{"data":[{"dar_ID":11623,"expiration_date":"2027-08-17 12:29:07","approval_date":"2026-08-17 12:29:07","initial_approval_date":"2025-07-15 11:33:59","project_name":"Genetic modifiers of Alzheimer's disease","status_name":"Approved","first_name":"Elizabeth","last_name":"Blue","institution":"University of Washington","dataset_accessions":["NG00093"],"publications":[],"dar_publications":[],"researchSTMT":"The objective of the proposed research is to identify and characterize genetic variants involved in Alzheimer's disease (AD) risk and AD-related phenotypes to ultimately identify potential avenues for therapeutic approaches and prevention of the disease. Our study design will use phenotypic (ex., AD diagnosis, age-at-onset, APOE genotype) and genomic data (ex., WGS, array, imputed genotypes) from NIAGADS studies to investigate genotype-phenotype associations. Strategies include association testing and haplotype- and family-based approaches, including estimates of relatedness and population genetics analyses as needed to perform the association testing (ex. control for population structure). \nNIAGADS data will not be used to investigate individual identity. Consent type and other Data Use Limitations (DUL) for each study will be respected in all analyses. Data from an individual with disease-specific consent will not be used in analyses outside of that restriction, including indirect uses such as imputation reference panels or variant summary statistics. When an individual’s DUL prohibits investigation of population genetics, population history or related issues, their data will be excluded from studies that address those issues. We intend to publish or otherwise broadly share any findings from this study with the scientific community. As such, genomic summary results from datasets with a “sensitive” designation will only be shared through publications to support study’s conclusions and through NIH-funded data repositories which maintain restricted access (ex. NIAGADS). Data from NIAGADS may be combined with non-NIAGADS data from the same or other studies (obtained from dbGaP or other sources), to improve the power for novel genetic discoveries, while respecting the consent of all participants. We expect that this activity creates no additional risks to participants. Data will be shared only among Internal Collaborators at the University of Washington. We do not plan to collaborate with External Collaborators at other institutions.","ntResearchSTMT":"We propose to identify and characterize genetic variation involved in Alzheimer's disease (AD), providing insight as to why some individuals develop or avoid AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. We will combine phenotype and genotype data using association testing and haplotype- and family-based approaches to identify and characterize associations and refine those signals with fine-mapping tools and external data. "},{"dar_ID":10085,"expiration_date":"2027-03-13 19:47:00","approval_date":"2026-10-21 19:47:42","initial_approval_date":"2020-10-14 14:30:45","project_name":"Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches","status_name":"Approved","first_name":"Sudha","last_name":"Seshadri","institution":"Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX","dataset_accessions":["NG00093"],"publications":["41560352","41720779","42024100"],"dar_publications":["41560352","41720779","42024100","38511601","39428839","40084664","37923804","37322115","37333771"],"researchSTMT":"Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease.\nAim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will:\nAim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data;\nAim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets.\nAim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results.\nAim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will:\nAim 2a. Estimate and account for global and local ancestry in all analyses;\nAim 2b. Perform admixture mapping in samples of admixed ancestry; and\nAim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses.\nAim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative omics analyses. We will:\nAim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data.\nAim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches.\nAnalyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, UT Health Science Center at Houston; Sudha Seshadri, UT Health Science Center at San Antonio; Ellen Wijsman, University of Washington; William Salerno, Baylor College of Medicine.","ntResearchSTMT":"This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s disease or provide protection from Alzheimer’s disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available."}]}