Overview
To access this data, please log into DSS and submit an application.
Within the application, add this dataset (accession NG00100) in the “Choose a Dataset” section.
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The p-value only files are available in the “Open Access Dataset” tab.
Description
A GWAS meta-analysis of 2,784 cases and 5,222 controls recruited from several case-control and family-based studies of African Americans was performed. A detailed description of the original cohorts and summary demographics of all samples included in this analysis are provided in the Supplementary Material of Kunkle et al. (Supplementary Note; Supplementary Tables 1-3). Imputation was performed with the African Genome Resources (AGR) panel. The final SNP set for analysis included 29,610,185 genotyped and imputed variants. Genotype dosages were analyzed within each dataset and subsequently meta-analyzed, adjusting for age, sex and PCs for population substructure (Model 1), and subsequently in addition for APOE genotype (Model 2). Additional details on these analyses and the methods for gene, pathway and expression association analyses can be found in the Supplementary Material of Kunkle et al.
Two datasets are provided.
The first one corresponds to the meta-analysis results obtained from model 1 (age, sex, and age adjusted) including genotyped and imputed data (African Genome Resources (AGR) panel of 2,784 Alzheimer’s disease cases and cases and 5,222 cognitively normal controls. The second one corresponds to the meta-analysis results of the same dataset and imputation, including adjustment for APOE in the model (model 2).
Each data file consists of information on SNP and its association to Alzheimer’s disease based on meta-analysis in the publication mentioned below. Although the individual datasets examined excluded any SNPs with call rates <95%, ADGC meta-analysis only analyzed SNPs either genotyped or successfully imputed in at least 30% of the AD cases and 30% of the control samples across all datasets. Please see the Supplementary methods for further details on quality control steps performed.
NOTE: The ADGC is releasing the summary results data from this analysis to enable other researchers to examine particular variants or loci for their evidence of association. We welcome your request with the provision that these summary data should not be used for research into the genetics of intelligence, education, social outcomes such as income, or potentially sensitive behavioral traits such as alcohol or drug addictions.
This dataset was originally published on the NIAGADS archive site on 08/20/2020 and was moved to DSS on 01/22/2025.
Available Filesets
| Name | Accession | Latest Release | Description |
|---|---|---|---|
| AD Risk Using African Genome Panel - Kunkle (2021); Full Summary Statistics (application needed) | fsa000120 | NG00100.v1 | Full Summary Statistics |
| AD Risk Using African Genome Panel - Kunkle(2021); P-values Only (open access) | fsa000119 | NG00100.v1 | P-values only |
View the File Manifest for a full list of files released in this dataset.
Related Studies
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A GWAS meta-analysis of 2,784 cases and 5,222 controls recruited from several case-control and family-based studies of African Americans was performed. A detailed description of the original cohorts and summary…
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 NG00100.
For investigators using Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. Kunkle et al. (2021)) (sa000066) data:
The Kunkle et al. ADGC African American GWAS meta-analysis was supported by NIH grants: RF1 AG054023, U24 AG056270, ADGC - U01 AG032984, NIA-LOAD – U24 AG026395, U24 AG026390 (PI Richard Mayeux, MD), NIAGADS – U24 AG041689 (PI Li-San Wang, PhD), WHICAP – R01 AG037212, R37 AG015473 (PI Richard Mayeux, MD), NCRAD - U24 AG021886 (PI Tatiana Foroud, PhD), Indianapolis AA – R01 AG009956, RC2 AG036650 (PI Kathleen Hall, PhD), ACT – U01 AG06781, U01 HG004610 (PI Eric Larson, MD, MPH), MIRAGE – R01 AG009029 (PI Lindsay Farrer, PhD), GenerAAtions – 5R01 AG20688 (PI M. Daniele Fallin, PhD), Pittsburg – P50 AG005133 (PI O. Lopez), AG030653, AG041718, AG064877 (PI M. Ilyas Kamboh, PhD), Case Western Reserve University – R01 AG019085 (PI Jonathan Haines, PhD), CHAP – R01 AG11101, R01 AG030146, RC2 AG036650 (PI Denis Evans, MD), ROS/MAP - P30 AG10161, R01 AG15819, R01 AG30146, R01 AG17917, R01 AG15819 (PI David Bennett, MD), African-American AD Genetics Study – R01 AG028786 (PI Jennifer Manly, PhD), MARS/CORE – R01 AG22018, P30 AG10161 (PI Lisa Barnes, PhD), Mayo – P50 AG0016574, R01 032990, KL2 RR024151 (PIs Ronald C. Petersen, MD, PhD, Nilufer Ertekin-Taner, MD, PhD and Neill Graff-Radford, MD), Miami – R01 AG027944, R01 AG028786 (PI Margaret Pericak-Vance, PhD), Wake Forest (PI Goldie Byrd, PhD), MSSM (PI Joseph Buxbaum, PhD) and MSSM – P50 AG05681, P01 AG03991, P01 AG026276 (PI Alison Goate). CR was further supported by NIH grants (RF1AG054080, U01AG052410, AG0087202). The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG062428-01 (PI James Leverenz, MD) P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), AG045058 (PI Thomas Obisesan, MD, MPH), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P30 AG062421-01 (PI Bradley Hyman, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P30 AG062429-01(PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P30 AG062715-01 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John C. Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).
Publications
- Kunkle BW. Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. JAMA neurology. 2021 Jan 1. PubMed link
Approved Users
- 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 Initial Approval:January 6, 2025Current Status:ApprovedPublications:Show publications (4)
- Cook N. Integrative Genetic, Proteogenomic, and Multi-omics Analyses Reveal Sex-Biased Causal Genes and Drug Targets in Alzheimer's Disease. medRxiv : the preprint server for health sciences. 2025 Nov 2. PubMed link
- Zeng Y. APOE*4 Risk-Modifying Genes and Drug Targets in Alzheimer's Disease through Cell-Type Specific Genomic Analyses. medRxiv : the preprint server for health sciences. 2025 Dec 4. PubMed link
- Lona-Durazo F. Sex-aware causal inference assessment of the immune system in complex neurodegenerative diseases. Brain : a journal of neurology. 2026 Aug 3. PubMed link
- Belloy ME. A quantitative trait locus for reduced microglial APOE expression associates with reduced cerebral amyloid angiopathy. Nature genetics. 2026 Feb. PubMed link
Research 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:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Initial Approval:January 23, 2019Current Status:ApprovedPublications:Show publications (10)
- Cruchaga C. Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2018 Feb. PubMed link
- Fernández MV. Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease. PLoS genetics. 2017 Nov. PubMed link
- Ridge PG. Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer's disease resilience. Genome medicine. 2017 Nov 29. PubMed link
- Fernández MV. Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease. Frontiers in neuroscience. 2018. PubMed link
- Olive C. Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes. Journal of Alzheimer's disease : JAD. 2020. PubMed link
- Kirola L. Lack of evidence supporting a role for DPP6 sequence variants in Alzheimer's disease in the European American population. Acta neuropathologica. 2021 Apr. PubMed link
- Moreno-Grau S. Long runs of homozygosity are associated with Alzheimer's disease. Translational psychiatry. 2021 Feb 24. PubMed link
- Cruchaga C. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease. Research square. 2023 Jun 9. PubMed link
- Wang L. Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease. Molecular neurodegeneration. 2024 Jan 3. PubMed link
- Patel M. Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis. Human molecular genetics. 2025 Apr 6. PubMed link
Research Use Statements:Show statementsTechnical Research Use Statement: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 studiesNon-Technical Research Use Statement: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. - Investigator:Engelman, CorinneInstitution:University of Wisconsin - MadisonProject Title:AD Risk PredictionDate of Initial Approval:February 26, 2021Current Status:ApprovedPublications:None ReportedResearch Use Statements:Show statementsTechnical Research Use Statement:Currently, no combined measure of rare (minor allele frequency [MAF] less than 0.5%), low frequency (MAF 0.5% to 5%), and common variant (MAF 5% or higher) risk of Alzheimer’s disease (AD) exists. Common variant polygenic risk scores (PRSs) have been a central approach to predicting genetic risk of AD, however, we know that rare and low frequency variants can account for the missing heritability in AD. One objective of this project is to determine the risk for AD based on variants across the full allele frequency spectrum. An additional objective of this project is to utilize GWAS summary statistics from multiple ancestries to calculate the PRS and AD risk. To accomplish these objectives, we will leverage sequencing data from the ADSP (our study is contributing 1,531 samples to the Follow Up phase) and summary statistics from published GWAS from different ancestries. To generate the common variant PRS, we will use these summary statistics and methods such as PRS-CSx. We will determine the carrier status for rare and low frequency AD risk alleles based on the literature and bioinformatics tools. Prediction of AD case-control status and age-at-onset for quantiles of the common variant PRS and carrier status will be characterized with an empirical receiver operating characteristic (ROC) curve. The statistical software R will be used to perform regression analyses and to evaluate the AUC.Non-Technical Research Use Statement:Currently, risk prediction for the later-onset form of Alzheimer’s disease (AD) focuses on genetic variants that are more common in the population, but ignores less common variants. Prediction is also largely based on data from populations of European ancestry. The goals of this project are to incorporate genetic variants across the full allele frequency spectrum (more and less common genetic variants) and to include more ancestrally diverse populations. To accomplish these goals, we will leverage genetic data from the ADSP and summary statistics (results) from published studies in diverse populations. We will determine the prediction of AD case-control status and age-at-onset.
- 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 Initial Approval:September 17, 2018Current Status:ApprovedPublications:Show publications (26)
- Chia R. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nature genetics. 2021 Mar. PubMed link
- Novikova G. Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nature communications. 2021 Mar 12. PubMed link
- Riaz M. Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population. Aging cell. 2021 Jun. PubMed link
- Wu HM. Heterogeneous effects of genetic risk for Alzheimer's disease on the phenome. Translational psychiatry. 2021 Jul 23. PubMed link
- Huq AJ. Polygenic score modifies risk for Alzheimer's disease in APOE ε4 homozygotes at phenotypic extremes. Alzheimer's & dementia (Amsterdam, Netherlands). 2021. PubMed link
- Kapoor M. Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. Nature communications. 2021 Aug 20. PubMed link
- McInerney TW. A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants. BMC bioinformatics. 2021 Sep 1. PubMed link
- Farrell K. Genome-wide association study and functional validation implicates JADE1 in tauopathy. Acta neuropathologica. 2022 Jan. PubMed link
- Horgusluoglu E. Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Jun. PubMed link
- Bowles KR. Dysregulated coordination of MAPT exon 2 and exon 10 splicing underlies different tau pathologies in PSP and AD. Acta neuropathologica. 2022 Feb. PubMed link
- Reyes-Dumeyer D. The National Institute on Aging Late-Onset Alzheimer's Disease Family Based Study: A resource for genetic discovery. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Oct. PubMed link
- Xue D. Large-scale sequencing studies expand the known genetic architecture of Alzheimer's disease. Alzheimer's & dementia (Amsterdam, Netherlands). 2021. PubMed link
- Bellenguez C. New insights into the genetic etiology of Alzheimer's disease and related dementias. Nature genetics. 2022 Apr. PubMed link
- Tcw J. Cholesterol and matrisome pathways dysregulated in astrocytes and microglia. Cell. 2022 Jun 23. PubMed link
- Podlesny-Drabiniok A. BHLHE40/41 regulate macrophage/microglia responses associated with Alzheimer's disease and other disorders of lipid-rich tissues. bioRxiv : the preprint server for biology. 2023 Feb 13. PubMed link
- Andrews SJ. The complex genetic architecture of Alzheimer's disease: novel insights and future directions. EBioMedicine. 2023 Apr. PubMed link
- Podleśny-Drabiniok A. BHLHE40/41 regulate microglia and peripheral macrophage responses associated with Alzheimer's disease and other disorders of lipid-rich tissues. Nature communications. 2024 Mar 6. PubMed link
- Farrell K. Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes. Nature communications. 2024 Sep 9. PubMed link
- Nguyen L. CASP8 intronic expansion identified by poly-glycine-arginine pathology increases Alzheimer's disease risk. Proceedings of the National Academy of Sciences of the United States of America. 2025 Feb 18. PubMed link
- Humphrey J. Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing. Nature genetics. 2025 Mar. PubMed link
- Wang D. Frequency of variants in Mendelian Alzheimer's disease genes within the Alzheimer's Disease Sequencing Project. Journal of Alzheimer's disease : JAD. 2025 Apr. PubMed link
- Phillips JM. Novel modelling approaches to elucidate the genetic architecture of resilience to Alzheimer's disease. Brain : a journal of neurology. 2025 Aug 1. PubMed link
- Cruchaga C. GWAS meta-analysis of CSF Alzheimer's disease biomarkers 18,948 individuals reveal novel loci and genes regulating lipid metabolism, brain volume and autophagy. Research square. 2025 May 21. PubMed link
- Nicolas A. Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations. Nature genetics. 2025 Jul. PubMed link
- Rajabli F. Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies known and novel cross-population and ancestry-specific associations as novel risk loci for Alzheimer's disease. Genome biology. 2025 Jul 17. PubMed link
- Chatterjee A. Evaluating the causal effect of mitochondrial dysfunction on Alzheimer's and Parkinson's disease using Polygenic Risk Scores and Mendelian Randomization. medRxiv : the preprint server for health sciences. 2025 Sep 27. PubMed link
Research 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:Kamboh, M. IlyasInstitution:University of PittsburghProject Title:Genetics of Alzheimer's Disease and EndophenotypesDate of Initial Approval:January 7, 2025Current Status:ApprovedPublications:None ReportedResearch 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:Kim, Jong HunInstitution:KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATIONProject Title: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 OutcomesDate of Initial Approval:July 20, 2026Current Status:ApprovedPublications:None ReportedResearch Use Statements:Show statementsTechnical Research Use Statement: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. Study 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. Multi-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. Analysis 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.Non-Technical Research Use Statement: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. Our 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. The 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.
- Investigator:Konermann, SilvanaInstitution:Arc instituteProject Title:Modeling Alzheimer’s disease risk and associated molecular phenotypesDate of Initial Approval:August 8, 2025Current Status:ExpiredPublications:None ReportedResearch Use Statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Initial Approval:December 18, 2019Current Status:ApprovedPublications:Show publications (3)
- Knutson KA. MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits. Human molecular genetics. 2023 Apr 6. PubMed link
- He R. Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease. PLoS genetics. 2025 Apr. PubMed link
- Ren J. Large-Scale Genotype-Based Trait Imputation With Multi-Ancestry GWAS Data. Genetic epidemiology. 2026 Feb. PubMed link
Research Use Statements:Show statementsTechnical Research Use Statement: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: Aim1. 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. Aim 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. Aim 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.Non-Technical Research Use Statement: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. - Investigator:Pathak, GitaInstitution:Institute for Genomic Health, Genetics and Genomic Sciences at Mount SinaiProject Title:Multi-modal analysis of psychiatric and dementia outcomesDate of Initial Approval:August 12, 2025Current Status:ApprovedPublications:None ReportedResearch Use Statements:Show statementsTechnical Research Use Statement:a. Objectives of the Proposed Research This study aims to investigate the relationship between psychiatric traits and age-related cognitive decline, addressing a critical knowledge gap in understanding how mental health influences aging outcomes. b. Study Design The study employs a multi-level investigative approach combining epidemiological, genetic, and molecular methodologies. The design incorporates three complementary components: first, identification of phenotypic associations between psychiatric traits and MCI/AD through comprehensive clinical assessment; second, investigation of genetic architecture through analysis of coding and non-coding variants, genetic correlation assessments, polygenic scoring, and Mendelian randomization for causal inference; and third, examination of molecular mechanisms through genetically regulated epigenetic and proteomic processes. The study design enables stratified analyses by sex and ethnicity while controlling for demographic and lifestyle confounders, providing a comprehensive framework for understanding the psychiatric-cognitive decline relationship across multiple biological levels. c. Analytical Plan The analytical approach will proceed in sequential phases, beginning with statistical modeling to identify psychiatric traits significantly associated with MCI and AD outcomes while adjusting for demographic and lifestyle factors. Genetic analyses will employ polygenic risk scores and Mendelian randomization techniques to establish causal relationships between psychiatric conditions (particularly depression and alcohol use disorder) and cognitive outcomes. Molecular analyses will focus on identifying shared genetic loci between psychiatric and cognitive phenotypes, followed by investigation of genetically regulated methylation and proteomic markers as potential mediators. The analysis plan includes development of molecular weights to aid causal inference analyses and determination of effect directionality, with stratified results reported by sex and ethnicity to identify population-specific risk patterns and potential intervention targets.Non-Technical Research Use Statement:This research examines how mental health conditions like depression and anxiety may increase the risk of memory problems and Alzheimer's disease as people age. Using genetic data and biological markers, we'll study whether psychiatric conditions directly cause cognitive decline or if they share common underlying causes. The study will identify which mental health factors pose the greatest risk for dementia, particularly looking at differences between men and women and various ethnic groups. Results could help better predict and prevent cognitive decline by addressing mental health early in life, potentially improving outcomes for millions facing both psychiatric and age-related brain conditions.
- Investigator:Saykin, AndrewInstitution:Indiana University School of MedicineProject Title:Alzheimer's Disease Genomics: Systems Biology and EndophenotypesDate of Initial Approval:November 15, 2018Current Status:ApprovedPublications:Show publications (142)
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- Patterson C. Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning. bioRxiv : the preprint server for biology. 2026 May 23. PubMed link
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Research Use Statements:Show statementsTechnical Research Use Statement:Alzheimer’s disease (AD) and related genomic data sets including sequencing, GWAS and phenotypic data will be combined with longitudinal clinical, demographic, cognitive, MRI, PET, CSF and blood endophenotype data, where available, to investigate the genetic architecture of Alzheimer’s disease and related disorders (ADRD) and brain aging. The overall goal to gain a better understanding of fundamental disease mechanisms, genetic susceptibility and protective factors, and the relationship of genetic factors to disease heterogeneity, progression and different trajectories across biomarker profiles. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) will be combined with ADSP and other data sets to increase detection power and for replication across samples. Analyses will include conventional statistical association, multivariate profiling of endophenotypes, biological pathway and network approaches, longitudinal models and combinatorial machine learning. Deliverables will include reports of new prioritized lists of candidate genes and variants for further investigation in new samples, functional experiments and in model systems. The ultimate goal is discovery of novel potential diagnostic markers and therapeutic targets that will help provide the foundation for a precision medicine approach to AD/ADRD.Non-Technical Research Use Statement:Alzheimer’s disease (AD) and related genomic data sets will be combined with longitudinal clinical, demographic, cognitive, MRI, PET, CSF and blood biomarker data to investigate the genetic architecture of Alzheimer’s disease and related disorders (ADRD) and brain aging. The overall goal to gain a better understanding of fundamental disease mechanisms, genetic susceptibility and protective factors, and the relationship of genetic factors to disease heterogeneity, progression and different trajectories across biomarker profiles. Data will be combined across studies to increase detection power and for replication. Analyses will include conventional statistical association and advanced analytic approaches including multivariate profiling, biological pathway and network analysis and machine learning. The ultimate goal is discovery of novel potential diagnostic and therapeutic markers that will help provide the foundation for a precision medicine approach to AD/ADRD. - Investigator:Seshadri, SudhaInstitution:Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TXProject Title:Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approachesDate of Initial Approval:October 14, 2020Current Status:ExpiredPublications:Show publications (6)
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- Horimoto ARVR. Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations. HGG advances. 2023 Jul 13. PubMed link
- Tin A. Identification of circulating proteins associated with general cognitive function among middle-aged and older adults. Communications biology. 2023 Nov 3. PubMed link
- Wang Y. Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 May. PubMed link
- Lee WP. Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Dec. PubMed link
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Research Use Statements:Show statementsTechnical Research Use Statement:Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 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: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 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. Aim 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. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses 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, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of MedicineNon-Technical Research Use Statement: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. - Investigator:Xavier, Rose MaryInstitution:UNC Chapel HillProject Title:Sleep Disturbance and Cognitive Function in Alzheimer’s Disease: The Shared Genetic BasisDate of Initial Approval:February 4, 2025Current Status:ClosedPublications:None ReportedResearch Use Statements:Show statementsTechnical Research Use Statement:Sleep disturbances (SD) are linked to cognitive function (CF) and Alzheimer’s disease (AD), but the genetic mechanisms, especially in non-European populations, are underexplored. With the goal to further the understanding of the genetic architecture of AD and promote the development of early prediction of AD, this research will use large-scale data to investigate the genetic basis underlying SD and CF in AD. Objectives 1. Identify unique and shared genetic basis of SD and CF in AD. 2. Examine the associations between genetic liability and measurement of SD and CF in AD progression. Study Design Objective 1 will be a genome wide association study (GWAS). Objective 2 will be a polygenic risk score (PRS) study. Analysis Plan We will combine the genotype data from NIAGADS with phenotype data from the NACC to conduct the proposed projects. Specifically, we propose to use the Alzheimer’s Disease Research Centers (ADRC) GWAS Datasets ADC1-15 to maximize the sample that matches with NACC phenotypes (SD, CF, and AD). Analyses will follow the established Ricopili protocol (https://sites.google.com/a/broadinstitute.org/ricopili/). First, we will conduct quality control (QC) checks on genotype data following standard protocols. Second, we will conduct data imputation where genetic variants were not genotypes using Michigan Imputation Server following standard protocols using the 1000 Genomes Phase 3 Reference data. For objective 1, we will conduct the phenotype-specific GWAS on SD, CF, and AD. We will then run cross-phenotype meta-analysis to examine the shared genetic basis of sleep disturbance and cognitive function in AD. For objective 2, we will construct PRSs for SD, CF and AD using PRS-CSx approach. Linear and logistic regressions will be used to examine the associations between PRS and measurements of SD and CF, and AD. Cox proportional hazard regression will be used to examine the association between SD PRS and longitudinal CF changes in AD progression. Outcomes, such as summary statistics of GWAS and constructed PRSs, will be submitted to NIAGADS.Non-Technical Research Use Statement:Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss and cognitive deterioration. It affects approximately 34 million people worldwide, yet reliable early prediction methods remain elusive. Prior research has implicated the impact of sleep disturbance on cognitive decline and AD pathophysiology. However, few studies have explored the genetic correlation between sleep disturbance and cognitive function in the context of AD, especially among non-European populations. To address these research gaps, the proposed research will employ bioinformatic and computational techniques to analyze largescale databases to further understand the unique and shared genetic variants that contribute to sleep disturbance and cognitive function in AD. This exploration of the genetic correlation between sleep disturbance and cognitive function in AD will inform future research to improve early detection of AD risk in individuals with pre-clinical symptoms and prediction of cognitive deterioration through AD development and progression.