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基于基因座的 GWAS 荟萃分析的基因组趋同将 AXIN1 鉴定为一个新的帕金森基因。

Genomic convergence of locus-based GWAS meta-analysis identifies AXIN1 as a novel Parkinson's gene.

机构信息

Consultant Rheumatology and Immunogenetics, ImmunoCure, Clinic and Lab, Suite 116, 1st Floor, The Plaza, 2-Talwar, Main Clifton Road, Karachi, Pakistan.

出版信息

Immunogenetics. 2018 Sep;70(9):563-570. doi: 10.1007/s00251-018-1068-0. Epub 2018 Jun 19.

Abstract

Parkinson's disease (PD) is a common, disabling neurodegenerative disorder with significant genetic underpinnings. Multiple genome-wide association studies (GWAS) have been conducted with identification of several PD loci. However, these only explain about 25% of PD genetic risk indicating that additional loci of modest effect remain to be discovered. Association clustering methods such as gene-based tests are more powerful than single-variant analysis for identifying modest genetic effects. Combined with the locus-based algorithm, OASIS, the most significant association signals can be homed in. Here, two dbGAP GWAS datasets (7415 subjects (2750 PD and 4845 controls) genotyped for 0.78 million SNPs) were analyzed using combined clustering algorithms to identify 88 PD candidate genes in 24 loci. These were further investigated for gene expression in substantia nigra (SN) of PD and control subjects on GEO datasets. Expression differences were also assessed in normal brains SN versus white matter on BRAINEAC datasets. This genetic and functional analysis identified AXIN1, a key regulator of Wnt/β-catenin signaling, as a novel PD gene. This finding links PD with inflammation. Other significantly associated genes were CSMD1, CLDN1, ZNF141, ZNF721, RHOT2, RICTOR, KANSL1, and ARHGAP27. Novel PD genes were identified using genomic convergence of gene-wide and locus-based tests and expression studies on archived datasets.

摘要

帕金森病(PD)是一种常见的、致残性的神经退行性疾病,具有重要的遗传基础。已经进行了多次全基因组关联研究(GWAS),确定了几个 PD 基因座。然而,这些只能解释约 25%的 PD 遗传风险,表明还有其他一些中等效应的基因座有待发现。关联聚类方法,如基于基因的测试,比单变异分析更能识别中等遗传效应。与基于基因座的算法 OASIS 相结合,可以确定最显著的关联信号。在这里,使用联合聚类算法分析了两个 dbGAP GWAS 数据集(7415 名受试者(2750 名 PD 和 4845 名对照),对 780 万个 SNP 进行了基因分型),以确定 24 个基因座中的 88 个 PD 候选基因。这些基因在 GEO 数据集的 PD 和对照受试者的黑质(SN)中进一步进行了基因表达研究。在 BRAINEAC 数据集上,还评估了正常大脑 SN 与白质之间的表达差异。这项遗传和功能分析确定了 AXIN1,一种 Wnt/β-catenin 信号的关键调节剂,作为一个新的 PD 基因。这一发现将 PD 与炎症联系起来。其他显著相关的基因是 CSMD1、CLDN1、ZNF141、ZNF721、RHOT2、RICTOR、KANSL1 和 ARHGAP27。使用基于基因座的测试和存档数据集上的表达研究,通过基因全基因组和基因座测试的基因组收敛,鉴定了新的 PD 基因。

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