NIA GENETICS OF ALZHEIMER’S DISEASE DATA STORAGE SITE (NIAGADS): ALZHEIMER’S GENOMICS DATABASE – 2019 UPDATE

Abstract

The Alzheimer's Genomics Database (https://ww.niagads.org/genomics) provides public access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD. This resource makes available 54 comprehensive summary statistics datasets from 14 GWAS studies for real-time mining with a flexible search interface and exploration via detailed reports and a genome browser. The GenomicsDB is powered by a big-data optimized relational database system. It uses OBO Foundry ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis. AD-risk associated variants from GWAS datasets are mapped to up-to-date annotations from dbSNP, ExAC, and similar resources. Variants are also mapped against the GRCh37.p13 assembly and linked to proximal and colocated genes and regulatory regions from functional genomics data repositories (e.g., ENCODE, Roadmap, FANTOM5). The Alzheimer's GenomicsDB efficiently compiles detailed variant and gene reports to assist researchers in interpreting their potential functional or regulatory role in the context of AD neurodegeneration. Updated variant reports highlight those discovered and QC'd by the Alzheimer's Disease Sequencing Project (ADSP) and include ranked predicted variant consequences generated with the ADSP's Annotation Pipeline (Butkiewicz et al. 2018). Other new features include linkage disequilibrium associations, which can be explored via interactive LocusZoom plots. Gene reports have also been updated and now include meta-analysis results from aggregate association tests performed by the ADSP (Bis et al. 2018), allowing us to flag those with genetic-evidence for AD. The system powering the GenomicsDB allows researchers to not only mine, but also analyze and annotate search results or uploaded data. In addition to functional and pathway enrichment analyses of gene lists, researchers can now run INFERNO (Amlie-Wolf et al. 2018) to compare variant lists against transcription factor binding from ENCODE, histone modification sites from Roadmap, FANTOM5 expressed enhancer regions, and GTex eQTL sites to identify potential regulatory elements, tissue contexts, and target genes. Providing access to genetic evidence for AD, the publicly available and user-friendly Alzheimer's GenomicsDB is a rich resource for the AD research community.