{"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":["NG00131"],"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":10766,"expiration_date":"2026-11-14 22:26:54","approval_date":"2025-11-14 22:26:54","initial_approval_date":"2023-03-28 11:37:04","project_name":"Classification of Alzheimer’s disease with Genetic Data and Artificial Intelligence","status_name":"Approved","first_name":"Jingchun","last_name":"Chen","institution":"University of Nevada, Las Vegas","dataset_accessions":["NG00131"],"publications":[],"dar_publications":[],"researchSTMT":"Alzheimer's disease(AD) is the most common cause of dementia, accounting for 60% to 80% of cases that affect over six million people in the United States. The disease gradually progresses from mild cognitive impairment(MCI) to dementia, which takes more than a decade. Identifying individuals who have a high risk of AD earlier is essential for AD prevention and intervention. As the heritability of AD is high(up to 79%), genetic data should be powerful to identify individuals at high risk. Indeed, polygenic risk score (PRS), designed to estimate individual genetic liability by integrating large GWAS summary statistics and individual genotype data, has been shown to be promising for AD risk prediction(AUCs up to 84%). However, the prediction accuracy using a single PRS is still not sufficient for MCI and AD classification in clinical practice. \nWe hypothesize that convolution neural network(CNN) models can improve the classification of AD and MCI by multiple integrating PRSs from multiple traits, multi-omics data (genotyping data, scRNA-seq), clinical data, and imaging data. The objective is to develop advanced AI algorithms and build data-driven models for disease risk assessment, earlier identifying individuals with high risk for MCI and AD. Our long-term goal is to develop and validate a prediction model that can be translated into clinical practice. Our CNN model has recently shown an improved performance for AD with PRSs from multiple traits(AUC 92.4%). We want to extend our approach to predicting AD and MCI in different ethnic groups and validate the results with independent datasets. To this end, we would like to apply for multi-omics data in NG00067.v9 from  https://dss.niagads.org/datasets/ng00067/. With an extensive experience in genetic studies on complex disorders and disease modeling, we are confident that we will achieve the specified goals and promote the integration of genetic data with AI algorithms, facilitating data-driven, personalized care of AD. We expect to finish this study within 2 years with publication and grant application. We have IRB approval and will follow the rules for data sharing and acknowledgment. \n","ntResearchSTMT":"Alzheimer’s disease (AD), the most common form of dementia, that usually develops from mild cognitive impairment to dementia. There is currently no treatment to slow the progression of this disorder. But earlier identification of the individuals with higher risk maybe critical to prevent the disease. We propose a new approach to create models for classification of AD and MCI with artificial intelligence and genetic data. This study will have a significant value in personalized medicine for AD risk assessment, classification, and earlier intervention. \n\nWe don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.\n"},{"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":["NG00131"],"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":10008,"expiration_date":"2027-07-08 15:00:01","approval_date":"2026-07-08 15:00:01","initial_approval_date":"2018-09-17 16:19:09","project_name":"Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes","status_name":"Approved","first_name":"Alison","last_name":"Goate","institution":"Icahn School of Medicine at Mount Sinai","dataset_accessions":["NG00131"],"publications":["40111762"],"dar_publications":["40111762","41040731","40676597","40533518","40470240","40084664","40033057","39937857","38448474","39251599","36907103","36824752","35005195","35379992","34978149","34874463","34757660","34719765","35750033","33712570","34470617","34417470","34386572","34301914","34041846","33589841"],"researchSTMT":"Alzheimer's disease (AD) is the most common form of dementia but has no effective prevention or treatment. Developing a comprehensive picture of the genetic architecture of AD including a network level functional assessment of risk/resilience genes is essential to develop novel therapeutic targets. The overarching goals of this study are to use genetic and genomic approaches to: 1) identify genes and variants that are involved in the development of AD and related disorders; 2) identify functional networks enriched for AD or related disorder risk and protective loci; 3) determine how cellular function and physiology is impacted by these genetic factors in disease-relevant cell types and animal models. This study will use publicly available whole genome/exome sequence data generated by the Alzheimer’s Disease Sequencing Project (ADSP) and genome-wide association study (GWAS) data from the International Genomics of Alzheimer’s Project (IGAP) and others. We will apply a suite of case-control and family approaches to investigate genetic association with dichotomous and continuous disease traits. This study will not only further our understanding of the genetic architecture of AD but also provide key information regarding the molecular mechanisms, setting the stage for novel therapeutic development.\n","ntResearchSTMT":"Alzheimer’s disease (AD) is the only disease among the top ten killers in the U.S. without a disease modifying therapy. Genetic studies provide a powerful means to identify genes and pathways that are causally linked to disease etiology. We propose to use genomic and functional approaches to identify genes that alter the risk of AD and investigate how these genes disrupt cellular pathways leading to disease.\n"},{"dar_ID":10018,"expiration_date":"2027-03-31 16:12:46","approval_date":"2026-03-31 16:12:46","initial_approval_date":"2019-01-10 10:16:04","project_name":"Examining Genetic Associations in Neurodegenerative Diseases","status_name":"Approved","first_name":"Michael","last_name":"Greicius","institution":"Stanford University School of Medicine","dataset_accessions":["NG00131"],"publications":[],"dar_publications":[],"researchSTMT":"We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.","ntResearchSTMT":"Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes. "},{"dar_ID":11542,"expiration_date":"2027-03-31 19:56:47","approval_date":"2026-03-31 19:56:47","initial_approval_date":"2025-01-07 19:16:05","project_name":"Genetics of Alzheimer's Disease and Endophenotypes","status_name":"Approved","first_name":"M. Ilyas","last_name":"Kamboh","institution":"University of Pittsburgh","dataset_accessions":["NG00131"],"publications":[],"dar_publications":[],"researchSTMT":"Objectives: \nWe are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995.  We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses. \nStudy Design: \nA case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations.\nAnalysis Plan: \nWe will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).\n","ntResearchSTMT":"Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh  Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes."},{"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":["NG00131"],"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":["NG00131"],"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":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":["NG00131"],"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":"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&#39;s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX","dataset_accessions":["NG00131"],"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."},{"dar_ID":10851,"expiration_date":"2027-05-18 16:42:47","approval_date":"2026-05-18 16:42:47","initial_approval_date":"2023-05-30 15:05:09","project_name":"Artificial Intelligence Strategies for Alzheimer's Disease Research","status_name":"Approved","first_name":"Li","last_name":"Shen","institution":"University of Pennsylvania","dataset_accessions":["NG00131"],"publications":["40417489","39348263","38941858","40502260","40502235","39358793","40229250","39394173","39354639","38723657","39842860","39670388","39526853","41231230","41139314","40426270","39766777","39670375","39670392"],"dar_publications":["40417489","39348263","38941858","40502260","40502235","39358793","40229250","39394173","39354639","38723657","39842860","39670388","39526853","41231230","41139314","40426270","39766777","39670375","39670392","38848574","37507059","38433927","38463442","38830083","37796839","38635981","38827072","37350896","37350880","38827078"],"researchSTMT":"The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. Our proposed AI methods and software will be open-source, user-friendly, and freely available for all to use. Specifically, we will analyze ADSP data sets using three novel informatics methods to tailor our automated machine learning (AutoML) tool to the analysis of Alzheimer’s disease (AD) data. First, we will develop a Multi-Modal Interaction (M2I) feature selection algorithm for identifying genetic interactions that are predictive of AD (AIM 1). Second, we will develop a Knowledge-driven Multi-omics Integration (KMI) algorithm for combining omics features for AI analysis of AD (AIM 2). Third, we will develop a Multidimensional Brain Imaging Omics (MBIO) integration framework for the joint analysis of multisource large-scale data for predicting AD. Finally, we will integrate all three biomedical informatics methods into our open-source AutoML software package and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.","ntResearchSTMT":"The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. We will develop three biomedical informatics methods with focuses on genetics, genomics and imaging respectively. We will integrate these methods into our open-source AutoML software package, and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support."},{"dar_ID":11321,"expiration_date":"2027-03-31 21:21:07","approval_date":"2026-03-31 21:21:07","initial_approval_date":"2025-01-06 20:09:42","project_name":"NIAGADS Metalbolomics","status_name":"Approved","first_name":"Rudolph","last_name":"Tanzi","institution":"Massachusetts General Hospital","dataset_accessions":["NG00131"],"publications":[],"dar_publications":[],"researchSTMT":"We propose to analyze and replicate MGWAS (Metabolome-Wide Association Studies) findings using blood samples from several population-based cohorts. This dataset will serve to replicate metabolite-SNP association signals from blood from several population-based cohorts.\nAdditionally, we plan to conduct Mendelian Randomization (MR) and mediation analyses for pleiotropic SNPs (single nucleotide polymorphisms) identified in our study. Finally, we will utilize this dataset to test and fine-tune a multiomics machine learning prediction model for Alzheimer's Disease (AD).\nSpecifically, this metabolomics dataset, obtained from both brain and cerebrospinal fluid (CSF), will be instrumental in validating our findings from blood metabolomic datasets. This validation step is crucial for establishing a systematic understanding of the shared genetic basis of metabolites and AD, potentially leading to early prediction and intervention strategies for AD.\nBy integrating data from multiple biological sources, we aim to enhance the robustness and reliability of our findings. This dataset will serve as an additional validation resource, providing a solid foundation for advancing our knowledge of AD's genetic underpinnings and developing predictive models.","ntResearchSTMT":"Metabolites (i.e. quantified small molecules) can reflect what has been encoded by the genome and modified by environmental factors at a specific point in time. Such biomarkers could be particularly useful for diagnosis improvement, potential therapeutic targets, as well as additional insights into disease pathogenesis. We hypothesize that many metabolites are strong intermediate variables (mediators) of genetic effects and/or environmental effects on AD. Specifically, several metabolite quantitative trait loci (metQTLs) affect biomarkers across the human life course starting from childhood and indirectly leading to AD, while others are influenced by lifestyle and environment later in life. To understand AD mechanisms, we will systematically study SNP-metabolite associations in blood and integrate them with AD susceptibility loci. We will use the NG00131 cohort to validate our findings in metabolites from brain and CSF, as the tissues of interest for AD."},{"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":["NG00131"],"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":11491,"expiration_date":"2027-01-21 15:15:35","approval_date":"2026-01-21 15:15:35","initial_approval_date":"2026-01-21 15:15:35","project_name":"Identifying Alzheimer's Disease Genetic Risk Factors By Integrated Genomic and Proteomic Analysis","status_name":"Approved","first_name":"Thomas","last_name":"Wingo","institution":"University of California Davis","dataset_accessions":["NG00131"],"publications":[],"dar_publications":[],"researchSTMT":"We aim to uncover new genetic risk variants for Alzheimer’s disease (AD), AD-related dementia (ADRD), and behavioral and psychiatric symptoms (BPS) associated with AD/ADRD.  We expect to use whole-genome sequencing (WGS), whole-genome genotyping (WGG), and whole-exome sequencing (WES) data. Additionally, we will use the results of brain proteomic analysis to nominate genes and pathways for AD, ADRD, and dementia BPS.  We plan to publish our findings to share them with the scientific community.\n\nOutcomes that will be tested include: (1) clinical disease status, (2) pathologic characterization (e.g., measures of beta-amyloid, tau, etc.), (3) cognitive decline, (4) BPSD, and (5) outcomes related to AD/ADRD severity. For sequencing data, we will extract raw sequencing reads from CRAM/BAM (or equivalent encrypted files) and re-map those to hg38 build of the human genome using PEMapper. Bascalling will be performed using PECaller using default settings. Variant annotation will use Bystro and quality control will follow approaches to assess completeness and account for ancestry as is customary in our lab. For rare variants, we will a variety of kernel-based approaches and for common variants, use standard statistical modeling. For all analyses, we plan to control for population structure deriving principal components from the underlying sequencing or genotyping data.\n","ntResearchSTMT":"Our aim is to identify genetic variants that are associated with Alzheimer's Disease (AD) to uncover new genetic associations. We will examine the role of important risk factors for AD (e.g., age and sex) in our analyses. Separately, we will perform integration of genetic findings for AD with information about how genetic variants influence or are associated with gene expression in the brain, cerebrospinal fluid, or blood to uncover new pathways of disease. Our overarching aim is to use genetic discoveries to identify mechanisms of AD pathogenesis to help nominate new treatment targets."},{"dar_ID":10065,"expiration_date":"2024-11-21 16:34:09","approval_date":"2023-11-21 16:34:09","initial_approval_date":"2019-03-19 11:04:52","project_name":"Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach","status_name":"Closed","first_name":"Jinying","last_name":"Zhao","institution":"University of Florida","dataset_accessions":["NG00131"],"publications":[],"dar_publications":["31785839","30660039","35959851","30931973","30847004"],"researchSTMT":"GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.","ntResearchSTMT":"Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets. "},{"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":["NG00131"],"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."}]}