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
- 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…
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 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).
Related Publications
Kunkle BW,. et al. Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis.JAMA Neurol.2021 Jan 1.doi: 10.1001/jamaneurol.2020.3536. Pubmed Link
Approved Users
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 18, 2025Request status:ApprovedResearch 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:Konermann, SilvanaInstitution:Arc instituteProject Title:Modeling Alzheimer’s disease risk and associated molecular phenotypesDate of Approval:August 8, 2025Request status:ApprovedResearch 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 Approval:March 25, 2025Request status:ApprovedResearch 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.Non-Technical Research Use Statement:We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two 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. The proposed research is feasible, promising and potentially significant to AD research. 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 Approval:August 12, 2025Request status:ApprovedResearch 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: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 Approval:August 12, 2025Request status:ApprovedResearch 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 Approval:July 16, 2025Request status:ApprovedResearch 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.