About
GenomicsDB
Back to topThe 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.
The GenomicsDB is a collaboration between the University of Pennsylvania's Computational Biology and Informatics Laboratory (CBIL) and NIAGADS.
Contact Us
Back to topContact us at genomicsdb@niagads.org to provide feedback or report an issue.
The NIAGADS GenomicsDB Team
Back to topChris 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 Cite
Back to topWhen 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.
An example acknowledgement statement is as follows:
The data used for the analyses described in this manuscript were obtained from the NIAGADS GenomicsDB on MM/DD/YY.
Funding
Back to topVersion
GenomicsDB
Back to top- Version: 3.3
- Release Date: Oct 12, 2018
- Release Notes:
- New NIAGADS GWAS Summary Statistics Datasets added: NG00052
- ADSP Annotation (NG00061) now available, including ADSP variants, QC filters, and ADSP variant tracks on the genome browser
- ADSP Case/Control Gene-level rare-variant aggregation AD-association tests (NG00065) added
- NEW Variant Annotations: ExAC allele frequencies, CADD deleterious, CATO transcription factor binding site overlap
- NEW Feature: Filter Variant results for ADSP variants: see it in action
Strategies-WDK
Back to topThe 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 Database
Back to topNIAGADS 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 Browser
Back to topThe 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
FAQ
What web browsers are supported?
Back to top- Mozilla Firefox
- Google Chrome
- Safari
- Microsoft Edge
Can I download raw summary-statistics data?
Back to topWhat are favorites?
Back to topWhat is the basket?
Back to topHow do I print or save a genome browser view?
Back to topResources
Reference Genome
Back to topNIAGADS Accessions
Back to topVariant Annotation
Back to topADSP Variant Annotation
Back to topGene Annotation
Back to topFunctional Genomics
Back to topOntologies
Back to topMethods
Variant Effect Prediction
Back to topVariant 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 Membership
Back to topGene 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 Significance
Back to topVariants 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 Regions
Back to topThe region 1000bp upstream of the gene transcription start site was used as a proxy for the gene promoter region.
Promoter Region TF Binding Sites
Back to topWe 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 Enhancers
Back to topWe used genomic co-location queries to find FANTOM5 identified expressed enhancer sites (tissue-independent) proximal to gene transcription start sites.
Functional and Pathway Enrichment Analysis
Back to topThe 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.
The test is implemented using the statistical function library packaged with the Scientific Computing Tools for Python (SciPy) toolkit.
Multiple hypothesis testing corrections were performed using the python package statsmodel.