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. Author manuscript; available in PMC: 2014 May 1.
Published in final edited form as: Addict Biol. 2012 Sep 12;18(3):523–536. doi: 10.1111/j.1369-1600.2012.00490.x

The Association between DRD2/ANKK1 and Genetically Informed Measures of Alcohol Use and Problems

Jacquelyn L Meyers 1, Emma Nyman 2, Anu Loukola 3,4, Richard J Rose 5, Jaakko Kaprio 2,3,4, Danielle M Dick 1
PMCID: PMC3522787  NIHMSID: NIHMS399430  PMID: 22970887

Abstract

Background

In 1990, Blum and colleagues first reported an association between DRD2 and alcoholism. While there have been subsequent replications of this genetic association, there have also been numerous studies that failed to detect an association between DRD2 and alcohol dependence. We propose that one aspect contributing to this inconsistency is the variation in alcohol phenotype used across studies.

Methods

Within the population based Finnish twin sample, FinnTwin16, we previously performed multivariate twin analyses to extract latent genetic factors which account for the variation across seven measures of alcohol consumption (frequency of drinking, frequency × quantity, frequency of heavy drinking, frequency of intoxication, and maximum drinks in a 24 hour period) and problems (the Rutgers Alcohol Problem Index-RAPI and the Mälmö-modified Michigan Alcohol Screen Test - MmMAST) in 3,065 twins. In the present study, we examined the association between thirty-one DRD2/ANKK1 SNPs and the genetic factor scores generated by twin analyses in a subset of FinnTwin16 (n=602). We focus on two of the genetic factors: a general alcohol consumption and problems factor score which represents shared genetic variance across alcohol measures, and an alcohol problems genetic factor score which loads onto the two indices of problematic drinking (MAST and RAPI).

Results

After correction for multiple testing across SNPs and phenotypes, of the thirty-one SNPs genotyped across DRD2/ANKK1, one SNP (rs10891549) showed significant association with the general alcohol consumption and problems factor score (p=0.004), and four SNPs (rs10891549, rs1554929, rs6275, rs6279), representing 2 independent signals after accounting for LD, showed significant association with the alcohol problems genetic factor score (p=0.005, p=0.005, p=0.003, p=0.003).

Conclusions

In this study, we provide additional positive evidence for the association between DRD2/ANKK1 and alcohol outcomes, including frequency of drinking and drinking problems. Additionally, post hoc analyses indicate stronger association signals using genetic factor scores than individual measures, which suggests that accounting for the genetic architecture of the alcohol measures reduces genetic heterogeneity in alcohol dependence outcomes in this sample and enhances the ability to detect association.

Introduction

Alcohol consumption and problems are complex human behaviors that are influenced by both genetic and environmental risk factors (Kendler et al., 1992; Kendler et al., 1994). One strong candidate gene for alcohol-related outcomes is the dopamine receptor D2 gene (DRD2). In 1989, it was hypothesized that the rewarding effects of alcohol are mediated through the mesolimbic dopamine system (Wise and Rompre, 1989). The association between DRD2 and alcoholism was first reported by Blum and colleagues, who found that an increased frequency of the Taq1A1 restriction fragment length polymorphism was observed in postmortem brain tissue from alcoholics (as compared to nonalcoholic controls) (Blum et al., 1990). Since this initial report, there has been an extensive literature examining the relationship between DRD2 and alcohol-related outcomes. While there have been subsequent replications of this genetic association (Blum et al., 1991; Comings et al., 1991; Parsian et al., 1991; Amadeo et al., 1993; Noble et al., 1994; Higuchi et al., 1994; Neiswanger et al., 1995; Hietala et al., 1997; Kono et al., 1997; Ishiguro et al., 1998; Noble, 2003; Foley et al., 2004; Konishi et al., 2004), there have also been numerous studies across a variety of samples, populations, and study designs which fail to find an association between DRD2 and alcohol outcomes (Arinami et al., 1993; Bolos et al., 1990; Chen et al., 1996, 1997, 2001; Cook et al., 1992; Cruz et al., 1995; Edenberg et al., 1998; Gelernter and Kranzler, 1999; Gelernter et al., 1991; Goldman et al., 1992, 1997; Lee et al., 1999; Lobos and Todd, 1998; Lu et al., 1996; Parsian et al., 2000; Sander et al., 1995, 1999; Schwab et al., 1991; Suarez et al., 1994; Turner et al., 1992; Waldman et al., 1999). Critics have proposed that much of this mixed literature resulted from the limitations of early genetic studies including small sample sizes and limited ability to tag all regions of a gene. However, results from more recent genetic association studies remain inconsistent with both positive (Hack et al., 2010, Filbey et al., 2011; Landgren et al., 2011; Van der Zwaluw et al., 2011; Bhaskar et al., 2011) and negative (Kasiakogia-Worlley et al., 2011; Creemers et al., 2011, Heath et al., 2011, Wang et al., 2011, Luo et al., 2011, Schumann et al., 2011) evidence for association between DRD2 and alcohol problems. Interpreting this literature is further complicated by the 2004 discovery that the Taq1A polymorphism that had been most extensively studied was actually located 10 kb downstream from DRD2 in a neighboring gene, ankyrin repeat and kinase domain containing 1 (ANKK1) (Neville et al., 2004). The Taq1A variant is located within an exon of ANKK1, causing a non-synonymous coding change that may affect the substrate binding specificity of the gene product. It has been hypothesized that ANKK1 may be involved in the dopaminergic reward pathway through signal transduction (Neville et al., 2004). There have been many reviews of the DRD2 literature that provide detailed analysis of the variation across these genetic association studies (Goldman, 1998; Noble et al., 2000, Le Foll et al., 2009). However, little attention has been given to variability in the measurement of alcohol problems across these studies.

Many of the aforementioned studies used standard measures of alcohol use and/or problems including the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, the Alcohol Dependence Scale (ADS), the Alcohol Expectancy Scale (AES), and the Alcohol Use Disorders Identification Test (AUDIT). Measures of alcohol problems vary by scientific field, setting (clinical vs. research), historical trend (DSM-III vs. DSM-IV), and availability. However, there is evidence to suggest that genetic association results may vary as a function of the alcohol measure used in the analysis. In 2002, Connor and colleagues tested the association between DRD2 and a variety of alcohol phenotypes, finding association with certain alcohol phenotypes (alcohol quantity, alcohol consumed per week, alcohol dependence scale score) and not others (frequency of alcohol use). This is an example of how even when using an identical sample and method in genetic association analyses the measure of the phenotype can affect the results.

Twin studies provide a method for examining the genetic relationship between different measures of alcohol use and problems. While some twin studies indicate that the genetic correlation between measures of regular alcohol consumption and problems is strong (Grant et al., 2009; Kendler et al., 2010), there is also evidence that there are genetic risk factors unique to alcohol problems (Dick et al. 2011). Additionally, recent twin studies examining the genetic relationship between the DSM-IV alcohol dependence criteria have indicated that the seven items are not genetically homogeneous (Kendler et al, 2011). Therefore, different measures of alcohol use and problems may be mediated by different genetic factors. This has implications for gene identification studies in that there are valid reasons why true genetic findings may not replicate across studies that have assessed different aspects of alcohol use and dependence.

We previously reported analyses conducted within the Finnish population-based twin sample, FinnTwin16, to examine the genetic architecture across seven measures of alcohol consumption (frequency of drinking, frequency × quantity, frequency of heavy drinking, frequency of intoxication, and maximum drinks in a 24 hour period) and problems (the Rutgers Alcohol Problem Index-RAPI and the Mälmö-modified Michigan Alcohol Screen Test - MmMAST) (Dick et al., 2011). Our results yielded a model suggesting four latent factors that account for the genetic variance across the measures of alcohol consumption and measures of problems. The first two latent genetic factors loaded onto all of the drinking measures (consumption and problems), the third latent genetic factor loaded exclusively onto maximum drinks in a 24 hr period and the MmMAST, and the fourth latent genetic factor loaded onto the two indices of problems (the MmMAST and the RAPI). Using comparable measures of alcohol consumption and problems, data from an independent twin sample, the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders, also indicated a parallel genetic architecture (Dick et al., 2011). This previously reported model from the Finntwin16 sample is depicted in Figure 1.

Figure 1.

Figure 1

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Best Fitting Model of the Genetic Architecture of Measures of Alcohol Consumption and Problems in the Full Finntwin16 Sample (previously published in Dick et al. 2011)

In the present study, we extended these twin study results to examine the relationship between these measures of alcohol use/problems and DRD2/ANKK1. We hypothesized that examining association with genetic factor scores (previously implicated by the twin analyses within the same sample) would decrease the genetic heterogeneity and consequently increase power to detect genetic association between DRD2/ANKK1 and alcohol outcomes. We were primarily interested in the shared genetic variance across all alcohol measures (Figure 1. latent genetic factor A1) and the shared genetic variance across the two indices of problematic alcohol use (Figure 1. latent genetic factor A6). Additionally, we conducted post hoc analyses of the association between DRD2/ANKK1 and multiple measures of both alcohol consumption and problems in an effort to evaluate whether using genetic factor scores was an improvement upon using individual measures of alcohol consumption and problems.

Methods

Sample

Finntwin16 (FT16) is a population-based study consisting of five consecutive birth cohorts of twins born between 1975 and 1979 (Kaprio et al., 2002). The five birth cohorts contained 3,065 families of twins in which both twins were living and residing in Finland at the age of 16. Details about data collection have been previously published (Kaprio et al., 2002; Kaprio et al., 2006). All twins were identified through Finland’s Population Register Center, permitting exhaustive and unbiased ascertainment. Zygosity was determined using a well-validated questionnaire completed by both co-twins at the baseline (Kaprio et al., 1991). Here we focus on assessments of alcohol consumption and alcohol problems in young adulthood. The average age for the respondent twins at this assessment was 24.4 years (SD=1.50, range 22.8–27.2), with a response rate of 88.1%. Of these individuals, genotypic data was collected on 602 subjects, 36.0% were monozygotic (MZ) twins (n=216), 63.5% were dizygotic (DZ) twins (n=382). The 602 genotyped individuals were selected from twin pairs extremely discordant and concordant (EDAC selection) for alcohol-related problems, using a 22- item version of the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989) administered at age 18.5. EDAC selection has the benefit of enhancing statistical power by focusing on the most informative sibpairs, and it has been suggested as an advantageous sampling method for genetic linkage analyses (Gu et al. 1996). A full description of this selection is described in Latvala et al., 2011 (Latvala et al., 2011).

This was supplemented by parental information and comparisons of school photographs for the 2.9% (n=14) of twins whose zygosity could not be determined definitively from information in the questionnaires (Kaprio et al., 2002; Kaprio et al., 2006b) and later by DNA confirmation.

Measures

Measures of alcohol consumption and problems are described in detail in (Dick et al. 2011). Briefly, consumption measures included: Frequency (how often do you drink alcohol at all?), Frequency × Quantity (the frequency of reported use in the past 28 days multiplied by the quantity of drinks consumed per drinking day during the past 28 days; drinks defined as 1 beer, 1 glass of wine, or 1 mixed drink containing hard liquor equivalent to 10 grams of ethanol), Frequency of Heavy Drinking (at the present, how often do you within one occasion consume more than five bottles of beer, or more than a bottle of wine, or more than half a bottle of hard liquor?), Frequency of Intoxication (how often do you use alcohol to get drunk?), and Max Drinks (the maximum number of drinks twins reported ever consuming in a 24 hour period). Alcohol problem measures included: The Mälmö-modified MAST (Mm-MAST;(17)), a 9-item self-report scale of drinking patterns and problems designed for application in Nordic cultures (18)) and the 22 items from the Rutgers Alcohol Problem Index (RAPI)(19), a reliable scale designed to assess problematic drinking. Parallel to current practice in gene identification efforts for alcohol dependence, only individuals who had evidence of alcohol exposure were included in twin analyses, so that genetic/environmental influences on the decision to initiate alcohol are not confounded with genetic/environmental influences on alcohol consumption or problems. Altogether 2% of the sample had never had a full alcoholic beverage and were excluded from analyses. All measures were coded so that higher scores indicated more frequent drinking or more drinking problems.

Twin Modeling

The twin model we employed has been described in detail elsewhere (Dick et al., 2011). Briefly, a multivariate Cholesky model was fit to the measures of alcohol consumption and problems in order to estimate (1) the magnitude of genetic and environmental influences on each phenotype and (2) the extent to which these influences contributed to the covariation between the phenotypes. Using the statistical software package Mx (Neale and Cardon, 1992), we generated individual scores for each subject weighted by the loadings implicated by the genetic architecture from the best fitting twin model. When the best fitting model (Figure 1) from the full sample (n=2,500) was fit in the genotyped subset (n=602), there was not a significant decrease in model fit (χ2=3.28, p=1.00), however a model with two genetic factors (A1 and A6) fit the genotyped subsample best (AIC = −352.334). Thus, we moved two genetic factors forward in creating individual genetic factor scores for each person within the genotyped sample; (1) A general factor which loads onto measures of alcohol consumption and problems and (2) an alcohol problems factor which loads onto the Mm-MAST and the RAPI. This genetic factor score is similar to a phenotypic factor score in that it encompasses all shared variance across various measures. It differs in that it incorporates genetic information gained from twin data, therefore partitioning this shared variance into shared genetic variance across various measures. Thus, if an individual has an increased score on the specific alcohol measures that are loaded on by the latent genetic factor (e.g., Mm-MAST and RAPI), that individual will also to have an increased score on the genetic factor score (e.g., Alcohol Problems Genetic Factor, which loads onto Mm-MAST and RAPI).

Genotyping

A total of 602 individuals were genotyped using Sequenom’s homogeneous Mass Extend (hME) and iPLEX Gold technology (Sequenom, San Diego, CA, USA). Thirty-one tagging single-nucleotide polymorphisms (SNPs) in DRD2/ANKK1 were selected based on the HapMap Project (http://www.hapmap.org) and NCBI (http://www.ncbi.nlm.nih.gov) databases. The selected variants were bi-allelic and had a minor allele frequency (MAF) >10% in the Caucasian population. The ability to amplify the flanking regions of each SNP was determined by using the applications SNPper (http://www.snpper.chip.org) and RealSNP (http://www.realsnp.com), which define the most reliable regions for designing primers and the quality of the amplicons, respectively. All tagging SNPs failing during the procedure were replaced by newly generated tagging SNPs proposed by Haploview (Barrett, Fry, Maller, & Daly, 2005). The PCR and extension primers were designed using Sequenom’s Mass ARRAY Assay Design software (version 2.0). SNPs were genotyped in 384-well plates according to manufacturer’s instructions. For quality controls, each plate contained at least eight water controls and 22 duplicate samples. PCR reactions were performed in a total reaction volume of 5μl using 20ng of genomic DNA obtained by blood draw (Kettunen et al, Nat Genet 2012). The alleles were automatically called by Sequenom’s Mass ARRAY Typer Analyzer software and verified by two independent persons. Further marker-specific quality controls included a call rate >80% and a Hardy-Weinberg equilibrium (HWE) p-value >0.01 (estimated using unrelated individuals). Mendelian errors were excluded using PedCheck (O’Connell & Weeks, 1998).

Once data were cleaned for quality control, genotypic data was available on 580 individuals of Finnish descent. An analysis of the population structure of the sample conducted in Eigenstrat (Price et al., 2006), a software program that uses principal components analysis to detect population stratification in genome-wide association studies, indicated a single ethnicity factor; thus all individuals were included in association analyses. Information on the genotyped SNPs, including chromosomal location and minor allele frequency is provided in Table 1. These thirty-one SNPs represent five different haplotype blocks across DRD2/ANKK1 (Figure 2). These SNPs are correlated (r2 range from .21–.93) yet represent five semi-independent signals (defined by r2<0.5) across DRD2/ANKK1 as indicated by a Nyholt correction for related SNPs (Nyholt et al., 2004).

Table 1.

Linear Regression of DRD2/ANKK1 SNPs on Genetic Factor Scores

DRD2 SNP Information Genetic Factor Scores
Chr Gene SNP Base Pair Location Alleles Major; Minor MAF Alcohol Consumption and Problems Alcohol Problems (MAST and RAPI)
Beta p-value Beta p-value
11 ANKK1 rs2734849 112775370 A;G 0.282 0.094 0.047 0.127 0.006
11 ANKK1 rs2734848 112775584 T;C 0.220 −0.040 0.401 −0.040 0.391
11 ANKK1 rs1800497 112776038 G;A 0.330 −0.003 0.945 −0.035 0.451
11 DRD2 rs11214599 112776570 C;T 0.330 −0.007 0.886 −0.043 0.353
11 DRD2 rs11214601 112777972 C;T 0.330 −0.004 0.936 −0.041 0.373
11 DRD2 rs2587550 112778135 A;G 0.120 −0.096 0.042 −0.103 0.026
11 DRD2 rs12422191 112779220 G;A 0.900 0.001 0.981 0.034 0.460
11 DRD2 rs10891549 112783657 T;C 0.235 0.098 0.004 0.130 0.005
11 DRD2 rs2234689 112783693 C;G 0.220 0.040 0.401 0.040 0.391
11 DRD2 rs1554929 112783974 C;T 0.235 0.098 0.039 0.130 0.005
11 DRD2 rs6279 112786283 C;G 0.118 −0.096 0.042 −0.103 0.003
11 DRD2 rs1124491 112787300 G;A 0.330 0.005 0.914 −0.042 0.367
11 DRD2 rs1079595 112787879 A;C 0.330 −0.004 0.936 −0.041 0.373
11 DRD2 rs6275 112788687 G;A 0.117 −0.099 0.038 −0.102 0.003
11 DRD2 rs2440390 112792088 C;T 0.080 −0.051 0.285 −0.014 0.757
11 DRD2 rs1079727 112794392 T;C 0.030 0.006 0.906 −0.035 0.444
11 DRD2 rs2734833 112798130 A;G 0.241 −0.098 0.038 −0.108 0.019
11 DRD2 rs1076562 112801218 G;A 0.095 −0.107 0.024 −0.087 0.060
11 DRD2 rs7131440 112805120 T;C 0.254 −0.104 0.028 −0.105 0.023
11 DRD2 rs17115583 112814112 G;A 0.043 −0.091 0.056 −0.081 0.081
11 DRD2 rs11214606 112815079 C;T 0.010 −0.007 0.875 −0.012 0.794
11 DRD2 rs4648318 112818599 T;C 0.103 −0.105 0.026 −0.074 0.111
11 DRD2 rs17529477 112822277 G;A 0.033 0.052 0.267 0.042 0.359
11 DRD2 rs17601612 112822955 G;C 0.063 0.025 0.595 0.035 0.446
11 DRD2 rs4245147 112823217 T;C 0.099 0.033 0.494 0.068 0.143
11 DRD2 rs4245148 112825629 C;T 0.060 0.033 0.491 0.089 0.053
11 DRD2 rs7131056 112834984 C;A 0.226 0.078 0.100 0.040 0.391
11 DRD2 rs4245149 112843567 G;A 0.052 −0.070 0.141 −0.079 0.087
11 DRD2 rs1799978 112851561 A;G 0.050 −0.044 0.255 0.019 0.684
11 DRD2 rs12364283 112852165 A:G 0.011 −0.021 0.655 0.000 0.997
11 DRD2 rs10891556 112857971 G;T 0.052 −0.073 0.126 −0.072 0.120
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Note: SNPs that passed Nyholt threshold for significant association (p<0.005) are bolded. The reference build used in this table was HapMap Data Release 28 Phase II+III, August10, on NCBI B36 assesmbly dbSNP b126. The major allele frequencies (MAF) presented in this table were calculated using only one individual per family.

Figure 2.

Figure 2

Figure 2

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LD structure of DRD2/ANKK1

Legend: Location of (A) and correlations between (B and C) the single-nucleotide polymorphisms (SNPs) genotyped in the DRD2/ANKK1 gene complex (B) in the CEPH (Centre d’Etude du Polymorphisme Humain) data obtained from the HapMap database (The International HapMap Consortium, 2003) and (C) in the Finntwin16 data, Shading indicates the degree of correlation as measured by D′ (Hedrick & Kumar, 2001); darker shading indicates higher correlations, and white shading indicates that markers are unlinked or uncorrelated. The numbers inside the diamonds are R2 values, another measure of correlation between SNPs. The black triangles grouping subsets of SNPs indicate blocks of SNPs that are highly correlated (as defined by criteria detailed in Gabriel et al., 2002). Not all SNPs genotyped in the Finntwin16 sample were available in the HapMap database; in these cases, proxy SNPs that were the SNPs most highly correlated with the genotyped SNPs are listed. In the Finntwin16 sample, the LD blocks were similar to those in the HapMap CEPH data, and the somewhat stronger LD between markers is in agreement with previous findings from the Finnish population (Service et al., 2006).

Genetic association analyses

Linear regression was used to analyze the association between each of the SNPs and each of the genetic factor scores. The degree of relatedness (~50% for DZ twins and ~100% for MZ twins) was accounted for in the models using the GENMOD command in SAS 8.2 (SAS Institute, 2008). All p-value results from the association analyses were corrected for the number of independent tests conducted; the Nyholt correction indicated a significant threshold of p<0.005. Male and female data were collapsed in the genotypic analyses in order to maximize power to detect genetic association and to mirror the best fitting model from the twin analyses. Additionally, we conducted post hoc analyses of the association between DRD2/ANKK1 and the seven individual measures of alcohol consumption and problems in order to test whether using genetic factor scores would result in different conclusions than had we analyzed multiple individual measures of alcohol use/problems. When evaluating results for the seven alcohol phenotypes, the Nyholt correction indicated a significant threshold of a p<0.001 to take into account the additional tests.

Results

Twin Analyses

The phenotypic correlations across the measures of alcohol consumption and problems ranged from .45–.99 and were virtually identical to those previously reported in the full sample (Dick et al. 2011). Polychoric correlations were computed on only one twin from each pair, chosen randomly. MZ and DZ twin correlations for each of the measures were described previously (Dick et al. 2011). For the first genetic factor score (General Alcohol Consumption and Problems), scores ranged from −2.50 to 4.25 (mean=0, SD= 0.86). For the second genetic factor score (Alcohol Problems), scores ranged from −0.28 to 1.54 (mean=0, SD=0.52).

Genetic Association Analyses

Recall that the Nyholt threshold for a significant p-value for the two genetic factor scores is p<0.005. Of the thirty-one SNPs genotyped across DRD2/ANKK1, one SNP (rs10891549) showed significant association with the general alcohol consumption and problems factor score (p=0.004). Four SNPs (rs10891549, rs1554929, rs6275, rs6279) showed significant association with the alcohol problems genetic factor score (p=0.005, p=0.005, p=0.003, p=0.003, respectively). These results are detailed in Table 1. In addition, we conducted post hoc analyses in which we examined the association between DRD2/ANKK1 SNPs and the individual seven phenotypic measures of alcohol consumption and problems. These results are detailed in Table 2. Recall that the Nyholt corrected p-value for the seven alcohol outcomes is p<0.001. Using this criterion, none of the DRD2/ANKK1 SNPs were significantly associated with any of the individual alcohol measures.

Table 2.

Linear Regression of DRD2/ANKK1 SNPs on Individual Measures of Alcohol Consumption and Problems

Alcohol Measures (p-values)
SNP Frequency Of Drinking Frequency × Quantity Frequency of Heavy Drinking Frequency of Intoxication Max Drinks 24 hr. Period MAST RAPI
rs2734849* .016 .443 .032 .032 .278 .012 .007
rs2734848* .119 .637 .379 .329 .455 .378 .522
rs1800497* .662 .668 .839 .925 .802 .650 .337
rs11214599 .816 .593 .706 .729 .718 .512 .278
rs11214601 .777 .654 .749 .776 .667 .538 .293
rs2587550 .005 .695 .045 .036 .335 .027 .046
rs12422191 .404 .541 1.00 .732 .739 .225 .981
rs10891549 .012 .441 .028 .024 .230 .009 .006
rs2234689 .119 .637 .379 .329 .455 .378 .522
rs1554929 .012 .441 .028 .024 .230 .009 .006
rs6279 .005 .695 .045 .036 .335 .027 .046
rs1124491 .793 .640 .723 .759 .683 .549 .275
rs1079595 .777 .654 .749 .776 .667 .538 .293
rs6275 .004 .616 .042 .032 .407 .026 .053
rs2440390 .221 .806 .262 .108 .407 .750 .718
rs1079727 .566 .783 .885 .916 .756 .632 .430
rs2734833 .046 .345 .027 .013 .226 .034 .015
rs1076562 .010 .473 .010 .011 .261 .058 .073
rs7131440 .045 .294 .018 .008 .210 .034 .021
rs17115583 .039 .332 .063 .043 .579 .094 .084
rs11214606 .937 .927 .893 .752 .642 .816 .755
rs4648318 .014 .575 .028 .013 .311 .103 .126
rs17529477 .184 .388 .411 .090 .229 .424 .239
rs17601612 .632 .835 .853 .534 .482 .515 .327
rs4245147 .444 .800 .912 .586 .348 .209 .101
rs4245148 .309 .298 .927 .782 .343 .073 .080
rs7131056 .037 .160 .087 .075 .702 .550 .368
rs4245149 .023 .429 .258 .152 .729 .115 .102
rs1799978 .530 .528 .263 .154 .325 .357 .768
rs12364283 .568 .448 .671 .656 .811 .879 .935
rs10891556 .017 .434 .228 .129 .743 .140 .147
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*

Located in ANKK1

Conclusions

Two-decades of genetic studies have left the relationship between DRD2/ANKK1 and alcoholism indeterminate. Many reasons have been put forth to explain the mixed association results. Among them, poor DNA extraction techniques, population stratification, and failure to properly screen controls for drug and alcohol disorders. Previous reviews of this literature have detailed the variability and limitations of these studies (Goldman, 1998). A 2000 review by Noble (Noble, 2000) focused on sample size, types of alcoholics analyzed, and the nature of comparative controls employed in a variety of previously published studies. He reviewed several samples each of which used varying measures of alcoholism (The Michigan Alcoholism Screening Test, the presence or absence of medical complications of alcoholism, alcohol consumption, Severity of Alcohol Dependence Questionnaire (SADQ), and the DSM-III-R criteria). In this paper, we focus on the variability in the measure of the phenotype used across this literature in an effort to understand how this variability may effect the conclusions one would draw about the evidence for association with DRD2/ANKK1.

The 36 studies published between 1990 and 2011(Table 3), have yielded both positive and negative evidence of association across a variety of alcohol phenotypes. If more weight is placed on the recently published studies (Dick et al., 2004; Hack et al., 2011; Creemers et al., 2011; Schumann et al., 2011), which are presumably better powered to detect genetic association in that they use larger sample sizes and test a greater number of markers across DRD2/ANKK1 gene, and considering the publication bias that leaves many null results unreported, there is little evidence of association between DRD2/ANKK1 and alcohol phenotypes. It does appear however, that most of the studies that used quantitative/continuous measures of alcohol use and problems provide positive evidence of genetic association between DRD2/ANKK1 and alcohol related traits. This may reflect the fact that using quantitative measures can increase power to detect genetic association (Waldman et al., 1999, Kuo et al., 2010). However, it is of note that the largest of the aforementioned studies (Schumann et al., 2011), a meta-analyses of alcohol consumption GWAS on over 21,000 individuals, did not produce a genome wide significant variant in either DRD2 or ANKK1. The association with DRD2/ANKK1 appears to be contingent upon the specific measure of the phenotype, specific SNPs, and specific population used in a study. This is consistent with the implications of our twin studies that indicate that different genetic factors may contribute to risk for different measures of the “same” outcome (Dick et al., 2011). Moreover, while two measures of alcohol problems can both be valid and widely used, they are not necessarily genetically homogenous.

Table 3.

Previously Published Studies on the Genetic Association between DRD2/ANKK1 and Alcohol Phenotypes

Study Measure of the Phenotype Study Design Sample Size SNPS Evidence of Association
Blum et al., 1990 DSM-III-R Alcohol Dependence and/or Abuse Case/Control Brain tissue from 35 cases; 35 controls Tag1 A1 Positive (post mortem samples)
Blum et al., 1991 Severe alcoholics Case/Control 96 cases (52 severe) Taq1 A1 Positive (post mortem samples)
Comings et al., 1991 Michigan Alcohol Screen Test** × stress exposure Cross-sectional 309 Honduran males Taq1 A1 Positive (with stress exposure)
Gelernter et al., 1991 DSM-III-R Alcohol Dependence Case/Control 44 white cases; 68 controls Taq1 A1 Negative
Turner et al., 1992 DSM-III-R Alcohol Dependence; AD+medical complications Cross-sectional 47 white males Taq1 A1 Negative
Amadeo et al., 1993 DSM-III-R Alcohol Dependence Case/Control 69 French Polynesian cases; 57 controls Taq1 A1 Positive (combination of ADH2 and DRD2)
Arinami et al., 1993 DSM-III-R Alcohol Dependence; Greater severity Case/Control 70 Japanese cases; 100 Japanese controls (unscreened) Taq1 A1 Positive
Bolos et al., 1990 DSM-III-R Alcohol Dependence Case/Control 40 white cases; 127 controls Taq1 A1 Negative
Higuchi et al., 1994 DSM-III-R Alcohol Dependence; Greater severity (Feigner Criteria) Case/Control 280 Japanese cases; 289 controls Taq1 A1 (+) Positive
Noble, 1994 SADQ (Severity) Case/Control 73 cases; 80 controls Taq1 A1 Positive
Suarez et al., 1994 Medical complications from Alcoholism Case/Control 88 white cases; 89 controls Taq1 A1 (+) Negative
Geijer et al., 1994 DSM-III-R Alcohol Dependence Case/Control 74 cases; 81 controls Taq1 A1/B1 Negative
Cruz et al., 1995 Alcohol Withdrawal Symptoms Case/Control 38 Mexican cases; 38 controls Taq1 A1 Negative
Lu et al., 2001 DSM-III-R Alcohol Dependence; Conduct Disorder (CD) Case/Control 34 cases with CD, 63 cases without CD; 85 controls Taq1 A1/B1 Positive
Hietala et al., 1997 SADQ (Severity); MAST Case/Control 70 Finnish male cases; 50 controls Taq1 A1 Positive
Kono et al., 1997 DSM-III-R Alcohol Dependence; Early onset Case/Control 100 Japanese cases; 93 controls Taq1 A1 Positive
Ishiguro et al., 1998 DSM-III-R Alcohol Dependence Case/Control 209 Japanese cases; 152 controls Taq1 A1 Positive
Lobos and Todd, 1998 DSM-III-R Alcohol Dependence; Severity (Feigner Criteria) Case/Control 55 cases; 80 controls 5 SNPs (6 haplotypes) Negative
Edenberg et al., 1998 DSMIII-R AD and Feigner Criteria Linkage 433 cases; 401 controls Taq1 A1 Negative
Sander et al., 1999 DSMIII-R AD; Family history of Alcoholism Case/Control 310 German cases; 196 controls TaqI A (+) Negative
Waldman et al., 1999 Quantitative Alcohol Measures** TDT 433 cases; 401 controls (COGA) Taq1 A1 Positive
Gelernter & Kranzler, 1999 DSM-III-R Alcohol Dependence Case/Control 160 EA cases; 136 controls Taq1 A1/B1 Negative
Lee et al., 1999 DSM-III-R Alcohol Dependence Case/Control 128 cases; 85 controls Taq1 A1 Negative
Parsian et al., 2000 Medical complications from alcoholism; Feigner Criteria; Cloninger Criteria Case/Control 173 cases; 88 controls TaqI A (+) Negative
Chen et al., 2001 DSM-IV Alcohol Dependence Case/Control 203 cases; 213 controls −141C Ins/Del Positive
Foley et al., 2004 Alcohol Consumption from medical records** Taq1 A1/B1 Positive
Konishi et al., 2004 DSM-IV Alcohol Dependence Case/Control 200 Mexican American cases; 351 controls TaqI A1/B1 Positive
Dick et al., 2007 DSM-III-R Alcohol Dependence; Feigner Criteria Family based association 219 Caucasian families (n = 1,923) (COGA) 26 single nucleotide polymorphisms (SNPs) across DRD2/ANKK1 Positive
Yang et al., 2008 DSM-III-R or DSM-IV Alcohol Dependence and/or Drug Dependence Case/Control 136 AD+DD cases;166 AD cases; 414 controls 43 SNPs across DRD2/ANKK1, TTC12, NCAM1 Positive
Hack et al., 2010 DSM-IV Alcohol Dependence; Case/Control 545 Irish cases; 509 controls 15 DRD2 SNPs (excluding Taq1A1) Negative
Filbey et al., 2011 Impulsive behavior on the Go/NoGo task Heavy Alcohol Drinking** Cross-sectional 53 cases rs1799732 Positive
Van der Zwaluw et al., 2011 Adolescent Binge Drinking Cross-sectional 282 Dutch adolescent cases Taq1A Positive
Bhaskar et al., 2011 Michigan Alcohol Screen Test** Case/Control 81 cases; 151 controls 6 DRD2 SNPs Positive
Creemers et al., 2011 Adolescent Regular alcohol use Cross-sectional 1192 Dutch adolescents Taq1A1 Negative
Schumann et al., 2011 Alcohol Consumption Cross-sectional 21,607 drinkers Affymetrix 500K coverage of DRD2 Negative
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**

Measure used in the present study

In the present study, we modeled the genetic architecture of the alcohol outcomes available in the Finntwin16 sample in an attempt to examine more genetically homogenous alcohol phenotypes. We found modest evidence of association between DRD2/ANKK1 SNPs and both genetically informed measures of alcohol consumption and problems. As rs10891549 and rs1554929 are highly correlated (r2=.98) and rs6275 and rs6279 are highly correlated (r2=0.87), there were two true independent signals detected in this sample. The first of these signals (rs10891549/rs1554929) is highly correlated with the SNPs within the ANKK1 gene, and may be indirectly associated with ANKK1, the original locus detected in association with alcohol problems. The association between the rs10891549/rs1554929 locus was found with both general alcohol consumption and problems in this sample. The second signal (rs6275/rs6279) may be potentially functional as rs6275 and rs6279 are non-synonymous polymorphisms that are located on the 3′UTR and may have a regulatory effect. This locus was only significantly associated with alcohol problems in the Finntwin16. Perhaps multiple independent signals within the DRD2/ANKK1 gene complex are differentially associated with alcohol outcomes; this may provide some explanation of the inconsistent genetic association findings.

In an effort to assess the utility of the genetic factor score, we also examined the association between DRD2/ANKK1 SNPs and the individual phenotypic measures of alcohol consumption and problems. As the inclusion of seven outcomes required a more stringent statistical test correction, no SNP passed the significance threshold put forth to correct for the multiple tests conducted. These results may suggest that we are indeed reducing genetic heterogeneity in the alcohol measures using the genetic factor scores. Additionally, we increase power to detect association in reducing the number of phenotypes examined (we correct for the analysis of two factor scores versus seven measures of alcohol consumption and problems). Thus, one can increase power to detect genetic association by (1) reducing the number of tests conducted, and (2) modeling the genetic architecture of the trait/disorder within your sample. Further, the need to refine these phenotypes to obtain adequate power to demonstrate association argues against claims of robust association between DRD2/ANKK1 and alcohol dependence. These results should be considered in light of several limitations. First, the generalizability of these results may be limited as analyses were conducted using twin data from a relatively homogenous Finnish population. In addition, the relative youth of the sample is relevant. With an average age of 24.4 years, it is likely that many participants may be just entering, or have not yet aged into the period of highest risk for heavy/problem use.

In summary, we provide modest evidence for the association between DRD2/ANKK1 and alcohol use/problems. In capturing the genetic heterogeneity across alcohol measures in genetic factor scores, we found association between DRD2/ANKK1 SNPs with both regular and problematic drinking. It should be noted that the β values associated with each significant DRD2/ANKK1 SNP range from 0.001–1.30, indicating that a very small portion of the variation in alcohol behavior is accounted for by DRD2/ANKK1 SNPs. In this study, we also demonstrated how to maximize the information obtained by twin analyses and molecular analyses within the same sample. By reducing the genetic heterogeneity inherent in the alcohol phenotype and the number of phenotypes analyzed, we detect a genetic association between DRD2/ANKK1 and alcohol use and problems, which would have been deemed nonsignificant had we not incorporated the genetic architecture across the traits.

Acknowledgments

The Finnish Twin studies have been supported by the National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, and AA-09203 to RJR and AA15416 and K02AA018755 to DMD), the Academy of Finland (grants 100499, 205585, 141054 and 118555 to JK), and the Academy of Finland Centre of Excellence Programme (to LP & JK).

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