In this work, we applied a unique cross-species, evidence-based gene prioritization strategy for genes involved in alcoholism. We started with a set of genes with prior microarray expression evidence of involvement in ethanol response, representing approximately 10% of the human protein-coding genes. These genes were ranked using additional sources of evidence across multiple species, including humans, mice, C. elegans and Drosophila. We used the COGA GWAS dataset and applied permutation analysis to evaluate the best weighting score matrix for gene ranking. Based on these results, we selected the top 47 genes with the best evidence for follow up bioinformatics analysis. Our functional enrichment test of these 47 genes suggested that this ranking algorithm identifies gene sets with coherent biological functions relevant to brain responses to ethanol and neural adaptations occurring with alcoholism. Remarkably, higher ranking scores were predictive of genes containing an enrichment of significant SNP associations in the context of COGA alcohol dependence GWAS results. These results provide initial evidence that a cross-species analysis of gene networks correlated with molecular or behavioral responses to ethanol may provide a powerful strategy to identify candidate genes that contribute to alcoholism.
