The NIAGADS GenomicsDB is a searchable annotation resource that provides access to publicly available NIAGADS summary statistics datasets for Alzheimer's Disease and related neuropathologies. These data are curated along with 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 Pennslyania's Computational Biology and Informatics Laboratory (CBIL) [PI: Chris Stoeckert] and NIAGADS [PI: Li-San Wang].

Contact Us

Contact us to provide feedback or report an issue.

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

The data in the NIAGADS GenomicsDB is 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 each resource.



  • 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.
Previous Releases


The NIAGADS GenomicsDB site is built using the Strategies-WDK system, a graphical search interface and web development kit for functional genomics databases.

GUS Database

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.

Genome Browser

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


What web browsers does the NIAGADS GenomicsDB Support?

To make sure you have the best experience possible, we recommend using the most up-to-date version of the following browsers:
  • Mozilla Firefox
  • Google Chrome
  • Safari
  • Microsoft Edge

Can I download a NIAGADS GWAS Summary Statistics dataset?

Registered users may request access to full downloads of data reposited in the NIAGADS warehouse. To request access, follow the provided link (see listing below) for the NIAGADS accession associated with the dataset and follow the provided instructions.

What are favorites?

Favorites are bookmarks that allow users to flag and annotated records (Genes, SNPs, or regions) of interest. This feature is only available to registered users.

What is the basket?

The basket is a tool that allows users to select and group records (Genes, SNPs, or regions) from a result or a detail summary page for further analysis. This feature is only available to registered users.

How do I print or save a genome browser view?

To print or save a genome view for use in a presentation or publication, you will need to either take a screen snapshot, or use your browser's "Print" option to Print to a PDF file.

Why aren't the newest datasets listed on my search page?

Modern web browsers have long memories. Try cleaning you browser cache, including hosted app data.


GWAS Summary Statistics

Functional Genomics

Gene Annotation

Variant Annotation

Reference Genome



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.

Genome-wide Significance

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.

The test is implemented using the statistical function library packaged with the Scientific Computing Tools for Python (SciPy) toolkit.

Mutliple hypothesis testing corrections were performed using the python package statsmodel.