ArticleGenome biology2026
Regulatory mechanisms driven by functional 3'-UTR variants in alcohol use disorder and related traits.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
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Abstract
backgroundGenetic variants in the 3' untranslated regions (3'-UTRs) of mRNAs can alter binding of RNA-binding proteins and microRNAs and thereby influence regulation by affecting RNA stability, localization, and translation. Despite their potential impact on the risk for complex traits, including alcohol use disorder, the contribution of 3'-UTR variants has not been systematically explored. We evaluate the impact of 3'-UTR variants within loci associated with substance use and neurological disorders using a massively parallel reporter assay (MPRA) in neuroblastoma and microglia cells.
resultsOf the 13,515 variants tested, 400 and 657 variants significantly alter gene expression in neuroblastoma and microglia cells, respectively. These functionally impactful variants account for more heritability of alcohol-related traits than non-functional variants. We develop a computational framework, MPRA-mediated Gene Expression Association (MGExA), that combines MPRA-derived variant effects with GWAS summary statistics and identify 31 genes whose expression changes may contribute to alcohol-related traits. CRISPR inhibition of 7 of these genes in neuronal cells leads to gene expression changes associated with neurodegenerative disorders and the oxidative phosphorylation pathway. Pharmacoepidemiological analysis of drugs that had similar effects on gene expression linked RBM14 and KANSL1 to risk for alcohol use disorder.
conclusionsWe identify genetic variants in 3'-UTR regions that affect gene expression. By integrating these functional genomics data and pharmacoepidemiological assessment with GWAS analysis, we identify genes whose expression differences could contribute to alcohol related traits. This approach provides a framework for moving from GWAS data to identifying biologically and clinically relevant genes associated with complex disorders.
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