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      Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

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          Abstract

          Genome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-responsive and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

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          A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

          The Lancet, 380(9859), 2224-2260
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            Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117)

            Alcohol consumption has been linked to over 200 diseases and is responsible for over 5% of the global disease burden. Well-known genetic variants in alcohol metabolizing genes, for example, ALDH2 and ADH1B, are strongly associated with alcohol consumption but have limited impact in European populations where they are found at low frequency. We performed a genome-wide association study (GWAS) of self-reported alcohol consumption in 112 117 individuals in the UK Biobank (UKB) sample of white British individuals. We report significant genome-wide associations at 14 loci. These include single-nucleotide polymorphisms (SNPs) in alcohol metabolizing genes (ADH1B/ADH1C/ADH5) and two loci in KLB, a gene recently associated with alcohol consumption. We also identify SNPs at novel loci including GCKR, CADM2 and FAM69C. Gene-based analyses found significant associations with genes implicated in the neurobiology of substance use (DRD2, PDE4B). GCTA analyses found a significant SNP-based heritability of self-reported alcohol consumption of 13% (se=0.01). Sex-specific analyses found largely overlapping GWAS loci and the genetic correlation (rG) between male and female alcohol consumption was 0.90 (s.e.=0.09, P-value=7.16 × 10−23). Using LD score regression, genetic overlap was found between alcohol consumption and years of schooling (rG=0.18, s.e.=0.03), high-density lipoprotein cholesterol (rG=0.28, s.e.=0.05), smoking (rG=0.40, s.e.=0.06) and various anthropometric traits (for example, overweight, rG=−0.19, s.e.=0.05). This study replicates the association between alcohol consumption and alcohol metabolizing genes and KLB, and identifies novel gene associations that should be the focus of future studies investigating the neurobiology of alcohol consumption.
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              PINA v2.0: mining interactome modules

              The Protein Interaction Network Analysis (PINA) platform is a comprehensive web resource, which includes a database of unified protein–protein interaction data integrated from six manually curated public databases, and a set of built-in tools for network construction, filtering, analysis and visualization. The second version of PINA enhances its utility for studies of protein interactions at a network level, by including multiple collections of interaction modules identified by different clustering approaches from the whole network of protein interactions (‘interactome’) for six model organisms. All identified modules are fully annotated by enriched Gene Ontology terms, KEGG pathways, Pfam domains and the chemical and genetic perturbations collection from MSigDB. Moreover, a new tool is provided for module enrichment analysis in addition to simple query function. The interactome data are also available on the web site for further bioinformatics analysis. PINA is freely accessible at http://cbg.garvan.unsw.edu.au/pina/.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 April 2019
                2019
                : 14
                : 4
                : e0202063
                Affiliations
                [1 ] Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [2 ] VCU Alcohol Research Center, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [3 ] VCU Center for Clinical & Translational Research, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [4 ] Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [5 ] Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, Virginia, United States of America
                Oregon Health and Science University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1532-584X
                Article
                PONE-D-18-21853
                10.1371/journal.pone.0202063
                6481773
                31017905
                d543c1e6-0576-4d2f-97af-61ef9a713da6
                © 2019 Mignogna et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 July 2018
                : 7 April 2019
                Page count
                Figures: 5, Tables: 6, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000027, National Institute on Alcohol Abuse and Alcoholism;
                Award ID: P50AA022537
                Funded by: VCU Alcohol Research Center
                This work was supported by NIAAA grant P50AA022537 (BR, SB, MFM) and by intramural funds of the VCU Alcohol Research Center ( https://arc.vcu.edu). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                Most relevant data are contained within the paper and Supporting Information. Additional raw microarray data are found on GeneNetwork ( www.genenetwork.org) under accession numbers GN135-137, GN154-156 and GN228-230 and in the Gene Expression Omnibus (GEO) database (accession number GSE28515). Primary human GWAS data is available from cited publications.

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