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Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm

¹ Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit
² Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit
³ Department of Surgery, Wayne State University School of Medicine, Detroit
⁴ Department of Neurology, Wayne State University School of Medicine, Detroit
⁵ Applied Genomics Technology Center, Wayne State University, Detroit, Michigan
⁶ Department of Neurosurgery, Kuopio University, Kuopio, Finland
⁷ Department of Clinical Medicine, Neurology, and Pathology, Kuopio University, Kuopio, Finland

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 nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased 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 nonaneurysmal 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 high. 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.

pathway analysis; adherens junction; Notch signaling; MAPK signaling

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