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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.

The GenomicsDB is a collaboration between the University of Pennsylvania's Computational Biology and Informatics Laboratory (CBIL) and NIAGADS.

Contact Us

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Contact us at genomicsdb@niagads.org to provide feedback or report an issue.

The NIAGADS GenomicsDB Team

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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)
Conor Klamann, Ph.D Web Developer (NIAGADS)
Yuk Yee (Fanny) Leung, Ph.D Research Assistant Professor (NIAGADS)
Otto Valladares System Administrator (NIAGADS)
Prabhakaran Gangadharan Bioinformatics Specialist (NIAGADS)

How to Cite

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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.

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.


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NIAGADS is funded by NIA U24-AG041689.



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  • Version: 3.4
  • Release Date: Feb 27, 2019
  • Release Notes:
      • New NIAGADS GWAS Summary Statistics Datasets added: NG00058
      • ADSP INDELs now available
      • ADSP Case/Control Gene-level single-variant AD-association tests (NG00065) added
      • NEW Feature: Gene link outs to UniProtKB
Previous Releases


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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 Database

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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.

Genome Browser

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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 are supported?

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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 raw summary-statistics data?

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Registered users may request access to full downloads of data reposited in the NIAGADS data repository. To request access, follow the provided link (see listing below) for the NIAGADS accession associated with the dataset, which will take you to an informational page about the accession in the NIAGADS repository. Follow the instructions on the accession page to download the data or request access to protected data.

What are favorites?

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

What is the basket?

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

How do I print or save a genome browser view?

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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.


Reference Genome

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NIAGADS Accessions

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Variant Annotation

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ADSP Variant Annotation

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Gene Annotation

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Functional Genomics

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Variant Effect Prediction

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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 Membership

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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 Significance

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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 Regions

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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

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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

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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

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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.

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