Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts

Abstract

Objective: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits.

Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P).

Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (r_g=0.76-0.92) and DSM-IV alcohol dependence (r_g=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (r_g=0.22), major depressive disorder (r_g=0.26), and attention deficit hyperactivity disorder (r_g=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (r_g=-0.24) and ADHD (r_g=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (r_g=0.82) while retaining most subjects.

Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Keywords: Alcohol Abuse; Alcohol Consumption; Alcohol Dependence; Epidemiology; Genetics; Genome-Wide Association Studies.

Figure 1.

Manhattan and QQ plots for the SNP-based GWAS meta-analysis of AUDIT total score (N = 141,932)

Figure 2.

Genetic correlations between the three AUDIT phenotypes (total score, AUDIT-C, AUDIT-P) and several traits measured in independent cohorts as described in the Supplementary Tables 3–5: alcohol-related traits, tobacco and cannabis use, neuropsychiatric, personality, cognition, anthropomorphic and blood lipids. ADHD, attention-deficit/hyper-activity disorder; BMI, body mass index; SE, standard error; IQ, intelligence quotient; HDL, high-density lipoprotein. * p < 0.05, ** p < 0.01, *** p < 0.0001; # AUDIT-P vs AUDIT-C, p < 0.01 FDR 5%, (#) AUDIT-P vs AUDIT-C, p < 0.05

Figure 3.

Genetic correlations between AUDIT cases (8 [N = 25,423], 10 [N = 15,151], 12 [N = 9,130], 15 [N = 4,471], 18 [N = 2,099], 20 [N = 1,290]) vs controls (2, 3, 4) in the UK Biobank and DSM-IV derived alcohol dependence from the Psychiatric Genetics Consortium. The orange line is a visualization of the number of cases used at each threshold, corresponding to the N on the right hand y-axis.