Overview
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Description
This dataset includes summary statistics for a genome-wide association study meta-analysis of eleven neuropathology endophenotypes. Included neuropathology endophenotypes are neuritic plaques, tau neurofibrillary tangles, cerebral amyloid angiography, amyloid-beta plaques, TDP-43 deposits, Lewy body deposits, cerebral atherosclerosis, cerebral arteriolosclerosis, gross infarcts, microinfarcts, and hippocampal sclerosis. The individual studies used in this project were National Alzheimer’s Coordinating Center (NACC), Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), and Adult Changes in Thought (ACT; n =7,804 total autopsied participants across all four studies). Each study was analyzed individually (ROS and MAP were analyzed together) with meta-analysis being subsequently performed.
Available Filesets
| Name | Accession | Latest Release | Description |
|---|---|---|---|
| Neuropathology Endophenotypes GWAS; Full Summary Statistics (application needed) | fsa000145 | NG00175.v1 | Full Summary Statistics |
| Neuropathology Endophenotypes GWAS; P-Value Only (open access) | fsa000146 | NG00175.v1 | P-value Only |
View the File Manifest for a full list of files released in this dataset.
Data Dictionary Files
Data Releases
Related Studies
- Genome-wide association studies (GWAS) have identified >80 Alzheimer’s disease and related dementias (ADRD)-associated loci. However, the clinical outcomes used in most prior studies belie the complex nature of underlying neuropathologies.…
Phenotype Harmonization
Consent Levels
| Consent | Number of Subjects |
|---|---|
| DS-ADRD-IRB-PUB-NPU | NA |
Visit the Data Use Limitations page for definitions of the consent levels above.
Acknowledgement
Acknowledgment statement for any data distributed by NIAGADS:
Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.
Use the study-specific acknowledgement statements below (as applicable):
For investigators using any data from this dataset:
Please cite/reference the use of NIAGADS data by including the accession NG00175.
For investigators using GWAS of multiple neuropathology endophenotypes identifies new dementia risk loci (sa000072) data:
The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: RF1AG082339 (to Yuriko Katsumata, Steven A Claas, Mark TW Ebbert, Peter T Nelson, and David W Fardo), R56AG057191 (to Yuriko Katsumata, Steven A Claas, and David W Fardo), F30NS124136 (to Lincoln MP Shade), P30 AG028383 (to Peter T Nelson), R35GM138636 (to Bernardo Aguzzoli Heberle, J Antony Brandon, Madeline L Page, and Mark TW Ebbert), R01AG068331 (to Bernardo Aguzzoli Heberle, J Antony Brandon, Madeline L Page, and Mark TW Ebbert), U01AG058654 (to Jonathan L Haines), P01AG078116 (to Yuriko Katsumata and Khine Zin Aung), R01AG082730 (to David W Fardo), R01LM012535 (to Kwangsik Nho), U19AG024904 (to Andrew J Saykin), U01AG068057 (to Andrew J Saykin), U01AG072177 (to Andrew J Saykin), U19AG074879 (to Kwangsik Nho and Andrew J Saykin), U24AG072122 (to Walter A Kukull), P30AG072976 (to Andrew J Saykin), the BrightFocus Foundation (A2020161S to Mark TW Ebbert), Alzheimer’s Association (2019-AARG-644082 to Mark TW Ebbert), the University of Kentucky Center for Clinical and Translational Science TL-1 Fellowship (TL1TR0019970), the National Center for Advancing Translational Sciences (UL1TR001998) and the Dean of the College of Medicine, University of Kentucky. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH), the University of Kentucky or other participating institutions. The NACC database is funded by NIA/NIH under grant U01AG016976. NACC data are contributed by the NIA-funded ADCs—P30 AG019610 (E Reiman), P30 AG013846 (N Kowall), P50 AG008702 (S Small), P50 AG025688 (A Levey), P50 AG047266 (T Golde), P30 AG010133 (A Saykin), P50 AG005146 (M Albert), P50 AG005134 (B Hyman), P50 AG016574 (R Petersen), P50 AG005138 (M Sano), P30 AG008051 (T Wisniewski), P30 AG013854 (R Vassar), P30 AG008017 (J Kaye), P30 AG010161 (D Bennett), P50 AG047366 (V Henderson), P30 AG010129 (C DeCarli), P50 AG016573 (F LaFerla), P50 AG005131 (J Brewer), P50 AG023501 (B Miller), P30 AG035982 (R Swerdlow), P30 AG028383 (L Van Eldik), P30 AG053760 (H Paulson), P30 AG010124 (J Trojanowski), P50 AG005133 (O Lopez), P50 AG005142 (H Chui), P30 AG012300 (R Rosenberg), P30 AG049638 (S Craft), P50 AG005136 (T Grabowski), P50 AG033514 (S Asthana), P50 AG005681 (J Morris) and P50 AG047270 (S Strittmatter). The results published here are in part based on data obtained from the AD Knowledge Portal. Genotyping was supported by the ADGC through the National Institute of Aging (U01AG032984 and RC2AG036528). Samples from the National Cell Repository for Alzheimer’s Disease, which receives government support under a cooperative agreement grant (U24AG21886) awarded by the NIA, were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families whose help and participation made this work possible. Data for this study were prepared, archived and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24AG041689-01). We thank the study participants and staff of the Rush Alzheimer’s Disease Center. The ROS and the Rush MAP are supported by grants from the NIH (P30AG10161, P30AG72975, R01AG15819, R01AG17917, R01AG22018, R01AG33678, R01AG34374, R01AG36042, R01AG40039, R01AG042210, U01AG46152, U01AG61356, R01AG47976, R01AG43379, RF1AG54057, R01AG56352, R01NS78009 and UH2NS100599) and the Illinois Department of Public Health. The ACT study was funded by the NIA (U19AG066567). Data collection for this work was additionally supported, in part, by previous funding from the NIA (U01AG006781). All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the NIA or the NIH. We thank the participants of the ACT study for the data they have provided and the many ACT investigators and staff who steward that data. You can learn more about ACT at https:// actagingstudy.org/.
Publications
- Shade LMP. GWAS of multiple neuropathology endophenotypes identifies new risk loci and provides insights into the genetic risk of dementia. Nature genetics. 2024 Nov. PubMed link
Third-Party Access
Approved Users
Total number of approved DARs: 8
- Investigator:Belloy, MichaelInstitution:Washington University in St LouisProject Title:Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omicsDate of Approval:March 31, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:• Objectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. • Study 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. • Analysis 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.Non-Technical Research Use Statement: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.
- Investigator:Fernandez, VictoriaInstitution:ACE Alzheimer CenterProject Title:GADIRDate of Approval:February 10, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The objective of this study is to contribute to our understanding of neurodegenerative diseases by examining the genetic contributors of major dementia neuropathological hallmarks (amyloid-β deposition, tau pathology, TDP-43, hippocampal sclerosis, Lewy body pathology, and cerebrovascular disease, among others. We will generate the largest Iberian database(N=3500) of neuropathologically curated brains (Aim 1) with a subset of those (N≈350) undergoing deep digital phenotyping (Aim 3). We will generate an associated genetic map (Aim 2) order to elucidate how common and rare genetic variants contribute to specific pathologies. We additionally aim to determine how polygenic risk scores (PRS) and pathway-specific PRS correspond to single and mixed neuropathological profiles, and to clarify the genetic architecture driving co-pathologies that frequently complicate clinical diagnosis. Eventually, we will replicate and fine-map our findings (Aim 4) leveraging available datasets at NIAGADS and other public repositories.Our analysis plan includes genome-wide association testing of ordinal, binary, and quantitative neuropathological traits; rare-variant burden analyses for coding and non-coding regions; PRS and pathway-PRS modeling across multiple dementia-related diseases; unsupervised clustering to identify variant sets defining specific endophenotypes; and pathway and network analyses to interpret significant signals. Colocalization and functional annotation approaches will integrate genomic findings with transcriptomic and proteomic resources.Data obtained from NIAGADS will be used to strengthen replication, broaden meta-analytic power, validate associations across independent neuropathology cohorts, and support functional interpretation using available genetic, expression, and multi-omic datasets. All analyses will use de-identified data in compliance with ethical and data-sharing standards.Non-Technical Research Use Statement:Dementia is an immensely challenging and prevalent condition, deeply impacting the lives of over 55 million individuals worldwide. While Alzheimer's disease stands as the most commonly recognized form of dementia, there exist other conditions that present comparable symptoms but distinct underlying pathological characteristics. To provide more effective support to patients and their families, we need to better understand the genetic causes associated to each of these brain pathologies, and to develop advanced tools for early classification and diagnosis. This grant proposal aims to tackle these challenges by establishing the largest Iberian (Spanish and Portuguese) database of dementia neuropathological cases, marked by a modernized and standardized neuropathological classification alongside comprehensive genomic data. Our goal is to delve further into the genetic architecture underpinning these pathological features and to refine existing risk assessment tools for more accurate diagnoses.
- Investigator:Goate, AlisonInstitution:Icahn School of Medicine at Mount SinaiProject Title:Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypesDate of Approval:June 1, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Hohman, TimothyInstitution:Vanderbilt University Medical CenterProject Title:Genetic Drivers of Resilience to Alzheimer's DiseaseDate of Approval:March 16, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:“Asymptomatic” Alzheimer’s disease (AD) is a phenomenon in which 30% of individuals over age 65 meet criteria for autopsy-confirmed pathological AD (beta-amyloid plaques and tau aggregation) but do not clinically manifest cognitive impairment.1-3 The resilience that underlies asymptomatic AD is marked by both protection from neurodegeneration (brain resilience)4 and preserved cognition (cognitive resilience).Our central hypothesis is that genetic effects allow a subset of individuals to endure extensive AD neuropathology without marked brain atrophy or cognitive impairment. We are uniquely positioned to identify resilience genes by leveraging the Resilience from Alzheimer’s Disease (RAD) database, a local resource in which we have harmonized a validated quantitative phenotype of resilience across 8 large AD cohort studies.Our strong interdisciplinary team represents international leaders in genetics, neuroscience, neuropsychology, neuropathology, and psychometrics who will leverage the infrastructure and rich resources of the AD Genetics Consortium, IGAP, ADSP, and our recently established and harmonzed continuous metric of resilience to fulfill the following aims:Aim 1. Identify and replicate common genetic variants that predict cognitive resilience (preserved cognition) and brain resilience (protection from brain atrophy) in the presence of AD pathology. We hypothesize that common genetic variation will explain variance in resilience above and beyond known predictors like education. Replication analyses will leverage age of onset data from IGAP to demonstrate that resilience loci predict a later age of AD onset.Aim 2. Identify and replicate rare and low-frequency genetic variants that predict cognitive and brain resilience. Rare and low-frequency variants with large effects have been identified in AD case/control studies, providing new insight into the genetic architecture of AD.Aim 3: Identify sex-specific genetic drivers of cognitive and brain resilience to AD pathology. Our preliminary results highlight sex differences in the downstream consequences of AD neuropathology, including sex-specific genetic markers of resilience.Non-Technical Research Use Statement:As the population ages, late-onset Alzheimer’s disease (AD) is becoming an increasingly important public health issue. Clinical trials targeted a reducing AD progression have demonstrated that patients continue to decline despite therapeutic intervention. Thus, there is a pressing need for new treatments aimed at novel therapeutic targets. A shift in focus from risk to resilience has tremendous potential to have a major public health impact by highlighting mechanisms that naturally counteract the damaging effects of AD neuropathology. The goal of the present project is to characterize genetic factors that protect the brain from the downstream consequences of AD neuropathology. We will identify both rare and common genetic variants using a robust metric of resilience developed and validated by our research team. The identification of such genetic effects will provide novel targets for therapeutic intervention in AD.
- Investigator:Kamboh, M. IlyasInstitution:University of PittsburghProject Title:Genetics of Alzheimer's Disease and EndophenotypesDate of Approval:March 31, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:Objectives: We 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. Study Design: A 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. Analysis Plan: We 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).Non-Technical Research Use Statement: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.
- Investigator:Park, Young HoInstitution:Seoul National University Bundang HospitalProject Title:Genome-wide search for pleiotropy in ischemic stroke, normal pressure hydrocephalus, and neuropathology endophenotypes linked to Alzheimer’s diseaseDate of Approval:June 1, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:- Objectives of the proposed research: Accumulating evidence indicates that ischemic stroke (IS) and normal pressure hydrocephalus (NPH) share common risk factors and critical mechanisms with Alzheimer’s disease (AD). This study aims to identify pleiotropy between (1) IS and AD-associated neuropathology endophenotypes (NPEs) and (2) NPH and AD-associated NPEs. Based on the identified pleiotropic variants or loci, we will also investigate shared pathways underlying biological mechanisms of IS, NPH, and AD.- Study design: We will conduct genome-wide pleiotropy analyses using summary statistics obtained from previous genome-wide association studies (GWAS) for IS, NPH, and AD-associated NPEs. Along with GWAS summary statistics for NPEs (Shade et al., 2024, Nat Genet), we will use GWAS summary statistics for IS (Malik et al., 2018, Nat Genet) and NPH (Rasanen et al., 2024, Neurology), generated in individuals of European ancestry.- Analysis plan: Two separate genome-wide pleiotropy analyses will be conducted: (1) pleiotropy GWAS for IS and AD-associated NPEs and (2) pleiotropy GWAS for NPH and AD-associated NPEs. We will evaluate pleiotropy for variants with minor allele frequency (MAF) > 0.01 and Z-scores < 80 using the R package PLACO, which examines the composite null hypothesis that a given variant is associated with a maximum of one trait, such that rejecting this hypothesis implies that the variant influences both traits and is thus pleiotropic. For top-ranked loci containing genome-wide significant pleiotropic variants, we will differentiate horizontal pleiotropy from mediated pleiotropy by conducting Bayesian colocalization analysis using the R package COLOC. We also plan to conduct pathway enrichment analyses for top-ranked pleiotropic variants to identify pathways underlying biological mechanisms shared by IS, NPH, and AD.- Planned collaboration: There are no planned collaborations with researchers at other institutions for this study.Non-Technical Research Use Statement:Accumulating evidence indicates that ischemic stroke (IS) and normal pressure hydrocephalus (NPH) are known to share common risk factors and critical mechanisms with Alzheimer’s disease (AD). Although large-scale genome-wide association studies (GWAS) have identified many variants or loci associated solely with IS, NPH, and AD, only a few studies have investigated shared genetic factors or pathways underlying their biological mechanisms. This study aims to identify variants or loci that influence AD-associated neuropathology endophenotypes (NPEs) and IS or AD-associated NPEs and NPH simultaneously. Using GWAS summary statistics for each neurodegenerative disease, we will conduct genome-wide pleiotropy analyses, differentiate horizontal pleiotropy (direct genetic effect on AD or through mechanisms that bypass IS or NPH) from mediated pleiotropy (genetic effect on AD through IS or NPH), and investigate biological pathways implicated in shared mechanisms of IS, NPH, and AD. Our findings may provide insight into the genetic basis and underlying mechanisms shared by IS, NPH, and AD.
- Investigator:Wingo, ThomasInstitution:University of California DavisProject Title:Identifying Alzheimer's Disease Genetic Risk Factors By Integrated Genomic and Proteomic AnalysisDate of Approval:January 21, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Outcomes 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.Non-Technical Research Use Statement: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.
- Investigator:Zhao, ZhongmingInstitution:University of Texas Health Science Center at HoustonProject Title:AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learningDate of Approval:June 1, 2026Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Study 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.Analysis 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.Non-Technical Research Use Statement: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.