM1 and opioid dependence (the first of which we are aware), only one of the three genotyped polymorphisms (Asn40Asp) was informative, and this SNP provided little support for an association of either allele with risk for opioid dependence. This absence of support is not due to population stratification, as family-based studies are immune to this bias, and is likely not due to insufficient power, as our design had power greater than 97% for detecting an additive effect of the polymorphism and greater than 70% for detecting a dominant or recessive effect with an odds ratio as small as 2.0. However, if the actual magnitude of the effect of this OPRM1 polymorphism on risk for opioid dependence were an odds ratio of 1.5 or smaller, we would have insufficient power (i.e., β<0.80) to reliably detect such an effect in our sample under either dominant or additive models.

The results of our family-based association analyses were bolstered by the results of our meta-analysis, which also found no significant evidence for this association in the collective body of case-control studies of opioid dependence published to date. This finding is also consistent with a recent meta-analysis conducted by Arias and colleagues (Arias et al., 2006) that found no evidence for an association of the Asn40Asp polymorphism and risk for substance dependence in general. In contrast to the prior meta-analysis, the current study focused specifically on exploring the association between the Asn40Asp SNP and opioid dependence.

It is important to note that significant heterogeneity is present in the current body of case-control studies of OPRM1 polymorphisms in opioid dependence, and this heterogeneity was detected (though not fully explained) in our meta-analysis. This heterogeneity is suggestive of potential moderators, mediators and/or confounders that possibly are affecting the conditions under which this association operates, and thus, the reliability with which it is reported in the literature. Although the effect was not significant within any given population, point estimates of the magnitude and nature of the association of opioid dependence with the Asn40Asp polymorphism varied by ancestry. As shown in Table 2, the frequency of the Asp allele varies considerably between populations as well. It is possible that this pattern of results is due to different patterns of linkage disequilibrium between the Asp40Asn polymorphism and an as-yet-undetected causal variant in its vicinity. In this event, the evidence for association of opioid dependence with the Asn40Asp polymorphism might be negligible when collapsed over all studies, but more profound effects of the Asn40Asp marker might be observed within subgroups of studies with similar patterns of linkage disequilibrium, which in this case, may correspond to the different ancestral groups represented.

Studies selected for our meta-analysis utilized various screening methodologies to ascertain cases and controls ranging from self-report to standardized clinical interviews (e.g., SCID), which may provide another source for the observed heterogeneity among the studies included in the meta-analysis. Thus, it is plausible that phenotypic differences within cases and controls are clouding the ability to observe a significant association. Interestingly, three studies (Bond et al., 1998; Crowley et al., 2003; Shi et al., 2002) utilized rigorous screening methodologies for controls but employed unstandardized screening methods for cases. An additional three studies (Gelernter et al., 1999; Li et al., 2000; Tan et al., 2003) utilized standardized screening methods for cases but did not use rigorous screening in the ascertainment of controls. This mismatch in screening methodology between cases and controls could potentially result in cases phenotypically similar to controls or the reverse, respectively, which will reduce the power to detect allelic association if it exists.

Recruitment source was also examined as a potential contributor to heterogeneity in the sample. Studies utilized several recruitment sources including detoxification centers, hospitals, and physician referral. Differences in the type of services offered by these sources may result in phenotypic discrepancies (e.g., severity) between subjects recruited from different sources. Patients recruited from hospital treatment programs may have significantly more co-morbid conditions than a patient receiving detoxification treatment or a patient referred to a study by a physician.

Examination of these potential moderating, mediating, and/or confounding factors revealed no significant effects on the nature or magnitude of the relationship between the Asn40Asp SNP and opioid dependence. However, detailed information on these variables was not uniform across studies, and thus measurement of these potentially influential variables was imprecise. Additionally, age, gender, duration of illness, and other potentially interesting moderator variables were not included in this analysis due to inconsistent reporting among the studies selected. This limitation demonstrates the value of establishing general guidelines for submission of association studies so that more informed meta-analyses can be conducted with greater precision. With uniform reporting of association studies, it is probable that the utility of meta-analysis in the field of psychiatric and addiction genetics will improve. However, uniform reporting cannot correct for poor methodology and thus the validity of meta-analysis will always be susceptible to the quality of the studies selected.

In conclusion, while an association between the Asn40Asp polymorphism and opioid dependence was not supported in the current family-based association analysis or a meta-analysis of case-control studies, further analysis is warranted in samples from specific ancestral groups (perhaps especially Hispanic populations) while controlling for potential admixture. In addition, it is critical that other OPRM1 variants, including all haplotype-tagging and amino-acid-coding SNPs, be tested for an influence on risk for opioid dependence, since the Asn40Asp polymorphism is only one of several hundred known mutations in the gene, and the critical risk-conferring SNP(s) may yet await discovery. Finally, it is important to recognize that, while the Asn40Asp polymorphism of OPRM1 may not have a direct bearing on whether or not an individual becomes dependent on opioids, this or other OPRM1 variants may influence other phenotypes relevant to the disorder. For example, while work seeking association of the Asn40Asp polymorphism with anxious and depressive symptoms (Jorm et al., 2002) or personality traits (Hernandez-Avila et al., 2004) has been negative, several studies have found effects of this SNP on therapeutic responses to naloxone among alcohol-dependent (Oslin et al., 2003) and opioid-dependent subjects (Hernandez-Avila et al., 2003), as well as the efficacy of nicotine replacement therapy among smokers (Lerman et al., 2004). Thus, continued examination of this and other OPRM1 polymorphisms in relation to quantitative traits and alternate phenotypes of substance use disorders appears warranted.

Acknowledgments

This project is a joint effort between the American and Chinese project sites and research team members. The authors would like to thank Xiaobo Yuan, Kejian Ma, Hongrui Ji, Li Wu, Hua Wang, Xianling Liu, Yu Li, Liping Yang, Jing Wang, Xuemei Gong, Huaihai Shi, Haibin Wang, Li Fu, Peikai Li, Jiucheng Shen, Yan Xu, Chunmei Duan, and Yuan Deng from the Yunnan Institute of Drug Abuse for their valuable contributions to the successful completion of this study. In addition, we thank the Human Genetics Resources Administration of China (HGRAC) for their support of this project. We also thank Dr. Margret Hoehe for providing genotype data not available in her original publication. This work was supported by a grant (R01DA012846) from the National Institutes of Health to Ming T. Tsuang.

Footnotes

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