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Description

The 90+ Study was initiated in 2003 to study the oldest-old, the fastest growing age group in the United States. The 90+ Study is one of the largest studies of the oldest-old in the world. More than 1,600 people have enrolled. Initial participants in The 90+ Study were once members of The Leisure World Cohort Study (LWCS), which was started in 1981. The 90+ sample set was genotyped at the Children’s Hospital of Philadelphia using the Illumina Infinium GSAMD-24v2-0_20024620_A1 BeadChip which captures genotype data on 759,993 genomic SNPs. The standard Alzheimer’s Disease Genetics Consortium (ADGC) quality control pipeline (Naj et al. 2011) was applied to this GWAS dataset. The first release (February 4, 2021) includes quality controlled GWAS data on 268 participants from the 90+ cohort. It also includes minimal phenotypes provided by the 90+ Study and covariates provided by the Alzheimer’s Disease Genetics Consortium (ADGC). The final QC’d dataset contains 733,861 SNPs and 268 samples.

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
90+ GWAS GSA snd10026GWAS268

Available Filesets

NameAccessionLatest ReleaseDescription
90+ Studyfsa000019NG00104.v1GWAS Data, Phenotypes, etc.

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

The 90+ sample set was genotyped at the Children's Hospital of Philadelphia using the Illumina Infinium GSAMD-24v2-0_20024620_A1 BeadChip which captures genotype data on 759,993 genomic SNPs. The standard Alzheimer's Disease Genetics Consortium (ADGC) quality control pipeline (Naj et al. 2011) was applied to this GWAS dataset. The final QC'd dataset contains 733,861 SNPs and 268 samples.

Sample SetAccession NumberNumber of Subjects
90+ GWAS GSA snd10026268
Consent LevelNumber of Subjects
GRU-IRB-PUB268

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

Total number of approved DARs: 5
  • Investigator:
    Farrer, Lindsay
    Institution:
    Boston University
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    January 24, 2023
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As part of the Collaborative for Alzheimer's Disease genetics REsearch (CADRE: NIA grant U01-AG058654), 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:
    Pendergrass, Rion
    Institution:
    Genentech
    Project Title:
    Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studies
    Date of Approval:
    August 1, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The purpose of our study is to identify novel genetic factors associated with Alzheimer’s Disease, corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP). This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. We will also evaluate genetic associations with sub-phenotypes individuals have within these broad disease categories, such as their Braak staging results which provide insights into the level of severity of Alzheimer’s. Thus we are requesting access to the set of genomic Whole Exome and Whole Genome Sequences (WES and WGS) have just been released through the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (DSS NIAGADS). The findings from our genetic association testing have the potential for identification of new therapeutic targets for Alzheimer's Disease, CBD, and PSP. The findings from our studies also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways. We will use standard genetic epidemiological methods to handle the WGS and WES data. We will also analyze cell type-specific expression differences in AD to identify biomarkers and disease pathways using standard gene expression analysis methods currently in use. We will also use other multi-omic and other genetic data that has now become available to further understand genetic association results we have found in AD.All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the DSS NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.
    Non-Technical Research Use Statement:
    Genetic variation and gene expression data allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype, disease traits, gene expression, and outcomes, through these datasets will allow us to pursue important new findings for disease treatment.
  • Investigator:
    Pericak-Vance, Margaret
    Institution:
    University of Miami
    Project Title:
    Collaboration on Alzheimer Disease Research
    Date of Approval:
    November 23, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We plan to analyze GWAS, 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 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:
    Zhao, Jinying
    Institution:
    University of Florida
    Project Title:
    Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach
    Date of Approval:
    October 18, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.
    Non-Technical Research Use Statement:
    Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.
  • Investigator:
    Zhi, Degui
    Institution:
    University of Texas Health Science Center at Houston
    Project Title:
    Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
    Date of Approval:
    July 14, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alzheimer’s disease (AD) affects 5.6 million Americans over the age of 65 and exacts tremendous and increasing demands on patients, caregivers, and healthcare resources. Our current understanding of the biology and pathophysiology of AD is still limited, hindering advances in the development of therapeutic and preventive strategies. Existing genetic studies of AD have some success but these explain only a fraction of the overall disease risk, suggesting opportunities for additional discoveries. The proposed project will leverage existing neuroimaging and genetic data resources from the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and will be conducted by a multidisciplinary team of investigators. We will derive AD endophenotypes from neuroimaging data in the UK Biobank using deep learning (DL). We will identify novel genetic loci associated with DL-derived imaging endophenotypes and optimize the co-heritability of these endophenotypes with AD-related phenotypes using UK Biobank genetic data. We will leverage resources and collaborations with AD Consortia and the power of DL-derived neuroimaging endophenotypes to identify novel genes for Alzheimer’s Disease and AD-related traits. Also, we will develop DL-based neuroimaging harmonization and imputation methods and distribute implementation software to the research community. We expect to discover new genes relevant to AD which may leads to understanding of molecular basis of AD and potential new treatment.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) exacts a tremendous burden on patients, caregivers, and healthcare resources. Our current understanding of the biology of AD is still limited, hindering advances in the development of treatment and prevention. Existing genetic studies of AD have some success but more studies are needed. The proposed project will leverage existing neuroimaging and genetic data resources from the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP) and other consortia and will be conducted by a multidisciplinary team of investigators. We will derive new AD relevant intermediate phenotypes from neuroimaging data using deep learning (DL), an AI approach. We will identify novel genetic loci associated with these phenotypes. Also, we will develop imaging harmonization and imputation methods and distribute implementation software to the research community. We expect to discover new genes relevant to AD which may leads to understanding of molecular basis of AD and potential new treatment.

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 NG00104.

For investigators using (sa000020) data:

The 90+ Study receives support through a National Institute on Aging (NIA) grant R01AG21055. We thank the staff and investigators of the study as well as the participants and their families, whose help and participation made this work possible.

Quality control procedures and data preparation on the GWAS was conducted by the Alzheimer’s Disease Genetics Consortium (ADGC) (UO1AG032984) and the NIA Genetics of Alzheimer’s Disease Storage Site (NIAGADS) (U24-AG041689), both funded by NIA.  

  • Corrada MM, Brookmeyer R, Paganini-Hill A, Berlau D, Kawas CH. Dementia incidence continues to increase with age in the oldest-old: The 90+ Study. Ann Neurol. 2010 Jan. doi: 10.1002/ana.21915 PubMed link
  • Corrada MM, Berlau DJ, Kawas CH. A population-based clinicopathological study in the oldest-old: the 90+ study. Curr Alzheimer Res. 2012 Jul. doi: 10.2174/156720512801322537 PubMed link
  • Corrada MM, Paganini-Hill A, Berlau DJ, Kawas CH. Apolipoprotein E genotype, dementia, and mortality in the oldest old: The 90+ Study. Alzheimers Dement. 2013 Jan. doi: 10.1016/j.jalz.2011.12.004 PubMed link