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NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's Disease genetic and genomic knowledge

By Emily Greenfest-Allen, Conor Klamann, Prabhakaran Gangadharan, Amanda Kuzma, Yuk Yee Leung, Otto Valladares, Gerard Schellenberg, Christian J Stoeckert, Li-San Wang

Posted 25 Sep 2020
bioRxiv DOI: 10.1101/2020.09.23.310276

INTRODUCTION: The NIAGADS Alzheimer's Genomics Database (GenomicsDB) is an interactive knowledgebase for Alzheimer's disease (AD) genetics that provides access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD and related dementia (ADRD). METHODS: The website makes available >70 genome-wide summary statistics datasets from GWAS and genome sequencing analysis for AD/ADRD. Variants identified from these datasets are mapped to up-to-date variant and gene annotations from a variety of resources and linked to functional genomics data. The database is powered by a big data optimized relational database and ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis and variant or gene report generation. RESULTS: Detailed variant reports provide tabular and interactive graphical summaries of known ADRD associations, as well as highlight variants flagged by the Alzheimer's Disease Sequencing Project (ADSP). Gene reports provide summaries of co-located ADRD risk-associated variants and have been expanded to include meta-analysis results from aggregate association tests performed by the ADSP allowing us to flag genes with genetic evidence for AD. DISCUSSION: The GenomicsDB makes available >150 million variant annotations, including ~30 million (5 million novel) variants identified as AD-relevant by ADSP, for browsing and real-time mining via the website. With a newly redesigned, efficient, search interface and comprehensive record pages linking summary statistics to variant and gene annotations, this resource makes these data both accessible and interpretable, establishing itself as valuable tool for AD research.

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