Sun Jiya, Song Fuhai, Wang Jiajia, Han Guangchun, Bai Zhouxian, Xie Bin, Feng Xuemei, Jia Jianping, Duan Yong, Lei Hongxing
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China.
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
J Alzheimers Dis. 2014;41(4):1039-56. doi: 10.3233/JAD-140054.
Meta-analysis of data from genome-wide association studies (GWAS) of Alzheimer's disease (AD) has confirmed the high risk of APOE and identified twenty other risk genes/loci with moderate effect size. However, many more risk genes/loci remain to be discovered to account for the missing heritability. The contributions from individual singe-nucleotide polymorphisms (SNPs) have been thoroughly examined in traditional GWAS data analysis, while SNP-SNP interactions can be explored by a variety of alternative approaches. Here we applied generalized multifactor dimensionality reduction to the re-analysis of four publicly available GWAS datasets for AD. When considering 4-order intragenic SNP interactions, we observed high consistency of discovered potential risk genes among the four independent GWAS datasets. Ten potential risk genes were observed across all four datasets, including PDE1A, RYR3, TEK, SLC25A21, LOC729852, KIRREL3, PTPN5, FSHR, PARK2, and NR3C2. These potential risk genes discovered by generalized multifactor dimensionality reduction are highly relevant to AD pathogenesis based on multiple layers of evidence. The genetic contributions of these genes warrant further confirmation in other independent GWAS datasets for AD.
对阿尔茨海默病(AD)全基因组关联研究(GWAS)数据的荟萃分析证实了APOE的高风险,并确定了其他20个效应大小中等的风险基因/位点。然而,仍有更多的风险基因/位点有待发现,以解释缺失的遗传力。在传统的GWAS数据分析中,已经对单个单核苷酸多态性(SNP)的贡献进行了深入研究,而SNP-SNP相互作用可以通过多种替代方法进行探索。在这里,我们将广义多因素降维应用于对四个公开可用的AD GWAS数据集的重新分析。当考虑4阶基因内SNP相互作用时,我们在四个独立的GWAS数据集中观察到发现的潜在风险基因具有高度一致性。在所有四个数据集中都观察到了10个潜在风险基因,包括PDE1A、RYR3、TEK、SLC25A21、LOC729852、KIRREL3、PTPN5、FSHR、PARK2和NR3C2。基于多层证据,通过广义多因素降维发现的这些潜在风险基因与AD发病机制高度相关。这些基因的遗传贡献值得在其他独立的AD GWAS数据集中进一步证实。