Weinsheimer Shantel, Lenk Guy M, van der Voet Monique, Land Susan, Ronkainen Antti, Alafuzoff Irina, Kuivaniemi Helena, Tromp Gerard
Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA.
Physiol Genomics. 2007 Dec 19;32(1):45-57. doi: 10.1152/physiolgenomics.00015.2007. Epub 2007 Sep 18.
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.
颅内动脉瘤（IA）是一种复杂的遗传疾病，迄今为止，已通过连锁分析确定了10个基因座。由于这些区域通常包含数百个基因，因此在这些基因座中鉴定赋予风险的基因已被证明很困难。一种为进一步研究对定位候选基因进行优先排序的方法是使用来自患病和未患病组织的基因表达数据。在患病或未患病组织中均不表达的基因被列为不太可能导致该疾病的基因。我们展示了一种整合表达和基因定位数据以识别可能参与疾病发病机制的途径的方法。我们将IA和非动脉瘤性颅内动脉（IVs）的表达谱与IA报告的10个连锁区间一起使用。对表达的基因进行了京都基因与基因组百科全书（KEGG）生物途径成员分析。这10个IA基因座包含1858个候选基因，其中1561个（84%）在微阵列上有代表。我们确定了810个在IVs或IAs中表达的IA定位候选基因。其中294个基因有途径信息，涉及至少2个基因座上代表的32条KEGG生物功能途径。计算了一个基于似然性的分数来对参与IA发病机制的途径进行排名。黏着连接、丝裂原活化蛋白激酶（MAPK）和Notch信号通路排名靠前。将IA的基因表达谱与基因定位数据整合提供了一种识别更可能在IA病理中起作用的候选基因的方法。
