Genetic Underpinnings of the Transition From Alcohol Consumption to Alcohol Use Disorder: Shared and Unique Genetic Architectures in a Cross-Ancestry Sample

Abstract

Objective: Recent genome-wide association studies (GWASs) of alcohol-related phenotypes have uncovered key differences in the underlying genetic architectures of alcohol consumption and alcohol use disorder (AUD), with the two traits having opposite genetic correlations with psychiatric disorders. Understanding the genetic factors that underlie the transition from heavy drinking to AUD has important theoretical and clinical implications.

Methods: The authors used longitudinal data from the cross-ancestry Million Veteran Program sample to identify 1) novel loci associated with AUD and alcohol consumption (measured by the score on the consumption subscale of the Alcohol Use Disorders Identification Test [AUDIT-C]), 2) the impact of phenotypic variation on genetic discovery, and 3) genetic variants with direct effects on AUD that are not mediated through alcohol consumption.

Results: The authors identified 26 loci associated with AUD and 22 loci associated with AUDIT-C score, including ancestry-specific and novel loci. In secondary GWASs that excluded individuals who report abstinence, the authors identified seven additional loci for AUD and eight additional loci for AUDIT-C score. Although the heterogeneity of the abstinent group biases the GWAS findings, unique variance between alcohol consumption and disorder remained after the abstinent group was excluded. Finally, using mediation analysis, the authors identified a set of variants with effects on AUD that are not mediated through alcohol consumption.

Conclusions: Differences in genetic architecture between alcohol consumption and AUD are consistent with their having different biological contributions. Genetic variants with direct effects on AUD are potentially relevant to understanding the transition from heavy alcohol consumption to AUD and may be targets for translational prevention and treatment efforts.

Keywords: Alcohol; Genetics/Genomics; Substance-Related and Addictive Disorders.

FIGURE 1.. Manhattan plot for cross-ancestry meta-analysis of AUD and AUDIT-C score^a

^aSample size–weighted meta-analyses were performed using METAL (17). In the upper plot, for AUD, Ns are 85,391 for case subjects and 308,488 for control subjects. In the lower plot, for AUDIT-C score, N=409,630. AUD=alcohol use disorder; AUDIT-C-consumption subscale of the Alcohol Use Disorders Identification Test.

FIGURE 2.. Heritability and genetic correlation analyses for AUD–less stringent, AUD, AUD–AUDIT-C >0, AUDIT-C, and AUDIT-C >0^a

^aPanel A shows effect sizes from the five cross-ancestry meta-analyses. All are correlated with AUD–stringent. In panel B, heritability is calculated for European Americans and African Americans using linkage disequilibrium score regression (LDSC). Error bars indicate standard error. Panel C shows genetic correlations with other phenotypes in European Americans calculated using LDSC. Nongray bars are significant at a Bonferroni-corrected p value of 1.95×10⁻⁴. Error bars indicate 95% confidence interval. AUD=alcohol use disorder; AUDIT-C=consumption subscale of the Alcohol Use Disorders Identification Test.

FIGURE 3.. Mediation analysis of variants associated with AUD^a

^aOdds ratios for direct and indirect effects are shown for variants significantly associated with AUD. The color of the points denotes the proportion mediated on the log scale (yellow=high mediation, green=moderate mediation, purple=low mediation). Size of the points denotes total effect. Variants are annotated with the nearest gene. AUD=alcohol use disorder.