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This GWAS dataset, ADC10, is the tenth set of ADC genotyped subjects used by the Alzheimer’s Disease Genetics Consortium (ADGC) to identify genes associated with an increased risk of developing Alzheimer’s disease. Provided here, are the PLINK genotype files that have undergone ADGC quality control procedures, imputation files (.bgen format) run through the TOPMED r2 pipeline, as well as basic phenotypes as provided by the Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC) derived from data provided by the National Alzheimer’s Coordinating Center (NACC). Additional phenotypic data are available for request through NACC. The ADSP-PHC has harmonized additional domains for the NACC participants and for more information about available data and where to request access, visit the ADSP data page (ng00067).

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
ADGC ADC Round 10snd10067Genotype SNP Array3,744

Available Filesets

NameAccessionLatest ReleaseDescription
ADGC ADC Round 10: GWAS, TOPMed Imputation, Phenotype filesfsa000082NG00138v.1GWAS, TOPMed Imputation, Phenotype files

View the File Manifest for a full list of files released in this dataset.

The ADC10 sample set was genotyped by the Center for Applied Genomics at the Children's Hospital of Philadelphia using the Illumina Infinium Global Screening Array (GSAMD-24v1-0_20011747_A1) BeadChip which captures genotype data on 700,078 genomic SNPs. The standard Alzheimer's Disease Genetics Consortium (ADGC) quality control pipeline (Naj et al. 2011) was applied to this GWAS dataset.

Sample SetAccession NumberNumber of Subjects
ADGC ADC Round 10snd100673,744
Consent LevelNumber of Subjects

Visit the Data Use Limitations page for definitions of the consent levels above.

Total number of approved DARs: 2
  • Investigator:
    Hohman, Timothy
    Vanderbilt University Medical Center
    Project Title:
    Genetic Drivers of Resilience to Alzheimer's Disease
    Date of Approval:
    April 11, 2024
    Request status:
    Research use statements:
    Show statements
    Technical 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 harmonized 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:
    Ma, Da
    Wake Forest University School of Medicine
    Project Title:
    Neuroimage Genomic analysis for Alzheimer's Subphenotypes
    Date of Approval:
    April 11, 2024
    Request status:
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective The objective of the proposed study is to establish the connection between Alzheimer’s Disease-related genomic markers and neuroimaging phenotypes and their association with the clinical onset of dementia. We hypothesize that a) genomic factors are associated with diverse Alzheimer’s Disease-related neuropathological and clinical progression patterns; and b) the genotype-phenotype interaction is dynamic along the Alzheimer’s Disease progression trajectory, which in turn regulates the clinical progression of dementia.Study design We plan to develop data-driven computational models using multi-modal imaging-genomics information, to test these hypotheses with the following two Specific Aims: (1) construct clinically relevant computational neuroimaging-genomic fingerprints to characterize distinctive subtypes of Alzheimer’s Disease neuropathological patterns, and (2) Construct clinically explainable subtype-aware AI models with effective genomic-neuroimaging information fusion to achieve accurate prediction of disease progression of Alzheimer’s Disease.Analysis plan I will construct and validate harmonized models by utilizing the available data from the Alzheimer's Disease Sequencing Project Phenotype Harmonization Consortium, which is a multi-institutional effort that harmonized phenotypical data of 22k participants collected from 31 AD-related cohorts to produce a large-scale, racially diverse, standardized set of clearly defined data.1. We will develop semi-supervised machine-learning-based classification frameworks to explore the complex genotype-phenotype associations that determine distinctive neuroimaging-based pathological progression patterns.2. We will also develop machine-learning model predictions of future AD-specific neuropathological biomarkers. More specifically, we aim to predict the progression of cortical Aβ levels for identifying pre-symptomatic subjects, and progression of tau levels for symptomatic subjects.
    Non-Technical Research Use Statement:
    Alzheimer’s Disease (AD) is a complex neurodegenerative disease with multiple variations of pathologies that affect the brain function, eventually leading to cognitive decline. Individual variations of our gene might be associated with different subtypes of the disease. Thus, it is important to explore the disease characteristics within the various AD subtypes to achieve personalized diagnosis and precision medicine, and eventually developing effective treatments for AD. The objective of this proposal is to study the connection between AD-related genomic markers and neuroimaging phenotypes and their association with the clinical onset of dementia.

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 NG00138.

For investigators using Alzheimer’s Disease Genetics Consortium (sa000003) data:

Use the following for use of any ADGC generated data:

The Alzheimer’s Disease Genetics Consortium (ADGC) supported sample preparation, sequencing and data processing through NIA grant U01AG032984. Sequencing data generation and harmonization is supported by the Genome Center for Alzheimer’s Disease, U54AG052427, and data sharing is supported by NIAGADS, U24AG041689. Samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886) awarded by the National Institute on Aging (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.

See below for additional dataset specific acknowledgments:

For use with GWAS Datasets ADC1-15:

The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). NACC phenotypes were provided by the ADSP Phenotype Harmonization Consortium (ADSP-PHC), funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716).

For use with the ADGC_AA_WES (snd10003) data:

NIH grants supported enrollment and data collection for the individual studies including: GenerAAtions R01AG20688 (PI M. Daniele Fallin, PhD); Miami/Duke R01 AG027944, R01 AG028786 (PI Margaret A. Pericak-Vance, PhD); NC A&T P20 MD000546, R01 AG28786-01A1 (PI Goldie S. Byrd, PhD); Case Western (PI Jonathan L. Haines, PhD); MIRAGE R01 AG009029 (PI Lindsay A. Farrer, PhD); ROS P30AG10161, R01AG15819, R01AG30146, TGen (PI David A. Bennett, MD); MAP R01AG17917, R01AG15819, TGen (PI David A. Bennett, MD); MARS R01AG022018 (PI Lisa L. Barnes).[CL1] [KA2] The NACC database is funded by NIA/NIH Grant U24 AG072122. 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), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P30 AG062421-01 (PI Bradley Hyman, MD, PhD), P30 AG062422-01 (PI Ronald Petersen, 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 Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).

For use with the ADGC-TARCC-WGS (snd10030) data:

This study was made possible by the Texas Alzheimer’s Research and Care Consortium (TARCC) funded by the state of Texas through the Texas Council on Alzheimer’s Disease and Related Disorders and the Darrell K Royal Texas Alzheimer’s Initiative.

Bellenguez C., et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature genetics. 2022 Apr. doi:10.1038/s41588-022-01024-z. PubMed link