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Human genetics and epigenetics of alcohol use disorder
Hang Zhou, Joel Gelernter
Hang Zhou, Joel Gelernter
Published August 15, 2024
Citation Information: J Clin Invest. 2024;134(16):e172885. https://doi.org/10.1172/JCI172885.
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Human genetics and epigenetics of alcohol use disorder

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Abstract

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

Authors

Hang Zhou, Joel Gelernter

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Figure 1

Workflow of GWAS.

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Workflow of GWAS.
In a typical GWAS study, participants are recruited an...
In a typical GWAS study, participants are recruited and provide written informed consent and blood or saliva samples for DNA extraction and genotyping using microarray (“00” indicates missing genotype call). Basic quality controls are performed to remove SNPs with low minor allele frequencies (MAF), high genotype missingness rate, or violation of Hardy Weinberg Equilibrium expectations (HWE) and remove samples with high genotype missingness. Since genetic factors often differ according to ancestry, principal component analysis (PCA) is performed on the data after quality controls with reference genomes — for example, the 1000 Genomes Project (165) — to infer the genetic ancestries of the study samples and remove genetic outliers (the results from different ancestry groups can then be combined by meta-analysis). Then, the remained samples and the data after quality control are imputed for millions more variants (imputed genotypes and SNPs [IMP], labeled in purple) using reference genomes (165–168). Imputation takes advantage of known patterns of linkage disequilibrium to provide useful data for many more variants than are genotyped directly. A study trait, in the context of either case-control status (for example, AUD) or continuous measurement (for example, AUD criterion counts), is assessed in the cohort. Regression models implemented in computational tools (169–175) are applied to test the association between each variant and the studied trait within the genetically inferred population group, adjusting for covariates including age, sex, and the top principal components of ancestry. Variants with P < 5 × 10-8 are considered genome-wide significant (GWS) after multiple testing corrections for the number of independent genomic regions evaluated (176).

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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