The Alzheimer's Disease Sequencing Project (ADSP) is a national sequencing initiative focused on identifying genetic risk and protective factors for AD. The projects' discovery phase includes whole exome sequencing (WES) of 10,061 unrelated non-White Hispanic (NHW) cases (N=5,096) and controls (N=4,965), and whole genome sequencing (WGS) of 584 NHW and Hispanic familial samples. The 'Discovery Extension Phase' of the project added WGS on ~430 additional familial samples. An initial 'Follow-Up Study (FUS)' Phase focused on examining candidate variants from the discovery phase, and identification of novel variants through combined analysis of diverse datasets is ongoing and focuses on additional existing cohorts with unrelated AD cases that 'encompass the richest possible ethnic diversity' as well as highly valuable set of autopsy confirmed cases and controls. In total we have already included in FUS over 14000 samples for sequencing including >2800 autopsy confirmed cases and controls, >6800 Hispanic (HI) cases and controls and > 5790 African American (AA) cases and controls. In this FUS2 application, we are proposing sequencing of an additional 4243 samples that will both increase our power to find effects but will also enhance our analysis by inclusion of several rich and unique sample sets. Additionally, these datasets will become an invaluable resource for the AD research community at-large. In summary ADSP will provide large ethnically diverse as well as unique datasets for both validation and generalization of ADSP discovery phase findings and discovery of novel risk and protective variants/genes for late onset AD (LOAD). Additionally, these datasets will become an invaluable resource for the AD research community at-large. Specifically we propose to: 1. Increase the diversity and further enrich the clinical phenotype data of the ADSP including data sets targeted to find protective effects through assembling samples from existing cohorts from unique populations that fulfill this goal. 2. Collaborate with the National Cell Repository for AD (NCRAD) in assembling DNA, blood and brain tissue on these existing cohorts, which will serve as a central resource for the AD research community. 3. Generate genome-wide SNP array data through the John P. Hussman Institute (HIHG) Center for Genome Technology (CGT) and whole genome sequencing data through the Uniformed Services University of the Health Sciences (USUHS) for all collected samples 4. Collaborate with NIA Genetics of AD Data Storage Site (NIAGADS), the Genome Center for AD (GCAD) and the University of Pennsylvania and the HIHG Center for Genetic Epidemiology and Statistical Genetics Quality Control Teams in processing, storage and dissemination of final data sets. Our overall goal is to enhance the discovery of AD risk and protective factors by facilitating research on AD in ethnically diverse and phenotypically rich datasets.
To gather data and sequence an additional 4000+ individuals from the inclusion of several rich and unique sample sets in the Alzheimer's Disease Sequencing Project (ADSP) creating an invaluable resource for the Alzheimer's disease (AD) research community at large. We will do this by 1. Increasing the diversity and further enrich the clinical phenotype data of the ADSP including data sets targeted to find protective effects through assembling samples from existing cohorts from unique populations that fulfill this goal. 2. Collaborating with the National Cell Repository for AD (NCRAD) in assembling DNA, blood and brain tissue on these existing cohorts, which will serve as a central resource for the AD research community. 3. Generating genome-wide SNP array data through the John P. Hussman Institute (HIHG) Center for Genome Technology (CGT) and whole genome sequencing data through the Uniformed Services University of the Health Sciences (USUHS) for all collected samples. 4. Collaborating with NIA Genetics of AD Data Storage Site (NIAGADS), the Genome Center for AD (GCAD), and the University of Pennsylvania (UPENN) and the HIHG Center for Genetic Epidemiology and Statistical Genetics (CGESG) Quality Control (QC) Teams in processing, storage and dissemination of final data sets.
