Alzheimer Disease Genetic Analysis to Identify Potential Therapeutic Targets (ADAPTT) - Alzheimer disease (AD) is the fifth most frequent cause of death in the U.S. and currently affects nearly 55 million individuals of all ancestries worldwide; this number is predicted to double over the next 20 years. Clinical trials for effective therapeutics have almost all failed, and the few currently approved treatments provide only modest slowing of progression for a subset of individuals, with potentially severe side effects. Thus, focusing on additional and alternative therapeutic targets is critical to address this increasing health crisis. Genetically driven targets significantly improve the probability of successful clinical trials, but despite the identification of numerous AD-associated loci through GWAS, few have advanced to potential therapeutic intervention. In part, this is because GWAS itself is a blunt instrument that cannot differentiate among the many genes often underlying an associated locus. This leaves a critical gap where numerous GWAS-identified loci have not been sufficiently examined to support or refute their candidacy as a therapeutic target. ADAPTT aims to fill this knowledge gap by leveraging all available data from the growing datasets of the Alzheimer Disease Sequencing Project (ADSP) and the Alzheimer Disease Genetics Consortium (ADGC). Analyses of these genetic data will be significantly enhanced through integration of the currently separate SNV, indel, and structural variant (SV) data. We will also leverage extant in silico data and use existing and generate new in vitro molecular genetic functional data. Our goal is to identify the most likely functional genes/variations lying under each GWAS- identified locus, providing the foundation for critically needed, and genetically-driven, therapeutic development. We will achieve this goal through three parallel specific aims: Specific Aim 1 will integrate and analyze data for all SNVs, indels, and SVs within 54 AD GWAS-identified loci. We will leverage the increasingly multi-ancestry and diverse ADSP and ADGC datasets to reduce the list of probable functional genes/variations. Specific Aim 2 will assess the impact of these 54 loci on the clinically critical endophenotypes of age-at-onset and disease progression. We will first model disease progression and age-at-onset using harmonized data from the ADSP’s Phenotype Harmonization Consortium and then test the influence of the GWAS-identified loci on these endophenotypes. Specific Aim 3 will integrate extant and new molecular genetics functional data to validate causal genetic variations driving the locus associations. The results of this study will generate a set of genetically-driven potential targets that will accelerate the development of new and better therapeutics for AD.
