Epilepsy is a common neurological disorder that affects mammalian species including human beings and dogs. In order to discover novel drugs for the treatment of canine epilepsy, multiomics data were analyzed to identify epilepsy associated genes. In this research, the original ranking of associated genes was obtained by two high-throughput bioinformatics experiments: Genome Wide Association Study (GWAS) and microarray analysis. The association ranking of genes was enhanced by a re-ranking system, HPO-Shuffle, which integrated information from GWAS, microarray analysis and Human Phenotype Ontology database for further downstream analysis. After applying HPO-Shuffle, the association ranking of epilepsy genes were improved. Afterward, a weighted gene coexpression network analysis (WGCNA) led to a set of gene modules, which hinted a clear relevance between the high ranked genes and the target disease. Finally, WGCNA and connectivity map (CMap) analysis were performed on the integrated dataset to discover a potential drug list, in which a well-known anticonvulsant phensuximide was included.