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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: 2
  • 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:
    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