Details

Autor(en) / Beteiligte

• Kapoor, Manav
• Wang, Jen-Chyong
• Farris, Sean P
• Liu, Yunlong
• McClintick, Jeanette
• Gupta, Ishaan
• Meyers, Jacquelyn L
• Bertelsen, Sarah
• Chao, Michael
• Nurnberger, John
• Tischfield, Jay
• Harari, Oscar
• Zeran, Li
• Hesselbrock, Victor
• Bauer, Lance
• Raj, Towfique
• Porjesz, Bernice
• Agrawal, Arpana
• Foroud, Tatiana
• Edenberg, Howard J
• Mayfield, R Dayne
• Goate, Alison

Titel

Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism

Ist Teil von

• Translational psychiatry, 2019-02, Vol.9 (1), p.89-89, Article 89

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Nexis

Beschreibungen/Notizen

• Alcohol exposure triggers changes in gene expression and biological pathways in human brain. We explored alterations in gene expression in the Pre-Frontal Cortex (PFC) of 65 alcoholics and 73 controls of European descent, and identified 129 genes that showed altered expression (FDR < 0.05) in subjects with alcohol dependence. Differentially expressed genes were enriched for pathways related to interferon signaling and Growth Arrest and DNA Damage-inducible 45 (GADD45) signaling. A coexpression module (thistle2) identified by weighted gene co-expression network analysis (WGCNA) was significantly correlated with alcohol dependence, alcohol consumption, and AUDIT scores. Genes in the thistle2 module were enriched with genes related to calcium signaling pathways and showed significant downregulation of these pathways, as well as enrichment for biological processes related to nicotine response and opioid signaling. A second module (brown4) showed significant upregulation of pathways related to immune signaling. Expression quantitative trait loci (eQTLs) for genes in the brown4 module were also enriched for genetic associations with alcohol dependence and alcohol consumption in large genome-wide studies included in the Psychiatric Genetic Consortium and the UK Biobank's alcohol consumption dataset. By leveraging multi-omics data, this transcriptome analysis has identified genes and biological pathways that could provide insight for identifying therapeutic targets for alcohol dependence.