Intracranial aneurysm (IA) is a complex genetic disease for which to date 10 loci have been identified by linkage. Identification of the risk conferring genes in the loci has proven difficult since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and non-diseased tissue. Genes that are not expressed, either in diseased or non-diseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and non-aneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK and Notch, signaling pathways ranked highly. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.