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Published in final edited form as: Addict Biol. 2017 Oct 23;24(1):121–131. doi: 10.1111/adb.12574

Genome-wide association study of Alcohol Use Disorder Identification Test (AUDIT) scores in 20,328 research participants of European ancestry

Sandra Sanchez-Roige 1, Pierre Fontanillas 2, Sarah L Elson 2; 23andMe Research Team2, Joshua C Gray 3, Harriet de Wit 4, Lea K Davis 5, James MacKillop 6, Abraham A Palmer 1,7,*
PMCID: PMC6988186  NIHMSID: NIHMS1067249  PMID: 29058377

Abstract

Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association (GWAS) study of the Alcohol Use Disorder Identification Test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20,328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the ‘chip-heritability’ of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10−7; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10−7; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using LD score regression, we identified positive genetic correlations between AUDIT score and high alcohol consumption, and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment, and additional unexpected negative correlations with BMI/obesity and attention-deficit/hyperactivity disorder (ADHD). We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating the etiology of AUD, in the context of alcohol consumption.

Keywords: Alcohol Use Disorder, alcohol-metabolizing enzymes, AUDIT, complex traits, genetic, GWAS

INTRODUCTION

The heritability of AUD and associated symptomatology such as high alcohol consumption has been estimated at ~50% by family and twin studies (Verhulst, Neale & Kendler 2015; Mbarek et al. Vink 2015), with a smaller proportion being attributable to additive effects of common genetic variation (e.g. 33% for AUD (Mbarek et al. 2015); 18% (Vrieze, McGue, Miller, Hicks & Iacono 2013) and 13% (Clarke et al. 2017) for alcohol consumption).

The search for specific genes that convey risk for AUD has been an active area of research for several decades. There have been numerous family-based linkage studies of AUD (Agrawal & Bierut 2012; Rietschel & Treutlein 2013; Enoch 2013; Edenberg & Foroud 2014), as well as candidate gene association studies. Robust linkage signals have been found near the cluster of aldehyde dehydrogenase (ALDH) genes on chromosome 12 (Wall, Luczak & Hiller-Sturmhöfel 2016), and alcohol dehydrogenase (ADH) genes on chromosome 4 (Long et al. 1998; Williams et al. 1999), a result that has also been identified by candidate gene association studies (Thomasson et al. 1991; Luo et al. 2006; Macgregor et al. 2009; van Beek, Willemsen, de Moor, Hottenga & Boomsma 2010; Li, Zhao & Gelernter 2011, 2012).

More recently, genome-wide association studies (GWAS) have been used to explore the genetic basis of AUD (Hart & Kranzler 2015; Tawa, Hall & Lohoff 2016). The most robust and replicated risk alleles in European, African American, and Asian ancestry populations map to alcohol-metabolizing enzyme genes on chromosome 4q22–23 and 12q24: ADH1B (Gelernter et al. 2014; Xu et al. 2015; Clarke et al. 2017), ADH1C (Treutlein et al. 2009; Edenberg et al. 2010; Frank et al. 2012; Gelernter et al. 2014; Clarke et al. 2017), ADH5 (Clarke et al. 2017), ADH7 (Park et al. 2013) and ALDH2 (Takeuchi et al. 2011; Yang et al. 2013; Park et al. 2013; Quillen et al. 2014; Jorgenson et al. 2017). More recent GWAS that target alcohol consumption rather than AUD have identified novel genes including KLB, which influence both high alcohol consumption in humans (Schumann et al. 2016; Clarke et al. 2017; Jorgenson et al. 2017) and ethanol preference in mice (Schumann et al. 2016).

In collaboration with the genetics company 23andMe, Inc., we performed a GWAS for alcohol misuse using the Alcohol Use Disorders Identification Test (AUDIT), a questionnaire developed to screen for alcohol misuse in the past year (Saunders et al. 1993). The estimated narrow-sense heritability of AUDIT dichotomous score is 60%, similar to the heritability of AUD (Mbarek et al. 2015). Self-reported AUDIT scores are predictive of future problematic drinking and higher AUD risk (Allen, Litten, Fertig & Babor 1997; Boschloo et al. 2010), with one study showing a high correlation (0.88) between AUDIT scores and AUD vulnerability (Bohn, Babor & Kranzler 1995). Indeed, high AUDIT scores (> 15) are consistent with a DSM-V diagnosis of severe AUD (Grant et al. 2015), perhaps because the AUDIT includes questions that are related to the criteria for AUD (e.g. DSM-V, criterion 2: “More than once wanted to cut down or stop drinking, or tried to, but couldn’t?” versus AUDIT, item 4: “How often during the last year have you found that you were not able to stop drinking once you had started?”). A previous GWAS of dichotomized AUDIT scores in 7,842 individuals in an unselected Dutch population did not reveal any significant associations (Mbarek et al. 2015). Here, to maximize power and to capture the dimensionality of alcohol misuse, we treated AUDIT scores from 20,328 research participants as a continuous trait rather than dichotomizing it by using a threshold score. We hypothesized that a GWAS for AUDIT scores might identify some of the same alleles that influence AUD, even though our cohort had relatively modest AUDIT scores.

MATERIALS AND METHODS

Sample

All participants included in the analyses were drawn from the customer base of 23andMe, Inc., a consumer genetics company. Participants provided informed consent and answered surveys online under a protocol approved by Ethical and Independent Review Services, an independent AAHRPP-accredited institutional review board (http://www.eandireview.com). We restricted the original sample (~25,000 individuals) to a set of unrelated participants of European ancestry (>97% as determined through an analysis of local ancestry [Durand, Do, Mountain & Macpherson 2014]; see Supplementary for additional details) for whom AUDIT data were available. Participants were not administered AUDIT if they reported that they never drank alcohol (N = 1,376; ‘Alcohol use’ questionnaire, Supplementary Table 1). The final number of participants included in the analysis was 20,328. Recruitment occurred over an approximately four-month period in 2015. Sociodemographic details are described in the Supplementary Table 1.

AUDIT scores

To evaluate alcohol misuse in the past year, participants completed the AUDIT (Saunders et al. 1993). We only included subjects who answered yes to the question “Have you ever in your life used alcohol” (i.e., “ever drinkers” vs “never drinkers”). The ten-item AUDIT questionnaire yields scores from 0 to 40. Since the scores were not normally distributed (by visual inspection, Figure 1), we used a log-10 transformation, which is frequently employed to approximate a normal distribution for AUDIT (Supplementary Table 2).

Figure 1.

Figure 1

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Distribution of AUDIT scores (prior to log transformation).

Genotyping, quality control and imputation

DNA extraction and genotyping were performed on saliva samples by CLIA-certified and CAP-accredited clinical laboratories of Laboratory Corporation of America. Samples were genotyped on 23andMe custom genotyping array platforms (Illumina HumanHap550+ Bead chip V1 V2, OmniExpress+ Bead chip V3, Custom array V4). Quality control of genetic variants and imputation were performed by 23andMe (see Supplementary Table 3). A full description of the methods have been reported elsewhere (Hyde et al. 2016; Lo et al. 2016).

Estimation of variance in AUDIT scores explained by the genotyped SNPs

To estimate the proportion of phenotypic variance explained (‘chip heritability’; hg2), we used a genomic restricted maximum likelihood (GREML) method implemented in Genetic Complex Trait Analysis (GCTA; Yang, Lee, Goddard & Visscher 2011). The GREML method estimates the proportion of variation in a phenotype that is due to all SNPs, and exploits the fact that genotypic similarity (i.e., “relatedness”, measured using genotyped SNPs) will be correlated with phenotypic similarity for heritable traits. Individual-level quality control was implemented, and distantly related individuals with pair-wise relationships were filtered at two thresholds (KIBS < 0.05 and KIBS < 0.03). We included age (inverse-normalized), self-reported sex (male/female), genotyping platform and top four principal components as covariates. GREML analyses were run using only directly genotyped SNPs.

Chip-heritability using LD Score Regression

We used a second method to measure chip heritability of AUDIT that is implemented by Linkage Disequilibrium Score Regression Coefficient (LDSC; Bulik-Sullivan et al. 2015b). To standardize the input file (GWAS summary statistics), we followed quality controls as implemented by the LDSC python software package. We used pre-calculated LD scores (“eur_w_ld_chr/” files (Finucane et al. 2015); MHC region excluded) for each SNP using individuals of European ancestry from the 1000 Genomes project, suitable for LD score analysis in European populations. We restricted the analysis to well-imputed SNPs: the SNPs were filtered to HapMap3 SNPs (International HapMap 3 Consortium et al. 2010), and were required to have a minor allele frequency (MAF) above 1%. InDels, structural variants, strand-ambiguous SNPs, and SNPs with extremely large effect sizes (χ2 > 80) were removed. In addition, this approach allowed us to distinguish between genomic inflation attributed to polygenic signal, from confounding biases such as population stratification or polygenicity (LD Score regression intercept > 1; Bulik-Sullivan et al. 2015b; Bulik-Sullivan et al. 2015a). As expected under polygenicity, we observed inflation of the median test statistic (Mean χ2 = 1.05), and adjusted for a genomic control inflation factor λ (the ratio of the observed median χ2 to that expected by chance) = 1.02. LD score intercept of 1.01 (SE = 0.01) suggested that deviation from the null was due to a polygenic structure rather than inflation due to population structure biases.

Genome-wide association analysis

For quality control of genotyped GWAS results, we removed SNPs with MAF of < 0.1%, a Hardy-Weinberg P <10−20 in Europeans, or a call rate of < 90%. We also removed SNPs that were only genotyped on the 23andMe V1 platform, due to limited sample size, and SNPs on chrM or chrY. Using trio data, we removed SNPs that failed a test for parent-offspring transmission; specifically, we regressed the child’s allele count against the mean parental allele count and removed SNPs with fitted β < 0.6 and P < 10−20 for a test of β < 1. We also tested genotyped SNPs for genotype date effects, and removed SNPs with P < 10−50 by ANOVA of SNP genotypes against a factor dividing genotyping date into 20 roughly equal-sized buckets. For imputed GWAS results, we removed SNPs with average r2 < 0.50 or minimum r2 < 0.30 in any imputation batch, as well as SNPs that had strong evidence of an imputation batch effect. The batch effect test is an F test from an ANOVA of the SNP dosages against a factor representing imputation batch; we removed results with P < 1 × 10−50. We also removed linear regression results for SNPs with MAF < 0.1% because tests of low frequency variants can be sensitive to violations of the regression assumption of normally distributed residuals. We performed association tests by linear regression assuming an additive model. We included age (inverse-normal transformed), sex, the top five principal components of genotype, and indicator variables for genotype platforms as covariates (Supplementary Table 4).

Phenotypic and genetic correlation analyses

We examined two distinct types of correlations: phenotypic correlations, where both variables were measured in the same individuals, and genetic correlations, where we used AUDIT data from this cohort in conjunction with summary statistics for GWAS conducted in other cohorts (Supplementary Table 9). The interpretation of these is different, since phenotypic correlations can be due to a combination of genetic and non-genetic factors, whereas genetic correlations measure only genetically driven correlations.

We used bivariate correlations to examine the direct phenotypic correlations between AUDIT and several variables of interest (age, gender, race, education, annual household), and to identify significant covariates for inclusion in GWAS analysis (Supplementary Table 5).

Using LDSC, we calculated genetic correlations (rg) between AUDIT and 30 other complex traits or diseases that have been previously associated with AUDIT or alcohol-related phenotypes, for which we had access to summary statistics. We used height as a control. References for the datasets used are identified in Supplementary Table 9. Files were standardized using the steps described in the section above (“Chip-heritability using LD Score Regression”). We did not constrain the intercepts in our analysis because the degree of sample overlap was unknown. We used False Discovery Rate (FDR) to correct for multiple testing (Benjamini & Hochberg 1995).

Query for expression quantitative trait loci (eQTL)

We queried eQTL evidence for our top (P < 10−7) GWAS SNPs using public online resources. We used the Genotype-Tissue Expression Portal (GTEx) to identify eQTLs associated with the SNPs; and the RegulomeDB (Boyle et al. 2012) to identify regulatory DNA elements in non-coding and intergenic regions of the genome in normal cell lines and tissues.

RESULTS

Demographics

Demographic data are shown in Supplementary Table 1. Mean age was 53.8 years (SD = 16.1), and 55.3% were women. The annual household income ranged from less than $14,999 (13.5%) to greater than $75,000 (21.5%), and the mean years of education completed was 16.8 (SD = 2.6). About half of the participants (49.3%) were married/partnered. Participants showed low to moderate alcohol use, average frequency of alcohol use was ~9 days per month (mean = 8.78, SD = 9.82); during the period of heaviest lifetime use, subjects reported reaching an average of 13.78±10.96 days over a 30-day period. Over the prior year, 78% of the participants reported drinking 1 or 2 drinks on a single day, and only 28% reported drinking more than 6 drinks on one occasion. Also over the prior year, 92% of the participants were able to stop drinking once they started, and 85% drank alcohol without feeling guilt or remorse.

AUDIT scores

The distribution of the AUDIT scores is shown in Figure 1 and Supplementary Table 2. 472 research participants had AUDIT scores > 15, and 127 had scores > 20, with an average AUDIT response of 3.84, (SD= 3.47). The phenotypic correlations between AUDIT and demographic variables measured in the same cohort are shown in the Supplementary Table 4. Age and sex were negatively correlated with AUDIT scores; younger individuals and males showed higher AUDIT scores (r = −0.15, P < 0.0001; r = −0.17, P < 0.0001, respectively). BMI was negatively associated with AUDIT (r = −0.07, P < 0.0001), whereas household income was positively correlated with AUDIT (r = 0.07, P < 0.0001). AUDIT scores were slightly higher in unmarried individuals (r = −0.02, P = 0.013) but we did not observe significant correlations with years of education (r = 0.01, P = 0.085). Measures of alcohol use were positively correlated with AUDIT scores (r = 0.50–0.52, P < 0.0001).

Chip-heritability estimates

We estimated the chip-heritability of AUDIT at 12.05% (± 1.91%, P = 2.70 × 10−11, GCTA; 8.8% ± 2.50%, LDSC), which is lower than previous chip-heritability estimates based on dichotomized AUDIT data (30% ±12%; Mbarek et al. 2015), and considerably lower than twin based heritabilities of alcohol abuse, dependence and alcoholism (~50%, (Goldman, Oroszi & Ducci 2005; Enoch 2013), as expected (e.g. Plomin, Haworth, Meaburn, Price & Davis 2013).

GWAS of AUDIT

The Manhattan and quantile-quantile (QQ) plots for AUDIT are shown in Figure 2 and Supplementary Figure 4, respectively. The most significant association was at rs182344113, located on chromosome 1 (P = 2.10 × 10−7; β = 0.168, SE = 0.03; MAF = 0.002; Supplementary Fig. 1). The association was in an intergenic region of the gene PIGM, and near KCNJ9 (GIRK3), which has been implicated in preclinical models of ethanol sensitivity. G-protein–gated inwardly rectifying potassium (GIRK) channels, which are coupled to GABA-B receptors, can be activated by ethanol (Aryal, Dvir, Choe & Slesinger 2009; Bodhinathan & Slesinger 2013). Interestingly, Kcnj9 knock-out mice exhibit excessive alcohol drinking (Dere et al. 2015).

Figure 2.

Figure 2

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Results of GWAS on AUDIT. (a) Manhattan plot of GWAS results indicating the strongest associations between the 22 autosomes, X chromosome, and AUDIT. Line denotes genome-wide significance (P < 5 × 10−8).

Several other SNPs also showed suggestive associations (Supplementary Table 6), including rs141973904 (P = 4.40 × 10−7, β = −0.05, SE = 0.01; MAF = 0.02; Supplementary Fig. 2) in an intron of ADH1C, replicating previous findings for that same SNP in a GWAS of alcohol consumption in males (Clarke et al. 2017), and broadly consistent with numerous previous genetic studies of AUD (Thomasson et al. 1991; Edenberg 2007; Frank et al. 2012; Biernacka et al. 2013).

Another suggestive association was at rs8059260 (P = 1.6 × 10−6, β = 0.017, SE = 0.004; MAF = 0.160; Supplementary Fig. 3), which is near the first exon of CLEC16A. Using the Genotype-Tissue Expression Portal (GTEx) database, we identified a cis- expression quantitative trait loci (eQTLs) for CLEC16A that co-localized with rs8059260 (r2 > 0.79; see Supplementary Table 7). We also found evidence of regulatory elements associated with rs8059260 using the RegulomeDB (Boyle et al. 2012; Supplementary Table 7).

Previously studied candidate genes

Our results did not strongly support any of the previously published candidate gene studies of AUD (reviewed in Bühler et al. 2015; Supplementary Table 8); various differences including the distinction between AUD and AUDIT, demographics characteristics and especially the low prevalence of AUD in our cohort could partially account for the lack of replication.

Genetic correlations

LD score regression (Bulik-Sullivan et al. 2015b) showed a genetic overlap between AUDIT and numerous traits (Fig. 3 and Supplementary Table 9). Both alcohol consumption and AUD showed the steepest correlations with AUDIT score (rg = 0.68 for both), while the better powered alcohol consumption traits (N = 70,460; N = 112,117) yielded a significant result (P = 3.40 × 10−3; P = 9.01 × 10−10) the P value for the genetic correlation with AUD (N = 7,280) fell just short of nominal significance (P = 6.41 × 10−2). We detected a significant negative genetic correlation between AUDIT and ADHD (rg = −0.29; P = 1.43 × 10−3). We observed a positive genetic correlation between AUDIT and lifetime tobacco use (rg = 0.42; P = 1.52 × 10−3). Unexpectedly, we identified a positive correlation between AUDIT and years of education (rg = 0.27; P = 3.14 × 10−5), college attainment (rg = 0.26; P = 8.11 × 10−3) and childhood IQ (rg = 0.42; P = 6.26 × 10−3). Also surprisingly, AUDIT was negatively genetically correlated with BMI (rg = −0.25; P = 1.48 × 10−4) and adulthood obesity (rg = −0.23; P = 2.06 × 10−3). Height, which is not strongly influenced by individual behavior and thus can be viewed as a negative control, was not genetically correlated with AUDIT (rg = 0.02; P = 7.12 × 10−1).

Figure 3.

Figure 3

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Genetic correlations between AUDIT and several traits measured in independent cohorts as described in the Supplementary Table 9: (a) neuropsychiatric, (b) smoking, (c) personality, (d) cognition, (e) anthropomorphic. * P < 0.05, ** P < 0.01, *** P < 0.0001.

DISCUSSION

With over 20,000 research participants, ours is by far the largest genetic study of AUDIT. By using a self-report measure of alcohol misuse, as opposed to recruiting a clinically-diagnosed population, we were able to rapidly and inexpensively ascertain a large number of participants. We identified rs141973904 (Supplementary Fig. 2) in the ADH cluster on chromosome 4q23, which has been previously associated with AUD (Edenberg et al. 2010; Frank et al. 2012; Gelernter et al. 2014; Treutlein et al. 2009). The same SNP has recently been associated (P = 1.22 × 10−15) with alcohol consumption using 53,089 males of European ancestry (Clarke et al. 2017). Furthermore, the most associated signal, rs182344113, which resides near the KCNJ9 (GIRK3) gene, was unknown, and is consistent with mouse studies of the homologous gene. The signal at rs182344113 was not significant and it will have to be replicated. We also identified a number of genetic correlations that have behavioral precedents, such as lifetime tobacco use, and several others that were unexpected, including lower BMI and obesity rates, and higher education. We found that AUDIT was genetically correlated and alcohol consumption, but fell out of significance for AUD, suggesting that a non-clinical population can be used as an alternative approach to study the genetics of AUD in the context of alcohol consumption.

In the absence of any GWAS significant results, we briefly discuss a few notable trend level results. We identified nominal associated variants in alcohol metabolizing genes. Genes influencing pharmacokinetics have previously been identified through linkage, candidate gene and genome-wide association studies for AUD and related traits (reviewed in Tawa et al. 2016). The most robust signal was located in the ADH1C gene, which contributes to ethanol oxidation. This signal was also identified in earlier GWAS studies for alcohol consumption (Clarke et al. 2017) and AUD status in both European (Clarke et al. 2017; Frank et al. 2012) and African American (Gelernter et al. 2014) populations, suggesting that pharmacokinetic factors are an important contributor to differences in both AUDIT score and AUD.

In addition to SNPs in the alcohol metabolizing genes, linkage and candidate gene studies have identified the GABRA2, OPRM1, DRD2 and ANNK1 genes, as candidate genes associated with AUD phenotypes (Bühler et al. 2015). However, we did not find robust signals for any of them (Supplementary Table 8), suggesting that previous studies may have overestimated the effects of these genes, or that these genes are associated with AUD but not AUDIT.

The strongest association we observed resides near KCNJ9; the frequency of the implicated imputed allele was very low (MAF = 0.002), suggesting the need for caution. The power to detect a significant association at this allele frequency was of XX. KCNJ9 encodes one of the G protein-activated inwardly rectifying K+ channels (GIRK3), which are expressed in the brain (Koyrakh et al. 2008), and can be directly activated by ethanol (Herman et al. 2015), even at low concentrations. In humans, two linkage studies have mapped this region for AUD (Hill et al. 2004), age of onset of drinking, harm avoidance, and novelty seeking (Dick et al. 2002). Additionally, DNA methylation levels of CpG in the promoter region of the GRIK3 gene showed altered expression in postmortem prefrontal cortex tissue of male alcoholics (Wang, Xu, Zhao, Gelernter & Zhang 2016). In mice, Kcnj9 also harbors a QTL for a variety of alcohol-related behaviors, including: ethanol preference (Tarantino, McClearn, Rodriguez & Plomin 1998), ethanol aversion (Risinger & Cunningham 1998), acute sensitivity to ethanol (Tipps, Raybuck, Kozell, Lattal & Buck 2016), and hypersensitivity to ethanol withdrawal (Kozell, Walter, Milner, Wickman & Buck 2009). Mice lacking GIRK3 in the brain have elevated alcohol drinking, without affecting the sensitivity to ethanol intoxication (Tipps, Raybuck, Kozell, Lattal & Buck 2016). Collectively, these results could provide an example of convergent results from humans and mice; however, until this non-significant observation is replicated it should be viewed with caution.

We hypothesized that the genetic risk for AUD is likely to overlap with numerous traits relevant to addiction and psychiatric phenotypes, based on previous epidemiological data (Compton, Thomas, Stinson & Grant 2007), twin studies (Kendler, Heath, Neale, Kessler & Eaves 1993; Pickens, Svikis, McGue & LaBuda 1995; Knopik, Heath, Bucholz, Madden & Waldron 2009) and recent genetic correlations between alcohol consumption and neuropsychiatric traits (Clarke et al. 2017). We showed positive genetic correlations between AUDIT and lifetime cigarette smoking, as previously observed between alcohol consumption and daily cigarettes, and tobacco initiation (Vink et al. 2014; Nivard et al. 2016). We also observed shared genetic architecture across AUDIT and other alcohol-related traits from independent cohorts: alcohol consumption (Schumann et al. 2016; Clarke et al. 2017), and AUD diagnosis (P = 0.062; Gelernter et al. 2014). With regards to other psychiatric traits, we found a negative correlation with ADHD; but there were no other significant genetic correlations between AUDIT and psychiatric traits. This result is generally consistent with a recent GWAS by Clarke et al (2017), where they did not observe strong correlations between alcohol consumption and psychiatric conditions.

Unexpectedly, we found positive genetic correlations between AUDIT and years of education, college attainment and childhood IQ. This association was suggestive for the within-sample phenotypic correlation (Supplementary Table 5) and was significant for the genetic correlation (Supplementary Table 9). Consistent with this finding, Clarke et al (2017) reported that college attainment and years of education were positively genetically correlated with alcohol consumption in females but not males.

Also unexpectedly, we observed negative genetic correlations between AUDIT and BMI and obesity, consistent with Clarke et al (2017). We also observed a phenotypic correlation between high AUDIT scores and low BMI. Previous studies have shown both positive and negative phenotypic correlations between alcohol use and BMI and obesity (Breslow & Smothers 2005; Tolstrup et al. 2005; Sobczyk-Kopciol et al. 2016), which may reflect differences in the populations used. The generalizability and biological meaning of these observations will require further research.

Our heritability estimate (12% ± 1.91) was lower the previous estimate by Mbarek et al (30% ± 12; 2015). While we cannot provide a definitive explanation for this discrepancy, we note that heritability estimates are not expected to be consistent across different populations. Differences between the two populations include geographic location (Dutch vs. US), the dichotomization of the phenotype by Mbarek et al (2015), heavier alcohol use in Mbarek’s cohort and limited AUDIT scores > 15 in our study.

Our study is not without limitations. Cumulative AUDIT scores reflect two distinct constructs: one measuring alcohol consumption and another measuring alcohol-related problems; thus, AUDIT scores may conflate multiple genetic signals (Bergman & Källmén 2002; Shevlin & Smith 2007). When we split AUDIT scores in three domains of consumption (items 1–3; AUDIT-C), dependence (items 4–6; AUDIT-D) and hazardous use (items 7–10; AUDIT-H), we observed higher scores for the domain of alcohol use (AUDIT-C: 2.96 ± 1.95; AUDIT-D: 0.203 ± 0.76; AUDIT-H: 0.665 ± 1.513). Our study may be tagging genetic risk for high quantity/frequency of alcohol consumption, as shown by the high genetic correlation with other alcohol-consumption traits, but may not overlap with other GWAS of AUD. In addition, our study focused on a cohort with relatively low levels of alcohol use; the unexpected positive genetic correlation between AUDIT and educational attainment, and the negative genetic correlation between AUDIT and both BMI/obesity and ADHD, may not generalize to cohorts with higher levels of alcohol use (Goldman et al. 2005) or AUD populations. Indeed, the selection of the 23andMe cohort (e.g. highly educated, with high socioeconomic background), or that of similar cohorts recruited from the general population such as UK Biobank, could have induced collider bias (Munafo, Tilling, Taylor, Evans & Smith 2017), underlying some of the paradoxical associations between AUDIT scores in our study, and high alcohol consumption in Clarke et al. (2017), and education/obesity. Another limitation of this study is the reliance on self-reported alcohol consumption, which may have induced biases and result in a more heterogeneous sample (Agrawal et al. 2012). Furthermore, AUDIT explicitly asks about alcohol use in the past year (i.e. state rather than trait), and this temporal specificity is not optimal for a genetic study. Nevertheless, AUDIT scores have a high correlation (r2=0.88) with scores on the Michigan Alcohol Screening Test (MAST), which assesses lifetime risk for AUDs, indicating that the temporal specificity of AUDIT may have a limited impact (Bohn, Babor & Kranzler 1995).

Although we used a unique and potentially powerful technique to examine the genetic basis of alcohol misuse, we recognize that alcohol consumption, misuse and dependence are influenced by numerous factors, both genetic and environmental (e.g. availability of alcohol, social norms, laws, psychosocial and personality factors, expectancies, health factors). Further, individuals vary in susceptibility at every stage of alcohol use from initiation to severe dependence, including the continued use after a first drink, the direct subjective and behavioral effects of the drug, withdrawal severity, tolerance and susceptibility to relapse, among others. Genetic factors are likely to influence variability at each of these stages, but different stages may be influenced by different sources of genetic variability. Thus, whereas we have investigated genetic variance related to an intermediate outcome measure of alcohol misuse (as measured by the AUDIT), it remains to be determined exactly how genetic sources of variation influence alcohol consumption. This ambiguity limits our ability to elucidate the underlying molecular mechanisms identified by GWAS of alcohol use and abuse.

Nonetheless, unlike studies of disease traits, which require careful diagnosis and ascertainment, we rapidly obtained a large cohort for which genotype data were available. We replicated a previously identified signal (ADH1C), and identified a novel GWAS signal (near KCNJ9) that has preclinical correlates. Our approach shows that genetic studies of AUDIT in community-based samples are an economical and effective alternative to rigorously diagnosed AUD cohorts that can nevertheless be used to gain insight into the biology of AUD, particularly aspects of alcohol drinking, and comorbid psychopathology.

Supplementary Material

supplemental

ACKNOWLEDGEMENTS

We would like to thank the 23andMe research participants and employees for making this work possible. J.M.’s contributions were partially supported by the Peter Boris Chair in Addictions Research. S.S-R is supported by the Frontiers of Innovation Scholars Program (FISP) and the Interdisciplinary Research Fellowship in NeuroAIDS (IRFN).

Footnotes

DISCLOSURE

Pierre Fontanillas, Sarah L. Elson, and members of the 23andMe Research Team are employees of 23andMe, Inc. The opinions and assertions expressed herein are those of the author(s) and do not necessarily reflect the official policy or position of the Uniformed Services University or the Department of Defense.

Supplementary Information with nine additional tables are available on the Addiction Biology website

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