Description

The first release, 2018.07.30, includes Compressed Sequence Alignment files (CRAMs) mapped to GRCh38 and GATK-called gVCFs from the ADSP and ADNI studies. These data were called by the Genome Center for Alzheimer’s Disease (GCAD) using VCPA 1.0, a functionally equivalent CCDG/TOPMed pipeline. GCAD processed a total of 4789 whole genomes, including, 876 ADSP Family Discovery and Discovery Extension samples, 3104 ADSP Case Control Extension samples, and 809 ADNI samples. The second data release, 2018.09.17, includes the ADSP quality control checked GATK joint called VCF containing all 4789 whole genomes.

Sample Summary per Data Type

WGS CRAMsWGS gVCFsGATK Called Genotypes
ADSP Discovery
snd10000
n = 580n = 580n = 580
ADSP Extension
snd10001
n = 3400n = 3400n = 3400
ADNI-WGS-1
snd10002
n = 809n = 809n = 809

Available Filesets

NameAccessionVersion/DateDescription/What’s New
ADSP/ADNI WGS Project Level VCFNA2018.09.17ADSP quality control checked GATK joint called VCF containing 4789 whole-genomes.
ADSP Discovery WGS CRAMs/GATK gVCFsnd10000VCPA1.0/2018.07.30Mapped to GRCh38
ADSP Extension WGS CRAMs/ GATK gVCFssnd10001VCPA1.0/2018.07.30Mapped to GRCh38
ADNI-WGS-1 CRAMs/ GATK gVCFssnd10002VCPA1.0/2018.07.30Mapped to GRCh38
ADSP/ADNI QC MetricsNAVCPA1.0/2018.07.30Sequencing Data Quality Control Metrics
ADSP/ADNI Phenotypes/Pedigreesdnd000012018.07.30Phenotypes and Pedigree structures for all whole-genome sequenced subjects
Sample SetAccession NumberNumber of Subjects
ADSP Discoverysnd10000574
ADSP Extensionsnd100013367
ADNI-WGS-1snd10002809
Consent LevelNumber of Subjects
DS-ADRDAGE-IRB-PUB214
DS-ADRD-IRB-PUB98
DS-ADRDMEM-IRB-PUB-NPU20
DS-AGEADLT-IRB-PUB173
DS-AGEADLT-IRB-PUB-NPU77
DS-AGEBRMEM-IRB-PUB-GSO7
DS-DEMND-IRB-PUB 186
DS-DEMND-IRB-PUB-NPU91
DS-ND-IRB-PUB61
DS-ND-IRB-PUB-MDS 4
DS-ND-IRB-PUB-NPU64
DS-NEURO-IRB-PUB168
DS-NEURO-IRB-PUB-NPU1
GRU-IRB-PUB3076
GRU-IRB-PUB-NPU36
HMB-IRB-PUB250
HMB-IRB-PUB-GSO102
HMB-IRB-PUB-NPU 122

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

  • Investigator:
    Ebbert, Mark
    Institution:
    Mayo Clinic
    Project Title:
    Resolving mutations in challenging genomic regions to test association with disease phenotypes
    Date of Approval:
    September 21, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    A majority of the human genome has been well characterized through the initial Human Genome Project and numerous large-scale sequencing studies such as the 1000 Genomes Project, Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer’s Disease Sequencing Project, and others. There are, however, many genome regions that are challenging to characterize using standard approaches that are important to human health and disease. We intend to (1) develop and test new methods to characterize mutations in these regions, and (2) test associations between these mutations and disease phenotypes. Data from the ADSP may be combined with other datasets, such as the Alzheimer's Disease Neuroimaging Initiative. All appropriate precautions will be taken to verify proper population stratification and eliminate any sample redundancy. Combining these data will not increase risk to participants, as all individual-level data will remain confidential. We may also use portions of the ADSP data as controls for other diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), though only in situations that do not violate genetic or data-use principles. Specifically, data that where participants consented for use only within Alzheimer's disease studies will not be used for any purpose outside Alzheimer's disease research.
    Non-Technical Research Use Statement:
    Many regions of the human genome present challenges that prohibit scientists from discovering potential disease-causing mutations. We are developing methods to characterize mutations in these regions to identify new genes involved in disease.
  • Investigator:
    Farrer, Lindsay
    Institution:
    Boston University
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    October 29, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As part of the Consortium for Alzheimers Sequence Analysis (CASA: NIA grant UF1-AG047133), we plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data will be generated by the National Human Genome Institute Large-Scale Sequence Program. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa versa. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Goate, Alison
    Institution:
    Icahn School of Medicine at Mount Sinai
    Project Title:
    Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes
    Date of Approval:
    September 17, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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:
    Haines, Jonathan
    Institution:
    Case Western Reserve University
    Project Title:
    Alzheimer Disease Sequence Analysis Collaborative (a.k.a. Collaborative Alzheimer Disease REsearch; CADRE)
    Date of Approval:
    October 22, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer disease (AD) and elderly normal controls. The goal of the planned analyses is to identify genes and other functional elements that have variations that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome data. We will also evaluate similar sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements) detected using both whole genome and whole exome data. All data will be analyzed separately and in an integrated fashion and will incorporate additional genetic and functional data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these two groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Mayeux, Richard
    Institution:
    Columbia University
    Project Title:
    Alzheimer's Disease Sequencing Project
    Date of Approval:
    November 16, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    For this study, we will analyze data from whole genome sequencing (WGS) of from the Alzheimer's Disease Sequencing Project (ADSP) discovery-replication phase families and independent case control data from ADSP extension study. We will also analyze WGS and whole exome sequencing (WES) data from the Alzheimer's Disease Neuroimaging (ADNI) study and the ADSP follow-up study (ADSP-FUS) as they become available. The overall goal of this project is to identify and annotate causal variants related to LOAD using sequencing data generated from families multiply affected by the disease and validate the results in independent case-control datasets. Using families as discovery and unrelated individuals as replication and having the ability to genotype additional family members can provide direct evidence of causality by establishing which variants co-segregate in families and are associated in the general population with disease.
    Non-Technical Research Use Statement:
    Analyses of whole genome, whole exome and targeted resequencing will continue to provide important new information regarding potential risk conferring genes, biochemical pathways involved in Alzheimer's disease and targets that may be suitable for pharmacological manipulation. While whole exome and targeted sequencing are powerful technologies, analysis of whole genomes will provide more information and allow discovery of rare, high risk variants.
  • Investigator:
    Pericak-Vance, Margaret
    Institution:
    University of Miami
    Project Title:
    Collaboration on Alzheimer Disease Research
    Date of Approval:
    November 5, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa veras. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Saykin, Andrew
    Institution:
    Indiana University School of Medicine
    Project Title:
    Alzheimer's Disease Genomics: Systems Biology and Endophenotypes
    Date of Approval:
    November 15, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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:
    Schellenberg, Gerard
    Institution:
    University of Pennsylvania
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    August 23, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As part of the Consortium for Alzheimers Sequence Analysis (CASA: NIA grant U19AG047133). We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data will be generated by the National Human Genome Institute Large-Scale Sequence Program. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa veras. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Wang, Li-San
    Institution:
    University of Pennsylvania
    Project Title:
    ADSP Data Processing
    Date of Approval:
    August 23, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    NIAGADS is the data coordinating center for ADSP. This request will allow us to access genotype and phenotype data for all ADSP samples and perform data processing and quality assurance, before distributing to the scientific community. Currently a data deposition plan is being developed by ADSP: 1. Plans for aggregating phenotype, GWAS, and exome chip genotype data are in place, and NIAGADS will work with data contributors to organize and review files before data are distributed to study investigators. 2. As suggested by dbGaP, NIAGADS will work with dbGaP/SRA and the three NHGRI large-scale sequencing centers to develop a plan for reviewing incoming sequencing data. This will be done in parallel with basic quality assurance procedures by dbGaP/SRA before data are promoted to archival status and ready for analysis. NIA is in discussion with other similar whole-genome and whole-exome sequencing projects. We plan to harmonize these additional datasets with the ADSP WGS/WES data so the community can combine these datasets for analysis. All associated phenotypes are minimized and there is minimal risk to the participants.
    Non-Technical Research Use Statement:
    NIAGADS is the data coordinating center for ADSP. This request will allow us to access genotype and phenotype data for all ADSP samples and perform data processing and quality assurance, before distributing to the scientific community.

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 ADSP data:

Please cite/reference the use of NIAGADS data by including the accession NG00067. The acknowledgment statement to use from the ADSP can be found here.

For investigators using ADNI data:

Please cite/reference the use of NIAGADS data by including the accession NG00066. The acknowledgement statement to use from ADNI can be found here.

  1. Nafikov RA. Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP. Genet Epidemiol. 2018 Jun. doi: 10.1002/gepi.22133. PubMed link
  2. Naj AC. Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer’s disease sequencing project. Genomics. 2018 May. pii: S0888-7543(18)30281-7. PubMed link
  3. Butkiewicz M. Functional Annotation of genomic variants in studies of Late-Onset Alzheimer’s Disease. Bioinformatics. 2018 Mar; doi: 10.1093/bioinformatics/bty177. PubMed link
  4. Vardarajan BN. Whole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer’s disease. Ann Clin Transl Neurol. 2018 Mar; 5(4): 406-417. PubMed link
  5. Blue EE. Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer’s Disease Sequencing Project. Dement Geriatr Cogn Disord. 2018 Feb; 45(1-2): 1-17. PubMed link
  6. Beecham GW. The Alzheimer’s Disease Sequencing Project: Study design and sample selection. Neurol Genet. 2017 Oct; 3(5): e194. PubMed link