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. 2021 Nov;116(11):3044-3054.
doi: 10.1111/add.15511. Epub 2021 May 6.

The impact of removing former drinkers from genome-wide association studies of AUDIT-C

Cecilia Dao  1   2 , Hang Zhou  2   3 , Aeron Small  3 , Kirsha S Gordon  2   3 , Boyang Li  1   2 , Rachel L Kember  4   5 , Yixuan Ye  1 , Joel Gelernter  2   3 , Ke Xu  2   3 , Henry R Kranzler  4   5 , Hongyu Zhao  1   2   3 , Amy C Justice  1   2   3
Affiliations

The impact of removing former drinkers from genome-wide association studies of AUDIT-C

Cecilia Dao et al. Addiction. 2021 Nov.

Abstract

Background and aims: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire screens for harmful drinking using a 12-month timeframe. A score of 0 is assigned to individuals who report abstaining from alcohol in the past year. However, many middle-age individuals reporting current abstinence are former drinkers (FDs). Because FDs may be more genetically prone to harmful alcohol use than lifelong abstainers (LAs) and are often combined with LAs, we evaluated the impact of differentiating them on the identification of genetic association.

Design and setting: The United Kingdom Biobank (UKBB) includes AUDIT-C and alcohol drinker status.

Participants: 131 510 Europeans, including 5135 FDs.

Measurements: We compared three genome-wide association (GWAS) analyses to explore the effects of removing FDs: the full AUDIT-C data, AUDIT-C data without FDs, and data from a random sample numerically matched to the data without FDs. Because prior studies show a consistent association of the ADH1B polymorphism rs1229984 with both alcohol consumption and alcohol use disorder, we compared allele frequencies for rs1229984 stratified by AUDIT-C value and FD versus LA status. Additionally, we calculated polygenic risk scores (PRS) of related diseases.

Findings: The rs1229984 allele frequencies among FDs were numerically comparable to those with high AUDIT-C scores and very different from those of LAs. Removing FDs from GWAS yielded a stronger association with rs1229984 (P value after removal: 1.9 × 10-70 vs 1.7 × 10-65 and 2.5 × 10-62 ), more statistically significant single nucleotide polymorphisms (SNPs) (after removal: 11 vs 9 and 8), and genomic loci (after removal: 11 vs 9 and 7). Additional independent SNPs were identified after removal of FDs: rs2817866 (PTGER3), rs7105867 (ANO3), and rs17601612 (DRD2). For PRS of alcohol use disorder and major depressive disorder, there are statistically significant differences between FDs and LAs.

Conclusions: Differentiating between former drinkers and lifelong abstainers can improve Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) genome-wide association results.

Keywords: AUDIT-C; Alcohol use disorder; former drinkers; genome-wide association study; polygenic risk score; sick quitters.

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Conflict of interest statement

Declaration of interests

Dr. Kranzler is a member of a scientific advisory board for Dicerna Pharmaceuticals and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative (ACTIVE Group), which over the past three years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. Dr. Kranzler and Dr. Gelernter are named as inventors on PCT patent application 15/878640 entitled: “Genotype-guided dosing of opioid agonists,” filed January 24, 2018.

Figures

Figure 1
Figure 1
Bar plot of AUDIT-C scores with stratification of alcohol status: lifelong abstainer, former drinker, and current drinker
Figure 2
Figure 2
Frequency of the ADH1B*rs1229984-C allele according to AUDIT-C score and with AUDIT-C = 0. Individuals are stratified by lifelong abstainer, current drinker, former drinker not due to illness, and former drinker due to illness
Figure 3
Figure 3
Standardized polygenic risk scores based on alcohol use disorder summary statistics
Figure 4
Figure 4
Standardized polygenic risk scores based on major depressive disorder summary statistics
Figure 5
Figure 5
Standardized polygenic risk scores based on coronary artery disease summary statistics
Figure 6
Figure 6
Standardized polygenic risk scores based on Type 2 diabetes summary statistics
See this image and copyright information in PMC

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