{"data":[{"dar_ID":11386,"expiration_date":"2027-03-31 17:02:38","approval_date":"2026-03-31 17:02:38","initial_approval_date":"2025-01-06 18:33:03","project_name":"Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics","status_name":"Approved","first_name":"Michael","last_name":"Belloy","institution":"Washington University in St Louis","dataset_accessions":["NG00166"],"publications":["41588232","41437896","41404291","41282793"],"dar_publications":["41588232","41437896","41404291","41282793"],"researchSTMT":"•\tObjectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. \n•\tStudy design: This study integrates large-scale population genetics with multi-omics and endophenotype analyses. We are integrating all data available from ADGC and ADSP, together with other data from AMP-AD and biobanks such as UKB, FinnGen, and MVP to conduct large-scale multi-ancestry GWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. We also particularly focus on X chromosome association studies. The study design also interrogates interactions with ancestry, hormone exposures, and with APOE*4, as well as comparisons to non-stratified GWAS/XWAS of Alzheimer’s disease. Further, we will also employ genetic correlation analyses, mendelian randomization, colocalization, and pleiotropy analyses, to interrogate overlap with other complex traits to better understand the mechanisms underlying sex dimorphism in Alzheimer’s disease. \n•\tAnalysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: Our phenotypes will include Alzheimer’s disease risk, conversion risk, various endophenotypes (including amyloid/tau biomarkers, brain imaging metrics, etc.) as well as molecular traits. As noted above, we will conduct large-scale multi-ancestry GWAS, XWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. Specific aims include interrogating these question and analyses on (1) the autosomes, (2) the X chromosome, and (3) leveraging sex stratified QTL studies to drive discovery of risk genes.\n","ntResearchSTMT":"Alzheimer’s disease (AD) manifests itself differently across men and women, but the genetic and molecular factors that drive this remain elusive. AD is the most common cause of dementia and till today remains largely untreatable. It is thus crucial to study the genetics of AD in a sex-specific manner, as this will help the field gain important insights into disease pathophysiology, identify novel sex-specific risk factors relevant to personalized genetic medicine, and uncover potential new AD drug targets that may benefit both sexes. This project uses large-scale genomics and multi-omics to elucidate novel sex agnostic and sex-specific AD risk genes. We will interrogate sex dimorphism for AD risk on the autosomes and the sex chromosomes. We similarly interrogate sex dimorphism in the genetic regulation of gene expression and protein levels, which we will integrate with genetic risk for Alzheimer’s disease to further discovery risk genes. Throughout, we will also interrogate how sex-specific risk for AD interactions with hormone exposures, ancestry, and the APOE*4 risk allele."},{"dar_ID":10026,"expiration_date":"2027-01-21 15:13:51","approval_date":"2026-01-21 15:13:51","initial_approval_date":"2019-01-23 09:28:16","project_name":"The Familial Alzheimer Sequencing (FASe) Project","status_name":"Approved","first_name":"Carlos","last_name":"Cruchaga","institution":"Washington University School of Medicine","dataset_accessions":["NG00166"],"publications":[],"dar_publications":["39927718","38172904","37333337","32894242","33591372","33627629","29670507","29183403","29091718","28943286"],"researchSTMT":"The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies","ntResearchSTMT":"Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease."},{"dar_ID":10530,"expiration_date":"2027-08-03 13:10:43","approval_date":"2026-08-03 13:10:43","initial_approval_date":"2022-05-31 11:31:55","project_name":"Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC)","status_name":"Approved","first_name":"Carlos","last_name":"Cruchaga","institution":"Washington University School of Medicine","dataset_accessions":["NG00166"],"publications":[],"dar_publications":["38072165","35103389","33591372","33279834","30108311"],"researchSTMT":"The ADSP-PHC was established to harmonize the rich endophenotype data across cohort studies to enable modern genomic analyses of ADRD with the ultimate goal to generate harmonized data that will become a “legacy” dataset perpetually curated and shared NIAGADS. The ADSP-PHC will facilitate phenotypic data harmonization for ADSP participants with genetic and genomic data. This effort represents a multi-disciplinary approach leveraging interdisciplinary expertise in large-scale genetic and genomic studies, clinical neuroscience, neuroimaging, psychometrics, and bioinformatics. This study will utilize a team structure consisting of two coordinating centers to oversee activities of all harmonization teams and to oversee coordination with other ADSP workgroups and initiatives, a Storage and Informatics Core that will oversee the coordination between LONI and NIAGADS for all data storage, compliance, and dissemination, a CHARGE Coordination Core to oversee the alignment of data and protocols with CHARGE workgroups, an Integration & Analytics Core that will enable data integration across phenotypes to facilitate downstream machine learning applications, and eight Domain-Specific Harmonization Teams tasked with harmonization in their area of expertise. The endophenotypes that will be harmonized by this project include Cognition, Fluid Biomarkers, Amyloid PET, Structural MRI, White Matter Hyperintensities, White Matter Integrity, Vascular Risk Factors, and Neuropathology. \nSite PIs on this project include: Jesse Mez (Boston University), Adam Brickman (Columbia University), Andy Saykin (Indiana University), Elizabeth Mormino (Stanford University), Pauline Maillard (UC Davis), Duygu Tosun-Turgut (UC San Francisco), Christos Davatzikos (University of Pennsylvania), Arthur Toga (USC); Mohamad Habes (University of Texas Health Science Center at San Antonio), Michael Cuccaro (University of Miami), Paul Crane (University of Washington), Bennett Landman (Vanderbilt University), Timothy Hohman (Vanderbilt University Medical Center), and Carlos Cruchaga (Washington University in St. Louis).\n","ntResearchSTMT":"The growing availability of endophenotypic data in cohort studies of Alzheimer’s disease and related dementias (ADRD) provides an exciting opportunity to further characterize the genetic architecture of this devastating disease. However, there is a pressing need to develop and apply advanced harmonization approaches to align ADRD endophenotypes across cohorts. The goal of this coordinated national initiative – the AD Sequencing Project Phenotype Harmonization Consortium, or “ADSP-PHC” – is to provide a centralized database of robust endophenotypes for large-scale genomic analyses that will accelerate the identification of novel targets for therapeutic intervention in ADRD. will accelerate the identification of novel targets for therapeutic intervention in ADRD."},{"dar_ID":11339,"expiration_date":"2027-08-17 12:31:23","approval_date":"2026-08-17 12:31:23","initial_approval_date":"2025-01-06 14:23:19","project_name":"CLEAR-AD","status_name":"Approved","first_name":"Nilufer","last_name":"Ertekin-Taner","institution":"Mayo Clinic","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"This U19 aims to bridge these knowledge gaps for discovery and validation of Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD) in multi-ethnic populations. CLEAR-AD U19 is based on the premise that AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood. The specific aims of CLEAR-AD are: 1) To discover CLPMS of the complex and heterogeneous AD pathophysiology and its co-pathologies. 2) To identify longitudinal CLPMS that detect and predict dynamic neuroimaging, fluid biomarker, and clinical changes across AD spectrum. 3) To characterize differences and similarities in CLPMS profiles across NHW, African American (AA) and Latino American (LA) participants to uncover biomarker patterns in multi-ethnic groups. 4) To make these vast resources available to the scientific community to amplify and accelerate its impact. In this U19, we will leverage NIH-funded ADNI, MCSA and ADRC cohorts of >3,700 multi-ethnic participants to generate >20,000 multi-omics measures (Omics Core) that will be processed and integrated with >48,000 harmonized AD cognitive, neuroimaging and fluid endophenotypes (Analytic Core). Using these data, we will identify brain region and cell-type specific CLPMS, which reflect biological subtypes of AD and disease stage (Project 1). We will discover longitudinal changes in CLPMS that predict cognitive and A/T/N/V progression (Project 2). We will define longitudinal cognitive and A/T/N/V changes and CLPMS in URP that are either conserved with NHW or population-specific (Project 3). This U19 will a) Identify the next generation of AD biomarkers with mechanistic insights; b) Establish a precision medicine approach for rigorous multi-omics biomarker discovery and validation in AD; c) Discover molecules that can serve as biomarkers and therapeutic targets; d) Enhance biomarker research in trial-ready multi-ethnic populations; and e) Generate and share a vast and harmonized resource of endophenotype and multi-omics data in NIH-funded cohorts.","ntResearchSTMT":"There is a clear and immediate need for the discovery of peripheral molecular signatures linked to central disease processes, core and co-pathologies in Alzheimer’s Disease (AD), that will serve as precision medicine blood-based biomarkers for diagnostic, prognostic, theragnostic and therapeutic purposes. AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood, i.e. centrally-linked peripheral molecular signatures (CLPMS). This\nU19 will leverage deeply phenotyped, longitudinal NIH-funded multi-ethnic cohorts and cross-disciplinary expertise for multi-omics data generation and its integration with harmonized AD endophenotypes, will share these data and utilize them in integrated U19 projects to discover CLPMS that will serve as the next generation of AD biomarkers."},{"dar_ID":12385,"expiration_date":"2027-07-20 18:24:08","approval_date":"2026-07-20 18:24:08","initial_approval_date":"2026-07-20 18:24:08","project_name":"Discovery of APOE-Interacting Genes Through Trans-Ancestry and Sex-Stratified Analysis to Elucidate Alzheimer's Disease Risk Mechanisms and Stratify ARIA Risk Using Proxy Outcomes","status_name":"Approved","first_name":"Jong Hun","last_name":"Kim","institution":"KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"Objectives: This project identifies ancestry- and sex-specific APOE ε4 modifier genes—variants that amplify or attenuate APOE ε4’s effect on AD risk and ARIA susceptibility from anti-amyloid immunotherapy. Aim 1: Trans-ancestry sex-stratified GWIS to construct an APOE-Wide Epistasis Map. Aim 2: Mechanistic validation via eQTL/pQTL colocalization and epistasis network. Aim 3: Explainable AI (XAI) integrating modifier SNPs, multi-omics subtypes, and ARIA proxy outcomes to stratify pre-treatment ARIA risk.\nStudy Design: Multi-cohort secondary analysis using NIAGADS-controlled ADSP data exclusively. Individual-level data from all 15 ADC cohorts (NG00022–NG00151) and multi-ancestry ADSP WGS (NG00067, NG00166) span European, African American, Hispanic/Latino, and South/East Asian ancestries. Functional datasets (eQTL/pQTL: NG00102, NG00118, NG00120, NG00130) support Aim 2; imaging and neuropathology datasets (NG00103, NG00147, NG00175) enable Aim 3 ARIA proxy development. No prospective recruitment.\nMulti-dataset rationale: GWIS requires 4–8× more samples than standard GWAS (Gauderman 2002); no single cohort is independently powered—all 15 ADC cohorts must be pooled. Trans-ancestry GWIS requires ancestry-matched datasets (NG00100/African, NG00106/South Asian, NG00141/Hispanic) because population-specific LD cannot be imputed from summary statistics. Functional datasets (eQTL, pQTL, methylation) are non-redundant—each covers a distinct regulatory layer for Aim 2. All datasets are AD-specific; non-AD neurodegeneration data are excluded.\nAnalysis Plan: Phenotypes: AD case/control (primary); APOE ε4 × SNP interaction; lobar microbleed count (ARIA-H proxy); SVD score (WMH, lacunar infarcts, perivascular spaces); longitudinal cognitive decline. Covariates: age, sex, top 20 ancestry PCs, stratum. Methods: logistic GWIS; trans-ancestry meta-analysis (METAL/MR-MEGA); sex-stratified/X-chromosome analyses; eQTL/pQTL colocalization (COLOC2/SMR); XGBoost XAI with 5-fold CV and SHAP.\n","ntResearchSTMT":"Alzheimer’s disease affects tens of millions worldwide. Lecanemab, approved in 2024, slows Alzheimer’s progression by removing amyloid plaques—but causes dangerous brain side effects (ARIA: Amyloid-Related Imaging Abnormalities) especially in APOE ε4 carriers, who also most need treatment. Currently, doctors cannot predict which APOE ε4 carriers will benefit versus be harmed.\n\nOur research identifies modifier genes controlling how dangerous APOE ε4 is. We leverage the ADSP’s diverse dataset spanning 15+ cohorts across European, African American, Hispanic/Latino, and Asian ancestries—a scale statistically necessary because detecting gene–gene interactions requires 4–8× more samples than standard genetic studies. Population-specific patterns allow high-confidence modifier identification. MRI-based brain bleeds and vascular markers serve as validated ARIA surrogates available at scale.\n\nThe result is an explainable AI tool that predicts—before treatment begins—which APOE ε4 patients face high ARIA risk and which will benefit from lecanemab, enabling precision Alzheimer’s therapy.\n"},{"dar_ID":11841,"expiration_date":"2026-08-08 20:45:40","approval_date":"2025-08-08 20:45:40","initial_approval_date":"2025-08-08 20:45:40","project_name":"Modeling Alzheimer’s disease risk and associated molecular phenotypes","status_name":"Expired","first_name":"Silvana","last_name":"Konermann","institution":"Arc institute","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk.","ntResearchSTMT":"Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD."},{"dar_ID":11408,"expiration_date":"2026-11-26 17:33:08","approval_date":"2025-11-26 17:33:08","initial_approval_date":"2025-01-21 16:43:31","project_name":"Alzheimer's disease(AD) subtype analysis using genome sequencing data","status_name":"Approved","first_name":"Kun Ho","last_name":"Lee","institution":"Chosun University","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"Objectives of the Proposed Research\nAlzheimer’s disease (AD) is a common degenerative disease, causing irreversible dementia. Early diagnosis is difficult due to a long asymptomatic period and requires invasive, expensive procedures. A screening method to classify high-risk groups for early AD diagnosis is needed.\nStudy Design\nEarly AD risk prediction can use genomic variants like the Polygenic Risk Score (PRS), which predicts high-risk groups but shows performance differences due to genetic heterogeneity and ethnic specificity. To address this, ethnicity-specific analysis is considered and validated with different ethnic datasets.\nThis study aims to develop Korea-specific PRS models for early AD risk prediction using genomic data from a Korean cohort and the ADSP. Trans-ethnic genomic data will be created by combining GARD and ADSP data, including African American (AA), non-Hispanic Whites (NHW), and East Asian (EA) data. Cross-validation (CV)\nanalysis will divide data into training and test sets. Genomic variants' importance (e.g., p-values, BLUP) will be calculated, and selected variants applied to PRS. PRS models will be evaluated using CV-divided test data to select the best model. Trans-ethnic and ethnicity-specific PRS models will be validated using reserved validation data.\nAnalysis Plan\nThe proposal aims to identify ethnicity differences in genomic prediction built with Caucasian-centric GWA SNVs and improve the model for trans-ethnic groups, particularly East Asians. A Bayesian machine learning approach transfers genetic risk model knowledge from the NHW dataset to other ethnic groups for better accuracy. Genotype datasets from all ancestry groups are used together. Instead of trans-ethnic meta-analysis, the approach by Gim et al. is adopted. Each ethnic group dataset is divided for cross-validation. Training datasets are analyzed to evaluate p-values and BLUP of SNVs. Summary statistics are used to build the prediction model and apply nested-CV for model selection. The best model for each ethnic group is tested using the test dataset. Data is analyzed similarly by learning from ethnic-specific variants and building a prediction model with the new method.\n","ntResearchSTMT":"Alzheimer’s disease (AD) is the leading cause of dementia and is irreversible once symptoms appear. A long asymptomatic period of AD complicates early diagnosis requiring invasive and costly procedures like CSF extraction or PET scans. Therefore, a screening method to identify high-risk groups for early AD diagnosis is necessary.\n\nOne approach uses the Polygenic Risk Score (PRS), which calculation is based on multiple genomic variants associated with AD. However, PRS predictions vary significantly (60-80%) due to genetic heterogeneity and ethnic specificity. Thus, data from multiple ethnicities must be analyzed. Although Asia accounts for over 50% of global dementia cases, most large-scale AD cohorts are predominantly White, lacking studies on Asians.\n\nThis study aims to develop trans-ethnic and ethnicity-specific PRS models for early AD risk prediction using genomic data from the GARD cohort, centered on Koreans, and the ADSP, which includes various European ethnicities. It investigates AD’s genetic heterogeneity due to ethnic differences and proposes methods to adjust for variability."},{"dar_ID":11356,"expiration_date":"2026-12-19 14:37:30","approval_date":"2025-12-19 14:37:30","initial_approval_date":"2025-12-19 14:37:30","project_name":"Investigating the relationships between polygenic risk scores and dementia and cognition across and within populations of different ancestry","status_name":"Approved","first_name":"Karen","last_name":"Mather","institution":"UNSW Sydney","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"Background: The Cohort Studies of Memory in an International Consortium (COSMIC) studies the factors linked to cognitive decline and dementia in a diverse range of populations from around the world. COSMIC is headed by the Centre for Healthy Brain Ageing (CHeBA), University of New South Wales (UNSW), Australia (see https://cheba.unsw.edu.au/consortia/cosmic).\n\nAt present, there is limited knowledge available regarding the genetic factors associated with ageing-related complex phenotypes and diseases in non-European based cohorts, particularly in low- and middle-income countries and whether specific ancestry-based genetic association results are generalizable to populations of other ancestries. \n\nIn this study, we aim to study the genetic factors associated with dementia and related phenotypes to appraise if they can be used to predict age-related cognitive performance and decline and dementia in a wide range of diverse cohorts. We will use data collected by COSMIC Consortium studies but also from external studies wherever possible. Hence, the application to assess NIAGADS data to include as many ancestry-diverse studies as we can in this work. The data from these cohorts/studies will be analyzed by meta-analysis.\n\nObjectives: To assess if dementia/cognitive and other polygenic risk scores (PRS) generated from different ancestries (European, non-European and trans ethnic) predicts age-related cognitive performance and decline and dementia across populations of different ancestries and including studies from low and middle-income countries. \n\nStudy Design and Analysis Plan: Participants will be adults aged 45 and above without dementia at baseline. Different PRS (constructed using different methods, eg.SBayesRC and using different GWAS p-value thresholds using PLINK) will be undertaken using available GWAS summary statistics. Cognitive data both cross-sectionally and longitudinally will be used where available, with priority given to tests of memory. PRS-cognitive analyses will be performed using appropriate mixed models. Covariates will include age, sex, years of education and any study-specific covariates (e.g. PCs for population stratification).\n","ntResearchSTMT":"Most human genetic association studies have been undertaken in populations of European ancestry, despite >75% of the world’s population being of Asian or African ancestry. To date, most genetic variants for dementia have been identified using populations mainly of European ancestry and from high income countries, despite more than ~60% of dementia cases living in low and middle-income countries. In addition, many of the non-white genetic studies have had small sample sizes and lack replication. We need to increase our understanding of the genetic risk for dementia and its related traits in under-represented populations, such as the multi-ethnic cohorts of the Cohort Studies of Memory in an International Consortium (COSMIC). The current project aims to examine if polygenic risk scores for dementia, cognitive and other related phenotypes generated from populations of different ancestries predict performance and decline on cognitive tests and incident dementia in older adults from multi-ethnic populations using the COSMIC Consortium and external studies."},{"dar_ID":10141,"expiration_date":"2027-07-29 17:05:33","approval_date":"2026-07-29 17:05:33","initial_approval_date":"2019-12-18 15:47:57","project_name":"Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s disease","status_name":"Approved","first_name":"Wei","last_name":"Pan","institution":"University of Minnesota","dataset_accessions":["NG00166"],"publications":["40209152","41537459"],"dar_publications":["40209152","41537459","36179104"],"researchSTMT":"We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims:\nAim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. \nAim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.\nAim 3. Inferring (putative) causal genes/proteins/metabolites for AD. We will apply TWA/PWAS/MWAS/xWAS methods to ADSP, AD GWAS and other omic data to identify (putative) causal genes, proteins, metabolites and other molecular traits for AD. These methods may be based on standard linear models or emerging ML/AI methods. \n","ntResearchSTMT":"We propose applying our newly developed statistical and computational methods, along with other suitable existing methods, to the existing ADSP sequencing data, other AD GWAS data and other omic data to detect common or rare genetic variants and other molecular traits, such as genes/proteins/metabolites, associated with and/pr (putative) causal to Alzheimer’s disease (AD). The novelty and power of our new methods are in three aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD; third, we will develop and apply standard linear model- and emerging ML/AI-based causal inference methods to infer causal genes, proteins, metabolites and other traits for AD. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective."},{"dar_ID":10085,"expiration_date":"2026-08-12 00:23:32","approval_date":"2025-08-12 00:23:32","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":"Expired","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":["NG00166"],"publications":["38511601","39428839","40084664"],"dar_publications":["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\ninsertion/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\nacross 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\nsubjects 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\nanalyses. 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\nnetwork approaches.\nAnalyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No\nindividual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health\nScience Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U 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\nthe ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s\nDisease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately\naccounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of\nother genomic data available."},{"dar_ID":11020,"expiration_date":"2027-02-03 12:33:46","approval_date":"2026-02-03 12:33:46","initial_approval_date":"2024-04-03 19:21:21","project_name":"Uncovering the causal genetic variants, genes and cell types underlying brain disorders","status_name":"Approved","first_name":"Michael","last_name":"Wainberg","institution":"Sinai Health System","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"We propose a multifaceted approach to elucidate and interpret genetic risk factors for Alzheimer's disease. First, we propose to perform a whole-genome sequencing meta-analysis of the Alzheimer's Disease Sequencing Project with the UK Biobank and All of Us to associate rare coding and non-coding variants with Alzheimer's disease and related dementias. We will explore a variety of case definitions in the UK Biobank and All of Us, including those based on ICD codes from electronic medical records (inpatient, primary care and/or death), self-report of Alzheimer's disease or Alzheimer's disease and related dementias, and/or family history of Alzheimer's disease or Alzheimer's disease and related dementias. We will perform single-variant, coding-variant burden, and non-coding variant burden tests using the REGENIE genome-wide association study toolkit. \n\nSecond, we propose to develop statistical and machine learning models that can effectively infer (“fine-map”) the causal gene(s), variant(s), and cell type(s) underlying each association we find, as well as associations from existing genome-wide association studies and other Alzheimer's- and aging-related cohorts found in NIAGADS. In particular, we propose to improve causal gene identification by incorporating knowledge of gene function as a complement to functional genomics. For instance, we plan to develop improved methods for inferring biological networks, particularly from single-cell data, and integrate these networks with the results of the non-coding associations from our first aim to fine-map causal genes. To fine-map causal variants and cell types, we plan to integrate the associations from our first aim with single-nucleus chromatin accessibility data from postmortem brain cohorts to simultaneously infer which variant(s) are causal for each discovered locus and which cell type(s) they act through. ","ntResearchSTMT":"We have a comprehensive plan to understand and explain the genetic factors that contribute to Alzheimer's disease. Our approach involves two main steps.\n\nFirst, we'll analyze genetic information from large research databases to identify rare genetic changes associated with Alzheimer's and related memory disorders. We'll look at both specific changes in genes and other parts of the genetic code. We'll use data from different studies and combine them to get a clearer picture.\n\nSecond, we'll create advanced computer models that can help us figure out which specific genes, genetic changes, and cell types are responsible for these associations. This will help us pinpoint the most important factors contributing to Alzheimer's disease. We'll also analyze data from previous studies to build a more complete understanding of these genetic links."},{"dar_ID":11584,"expiration_date":"2027-06-01 19:42:46","approval_date":"2026-06-01 19:42:46","initial_approval_date":"2025-03-27 20:04:42","project_name":"AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning","status_name":"Approved","first_name":"Zhongming","last_name":"Zhao","institution":"University of Texas Health Science Center at Houston","dataset_accessions":["NG00166"],"publications":[],"dar_publications":[],"researchSTMT":"Objectives: The objective of our study is to advance our understanding of the genetic basis of Alzheimer’s Disease (AD) through the analysis of comprehensive genomic datasets such as Whole Exome Sequencing (WES), Whole Genome Sequencing (WGS), single-nuclei RNA sequencing, and Genome-Wide Association Studies (GWAS), as well as the related phenotype. We aim to identify genetic variants that are integral to the development and progression of AD. \n\nStudy Design: Our approach involves a detailed multi-omics analysis focusing on both coding and non-coding regions within these datasets. We will develop new analytical variables from existing data, ensuring that our research adheres to the established data use limitations and contributes meaningfully to the field of genetic research in AD. \n\nAnalysis Plan: The plan centers on investigating the correlation between genetic variants and AD, exploring how these variants influence the disease at a genetic level. We will employ cutting-edge computational methods to analyze interactions between these genetic markers and their potential role in AD pathogenesis. The integration of data from multiple sources will be carefully executed to maintain compliance with data use agreements, emphasizing the scientific exploration of AD.\n","ntResearchSTMT":"Our research is dedicated to unraveling the genetic components of Alzheimer’s Disease. By analyzing genetic sequences and variations through various genomic datasets, we seek to deepen the scientific understanding of how these genetic elements contribute to AD. The outcomes of this study will be shared with the public, enhancing general knowledge of Alzheimer’s Disease and supporting the global research community in its ongoing efforts to decode this complex condition."}]}