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Description

This GWAS dataset, ADC9, is the ninth 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 9snd10066Genotyping SNP Array2,084

Available Filesets

NameAccessionLatest ReleaseDescription
ADGC ADC Round 9: GWAS, TOPMed Imputation, Phenotype filesfsa000081NG00137.v1GWAS, TOPMed Imputation, Phenotype files

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

The ADC9 sample set was genotyped by the Center for Applied Genomics at the Children's Hospital of Philadelphia using the Illumina Infinium 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 SubjectsNumber of Samples
ADGC ADC Round 9snd100662,0842,084
Consent LevelNumber of Subjects
DS-ADRDAGE-IRB-PUB265
DS-ADRD-IRB-PUB226
DS-ADRDMEM-IRB-PUB-NPU53
DS-ND-IRB-PUB135
DS-ND-IRB-PUB-MDS4
DS-ND-IRB-PUB-NPU12
DS-NEURO-IRB-PUB-NPU68
GRU-IRB-PUB578
GRU-IRB-PUB-NPU36
HMB-IRB-PUB605
HMB-IRB-PUB-MDS102

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

Total number of approved DARs: 7
  • Investigator:
    Cruchaga, Carlos
    Institution:
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    May 9, 2024
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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 studies
    Non-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:
    Greicius, Michael
    Institution:
    Stanford University School of Medicine
    Project Title:
    Examining Genetic Associations in Neurodegenerative Diseases
    Date of Approval:
    November 25, 2024
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.
    Non-Technical Research Use Statement:
    Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.
  • Investigator:
    Hohman, Timothy
    Institution:
    Vanderbilt University Medical Center
    Project Title:
    Genetic Drivers of Resilience to Alzheimer's Disease
    Date of Approval:
    October 31, 2024
    Request status:
    Approved
    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
    Institution:
    Wake Forest University School of Medicine
    Project Title:
    Neuroimage Genomic analysis for Alzheimer's Subphenotypes
    Date of Approval:
    May 8, 2024
    Request status:
    Approved
    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.
  • Investigator:
    Sinclair, Lindsey
    Institution:
    University of Bristol
    Project Title:
    Is depression a modifiable risk factor for Alzheimer's disease?
    Date of Approval:
    September 17, 2024
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    ObjectivesI aim to identify whether individuals with normal cognition and a high genetic risk of depression are at higher risk of developing atrophy in brain regions affected by Alzheimer’s disease than those at lower genetic risk.Study Design Depression was identified by the Lancet commission in 2017 as a potentially modifiable risk factor for Alzheimer’s disease. The extent to which this risk is actually modifiable is unknown, as are the mechanisms by which depression increases dementia risk. We wish to use polygenic risk scores (PRS) to look at whether someone’s total genetic risk for depression makes them more likely to have shrinkage of brain regions affected in AD and/or involved in mood over time. If individuals at higher genetic risk of depression have greater shrinkage of brain regions affected early in AD e.g. medial temporal lobe, this could help to explain part of the increase in risk. We will do this using NACC and ROSMAP data on people with normal cognition at the time that their MRI scans were performed.We will use logistic regression and mediation analyses to examine whether social contact may mediate some of the increased risk for dementia in those who are depressed. We would like to be able to include genetic risk for depression and AD in this analysis.Analysis Plan I will use data from individuals in NACC and ROSMAP with normal cognition at the time of their MRI. I will examine whether the PRS is related to the risk of AD by comparing mean regional brain volumes for those in the highest and lowest quartile of PRS-D. Included individuals will need to have completed ≥1 depression rating scales and ≥1 MRI. In NACC depression will be defined as GDS ≥8 and/or NPI-depression severity ≥2 & distress ≥3. In ROSMAP depression will be determined using DSM-IV diagnoses & CESD ≥4. The PRS for depression will be calculated using R or PLINK using data from the most recent depression GWAS. I plan to use lassosum2, ldpred2 and PRS clumping & thresholding and to include a smaller GWAS with better phenotyping (Wray et al 2018) in a secondary analysis. The PRS for AD will also be calculated and included as a co-variate in analyses
    Non-Technical Research Use Statement:
    The development of depression in mid to later life increases a person's risk of developing Alzheimer's disease. Although depression can be treated, at the moment it is not clear if this treatment may also prevent AD later in life. It is therefore important to know how depression itself increases this risk.We aim to find out1. What changes in the brains of people who are depressed? 2. Could these changes increase dementia risk?Methods1. We will use existing data for brain tissue from individuals without dementia. We will use this to find out which pathways differ in people with depression. We will study both areas involved in mood and areas affected early in AD. 2. We will examine whether genetic risk of depression is linked to brain shrinkage. 3. We will assess the relationship between depression and loneliness/social isolation and whether social isolation may be a mechanism by which depression increases risk.
  • Investigator:
    Sinclair, Lindsey
    Institution:
    University of Bristol
    Project Title:
    Understanding genetic contributions to neuropsychiatric symptoms in dementia
    Date of Approval:
    November 14, 2024
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As well as progressive cognitive decline, most patients with Alzheimer’s disease and other dementias experience non-cognitive symptoms during disease progression. Twelve non-cognitive symptoms are measured on the ‘Neuropsychiatric Inventory’ (NPI), including apathy, agitation, irritability, depression, anxiety, aberrant motor behaviours, disinhibition, euphoria, sleep disturbances, appetite changes, as well as delusions and hallucinations.Project aims:1. Derive a statistical model of which NPI items most commonly occur together to identify NPI symptom profiles.2. Perform a genome wide association analysis (GWAS) on patients with the identified NPI symptoms profiles in AD compared to all other AD patients.3. Upstream analysis using the GWAS output for the NPI symptom profiles will include: Identification of genes involved in specific biological pathways, heritability of the profiles, and the polygenetic crossover of these profiles with other neurological disorders and dementias.Study Design:GWAS meta-analysis. We already have genetic and phenotypic data from GERAD, BDR and the HUNT study.Analysis Principal component analysis, hierarchical clustering, and confirmatory factor analysis will be performed on the 12 item NPI to identify symptom profiles.Genome wide association analysis (GWAS) between AD cases with the identified NPI symptom profiles vs all other AD cases will be performed. The exact case-control separation is undecided (i.e. dichotomous or continuous for the severity of the symptoms). However, the GWAS will control for confounders including sex, population stratification, and either age or time since disease diagnosis depending on data availability. Meta-analysis will be performed using METAL. Since all the participants in the GWAS are AD cases, we will assess and adjust for collider bias, as appropriate. Upstream GWAS analysis will include: Assessing the enrichment of SNPs in genes or biological pathways using MAGMA. Polygenic risk scores to assess genetic overlap with the identified NPI profiles and other characteristics. Genome Complex Trait Analysis (GCTA) will estimate the heritability of the NPI symptom profiles.
    Non-Technical Research Use Statement:
    Dementia is characterized by progressive cognitive decline, but non memory symptoms are also present in most people living with dementia. These can include psychological symptoms such as apathy and psychosis, as well as behavioural changes such as disinhibition. For those living with dementia, these symptoms can cause significant distress and reduced quality of life, as well as a greater need to access healthcare services and an increased chance of requiring residential care. The medications used to treat these symptoms have significant side effects including increased stroke risk. These symptoms have largely been studied singularly, which does not reflect the experience of individuals with disease. The aim of this study is to investigate the genetics of Alzheimer’s Disease patients which present with the different subsets of non-cognitive symptoms which most commonly occur together. This approach has the potential to develop more tailored treatments for patients with dementia.
  • Investigator:
    Xiao, Peng
    Institution:
    University of Nebrask Medical Center
    Project Title:
    Uncovering the genetic basis of Alzheimer's Diseases by integrating GWAS with multiomics approaches across different ethnicities
    Date of Approval:
    August 9, 2024
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    ObjectivesIn this study we aim to understand the system level understanding of Alzheimer's disease (AD) by integrating GWAS with robust multiomics datasets across diverse ethnic groups and harmonization of the results to include associated genes and pathways to understand underlying disease mechanisms and to inform our understanding of biological continuum of the diseases. Investigating AD associated variants as quantitative trait loci for epigenetic, transcriptomic, and proteomic layers to explore how variants in genes perturb pathways leading to AD.Study designWe will comprehensively examine the genetic architecture of Alzheimer's diseases based on different races (Caucasians, Latinos, Asians and Africans) GWAS data from publicly available datasets. We request access to as many datasets available in NIAGADS and other repositories like EADB-consortium, IGAP and we also have requested access to datasets through our literature search from corresponding authors for Asian cohorts. We will perform meta-analysis at two levels for GWAS datasets and find genome wide significant loci (GWS). Mendelian randomization analyses will be adapted to multi-omics setting through analyzing QTLs and GWS. We will perform correlation and enrichment analysis for significant findings from different omics layers.Analysis PlanGenome wide meta-analysis across different races to identify new loci and functional pathways influencing AD. Find genes most likely to be responsible for association signal with AD at each loci by applying mendelian randomization (MR) method. We plan to use MR method to combine multiomics (GWAS, eqtl, mqtl, aqtl and pqtl).
    Non-Technical Research Use Statement:
    To our knowledge our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of AD pathogenesis and define catalogues of candidate genes. In addition, rare or population specific common variants can be identified thus genes with underlying genetic support for an association with AD are likely to encode successful drug targets in clinical development. This should further lead to patient stratification.

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 NG00137.

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.

Kunkle, B.W., et al. Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. JAMA neurology. 2021 Jan. doi:10.1001/jamaneurol.2020.3536. PubMed link

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