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The NIAGADS GenomicsDB Team
|Chris Stoeckert, Ph.D.||Principal Investigator (CBIL)|
|Li-San Wang, Ph.D||Principal Invesigator (NIAGADS)|
|Amanda Partch||Project Manager (NIAGADS)|
|Emily Greenfest-Allen, Ph.D||Senior Research Investigator (CBIL)|
|Otto Valladares||System Administrator (NIAGADS)|
|Prabhakaran Gangadharan||Bioinformatics Specialist (NIAGADS)|
|Fanny Leung||Research Associate (NIAGADS)|
How to Cite
- Version: 2.1
- Release Date: 9/13/16
- Release Notes:
- New NIAGADS GWAS Summary Statistics datasets added: NG00045, NG00048, and NG00049.
- Search strategies illustrating example queries for exploring these and related datasets have also been added.
- p-values (and -log10 p-values) associated with specific variants can now be viewed on genome browser GWAS summary statistics tracks by mousing over the vertical bars on the track.
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
- PMID: 21705364
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.9.4.
- Version: 4.0
- PMID: 21705364
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: 12.0.1
- PMID: 1957090
What web browsers does the NIAGADS GenomicsDB Support?
- Mozilla Firefox
- Google Chrome
- Microsoft Edge
Can I download a NIAGADS GWAS Summary Statistics dataset?
What are favorites?
What is the basket?
How do I print or save a genome browser view?
Why aren't the newest datasets listed on my search page?
GWAS Summary Statistics
Variant Effect Prediction
Variant Effect Prediction performed by running snpEff version 4.2 (2015-12-05) with default options against all SNPs listed in dbSNP v. 142, with GRCh37.p13/hg19 as the reference genome.
Gene Pathway Membership
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 NCBI Gene reference.
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.
For some NIAGADS datasets, genome-wide significance was attached to variants identified by genomic coordinates (e.g., chr19:45412955) or exome probe identifiers (e.g., exm-rs769449). These were mapped to dbSNP rs ids, as indicated by the Mapped From field in relevant table reports.
Gene Promoter Regions
The region 1000bp upstream of the gene transcription start site was used as a proxy for the gene promoter region.
Promoter Region TF Binding Sites
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 Enhancers
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 Analysis
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.
Mutliple hypothesis testing corrections were performed using the python package statsmodel.