Alzheimer Disease Genetic Architecture in African Americans

摘要 阿尔茨海默病（AD）的部分遗传成分可以通过以下基因来解释： 定位克隆、靶向基因分析、GWAS和下一代测序方法。 除了少数明显的例外，这些基因的功能变异和精确的致病性 这些变异导致AD的机制尚不清楚。父母补助金将继续 将我们的努力集中在非洲血统的人身上，这是一个痴呆症发病率高的群体 但学习的人比欧洲血统（EA）少得多。我们将利用丰富的广告相关 内表型和其他风险因素数据，来自于由 阿尔茨海默病遗传学联盟（ADGC）和阿尔茨海默病测序项目 （ADSP）用于AD的遗传学研究，以促进AD相关基因和变体的进一步发现 以及它们导致AD的作用机制。这份关于补充父母的建议 赠款将大大加强我们的努力，把上述数据集和 百万退伍军人项目（MVP）数据集，目前正在开发和分析， 我们团队的AD遗传学。在这里，我们建议在VHA中识别潜在未诊断的ADRD 通过将现有的支持向量机（SVM）算法应用于电子健康记录， (EHR)所有年龄在65岁以上的退伍军人中，没有AD或AD相关痴呆（ADRD）的正式诊断。 该算法要求EHR中的结构化和非结构化数据都可用， 将来可以应用于MVP参与者。我们还将扩大目前的努力， 一组图表审查，可用于表型验证和进一步的算法开发 并实施与MVP AD/MCI工作组一致的报告模板。最后我们将 荟萃分析ADGC和MVP GWAS结果，代表最大的AA数据集， 为此目的的日期，用于按APOE ε4状态和年龄分层的各种模型，以检测基因- 基因（G×G）和基因-环境（G×E）相互作用。分析将单独进行， MVP、ADGC、ADSP和UK Biobank数据集，将通过荟萃分析合并结果。

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.