The Alzheimer Disease Sequence Analysis Collaborative

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The Alzheimer's Disease Sequencing Project (ADSP) is a national sequencing initiative focused on identifying genetic variants that either protect from or increase the risk for late onset Alzheimer Disease (LOAD), with the goal of accelerating development of effective therapeutics. The ADSP Discovery phase includes whole exome sequencing (WES) of 10,571 unrelated non-Hispanic White (NHW) cases (N=5,606) and controls (N=4,965), and whole genome sequencing (WGS) of 578 NHW and Hispanic (HI) familial samples. The Discovery Extension Phase of the project added WGS on 434 new familial samples and 3,343 NHW, AA, and HI cases and controls (collectively the ADSP-DEP). Preliminary analyses of these data confirm known LOAD genes and point toward several new LOAD-related genes and their functional relationships to APP processing, neuroinflammation, endocytosis, and cholesterol metabolism. The current Follow-Up Study phase (ADSP-FUS) will generate >15,000 additional WGS focused on samples that `encompass the richest possible ethnic diversity', which will include primarily HI and African-American (AA) datasets. This combination of diverse datasets derived from case-control, cohort, and family study designs requires an intensive and comprehensive analytical effort to uncover the wealth of information sequestered in the WGS. We (the Collaboration on Alzheimer Disease REsearch [CADRE]) hypothesize that protective and risk genomic variants will provide potential therapeutic targets for Alzheimer disease. Thus, the primary goal of this proposal is to integrate comprehensive genomic analysis of the combined ADSP data (WGS, WES, SNP array) with extant biological data to identify the highest priority variants and loci as candidates for downstream functional analysis. By leveraging the data derived from the AA and HI admixed populations, we use their increased diversity to accelerate and define likely targets. This goal will be met by: 1) Characterizing genomic variation in LOAD using ethnically diverse datasets. We will supplement the ADSP-DEP and ADSP-FUS with additional, separately funded WES and WGS data; 2) enhancing discovery and fine-mapping using admixture analyses in ethnically diverse datasets; and 3) Prioritizing variants and genes by integrating statistical and biological information. For the variants we identify, we will generate additional information from structural and gene expression data and integrate all data into a genomically driven comprehensive biological network that will be used to prioritize loci for functional testing as therapeutic targets.