Genetic Studies of Alzheimer's Disease in Jewish and Arab Populations

阿尔茨海默病（AD）的遗传基础的大多数发现是在欧洲血统的高加索人中进行的 (EAs)需要一万到十五万人的样本来检测。我们和其他人已经 表明AD风险变异的发现可以在遗传上更同质的队列中完成， 包括几千个或更少的受试者。对非EA人群的研究也提供了机会， 发现在EA中相对罕见或不存在的变体，并且由于以下原因，在EA中显示较小的效应大小 其他基因和环境因素的影响。我们将关注目前居住在以色列的犹太人和阿拉伯人 他们来自中东和北非（MENA）。虽然犹太人被同化到一些 在很大程度上与他们的非犹太邻居，他们一直保持着独特的基因概况，反映了一些 与非犹太人，古代犹太人背景的混合物，以及反映遗传漂变和新的 在过去的两千年里发生的变化。我们之前对生活在以色列村庄Wadi Ara的阿拉伯人的研究 揭示了AD与ACE的全基因组显著相关性，是SORL 1建立的核心。 作为一个AD基因，并有助于跨种族GWAS导致几个新的AD基因的发现。在 在这个项目中，我们将利用中东和北非犹太人和以色列阿拉伯人的遗传结构，以及他们独特的 环境暴露和生活方式，以促进发现AD相关基因和变体。我们特别 将在以色列的三个地点招募3,000名中东和北非地区的犹太人，以及在多个村庄招募1,000名以色列阿拉伯人 （总样本中AD病例和对照的数量相等）。我们将从每位参与者身上采集血样 用于DNA和生物标志物研究的样本，以及表型数据，包括临床、认知测试、病史 和生活方式信息，以及部分样本的脑部MRI数据。DNA样本将是完整的 基因组测序（WGS）。WGS数据将使用阿尔茨海默病研究所建立的管道进行处理。 测序项目（ADSP）。我们将使用混合映射和方法进行AD的GWAS， 变异和基因检测。将在德系犹太人和非犹太人数据集中复制最佳结果 由阿尔茨海默病遗传学联盟和ADSP使用跨种族分析组装， 这些方法关注影响蛋白质结构、转录和基因表达的变异。我们还将 使用单一结局和多效性模型对发病年龄和AD生物标志物进行GWAS。接下来我们就 通过使用以下进行表达定量性状基因座分析来鉴定排名靠前的SNP的基因靶标： 本地来源的和公开可用的数据，包括大脑和其他组织中的基因型和基因表达数据 组织，并建立排名靠前的SNP和基因之间的功能连接，使用途径，共同， 表达网络和孟德尔随机化分析。最后，我们将评估病毒之间的关联 使用机器学习方法和回归模型在AD的WGS数据中检测到。我们期望这 该项目将确定开发有效药物的新靶点，以治疗或延缓导致AD的过程。

Most discoveries of the genetic basis of Alzheimer disease (AD) were made in Caucasians of European ancestry (EAs) and required samples between 10,000 and 150,000 subjects to detect them. We and others have demonstrated that discovery of AD risk variants can be accomplished in more genetically homogeneous cohorts comprising several thousand or fewer subjects. Studies of non-EA populations also afford the opportunity to discover variants that are relatively rare or absent in EAs and that display a smaller effect size in EAs due to modification by other genes and environmental factors. We will focus on Jews and Arabs currently living in Israel who are descended from the Middle East and North Africa (MENA). Although MENA Jews assimilated to some extent with their non-Jewish neighbors, they have maintained a distinctive genetic profile that reflects some admixture with non-Jews, ancient Jewish background, and a unique component reflecting genetic drift and new mutations during the last two millennia. Our previous studies of Arabs living in the Israeli village called Wadi Ara revealed a genome-wide significant association for AD with ACE, were central to the establishment of SORL1 as an AD gene, and contributed to a trans-ethnic GWAS leading to the discovery of several novel AD genes. In this project, we will leverage the genetic architecture of MENA Jews and Israeli-Arabs, as well as their distinctive environmental exposures and lifestyles, to promote discovery of AD-related genes and variants. Specifically, we will recruit 3,000 MENA Jews at three sites in Israel, as well as 1,000 Israeli-Arabs located in multiple villages (equal numbers of AD cases and controls in the total sample). We will obtain from each participant a blood specimen for DNA and biomarker studies, and phenotypic data including clinical, cognitive test, medical history and lifestyle information, as well as brain MRI data for a portion of the sample. DNA specimens will be whole genome sequenced (WGS). WGS data will be processed using pipelines established by the Alzheimer Disease Sequencing Project (ADSP). We will conduct a GWAS for AD using admixture mapping and methods for single variant and gene-based tests. Top-findings will be replicated in Ashkenazi Jewish and non-Jewish datasets assembled by the Alzheimer Disease Genetics Consortium and ADSP using trans-ethnic analysis and approaches that focus on variants affecting protein structure, transcription, and gene expression. We will also conduct GWAS for age at onset and AD biomarkers using single outcome and pleiotropy models. Next, we will identify gene targets of the top-ranked SNPs by performing expression quantitative trait locus analysis using locally derived and publicly available data containing genotype and gene expression data in brain and other tissues, and establish functional connections among the top-ranked SNPs and genes using pathway, co- expression network, and Mendelian randomization analysis. Finally, we will evaluate the association of viruses detected in WGS data with AD using machine learning methods and regression models. We expect that this project will identify novel targets for development of effective drugs to treat or retard processes leading to AD.