Meta-Analysis of the Association of the Taq1A Polymorphism with the Risk of Alcohol Dependency: A HuGE Gene-Disease Association Review

¹ School of Health and Social Care, Oxford Brookes University, Marston, United Kingdom
² Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, United Kingdom
³ Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, King's College London, London, United Kingdom

Correspondence to Dr. Lesley Smith, School of Health and Social Care, Oxford Brookes University, Jack Straws Lane, Marston, OX3 0FL, United Kingdom (e-mail: lesleysmith@brookes.ac.uk ).

Received for publication February 22, 2007. Accepted for publication September 6, 2007.

The human dopamine 2 receptor Taq1A allele has been implicated as a vulnerability factor for alcohol dependence in a number of studies and reviews. To determine whether this allele is associated with alcoholism, the authors conducted a Human Genome Epidemiology review and meta-analysis. Forty-four studies with 9,382 participants were included. An odds ratio of 1.38 (95% confidence interval: 1.20, 1.58; heterogeneity, 50.5%) was found for the A1A1 + A1A2 versus the A2A2 genotype. Sensitivity analyses suggested lack of ethnic matching as a possible source of heterogeneity; a small, significant association was detected in studies with ethnic-matched controls (odds ratio = 1.26, 95% confidence interval: 1.02, 1.56; heterogeneity, 37%). Significant associations were also found in analyses restricted to studies reporting use of blinding and those with adequate screening of controls for alcohol dependency. For the A1A1 versus the A1A2 + A2A2 genotype, the odds ratio was 1.22 (95% confidence interval: 1.05, 1.43; heterogeneity, 0%). Sensitivity analyses on groups of studies reporting use of ethnic-matched controls and those that screened controls for alcohol dependency still showed significant associations. The relatively small effect for the association of the A1 allele, or another genetic variant linked to it, with alcohol dependence indicates a multigene causality for this complex disorder.

Keywords: alleles; association; dependency (psychology); DRD2; epidemiology; meta-analysis; review (gene-disease association); Taq1A
Abbreviations: CI, confidence interval; DRD2, dopamine receptor D2 gene; GABA, γ-aminobutyric acid; OR, odds ratio

Alcohol is known to increase dopaminergic function in the mesolimbic system, a brain reward system thought to be crucial in drug-mediated reinforcement behavior, and therefore may be involved in the pathogenesis of alcohol dependence. The dopamine receptor D2 gene (DRD2) has been one of the most extensively studied in addictive disorders, with the Taq1A polymorphism being the most frequently studied. The DRD2 Taq1A polymorphism is located more than 10 kilobase-pairs downstream from the coding region of the DRD2 gene at chromosome 11q23, and a mutation in this noncoding region would not be expected to produce a structural change in the dopamine receptor (1, 2). Therefore, the functional significance of the polymorphism is unclear. It is suggested that the Taq1A polymorphism may be in linkage disequilibrium with an upstream regulatory element or another functional gene that confers susceptibility to alcoholism. More recently, this DRD2-associated polymorphism has been more precisely located within the coding region of a neighboring gene, ANKK1 (ankyrin repeat and kinase domain containing 1), which may confer a change in the amino acid sequence (3).

The Taq1A allele has also been implicated in other addictive disorders such as polysubstance abuse (11); cocaine (12–14), opioid (15), methamphetamine (16, 17), and nicotine dependence (18); and gambling (19) as well as in other mental health disorders such as mood disorders, schizophrenia, and posttraumatic stress disorder (20, 21). However, no conclusive linkage or association has been found. Other candidate genes (alleles) implicated in alcohol dependency are the alcohol-metabolizing enzymes alcohol dehydrogenases and acetaldehyde dehydrogenase, and γ-aminobutyric acid (GABA), the major inhibitory neurotransmitter in the human brain (22). GABA acts via two receptor types, A and B. GABAA receptors are activated by benzodiazepines, which have pharmacologic properties similar to alcohol; thus, GABAA receptor genes are strong candidates. Several association studies point to the involvement of GABAA receptor subunit genes clustered on chromosome 5 and the development of alcohol dependence (23–29).

Prevalence data from population surveys in the United States have shown that about 6 percent of men and 2 percent of women are classified as alcohol dependent (30). Although a predisposition to alcohol dependency is thought to be partly attributable to genetic factors, a role for other risk factors has also been suggested. Previous work has linked alcohol abuse and dependence to an earlier age at drinking onset (31–38); being male (31, 33); being divorced, separated, or never married (31); having an early history of antisocial behavior (33, 39, 40); and belonging to a lower socioeconomic group (41). However, these findings are mostly from cross-sectional surveys, which are susceptible to recall bias, and there are few data confirming the association between specific risk factors and alcohol dependency from robust prospective studies with sufficient duration of follow-up and adequate control for confounding (31).

The aim of this Human Genome Epidemiology review was to systematically review and perform a meta-analysis of all available evidence from observational studies regarding the association of the DRD2 Taq1A allele with alcohol dependence.

Inclusion criteria
Studies were selected if they evaluated an association between the Taq1A allele of the DRD2 gene and alcohol dependency and included a non-alcohol-dependent concurrent control group. Only studies published in the English language were considered.

Identification and eligibility screening of studies
We identified eligible studies by searching Medline (National Library of Medicine, Bethesda, Maryland), Embase (Elsevier, Amsterdam, the Netherlands), and BIOSIS (Thomson Scientific, Stamford, Connecticut) from their inception to August 2006. We used the following thesaurus search terms (exploded): "alcohol-related disorders" OR "alcoholic beverages" OR "alcoholic drinking" combined with the free-text terms "allel*" OR "gene" OR "genes" OR "locus" OR "loci" OR "receptor*" OR "genotyp*"OR "polymorphi*" OR "RFLP" OR "genetic*" OR "mutation*" OR "variant*" AND "alcohol*" OR "drink*" AND "DRD2" OR "Taq1A." Two reviewers (L. S. and M. W.) screened the title and abstract of each electronic citation; full-text copies of potentially relevant studies were obtained. Each full-text copy was screened for eligibility (L. S. and M. W.). References from retrieved study reports and reviews were also screened for additional studies.

Data extraction
Two investigators (L. S. and M. W.) independently extracted data by using a structured form. Discrepancies were resolved by discussion and consultation with a third reviewer (D. F.). The following information was sought from each report: selection and diagnostic criteria of the alcohol-dependent group; selection and classification criteria of the control group; demographic information including ethnicity, age, and sex; all alleles investigated; method of ascertainment of genotype; blinding of personnel performing genotyping to clinical status of the study participants; methods used to create balanced groups (matching procedures or statistical adjustment methods); genotype and allelic frequencies; and statement of Hardy-Weinberg equilibrium. Studies were categorized as Caucasian based on the original study's use of the term to describe ethnic group or for studies conducted on White North Americans or Europeans.

Data analysis
We estimated unadjusted odds ratios for published genotype frequencies. Pooled odds ratios were calculated by using a random-effects model (42) stratified by geographic region/ethnicity. Geographic regions/ethnic groups were Caucasian, Native American, Japanese, Chinese, Hispanic, Indian, and Korean. Studies with mixed populations were added to the group that represented the majority of the participants. We quantified the extent of heterogeneity by using I2, which represents the proportion of variability between studies attributable to true variability rather than chance (43). There are several approaches to analyzing gene-disease association studies. We conducted two analyses; one assumed a dominant model of gene action (homozygous wild type vs. heterozygous and homozygous variant), the other a recessive model of gene action (homozygous wild type plus heterozygous vs. homozygous variant). We chose not to compare allele frequencies (codominant model) between cases and controls, because this process double-counts people, or to perform several pair-wise comparisons, because each pair-wise comparison leaves out valuable information. In addition, both analyses increase the likelihood of a type 1 error. We tested whether the genotype frequencies in the controls were in agreement with the expected distribution (Hardy-Weinberg equilibrium) by using Pearson's χ² with 1 degree of freedom, with Yates's correction for cells with values of less than 5.

We investigated heterogeneity through a number of preplanned sensitivity analyses in which studies were eliminated from analyses if they significantly deviated from Hardy-Weinberg equilibrium, did not use ethnic-matched controls, did not report use of blinding of case-control status and/or genotyping, and did not report adequate screening of control groups to exclude alcohol dependency. Cumulative meta-analyses were also performed. We used Stata software, version 8 (Stata Corporation, College Station, Texas) for cumulative meta-analyses and Hardy-Weinberg analysis, and the computer program RevMan, version 4.1 (The Nordic Cochrane Centre, The Cochran Collaboration, Copenhagen, Denmark) for all other analyses.

Description and quality of included studies
We screened 1,056 titles and abstracts and obtained 116 full-text papers. Of these, 44 met our eligibility criteria. We included 44 studies with a total of 9,382 participants. Forty-two were case-control studies, and two were cross-sectional surveys (44, 45). Characteristics of included studies are summarized in table 1. Three studies reported allelic frequencies only (6, 46, 47), but we were able to extract genotype frequencies from a review published by Noble (48) for the Bolos et al. (46) and Neiswanger (6) studies. All others reported genotype frequencies.

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Table 1  Characteristics of studies included in a meta-analysis of the association of the Taq1A polymorphism with the risk of alcohol dependency

Matching for sex was described in 10 studies (44, 51, 52, 59, 64, 69, 74, 76–78), for age in two (76, 77), and for ethnicity in 30 (5, 6, 44–46, 49, 52, 54, 56–60, 62, 64, 66–70, 75–84). Few (nine) studies reported blinding to case or control status of personnel who performed the genotyping (4, 5, 52, 56, 57, 62, 65, 75, 85). Polymerase chain reaction was used for genotyping in 25 studies (5, 45, 49, 50, 54, 57–61, 63, 64, 67, 69–71, 73, 74, 76–79, 81, 86, 87) and hybridization in 16 studies (4, 6, 46, 51–53, 55, 56, 62, 65, 66, 68, 72, 75, 84, 85); three did not report the method used (44, 47, 83). Genotyping was conducted in duplicate by only six studies (52, 57, 62, 67, 75, 76); in one study (87), 10 percent of the sample was checked; and, in one study, samples were genotyped in triplicate (4).

Overall effects
Forty-three studies, including a total of 5,273 cases and 3,995 controls, reported genotype frequencies for the Taq1A polymorphism and alcohol dependency.

We found evidence suggesting that genotypes in control groups were not in Hardy-Weinberg equilibrium (p < 0.05) in three studies (46, 68, 74).

For all studies combined, when we assumed the dominant model of gene action (A1A1 + A1A2 vs. A2A2), a small but significant association of alcohol dependency with being homozygote or heterozygote for the A1 allele was detected. The odds ratio was 1.38 (95 percent confidence interval (CI): 1.20, 1.58) when random effects were used, although substantial statistical heterogeneity was detected between studies (I² = 50.5 percent, p = 0.0001). Stratifying the studies into subgroups by ethnic group produced similar results, with significant associations detected in the two largest subgroups: Caucasian (odds ratio (OR) = 1.57, 95 percent CI: 1.29, 1.91) with substantial heterogeneity (I² = 57.2 percent, p = 0.0001) and Chinese (OR = 1.35, 95 percent CI: 1.04, 1.75) with no heterogeneity (I² = 0 percent, p = 0.71) (Web figure 1; this information is shown in the first of four supplementary figures; each is referred to as "Web figure" in the text and is posted on the website of the Human Genome Epidemiology Network (http://www.cdc.gov/genomics/hugenet/reviews.htm) as well as on the Journal's website (http://aje.oupjournals.org/)).

Pooling the results of the same studies, but assuming the recessive model of gene action (A1A1 vs. A1A2 + A2A2), also showed a small but significant positive association of alcohol dependency with being homozygote for the A1 allele (Web figure 2). The combined odds ratio was 1.22 (95 percent CI: 1.05, 1.43) using random effects this time, with no statistical heterogeneity detected between studies (I² = 0 percent, p = 0.92). Again, subgroup analyses showed no notable differences between different populations, with a significant association detected in the Caucasian subgroup (OR = 1.40, 95 percent CI: 1.03, 1.91) with no heterogeneity (I² = 0 percent, p = 0.92) and in the Chinese subgroup (OR = 1.41, 95 percent CI: 1.00, 2.00) with no heterogeneity (I² = 0 percent, p = 0.52).

Sensitivity analyses
Statistically significant departures from Hardy-Weinberg equilibrium were detected in three studies (46, 68, 74). Exclusion of these three studies from both analyses did not change the overall effect for the recessive model (OR = 1.22, 95 percent CI: 1.05, 1.43; I2 = 0 percent, p = 0.89) or remove heterogeneity between studies for the dominant model (OR = 1.38, 95 percent CI: 1.20, 1.60; I² = 53.1 percent, p < 0.0001). There was a slight increase in heterogeneity in the Caucasian subgroup (I² = 59.7 percent) with removal of the two studies, with significant departures from Hardy-Weinberg equilibrium.

An overall significant association was still detected using the dominant model in analyses restricted to studies reporting use of ethnic matching of controls (table 2), blinding (table 3), and screening of the control group to exclude alcohol dependents (table 4). Sensitivity analyses had little impact on heterogeneity, with the exception of the Caucasian subgroup when the analysis was restricted to studies using ethnically matched controls, where it was markedly reduced (37 percent, table 2). Similarly, for the recessive model, significant associations were still detected when restricting the analyses to studies with ethnically matched controls (table 3) and screened controls (table 4), with negligible heterogeneity.

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Table 2  Effect estimates in studies that reported use of a control group matched for ethnicity

 

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Table 3  Effect estimates in studies that reported use of blinding measures

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Table 4  Effect estimates in studies that reported screening for alcohol dependency in the control group

 
Results for the cumulative meta-analysis after each study from 1990 to 2004, and for each genetic model, are shown in Web figures 3 and 4. For the dominant model, the cumulative meta-analysis shows that, with each additional study, although being significantly greater than one since 1990, the magnitude of the effect decreased over time; the odds ratio came closer to one and remained relatively stable beginning in 2000. For A1 homozygotes versus both other genotypes (recessive model), the odds ratio became greater than one after 1991 and has remained so since then, reaching significance in 2001. The pooled odds ratio has changed very little since 2001. 

This large meta-analysis of mainly case-control studies found a small but significant association of the Taq1A polymorphism with alcohol dependency in both a dominant and a recessive model of gene action. Given the modest effect size found by our meta-analysis, many of the individual studies were obviously underpowered. However, there were substantial variations between studies, particularly in the Caucasian subgroup. The observed heterogeneity could be due to differences in how the samples were selected and screened or to methods of genotyping or interaction with other risk factors.

We tested whether genotype frequencies in the control groups were in agreement with Hardy-Weinberg equilibrium because it has been reported that this is miscalculated in many studies (88), and departure from Hardy-Weinberg equilibrium may point to genotyping error or other biases resulting in heterogeneity and misleading results (89). Exclusion of the three studies with significant deviation from Hardy-Weinberg equilibrium did not account for the heterogeneity or significantly change the pooled estimate. For many studies, although significant deviations were not found, agreement with Hardy-Weinberg equilibrium cannot be implied because there may have been insufficient information to detect a difference.

All of the studies had one or more serious methodological shortcomings: lack of matching for or adequate description of ethnicity of the control group, lack of blinding, inconsistent screening of control groups for alcohol dependence, inadequate description of genotyping methods, and potential variations in case definition due to imprecision of the different diagnostic criteria used. Selection of the control groups was especially problematic and in many studies included convenience samples rather than population-based controls. Population stratification cannot be ruled out because ethnicity was self-reported, and self-reported ancestry has been shown to be unreliable in outbred populations such as in the United States (90). One or more of these factors may have led to the observed heterogeneity in our meta-analysis. It is likely that each of these biases would affect the overall pooled estimate to a different extent, but it is unclear whether they would all work to bias the result in the same direction.

We investigated the impact of including studies with a greater likelihood of bias on the overall estimate by conducting a series of sensitivity analyses. Overall pooled estimates or heterogeneity values did not change markedly when potentially biased studies were excluded; however, heterogeneity was reduced substantially in the Caucasian subgroup and the overall group when the analysis was restricted to studies with ethnically matched controls.

An additional factor that has been cited as a probable cause of heterogeneity between studies is severity of alcohol dependency. We were unable to investigate the impact of severity of alcohol dependency on the pooled estimate because there was no consistent use of the term "severe." Medical complications, withdrawal symptoms, and Michigan Alcohol Screening Test scores were all used as criteria for defining the cases as severe. Severity of illness may reflect the duration of illness if based on an accumulation of complications and thus be unreliable.

The cumulative meta-analysis showed that, as more studies were published, the pooled odds ratio approached one, suggesting that one reason earlier studies showed strong associations may be chance because of small sample sizes. This finding is in agreement with an investigation of 55 meta-analyses of genetic association studies of various disease outcomes that showed that positive findings of early studies do not adequately predict establishment of an association (91).

Although we made a considerable effort to find published studies, the possibility of publication bias cannot be overlooked. It is difficult to predict the likely effect on the estimate. Most frequently, publication bias is due to preferential publication of favorable results, which tends to overestimate effects or associations; however, because of the polarity of opinion on the role of Taq1A, studies showing no association may not have been suppressed. We did not formally investigate publication bias through the use of funnel plots because it is unclear whether the assumptions for their interpretation are valid when observational studies, rather than randomized controlled trials, are pooled (92, 93).

Authors of previous reviews, meta-analyses, and primary studies have presented contrasting findings on the role of Taq1A and alcoholism. Reviews by Noble et al. (7, 48, 94, 95) reported on meta-analyses, mainly in Caucasian populations, and concluded that there is a strong association, particularly among people with "severe" alcohol dependency. These analyses were all based on comparing allele frequencies in people with alcohol dependency and nonalcoholic controls, analogous to the codominant model. This type of analysis tends to produce spurious associations or overinflate p values because of doubling of the sample size. It is also not clear whether, in these reviews, correct methods for pooling frequencies were used, which weights each study appropriately, or whether frequencies were pooled simply by adding them all and treating the data as if they were all from one large study.

A more cautious conclusion was drawn by Gelernter et al. (10), whose meta-analysis of studies published up to 1993 failed to confirm that a strong association was found. They suggested that factors contributing to the heterogeneity were lack of careful screening to exclude problem alcohol use by the control groups and lack of matching for ethnicity rather than the use of simple racial matching.

Although the modest strength of association found in this study is of a similar magnitude to odds ratios reported for meta-analyses of other candidate genes and mental health disorders (96, 97), because of the numerous potential sources of bias in the primary studies, overestimation of the association cannot be ruled out.

While there is convincing evidence for a genetic contribution to alcohol dependence derived from family, twin, and adoption studies, it cannot be explained by a single gene operating under Mendelian laws of inheritance.

Therefore, a single gene predisposing to alcoholism is not anticipated, and the likelihood is that many, of relatively modest influence, interact with environmental factors and operate during a process of development. As such, the relatively small effect identified by this study regarding the contribution of the A1 allele or another genetic variant linked to it is much more in keeping with the current understanding of the genetic contribution to such complex disorders and behaviors.

Conflict of interest: none declared.

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