Network Analysis of EtOH-Related Candidate Genes

Authors

  • An-Yuan Guo,

    1. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA (phone: +1-615-3439158; fax: +1-615-9368545)
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  • Jingchun Sun,

    1. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA (phone: +1-615-3439158; fax: +1-615-9368545)
    2. Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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  • Peilin Jia,

    1. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA (phone: +1-615-3439158; fax: +1-615-9368545)
    2. Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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  • Zhongming Zhao

    1. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA (phone: +1-615-3439158; fax: +1-615-9368545)
    2. Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37212, USA
    3. Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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Abstract

Recently, we collected many large-scale datasets for alcohol dependence and EtOH response in five organisms and deposited them in our EtOH-related gene resource database (ERGR, http://bioinfo.mc.vanderbilt.edu/ERGR/). Based on multidimensional evidence among these datasets, we prioritized 57 EtOH-related candidate genes. To explore their biological roles, and the molecular mechanisms of EtOH response and alcohol dependence, we examined the features of these genes by the Gene Ontology (GO) term-enrichment test and network/pathway analysis. Our analysis revealed that these candidate genes were highly enriched in alcohol dependence/alcoholism and highly expressed in brain or liver tissues. All the significantly enriched GO terms were related to neurotransmitter systems or EtOH metabolic processes. Using the Ingenuity Pathway Analysis system, we found that these genes were involved in networks of neurological disease, cardiovascular disease, inflammatory response, and small molecular metabolism. Many key genes in signaling pathways were in the central position of these networks. Furthermore, our protein–protein interaction (PPI) network analysis suggested some novel candidate genes which also had evidence in the ERGR database. This study demonstrated that our candidate gene selection is effective and our network/pathway analysis is useful for uncovering the molecular mechanisms of EtOH response and alcohol dependence. This approach can be applied to study the features of candidate genes of other complex traits/phenotypes.

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