Legacy Dataset Migration to DSS
Datasets housed in the original NIAGADS database will be moving to the Data Sharing Service (DSS) web portal.
DSS is a FISMA compliant service that follows the GDS policy for file sharing and is hosted via Amazon Web Services (AWS). This allows for an improved user experience through a streamlined application, management, and access process.
Impacted Datasets
See a full list of impacted datasets and their migration status below:
Accession Number | Title | Location |
|---|---|---|
NG00122 | Prediction of Psychosis in Alzheimer Disease Summary Statistics - DeMichele-Sweet, et. al. 2021 | NIAGADS |
NG00115 | Similar Genetic Architecture of Alzheimer’s Disease and Differential APOE Effect Between Sexes- Wang et al. 2021 | NIAGADS |
NG00112 | A novel age-informed approach for genetic association analysis in Alzheimer’s disease summary statistics - Guen et al. 2021 | NIAGADS |
NG00111 | Genome-wide association identifies the first risk loci for psychosis in Alzheimer disease | NIAGADS |
NG00110 | Exome-wide age-of-onset analysis reveals exonic variants in ERN1 and SPPL2C associated with Alzheimer's disease | NIAGADS |
NG00109 | Genetic architecture of subcortical brain structures in 38,851 individuals summary statistics- Satizabal et al. 2019 | NIAGADS |
NG00102 | Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders | DSS |
NG00100 | Novel risk loci and pathways associated with Alzheimer disease in African Americans: A GWAS and meta-analysis summary statistics- Kunkle et al. (2020) | NIAGADS |
NG00099 | Results of gene - based weighted burden analyses using SCOREASSOC and GENEVARASSOC and multivariate analyses of variants near APOE applied to the ADSP Discovery Case - Control Based Extension Study. | NIAGADS |
NG00098 | Case of CBD for determining Cryo-EM structure of 4R tau | NIAGADS |
NG00097 | TARCC GWAS | NIAGADS |
NG00096 | MTC GWAS | NIAGADS |
NG00095 | ROSMAP2 GWAS | NIAGADS |
NG00093 | WHICAP GWAS | NIAGADS |
NG00091 | Results of gene-based weighted burden analyses using SCOREASSOC and GENEVARASSOC applied to the ADSP discovery sample | NIAGADS |
NG00089 | CSF TREM2 Summary Statistics | NIAGADS |
NG00088 | GWAS Summary Statistics of informed conditioning analysis in African Americans- Mez et al. (2017) | NIAGADS |
NG00087 | WashU2 GWAS | NIAGADS |
NG00086 | NACC Polygenic Hazard Score | NIAGADS |
NG00085 | ExomeChip - WashU | NIAGADS |
NG00084 | Immune-related genetic enrichment in FTD summary statistics- Broce et al. 2018 | NIAGADS |
NG00083 | Circular RNAs in Alzheimer Disease Brains - RNA-seq Data | NIAGADS |
NG00082 | UAB/HudsonAlpha Families with Neurodegenerative Diseases | NIAGADS |
NG00081 | CHOP Exome Chip | NIAGADS |
NG00080 | Miami Exome Chip | NIAGADS |
NG00079 | Northshore Exome Chip | NIAGADS |
NG00078 | IGAP APOE-Stratified Analysis Summary Statistics- Jun et al. (2015) | NIAGADS |
NG00077 | Genetic analyses of patients with CTE | NIAGADS |
NG00076 | ADGC case-control summary statistics on 7050 samples not included in the IGAP-2013 discovery stage- Hu et al. (2019) | NIAGADS |
NG00075 | IGAP Rare Variant Summary Statistics- Kunkle et al. (2019) | NIAGADS |
NG00074 | Multi-cohort study of endo-lysosomal system genetics and dementia | NIAGADS |
NG00073 | Genome-wide summary statistics for cognitively defined late-onset Alzheimer’s disease subgroups | NIAGADS |
NG00072 | NLTCS (The National Long Term Care Survey) SNP DATA I | NIAGADS |
NG00071 | ADC7 - Alzheimer Disease Center Dataset 7 | DSS |
NG00070 | ADC6 - Alzheimer Disease Center Dataset 6 | DSS |
NG00069 | ADC5 - Alzheimer Disease Center Dataset 5 | DSS |
NG00068 | ADC4 - Alzheimer Disease Center Dataset 4 | DSS |
NG00065 | ADSP Discovery Case/Control Association Results | NIAGADS |
NG00063 | Prediction of Psychosis in Alzheimer Disease | NIAGADS |
NG00062 | Episodic Memory Trajectories (EMTs) of 13,037 elderly | NIAGADS |
NG00061 | Functional Annotation of genomic variants in studies of LOAD | NIAGADS |
NG00059 | Aging, Dementia, and TBI Study | NIAGADS |
NG00058 | Summary Statistics of IGAP Age at onset survival GWAS dataset - Huang KL et al. (2017) | NIAGADS |
NG00057 | Laser Capture and RNA sequencing of Microglia in human brain - Mastroeni et al.(2017) | NIAGADS |
NG00056 | Transethnic GWAS Summary Statistics - Jun et al. (2017) | NIAGADS |
NG00055 | CSF Aβ/ptau Summary Statistics - Deming Y et al. (2017) | NIAGADS |
NG00053 | IGAP Summary Statistics, ADGC subset- Lambert et al. (2013) | NIAGADS |
NG00052 | CLU, A potential endophenotype for AD: Summary Statistics- Deming et al. (2016) | NIAGADS |
NG00051 | SORL1 coding variants and risk for AD | NIAGADS |
NG00050 | GWAS of CLU, A potential endophenotype for Alzheimer's disease | NIAGADS |
NG00049 | CSF Summary Statistics- Cruchaga et al. (2013) | NIAGADS |
NG00048 | ADGC Age at Onset Summary Statistics- Naj et al. (2014) | NIAGADS |
NG00047 | Indianapolis African American GWAS | NIAGADS |
NG00045 | Progressive Supranuclear Palsy (PSP) Summary Statistics- Hoglinger et al. (2011) | NIAGADS |
NG00043 | MAYO GWAS | NIAGADS |
NG00042 | Miami, Vanderbilt, and Medical School of Mount Sinai (UMVUMSSM) GWAS | NIAGADS |
NG00041 | ADGC Neuropath Summary Stats and Phenotypes- Beecham et al. (2014) | NIAGADS |
NG00040 | Multi-Ethnic Exome Array Study of AD, FTD, and PSP | NIAGADS |
NG00039 | ADGC African American Summary Statistics- Reitz et al. (2013) | NIAGADS |
NG00038 | Expression Levels from AD Case-Control Study | NIAGADS |
NG00037 | Progressive Supranuclear Palsy (PSP) GWAS | NIAGADS |
NG00036 | IGAP Summary Statistics- Lambert et al. (2013) | NIAGADS |
NG00035 | GWAS of CSF tau levels identifies risk variants for AD | NIAGADS |
NG00034 | ACT and Genetic Differences GWAS | NIAGADS |
NG00033 | Identifying Rare Variants That Increase Risk For Alzheimer's Disease | NIAGADS |
NG00032 | NIA-LOAD (ADGC subset) GWAS | NIAGADS |
NG00031 | MIRAGE Caucasian GWAS | NIAGADS |
NG00030 | WashU1 GWAS | NIAGADS |
NG00029 | ROSMAP GWAS | NIAGADS |
NG00028 | TGEN II GWAS | NIAGADS |
NG00027 | ADGC Summary Statistics- Naj et al. (2011) | NIAGADS |
NG00026 | University of Pittsburgh GWAS | NIAGADS |
NG00025 | eGWAS Mayo | NIAGADS |
NG00024 | ADC3- Alzheimer Disease Center Dataset 3 | DSS |
NG00023 | ADC2- Alzheimer Disease Center Dataset 2 | DSS |
NG00022 | ADC1- Alzheimer Disease Center Dataset 1 | DSS |
NG00020 | NIA-LOAD GWAS | NIAGADS |
NG00017 | OHSU GWAS | NIAGADS |
NG00015 | Genetics Consortium for Late Onset of Alzheimers | NIAGADS |
NG00010 | Caribbean Hispanic AD Study | NIAGADS |
Accessing Impacted Datasets in the DSS
If you currently have an approved data access request in the old system that includes datasets that have not yet been moved over to the new repository, please be advised that you will need to maintain this request until all relevant datasets are migrated.
Existing DSS DAR, Research Use Applies
If you have an active DSS DAR and the research use of that DAR applies to the migrated dataset, you may revise your DAR to include the migrated dataset. Please reference the “Editing an Application” section of the https://niagads.scrollhelp.site/support/pi-application-user-guide#PIApplicationUserGuide-EditinganApplication for instructions on how to revise a DAR.
No Active DSS DAR or Have an Existing DSS DAR Where Research Use Doesn’t Apply
If you do not have an active DSS DAR or the research use of an existing DSS DAR does not apply to the migrated dataset, please submit a new DAR for the migrated datasets in the DSS. Please visit https://niagads.scrollhelp.site/support/application-instructions to find information about submitting a DAR for DSS datasets.
Frequently Asked Questions
Can I still access NGXXXXX in the NIAGADS database if it has been migrated to the DSS database?
No, once datasets are available in the DSS, they can no longer be accessed through the original NIAGADS database. To access these datasets, you must submit a new Data Access Request for that dataset in the DSS or modify an existing DAR (if the research use is applicable to the dataset) in the DSS.
How can I tell if I have access to an impacted dataset?
Log into your account at Log in or create an account | NIAGADS and view your DAR in the old system. Here you can see the datasets you have access to within your DAR.
If you have any issues determining which datasets you have access to in the old NIAGADS system, please email niagads@pennmedicine.upenn.edu with your DAR ID number for assistance.
How can I tell if a dataset has been moved?
If you have approved access to the dataset in the old system, you will receive an email notifying you that the dataset has been moved to the DSS.
Additionally, the table above will be updated continuously as datasets are migrated and a blog post will be released when datasets are moved on the DSS and NIAGADS sites in the news section.
What is the anticipated timing to complete the move?
Currently the move is tentatively anticipated to be complete at the end of 2024.
Do I have to keep my DAR active in the old NIAGADS system?
Only if you have access to datasets in the old system that have yet to be migrated to the DSS.