Lack of associations of the opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with alcohol dependence: review and meta-analysis of retrospective controlled studies
Studies have sought associations of the opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with alcohol-dependence, but findings are inconsistent. We summarize the information as to associations of rs1799971 (A > G) and the alcohol-dependence.
Methods
Systematically, we reviewed related literatures using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Embase, PubMed, Web of Knowledge, and Chinese National Knowledge Infrastructure (CNKI) databases were searched using select medical subject heading (MeSH) terms to identify all researches focusing on the present topic up to September 2016. Odds ratios (ORs) along with the 95% confidence interval (95% CI) were estimated in allele model, homozygote model, heterozygote model, dominant model and recessive model. Ethnicity-specific subgroup-analysis, sensitivity analysis, heterogeneity description, and publication-bias assessment were also analyzed.
Results
There were 17 studies, including 9613 patients in the present meta-analysis. The ORs in the 5 genetic-models were 1.037 (95% CI: 0.890, 1.210; p = 0.64), 1.074 (95% CI: 0.831, 1.387; p = 0.586), 1.155 (95% CI: 0.935, 1.427; p = 0.181), 1.261 (95% CI: 1.008, 1.578; p = 0.042), 0.968 (95% CI: 0.758, 1.236; p = 0.793), respectively. An association is significant in the dominant model, but there is no statistical significance upon ethnicity-specific subgroup analysis.
Conclusion
The rs1799971 (A > G) is not strongly associated with alcohol-dependence. However, there are study heterogeneities and limited sample sizes.
Abkürzungen
95% CI
95% confidence interval
CNKI
Chinese National Knowledge Infrastructure
HWE
Hardy-Weinberg equilibrium
MeSH
Medical Subject Heading
OPRM1
opioid receptor mu 1
OR
odds ratio
PCR-RFLP
polymerase chain reaction-restricted fragment length polymorphisms.
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Background
Alcohol-dependence is a common disorder involving psychological and physical alcohol-dependence despite frequent complications [1]. Based on DSM-IV criteria, no less than 3 out of 7 of the following criteria must be met during 12 months for alcohol-dependence: tolerance; use is continued in spite of knowledge of related harms; recreational, occupational or social pursuits are reduced or given up due to alcohol use; time is spent obtaining alcohol or recovering from effects; unsuccessful efforts or persistent desires to cut down on alcohol-use; use for longer periods or in larger amounts than intended; and withdrawal symptoms or clinically defined alcohol withdrawal syndrome [2]. There are around 76 million people suffered from alcohol dependence worldwide, which is one of the leading psychiatric disorders of adult patients [3]. Its etiology is still unclear [4]. There were some studies indicating heritability of this disorder (ranging from 49% to 64%) [5, 6]. Several studies concerning genome-wide or phenome-wide associations of alcohol dependence were listed in Table 1 [5, 7‐11]. These researches suggested that genetic factors might influence the patient susceptibility to alcohol dependence.
Table 1
Previous studies about genome- or phenome-wide association studies of alcohol dependence
1. They confirmed well-known risk loci mapped to alcohol-metabolizing enzyme genes, notably ADH1B in European-American (EA) and African-American (AA) populations and ADH1C in AAs, and identified novel risk loci mapping to the ADH gene cluster on chromosome 4 and extending centromerically beyond it to include GWS associations at LOC100507053 in AAs, PDLIM5 in EAs, and METAP in AAs.
2. They also identified a novel GWS association mapped to chromosome 2 at rs1437396, between MTIF2 and CCDC88A, across all of the EA and AA cohorts, with supportive gene expression evidence, and population-specific GWS for markers on chromosomes 5, 9 and 19.
1. The results confirmed significant associations of the well-known functional loci at ADH1B with MaxDrinks in EAs and AAs. The region of significant association on chromosome 4 was extended to LOC100507053 in AAs but not EAs.
2. They also identified potentially novel significant common SNPs for MaxDrinks in EAs: rs1799876 at SERPINC1 on chromosome 1 and rs2309169 close to ANKRD36 on chromosome 2.
1. GWAS SNP effect concordance analysis was performed between GWAS and a recent alcohol dependence GWAS using DSM-IV diagnosis. The twin-based heritability of alcohol dependence-AUDIT was estimated at 60% (55–69%).
2. GCTA showed that common SNPs jointly capture 33% of this heritability.
3. The top hits were positioned within 4 regions (4q31.1, 2p16.1, 6q25.1, 7p14.1) with the strongest association detected for rs55768019.
1. In the stage 1 sample, they observed 3 GWS SNP associations, rs200889048 and rs12490016 in EAs and rs1630623 in AAs and EAs meta-analyzed.
2. In the stage 2 sample, they replicated 278, 253 and 168 of the stage 1 suggestive loci in AAs, EAs, and AAs and EAs meta-analyzed, respectively. A meta-analysis of stage 1 and stage 2 samples identified 2 additional GWS signals: rs28562191 in EAs and rs56950471 in AAs
1. Ten correlated SNPs located in an intergenic region on chromosome 3q26 were associated with fast beta (20–28 Hz) EEG power at P < 5 × 10–8. The most significantly associated SNP, rs11720469 is an expression quantitative trait locus for butyrylcholinesterase, expressed in thalamus tissue.
2. Four of the genome-wide SNPs were also associated with alcohol dependence, and two (rs13093097, rs7428372) were replicated in an independent AA sample.
3. Analyses in the AA adolescent/young adult subsample indicated association of rs11720469 with heavy episodic drinking (frequency of consuming 5+ drinks within 24 h).
1. They replicated prior associations with drinking behaviors and identified multiple novel phenome-wide significant and suggestive findings related to psychological traits, socioeconomic status, vascular/metabolic conditions, and reproductive health.
2. They applied Bayesian network learning algorithms to provide insight into the causative relationships of the novel ADH1B associations: ADH1B appears to affect phenotypic traits via both alcohol-mediated and alcohol-independent effects. They replicated the novel ADH1B associations related to socioeconomic status (household gross income and highest grade finished in school).
3. For CHRNA3-CHRNA5 risk alleles, they replicated association with smoking behaviors, lung cancer, and asthma. There were also novel suggestive CHRNA3-CHRNA5 findings with respect to high-cholesterol-medication use and distrustful attitude.
A relevant neurotransmitter system is related to endogenous opioids pathway [12]. Drinking alcohol can first increase levels of endogenous opioids (e.g. β-endorphin). Opioid reward system in return can elicit seeking additional alcohol. In addition, binding of μ-opioid receptors to β-endorphin could reinforce alcohol-dependence through increasing dopamine expressions at reward-centers [12] and then affect individual responses to alcohol. Therefore, genetic variations of OPRM1 might have an effect upon the risks of alcohol-dependence [13]. The rs1799971 is in the OPRM1 coding-area [13]. Though lots of researches have sought associations of the OPRM1 A118G- polymorphism with alcohol-dependence, there was no consensuses. [14] A Swedish group found that the A118G-polymorphism was connected to an 11% risk of alcohol dependence [15] while Bergen et al. found no significant association. [16] We were thus prompted to perform a meta-analysis to provide a full picture of current progress on this topic.
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Methods
Article search and selection criteria
Two investigators searched CNKI, Embase, Web of Knowledge, and PubMed (up to Sep. 2016). Terms included “alcohol or alcoholic” and “rs1799971 or A118G or OPRM1”. Also, related references were scanned. Inclusion criteria and exclusion criteria are shown in Table 2.
Table 2
Inclusion criteria for this meta-analysis
Number
Inclusion criteria
1
Case-control studies.
2
The studies evaluated the associations between OPRM1 A118G polymorphism and alcohol dependence.
3
The studies included detailed genotyping data (total number of cases and controls, number of cases and controls with A/A, A/G, and G/G genotypes).
4
Studies focusing on human being.
Number
Exclusion criteria
1
The design of the experiments was not case-control.
2
The source of cases and controls, and other essential information were not provided.
3
The genotype distribution of the control population was not in accordance with the Hardy–Weinberg equilibrium (HWE).
4
Reviews and duplicated publications.
Data extraction
We sought these information: authors’ names, publication-year, nation, ethnicity (Asian, Caucasian, or others), genotyping ways, P value for Hardy-Weinberg equilibrium (HWE),total numbers of controls and cases, controls and cases with OPRM1-A118G polymorphism, with A/A, A/G, and G/G genotypes, and control sources (population-based or hospital-based).
Methodological qualities
Based on the methodological quality scale (see Table 3), 2 investigators estimated the study qualities independently. Disagreements were resolved by discussions. In the methodological quality assessment scale, five items (sample sizes, quality control of genotyping methods, source of controls, case representativeness, and HWEs) were checked. The scores range between 0 and 10, with 10 indicating highest quality.
Table 3
Scale for methodological quality assessment
Criteria
Score
1. Representativeness of cases
RA diagnosed according to acknowledged criteria.
2
Mentioned the diagnosed criteria but not specifically described.
1
Not Mentioned.
0
2. Source of controls
Population or community based
3
Hospital-based RA-free controls
2
Healthy volunteers without total description
1
RA-free controls with related diseases
0.5
Not described
0
3. Sample size
> 300
2
200–300
1
< 200
0
4. Quality control of genotyping methods
Repetition of partial/total tested samples with a different method
2
Repetition of partial/total tested samples with the same method
1
Not described
0
5. Hardy-Weinberg equilibrium (HWE)
Hardy-Weinberg equilibrium in control subjects
1
Hardy-Weinberg disequilibrium in control subjects
0
Statistical analysis
This analysis was in accord with the PRISMA checklist and guideline. ORs were computed in 3 steps: 1) for given individuals that have “B”, we computed the odds that the same individuals have “A”; 2) for given individuals that do not have “B”, we computed the odds that the same individuals have “A”; and 3) we divided the odds from step 1 by the odds from step 2, getting the ORs. The pooled ORs were estimated and used for comparisons in the 5 genetic models mentioned above. Ethnicity-specific subgroup-analyses were also made. To estimate the heterogeneities, we performed the I2 tests, Labbe plots, and Cochran’s Q-tests (see Table 4). As it seems likely that there are considerable phenotypic variations between populations in the different studies, we did all these analyses using the random-effects model. By contour-enhanced funnel plots and sensitivity-analysis plots (Table 4), we did publication-bias and sensitivity tests.
Table 4
The statistical methods used in this meta-analysis and there explanation
Statistic means
Goals and Usages
Explanation
Labbe plot
To evaluate heterogeneity between the included studies
In Labbe figure, if the points basically present as a linear distribution, it can be taken as an evidence of homogeneity.
Cochran’s Q test
To evaluate heterogeneity between the included studies
Cochran’s Q test is an extension to the McNemar test for related samples that provides a method for testing for differences between three or more matched sets of frequencies or proportions. Heterogeneity was also considered significant if P < 0.05 using the Cochran’s Q test.
I2 index test
To evaluate heterogeneity between the included studies
The I2 index measures the extent of true heterogeneity dividing the difference between the result of the Q test and its degrees of freedom (k – 1) by the Q value itself, and multiplied by 100. I2 values of 25%, 50% and 75% were used as evidence of low, moderate and high heterogeneity, respectively.
Sensitivity analysis
To examine the stability of the pooled results
A sensitivity analysis was performed using the one-at-a-time method, which involved omitting one study at a time and repeating the meta-analysis. If the omission of one study significantly changed the result, it implied that the result was sensitive to the studies included.
Contour-enhanced funnel plot
Publication bias test
Visual inspection of the Contour-enhanced funnel plots was used to assess potential publication bias. Asymmetry in the plots, which may be due to studies missing on the left-hand side of the plot that represents low statistical significance, suggested publication bias. If studies were missing in the high statistical significance areas (on the right-hand side of the plot), the funnel asymmetry was not considered to be due to publication bias
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A value of P < 0.01 was deemed of statistical significance. Statistical-analyses were conducted with Review Manager 5.3 and STATA 13.0.
Results
Search results and study characteristics
Figure 1 shows the processes of the literature-searching. 17 studies with 9613 patients were included. [15‐31] Nine studies involved Caucasian subjects and were done in the USA, [15, 16, 24, 28, 30] Germany, [19, 22, 27] and Spain [18] (8026 subjects in total). Eight involved Asian subjects and were done in China, [23, 26, 29, 31] India, [17] Japan, [25] and Korea [20, 21] (1587 subjects in total). Fourteen studies were written in English, [15‐25, 27, 28, 30] and three were in Chinese. [26, 29, 31] Alcohol dependence was defined by drinking history. Genotyping methods used included direct sequencing, polymerase chain reaction-restricted fragment length polymorphisms (PCR-RFLP), Puregene™ kit or standard phenol-chloroform method, TaqMan assay, and fluorescence resonance energy transfer method. Ten matchings for the controls were population-based, [15, 16, 18, 24‐29, 31] 3 were hospital-based, [20‐22] and 4 were mixed. [17, 19, 23, 30] The characteristics and methodological qualities are in Table 5.
Fig. 1
Literature search and selection of articles
Table 5
Characteristics of studies included in the meta-analysis
Author
Year
Country
Ethnicity
Disease type
Genotyping
Source of controls
Alcohol-dependence (n)
Controls (n)
P for HWE
Quality
Total
AA
AG
GG
Total
AA
AG
GG
Bergen et al.
1997
USA
Caucasian
Alcohol-dependence
Direct sequencing and PCR-RFLP
Population-based
160
123
35
2
264
204
59
1
0.1285
7
Sander et al.
1998
German
Caucasian
Alcohol-dependence
PCR-RFLP
Population-based
327
261
62
4
340
289
49
2
0.9606
6
Franke et al.
2001
German
Caucasian
Alcohol-dependence
Direct sequencing and PCR-RFLP
Mixed
221
170
50
1
365
284
74
7
0.4024
8
Schinka et al.
2002
USA
Caucasian
Alcohol-dependence
Puregene™ kit or standard phenol-chloroform method
Population-based
179
152
27
0
297
220
73
4
0.4531
7
Kim et al.
2004
Korea
Asian
Alcohol-dependence
PCR-RFLP
Hospital-based
100
46
47
7
128
54
53
21
0.2014
8
Kim et al.
2004
Korea
Asian
Alcohol-dependence
PCR-RFLP
Hospital-based
112
37
61
14
140
68
57
15
0.5582
7
Loh et al.
2004
China Taiwan
Asian
Alcohol-dependence
PCR-RFLP
Mixed
154
59
77
18
146
70
56
20
0.1136
8
Bart et al.
2005
USA
Caucasian
Alcohol-dependence
PCR-RFLP
Population-based
389
299
90
170
147
23
Not available
8
Nishizawa et al.
2006
Japan
Asian
Alcohol-dependence
PCR-RFLP
Population-based
64
12
37
15
74
26
33
15
0.4493
8
Zhang et al.
2006
USA and Russia
Caucasian
Alcohol-dependence
PCR-RFLP
Mixed
318
246
68
4
338
256
78
4
0.4713
7
Deb et al.
2010
India
Asian
Alcohol-dependence
PCR-RFLP
Mixed
53
16
32
5
82
44
30
8
0.3967
8
Miranda et al.
2010
USA
Caucasian
Alcohol-dependence
TaqMan assays
Population-based
27
13
14
160
134
26
> 0.05
8
Dou et al.
2011
China
Asian
Alcohol-dependence
PCR-RFLP
Population-based
118
48
53
17
218
74
110
34
0.5127
6
Koller et al.
2012
Germany
Caucasian
Alcohol-dependence
Fluorescence resonance energy transfer method
Hospital-based
1845
1461
353
31
1863
1417
419
27
0.5275
9
Huang et al.
2012
China
Asian
Alcohol-dependence
PCR-RFLP
Population-based
45
33
11
1
45
33
12
0
0.3021
6
Francesc
2015
Spain
Caucasian
Alcohol-dependence
PCR-RFLP
Population-based
630
425
190
15
133
101
30
2
0.893
7
Jin
2015
China
Asian
Alcohol-dependence
PCR-RFLP
Population-based
58
41
12
5
50
39
9
2
0.1487
7
×
Meta-analysis results
Related results are listed in Table 6. The Labbe plots are as Fig. 2a–c. Overall, statistically significant associations of OPRM1-A118G polymorphism with alcohol-dependence was detected only in the dominant model (OR 1.261, 95% CI 1.008, 1.578; p = 0.042; Fig. 6). In the other four models, any associations were not significant (allele model: OR 1.037, 95% CI 0.890, 1.210; p = 0.640; Fig. 3; homozygote model: OR 1.074, 95% CI 0.831, 1.387; p = 0.586; Fig. 4; heterozygote model: OR 1.155, 95% CI 0.935, 1.427; p = 0.181; Fig. 5; recessive model: OR 0.968, 95% CI 0.758, 1.236; p = 0.793; Fig. 7).
Table 6
The results of meta-analysis for various genotype models
Genetic model
Heterogeneity test
Test of Association
Publication bias
Name
Explanation
Ethnicity
Q value
d.f.
I-squared
Tau-squared
P Value
Heterogeneity
Effect model
Pooled OR
95% CI
Z value
P value
Statistical significance
Allele model
G vs. A
Caucasian
17.38
6
65.5%
0.0493
0.008
Yes
Random
0.985
[0.797, 1.217]
0.14
0.888
No
No
Asian
14.90
7
53.0%
0.0564
0.037
Yes
Random
1.100
[0.871, 1.390]
0.80
0.421
No
Total
34.85
14
59.8%
0.0487
0.002
Yes
Random
1.037
[0.890, 1.210]
0.47
0.640
No
Homozygote model
GG vs. AA
Caucasian
5.60
6
0.0%
NA
0.469
No
Random
1.119
[0.731, 1.714]
0.52
0.605
No
No
Asian
10.22
7
31.5%
NA
0.176
No
Random
1.146
[0.743, 1.767]
0.62
0.538
No
Total
15.81
14
11.4%
NA
0.325
No
Random
1.118
[0.830, 1.506]
0.74
0.462
No
Heterozygote model
AG vs. AA
Caucasian
16.71
6
64.1%
0.0575
0.010
Yes
Random
0.983
[0.780, 1.237]
0.15
0.882
No
No
Asian
15.58
7
55.1%
0.1296
0.029
Yes
Random
1.433
[1.015, 2.023]
2.04
0.041
No
Total
42.72
14
67.2%
0.1017
0.000
Yes
Random
1.155
[0.935, 1.427]
1.34
0.181
No
Dominant model
AG + GG vs. AA
Caucasian
41.43
8
80.7%
0.1518
0.000
Yes
Random
1.185
[0.882, 1.593]
1.13
0.259
No
No
Asian
16.65
7
58.0%
0.1310
0.020
Yes
Random
1.379
[0.983, 1.934]
1.86
0.063
No
Total
63.64
16
74.9%
0.1467
0.000
Yes
Random
1.261
[1.008, 1.578]
2.03
0.042
No
Recessive model
GG vs. AA + AG
Caucasian
5.24
6
0.0%
NA
0.513
No
Random
1.142
[0.746, 1.747]
0.61
0.542
No
No
Asian
6.21
7
0.0%
NA
0.516
No
Random
0.919
[0.673, 1.255]
0.53
0.595
No
Total
12.06
14
0.0%
NA
0.602
No
Random
0.991
[0.771, 1.275]
0.07
0.946
No
Fig. 2
Labbe plots, sensitivity analysis plots, and contour-enhanced funnel plots of the included studies focusing on the association of the OPRM1 A118G polymorphism with alcohol dependence risk. Labbe plots in allele model (a), heterozygote model (b), and dominant model (c). Sensitivity analysis in allele model (d), heterozygote model (e), and dominant model (f). Contour-enhanced funnel plots in allele model (g), heterozygote model (h), and dominant model (i)
Fig. 3
Forest plots (individual and pooled effects with 95% CI) regarding the association of the OPRM1 A118G polymorphism with alcohol dependence in the allele model
Fig. 4
Forest plots (individual and pooled effects with 95% CI) regarding the association of the OPRM1 A118G polymorphism with alcohol dependence in the homozygote model
Fig. 5
Forest plots (individual and pooled effects with 95% CI) regarding the association of the OPRM1 A118G polymorphism with alcohol dependence in the heterozygote model
×
×
×
×
The ethnicities are an Asian group and a Caucasian group. The corresponding results are shown in Table 6 and Figs. 3, 4, 5, 6, 7. For both the 2 subgroups, the OPRM1-A118G polymorphism had no association with alcohol-dependence in all these 5 genetic-models.
Fig. 6
Forest plots (individual and pooled effects with 95% CI) regarding the association of the OPRM1 A118G polymorphism with alcohol dependence in the dominant model
Fig. 7
Forest plots (individual and pooled effects with 95% CI) regarding the association of the OPRM1 A118G polymorphism with alcohol dependence in the recessive model
×
×
Sensitivity analysis and publication bias
The ORs were not influenced by removing any single article (Fig. 2d–f). We had searched all possible studies both in Chinese databases and English databases to reduce the publication bias. Contour-enhanced funnel plots demonstrated that the studies only had missing areas for high statistical significance instead of low significance areas, thus very little or none publication bias was detected (Fig. 2g–i).
Discussion
Alcohol dependence is estimated to exhibit heritability of more than 50% [5, 6], indicating genetic factors might play pivotal roles alcohol-dependence. Genome-wide or phenome-wide associations researches of alcohol-dependence was presented in Table 1. In view of the significances of μ-opioid receptor systems in physiologic mechanisms of reward centers, it is safe to say that OPRM1-polymorphisms had an influence on alcohol-dependence risks. [32, 33] Therefore, we focused our study on OPRM1 A118G, which is a functional allelic-variant with deleterious effects on protein and mRNA expressions. [34]
Close associations are suspected of the OPRM1 A118G polymorphism (A > G) with nicotine, alcohol, and opioid dependence. [13, 35, 36] Kapur et al. and Tan et al. discovered close associations between A118G-polymorphisms and heroin dependence. [37, 38] Modulation changes of kinase A are likely responsible for the close associations of the OPRM1 A118G polymorphism (A > G) with heroin dependence. [39] Recently, Frances et al. found that the OPRM1 A118G polymorphism (A > G) was associated with alcohol/tobacco-dependence in a Spanish population, and this association was related to several environmental and genetic factors. [18] However, the study from Rouvinen-Lagerstrom et al. suggested that the effect of A118G-polymorphism on the development of alcohol dependence was not statistically significant (P > 0.05). [40] In a study by Franke et al., data from ethnically homogenous samples detected no actual difference of the OPRM1 A118G polymorphism between alcohol dependent subjects and controls. [19]
We combed PubMed, Embase, Web of knowledge and CNKI databases in search of associations of alcohol dependence with the OPRM1 A118G polymorphism to cover the most information sourced from both Chinese and English studies. In our meta-analysis, significant associations between alcohol-dependence risks and A118G-polymorphisms were only found in the dominant model (OR 1.261, 95% CI 1.008, 1.578; p = 0.042). Association was non-significant in four other models. For subgroup analyses of Caucasian or Asian group each considered separately, the OPRM1 A118G polymorphism did not have association with alcohol dependence in all five genetic models.
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In the contour-enhanced funnel plots, each circle represented a study. If studies appeared to be missing in areas of low statistical significance (the left part of the plot), the asymmetry is likely to be due to publication-biases. [41] In the present study, funnel plots indicated no publication bias.
There are potential limitations in our meta-analysis. The numbers of studies (nine and eight) as well as sample sizes for each ethnicity were limited. Type-II error could not be dismissed. [42] In addition, effects of gene-environment interactions and gene-gene interactions were not analyzed as not all eligible articles included these type of data. Within those studies with genomic interaction data, confounding factors were controlled and reported differently. Last, ORs adjusted by patient characteristics including genders, ages, living styles, medication-consumptions and other exposure-factors using meta-regression could be calculated with higher accuracy if related data were available in the majority of eligible studies.
Conclusions
The opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) is not associated with alcohol dependence in Caucasian nor Asian populations.
Acknowledgments
We thank our colleagues at the Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College.
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Funding
This study was supported by China Scholarship Council of the Ministry of Education, P. R. China. This study was funded by Peking Union Medical College Youth Research Funds (2016) (Project No. 3332016010; Grant recipient: Xiangyi Kong). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The data are within the manuscript.
Ethics approval and consent to participate
Because this is only a systematic review of previous retrospective studies, and does not involve any human experiments or animal experiments, ethics approval and consent are not applicable.
Consent for publication
Not applicable.
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Competing interests
The authors state that there are no conflicts of interest to disclose.
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Lack of associations of the opioid receptor mu 1 (OPRM1) A118G polymorphism (rs1799971) with alcohol dependence: review and meta-analysis of retrospective controlled studies
verfasst von
Xiangyi Kong Hao Deng Shun Gong Theodore Alston Yanguo Kong Jingping Wang
