Alzheimer Disease Genetic Architecture in African Americans

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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.