Multi-species data integration and gene ranking enrich significant results in an alcoholism genome-wide association study.

Zhongming Zhao, An Yuan Guo, Edwin J.C.G. van den Oord, Fazil Aliev, Peilin Jia, Howard J. Edenberg, Brien P. Riley, Danielle M. Dick, Jill C. Bettinger, Andrew G. Davies, Michael S. Grotewiel, Marc A. Schuckit, Arpana Agrawal, John Kramer, John I. Nurnberger, Kenneth S. Kendler, Bradley T. Webb, Michael F. Miles

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


A variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits. Integration of these heterogeneous data can be used to produce a ranked target gene list for additional investigation. In this study, we performed a unique multi-species evidence-based data integration using three microarray experiments in mice or humans that generated an initial alcohol dependence (AD) related genes list, human linkage and association results, and gene sets implicated in C. elegans and Drosophila. We then used permutation and false discovery rate (FDR) analyses on the genome-wide association studies (GWAS) dataset from the Collaborative Study on the Genetics of Alcoholism (COGA) to evaluate the ranking results and weighting matrices. We found one weighting score matrix could increase FDR based q-values for a list of 47 genes with a score greater than 2. Our follow up functional enrichment tests revealed these genes were primarily involved in brain responses to ethanol and neural adaptations occurring with alcoholism. These results, along with our experimental validation of specific genes in mice, C. elegans and Drosophila, suggest that a cross-species evidence-based approach is useful to identify candidate genes contributing to alcoholism.

Original languageEnglish
Article numberS16
JournalUnknown Journal
Volume13 Suppl 8
StatePublished - 2012


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