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Integrating Genome-Wide Association Study and Brain Expression Data Highlights Cell Adhesion Molecules and Purine Metabolism in Alzheimer's Disease

机译:全基因组关联研究和脑表达数据的整合突出了阿尔茨海默氏病中的细胞粘附分子和嘌呤代谢

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摘要

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. However, a large proportion of AD heritability has yet to be explained. We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in two AD GWAS. However, it is unclear whether CAM is present in the Genetic and Environmental Risk for Alzheimer's Disease Consortium (GERAD) GWAS and brain expression GWAS. Meanwhile, we think integrating AD GWAS and AD brain expression datasets may provide complementary information to identify important pathways involved in AD. Here, we conducted a systems analysis using (1) KEGG pathways, (2) large-scale AD GWAS from GERAD (n = 11,789), (3) two brain expression GWAS datasets (n = 399) from the AD cerebellum and temporal cortex, and (4) previous results from pathway analysis of AD GWAS. Our results indicate that (1) CAM is a consistent signal in five AD GWAS; (2) CAM is the most significant signal in AD; (3) we confirmed previous AD risk pathways related to immune system and diseases, and cardiovascular disease, etc.; and (4) we highlighted the purine metabolism pathway in AD for the first time. We believe that our results may advance our understanding of AD mechanisms and will be very informative for future genetic studies in AD.
机译:阿尔茨海默氏病(AD)是老年人中最常见的神经退行性疾病。最近,全基因组关联研究(GWAS)已用于研究AD发病机理。但是,AD遗传力的很大一部分尚待解释。我们先前将细胞粘附分子(CAM)途径确定为两个AD GWAS中的一致信号。但是,目前尚不清楚CAM是否存在于阿尔茨海默氏病联盟(GERAD)GWAS和大脑表达GWAS的遗传和环境风险中。同时,我们认为整合AD GWAS和AD脑表达数据集可能提供补充信息,以识别参与AD的重要途径。在这里,我们进行了系统分析,使用了(1)KEGG途径,(2)来自GERAD的大规模AD GWAS(n = 11,789),(3)来自AD小脑和颞皮质的两个大脑表达GWAS数据集(n = 399) (4)AD GWAS路径分析的先前结果。我们的结果表明:(1)CAM是五个AD GWAS中的一致信号; (2)CAM是AD中最重要的信号; (3)我们确认了先前与免疫系统和疾病,心血管疾病等有关的AD风险途径; (4)我们首次强调了嘌呤的代谢途径。我们相信我们的结果可能会增进我们对AD机制的理解,并且对将来AD中的遗传研究很有帮助。

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