Alzheimer Disease Genetic Architecture in African Americans

摘要 阿尔茨海默病(AD)的一部分遗传成分是由位置基因识别的 克隆、靶向基因分析、GWAS和下一代测序方法。几乎没有什么值得注意的 例外情况，这些基因中的功能变异以及这些变异的确切致病机制 导致阿尔茨海默病的原因不明。我们将继续努力对付非洲血统的人，这是一个群体 有很高的痴呆症发病率，但研究比欧洲血统的人少得多。我们会 利用来自最大的AA集合的丰富的AD相关内表型和其他风险因素数据 由我们、阿尔茨海默病遗传学联合会(ADGC)和阿尔茨海默病测序组织 阿尔茨海默病遗传学研究项目(ADSP)，以促进进一步发现AD相关基因和变异 因为他们的行动机制导致了AD。在此之前，我们证明了AD与 SORL1、AKAP9和其他AAS基因使用标准和新的分析方法。在下一个项目中 期间，我们将对140多例AA AD患者的脑组织进行RNA测序，并 使用最先进的生物信息学方法控制和分析这些数据，以评估AD的影响 基因表达的风险变异。我们将构建特定于AA的遗传-贝叶斯弹性网络模型 使用AA脑群基因和RNAseq数据介导的基因表达。这些模型将是 应用于来自ADGC和ADSP(总计n=9,200)的AA队列，使用PrediXcan构建特定于AA的 表达预测此外，我们将通过(1)确定遗传影响AD风险的非遗传中介因素 使用已建立的几个AD危险因素的数据在AAS中执行AD的基因ˣ环境GWAS 来自ADGC/ADSP、英国生物库和百万退伍军人计划的数据；(2)应用孟德尔随机化 评估糖尿病、吸烟、高血压、高胆固醇血症、 AAs中肥胖和AD的研究使用现有的关于AAs中这些特征的Gwas汇总统计数据；以及(3)进行 通过推导AD的多基因风险分数来确定多效性的全基因组关联研究(Phewas) AAS并测试其与英国生物库内一系列表型的相关性。我们还将在体外进行 实验(包括siRNA敲除、基因和蛋白质过度表达、免疫荧光和 在人类神经细胞和诱导的含有AA AD风险的多能干细胞来源的神经元中 CRISPR插入的变体以了解AKAP9和其他有希望的基因的遗传变异是如何导致 以及提供可用于小分子药物筛选的模型 用于潜在的AD治疗。

ABSTRACT A portion of the genetic component of Alzheimer disease (AD) is explained by genes identified by positional cloning, targeted gene analysis, GWAS and next generation sequencing approaches. With few notable exceptions, the functional variants in these genes and precise pathogenic mechanisms by which these variants lead to AD are unknown. We will continue to direct our efforts on persons of African ancestry (AA), a group with a high incidence of dementia but studied much less than persons of European ancestry (EA). We will leverage rich AD-related endophenotype and other risk factor data from the largest collection of AAs assembled by us, the Alzheimer Disease Genetics Consortium (ADGC) and Alzheimer Disease Sequencing Project (ADSP) for genetic studies of AD to promote further discovery of AD-related genes and variants as well as their mechanisms of action leading to AD. Previously, we demonstrated significant association of AD with SORL1, AKAP9, and other genes in AAs using standard and novel analytic approaches. In the next project period, we will perform RNA sequencing on brain tissue obtained from more than 140 AA AD cases and controls, and analyze these data using state-of-the-art bioinformatics approaches to assess the influence of AD risk variants on gene expression. We will construct AA-specific Bayesian elastic-net models of genetically- mediated gene expression using the AA brain cohort genotype and RNAseq data. These models will be applied to AA cohorts from the ADGC and ADSP (total n=9,200) using PrediXcan to construct AA-specific expression predictions In addition, we will identify non-genetic mediators of genetic influences on AD risk by (1) performing gene ˣ environment GWASs of AD in AAs using data for several established AD risk factors using data from the ADGC/ADSP, UK Biobank and Million Veterans Program; (2) applying mendelian randomization to assess the causal relationship between diabetes, cigarette smoking, hypertension, hypercholesterolemia, obesity and AD in AAs using existing GWAS summary statistics for these traits in AAs; and (3) conducting a phenome-wide association study (PheWAS) to identify pleiotropy by deriving a polygenic risk score for AD in AAs and testing its association with a range of phenotypes within the UK Biobank. We will also perform in vitro experiments (including knockdown by siRNA, gene and protein over-expression, immunofluorescence, and ELISA) in human neuronal cells and induced pluripotent stem cell-derived neurons containing AA AD risk variants inserted by CRISPR to understand how genetic variation in AKAP9 and other promising genes leads to AD-related pathologic states as well as to provide models that can be used in small molecule drug screens for potential AD treatments.