This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) Genomics
Multi-species data integration and gene ranking enrich significant results in an alcoholism genome-wide association study
- Equal contributors
1 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
2 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
3 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
4 Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
5 Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA 23298, USA
6 Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA
7 Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
8 Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA
9 Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA 23298, USA
10 Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
11 Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110, USA
12 Department of Psychiatry, University of Iowa College of Medicine, Iowa City, IA 52242, USA
13 Institute of Psychiatric Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Citation and License
BMC Genomics 2012, 13(Suppl 8):S16 doi:10.1186/1471-2164-13-S8-S16Published: 17 December 2012
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.