Alzheimer Disease Genetic Architecture in African Americans

摘要 阿尔茨海默病(AD)的部分遗传成分是由下列基因解释的 定位克隆、靶向基因分析、GWAS和下一代测序方法。 除了几个值得注意的例外，这些基因中的功能变异和精确的致病 这些变异导致AD的机制尚不清楚。父母的赠款将继续 我们将努力针对非洲血统(AA)的人，这是痴呆症的高发群体 但与欧洲血统的人(EA)相比，他们的研究要少得多。我们将利用与富广告相关的 内表型和其他风险因素数据来自由 阿尔茨海默病遗传学联合会(ADGC)和阿尔茨海默病测序项目 (ADSP)AD遗传学研究，以促进AD相关基因和变异的进一步发现 以及它们导致AD的作用机制。这项为家长提供补充资料的建议 格兰特将通过将上述数据集和 目前正在开发和分析的大型百万退伍军人项目(MVP)数据集 我们团队的广告遗传学。在这里，我们建议在VHA中确定潜在的未诊断ADRD 将现有的支持向量机(SVM)算法应用于电子健康记录 (EHR)所有65岁以上未正式诊断为AD或AD相关痴呆(ADRD)的退伍军人。 此算法要求EHR内的结构化数据和非结构化数据均可用 可以应用于未来的MVP参与者。我们还将扩大目前的努力，发展 一组图表检查，可用于表型验证和进一步的算法开发 并执行与MVP AD/MCI工作组一致的报告模板。最后，我们会 Meta-Analyze ADGC和MVP Gwas结果，表示集合到 为此目的，对于按载脂蛋白Eε4状态和年龄分层的各种模型，检测基因- 基因(G×G)和基因-环境(G×E)互作。分析将在#年单独执行 MVP、ADGC、ADSP和UK Biobank数据集，结果将通过荟萃分析进行组合。

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. The parent grant is continuing 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 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. This proposal for a Supplement to the Parent grant will greatly enhance our efforts by bringing together the aforementioned datasets and the large Million Veterans Project (MVP) dataset that is currently being developed and analyzed for AD genetics by our team. Here, we propose to identify potentially undiagnosed ADRD in the VHA by applying an existing support-vector machine (SVM) algorithm to electronic health records (EHR) of all Veterans age 65+ who lack a formal diagnosis of AD or AD-related dementia (ADRD). This algorithm requires the availability of both structured and unstructured data within EHR and can be applied to MVP participants in the future. We will also expand the current effort to develop a set of chart reviews that can be used for phenotype validation and further algorithm development and implement a report template consistent with the MVP AD/MCI Working Group. Finally, we will meta-analyze ADGC and MVP GWAS results, representing the largest AA dataset assembled to date for this purpose, for a variety of models stratified by APOE ε4 status and age to detect gene- gene (G×G) and gene-environment (G×E) interactions. Analyses will be performed separately in MVP, ADGC, ADSP and UK Biobank datasets, and the results will be combined by meta-analysis.