通过对四个全基因组测序数据集中的临床阿尔茨海默氏症病例和代理阿尔茨海默氏症病例进行多家系元分析,确定 16 个新的阿尔茨海默氏症易感基因位点

Julian Daniel Sunday Willett, Mohammad Waqas, Younjung Choi, Tiffany Ngai, Kristina Mullin, Rudolph E Tanzi, Dmitry Prokopenko
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引用次数: 0

摘要

阿尔茨海默病(AD)是最普遍的痴呆症。虽然以前已经发现了许多与阿兹海默症相关的遗传决定因素,但很少有研究对非欧洲血统的个体进行分析。在此,我们利用来自 NIAGADS、NIMH、UKB 和 All of Us (AoU) 的全基因组测序数据,对 49149 例病例(12074 例临床诊断病例和 37075 例代理 AD 病例)和 383225 例对照进行了一项多血统全基因组关联研究。近一半的 NIAGADS 和 AoU 参与者为非欧洲血统。对于临床诊断的 AD,我们发现了 14 个新位点--5 个常见位点(FBN2/SCL27A6、AC090115.1、DYM、KCNG1/AL121785.1、TIAM1)和 9 个罕见位点(VWA5B1、RNU6-755P/LMX1A、MOB1A、MORC1-AS1、LINC00989、PDE4D、RNU2-49P/CDO1、NEO1 和 SLC35G3/AC022916.1)。对 UKB 和 AoU AD-by-proxy 病例进行的元分析发现了两个新的罕见基因位点(RPL23/LASP1 和 CEBPA/AC008738.6),这两个位点在 NIAGADS 中也具有名义意义。总之,我们为 16 个新的 AD 基因位点提供了证据,并提倡使用基于 WGS 的 GWAS 对不同队列进行更多研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of 16 novel Alzheimer's disease susceptibility loci using multi-ancestry meta-analyses of clinical Alzheimer's disease and AD-by-proxy cases from four whole genome sequencing datasets
Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD , we identified 14 new loci - five common (FBN2/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/ AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.
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