The Alzheimer’s Genomics Database is a user-friendly AD-centric interactive knowledgebase and genome browser, which provides unrestricted access to GWAS summary statistics from the NIAGADS repository, as well as to annotated variants called from ongoing the Alzheimer’s Disease Sequencing Project’s (ADSP) joint-genotype calling efforts. 

We sat down with Dr. Emily Greenfest-Allen, the architect of the Alzheimer’s Genomics Database, to learn more about the tool and how it can help accelerate ADRD research.

Tell us a little bit about the challenge the Alzheimer’s Genomics Database tries to address?

Access to genome-wide association study (GWAS) results in NIAGADS, as with most human genetic datasets, is restricted and can only be obtained by making a formal data access request.  On the other hand, most of these datasets are paired with publicly available summary statistics that summarize the GWAS results across the individuals in the study.  Although access to these summary data is unrestricted, their utility can be limited as a lot of specialized bioinformatics expertise is required to transform and annotate these data so they are useful for research. Furthermore, even though large collections of standardized GWAS summary statistics are available elsewhere, they contain few AD datasets and are disease agnostic, so they cannot help researchers explore data in an AD-relevant context.

The Alzheimer’s Genomics Database uses NIAGADS’s extensive collection of publicly available GWAS summary statistics (>60 datasets and growing) to establish an interactive knowledgebase for AD-genetics. We do the standardization and harmonization steps, so researchers don’t have to.  We then annotated the harmonized data and created tools for interactively exploring the data in a broader, AD-relevant, genomic context.  This makes this genetic data accessible to AD-researchers of all backgrounds, not just bioinformaticians.

What types of tools does the Alzheimer’s Genomics Database have for researchers?

With the Alzheimer’s Genomics Database, you can search for a gene or variant of interest or take an in-depth look at a specific GWAS summary statistics dataset.  Researchers can explore detailed reports for more than 250 million annotated variants called by the ADSP that summarize both genetic evidence for AD-risk from NIAGADS datasets and third-party genomic annotations, with quick links to related resources.  Information in these reports is compiled into interactive tables that can be filtered by the statistics and annotations, allowing real-time data mining.

The Genomics Database also hosts the NIAGADS Genome Browser, which allows researchers to visually inspect any of the GWAS summary statistics tracks in gene regions or other loci of interest. Users can customize the display of these tracks to:

  • Emphasize annotations
    • Compare summary statistics results
    • Compare tracks against annotated ADSP and dbSNP variant tracks
    • Compare tracks against a selection of functional genomics tracks pulled from FILER, NIAGADS’s harmonized functional genomics repository

How do you hope the Alzheimer’s Genomics Database will impact ADRD research?

It would be great if the Alzheimer’s Genomics Database became the go-to place for AD-researchers when they want to learn more about a gene or variant in the context of Alzheimer’s Disease.  Since we also link out to other resources, it is an ideal place to start when your primary concern is AD-relevance and then connect to other disease-agnostic gene or variant databases (e.g., NCBI Gene, UniProtKB, dbSNP) via our links.

It can also become a first stop for researchers considering making a data access request for GWAS data hosted at NIAGADS.   By interactively browsing the associated summary statistics dataset, researchers can see if there is something going on in their region of interest before requesting full access to the data and processing a large-scale genetic dataset that might not end up being statistically informative in that region or relevant to their research question.

Visit the NIAGADS Alzheimer’s Genomics Database to explore all it has to offer.

To learn more about this resource:

For a quick introduction, check out the NIAGADS YouTube page for video tutorials.

For in depth information about the design and all the knowledgebase has to offer, check out our newly published paper:
Greenfest-Allen E, Valladares O, Kuksa PP, et al.  NIAGADS Alzheimer’s GenomicsDB: A resource for exploring Alzheimer’s disease genetic and genomic knowledge.  Alzheimers Dement 2024; 20(2):1123-1136.  PMID:37881831 // doi: 10.1002/alz.13509

You can also come visit us at our booth, 1329D, at AAIC 2024 in Philadelphia to get a live-demo and chat with NIAGADS staff about how you can use the NIAGADS Alzheimer’s Genomics Database and other NIAGADS Open Access resources to support your research.

To provide feedback about your experience:

Feel free to email us at help@niagads.org, using the subject line “GenomicsDB”, with questions or suggestions.

Check out the papers that utilized the Alzheimer’s Genomics Database as part of their research process below:

Zhang J, Pandey M, Awe A, et al. The association of GNB5 with Alzheimer disease revealed by genomic analysis restricted to variants impacting gene function.  Am J Hum Genet. 2024; 111(3):473-486. PMID:38354736 // doi:10.1016/j.ajhg.2024.01.005

Kuksa PP, Liu CL, Fu W, et al. Alzheimer’s Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer’s Disease. J Alzheimers Dis. 2022;86(1):461-477. PMID:35068457 // doi:10.3233/JAD-215055

Kuksa PP, Greenfest-Allen E, Cifello J, et al. Scalable approaches for functional analyses of whole-genome sequencing non-coding variants. Hum Mol Genet. 2022;31(R1):R62-R72. PMID:35943817 // doi:10.1093/hmg/ddac191

Blue EE, Thornton TA, Kooperberg C, et al. Non-coding variants in MYH11, FZD3, and SORCS3 are associated with dementia in women. Alzheimers Dement. 2021;17(2):215-225. PMID:32966694// doi:10.1002/alz.12181

Fang J, Pieper AA, Nussinov R, et al. Harnessing endophenotypes and network medicine for Alzheimer’s drug repurposing. Med Res Rev. 2020;40(6):2386-2426. PMID:32656864 // doi:10.1002/med.21709

Butkiewicz M, Blue EE, Leung YY, et al. Functional annotation of genomic variants in studies of late-onset Alzheimer’s disease. Bioinformatics. 2018;34(16):2724-2731. PMID:29590295 // doi:10.1093/bioinformatics/bty177