GenomicsDBBack to top
The NIAGADS Alzheimer's GenomicsDB is a searchable annotation resource that provides access to publicly available NIAGADS summary statistics datasets for Alzheimer's Disease (AD) and related neuropathologies. These data are linked to AD-revelant variant and gene annotations and functional genomics datasets, allowing AD researchers to easily identify and interpret interesting genomic regions via interactive search strategies and the NIAGADS genome browser.
Contact UsBack to top
Contact us at email@example.com to provide feedback or report an issue.
The NIAGADS GenomicsDB TeamBack to top
|Chris Stoeckert, Ph.D.||Principal Investigator (CBIL)|
|Li-San Wang, Ph.D||Principal Investigator (NIAGADS)|
|Amanda Kuzma||Project Manager (NIAGADS)|
|Emily Greenfest-Allen, Ph.D||Senior Research Investigator (CBIL)|
|Yuk Yee (Fanny) Leung, Ph.D||Research Assistant Professor (NIAGADS)|
|Otto Valladares||System Administrator (NIAGADS)|
|Prabhakaran Gangadharan||Bioinformatics Specialist (NIAGADS)|
How to CiteBack to top
When citing the NIAGADS resource, please use:
NIAGADS: The NIA Genetics of Alzheimer's Disease Data Storage Site. Alzhemeier's and Dementia, 12(11): 1200-1203.
The data in the NIAGADS GenomicsDB are provided by independent researchers. A full listing of the GWAS Summary Statistics datasets and other resources incorporated in the GenomicsDB is provided in the Resources section below. When using these data in a publication, please cite the data providers. Relevant manuscripts or data-use policies for each NIAGADS accession can be found by following the link associated with the accession.
The data used for the analyses described in this manuscript were obtained from the NIAGADS GenomicsDB on MM/DD/YY.
FundingBack to top
GenomicsDBBack to top
- Version: 3.2
- Release Date: April 12, 2018
- Release Notes:
- NEW Feature: Filter Variant search results by top predicted SnpEff Effect: see it in action
- Bug Fix: Stop including deprecated merged/refSNP IDs GWAS summary statistic search results.
Strategies-WDKBack to top
The NIAGADS GenomicsDB site is built using the Strategies-WDK system, a graphical search interface and web development kit for functional genomics databases.
- Version: 2.2-build-26
GUS DatabaseBack to top
NIAGADS Genomics is powered by the Genomics Unified Schema (GUS), a relational database system comprising a modular schema capturing sequence data, functional genomics data, rich descriptions of methodology and study design using ontologies, and network models. The NIAGADS Genomics site is using GUS v. 4.0 in PostgreSQL v.9.5.
- Version: 4.0
- PMID: 21705364
Genome BrowserBack to top
The NIAGADS GenomicsDB genome browser is built on the GMOD:JBrowse framework, and allows users to browse tracks generated from NIAGADS GWAS summary statistics datasets and compare against personal data tracks or a set of reposited tracks, each relevant to Alzheimer's Disease
- Version: 1.12.3
- PMID: 1957090
What web browsers are supported?Back to top
- Mozilla Firefox
- Google Chrome
- Microsoft Edge
Can I download raw summary-statistics data?Back to top
What are favorites?Back to top
What is the basket?Back to top
How do I print or save a genome browser view?Back to top
Reference GenomeBack to top
GWAS Summary StatisticsBack to top
Variant AnnotationBack to top
Gene AnnotationBack to top
Functional GenomicsBack to top
OntologiesBack to top
Variant Effect PredictionBack to top
Variant Effect Prediction performed by running snpEff version 4.3i (2016-12-15) with default options against all SNPs listed in dbSNP v. 147, with GRCh37.p13/hg19 as the reference genome.
Gene Pathway MembershipBack to top
Gene membership in pathways was determined by using custom scripts to parse KEGG Markup Languange (KGML) representations of pathway maps procured via the KEGG REST API and to map KEGG genes and orthologs to the Ensembl Gene reference.
Genome-wide SignificanceBack to top
Variants supported by a p-value ≤ 5 x 10-8 were identified as having genome-wide significance in a NIAGADS GWAS summary statistics dataset. For exome array studies, a cutoff of p-value ≤ 1 x 10-3 was used.
Gene Promoter RegionsBack to top
The region 1000bp upstream of the gene transcription start site was used as a proxy for the gene promoter region.
Promoter Region TF Binding SitesBack to top
We used genomic co-location queries to find ChIP-Seq sites for transcription factor binding (from selected brain-relevant ENCODE tracks; see Resources) overlapping or contained within gene promoter regions.
Promoter Region Expressed EnhancersBack to top
We used genomic co-location queries to find FANTOM5 identified expressed enhancer sites (tissue-independent) proximal to gene transcription start sites.
Functional and Pathway Enrichment AnalysisBack to top
The functional enrichment analysis tool uses a one-sided Fisher's Exact test to evaluate the enrichment of a GO term or pathway in a gene list.
Multiple hypothesis testing corrections were performed using the python package statsmodel.