[Recorded Webinar are Available Now!] dkNET Webinar "The Accelerating Medicines Partnership Common Metabolic Diseases Knowledge Portal: A Resource for Research into the Genetic and Genomic Basis of Diabetes and Cardiometabolic Disease"

*Watch recorded webinar here: https://youtu.be/0sLKeCDw06U

Join dkNET Webinar on Friday, November 18, 2022, 11 am - 12 pm PT

Presenter: MacKenzie Brandes, Project Manager, Broad Institute

Abstract

Common metabolic diseases are complex, as many genetic loci influencing disease risk have been identified to date. Discovering the genes and biological mechanisms through which these variants impact specific diseases requires the integration of multiple data types. We have created the Common Metabolic Diseases Knowledge Portal (CMDKP; hugeamp.org) to promote understanding and treatment of common metabolic diseases. The CMDKP aggregates and integrates multiple data types for 433 traits: full GWAS summary statistics from over 400 datasets, many of which are not available elsewhere; tissue-specific epigenomic annotations from over 5,500 datasets, via the Common Metabolic Diseases Genome Atlas (CMDGA; cmdga.org); manually curated credible sets; and multiple expert-generated effector gene lists. Bioinformatic methods are implemented at scale across these data: novel sample overlap-aware genetic association meta/analysis meta-analysis; predictions of variant effects (VEP and BASSETT), gene-level association analysis (MAGMA); credible set calculations; and annotation global enrichments (GREGOR and S-LDSC). The integrated results are accessible via an open-access web portal, which offers both forward and reverse genetics workflows, with results publicly available through direct download and programmatic APIs. In a forward genetics approach, researchers can query a trait or disease and see genome-wide variant- and gene-level genetic associations and tissue-specific epigenomic annotations that are globally enriched for those genetic associations. In a reverse genetics approach, the Region, Gene, and Variant pages distill and summarize results for a gene or variant of interest. Interactive tools allow researchers to perform custom, on-the-fly analyses, such as generating gene-level association scores based on protected individual-level data and assessing the weight of genetic evidence linking a gene to disease. The Variant Sifter visualizer allows users to explore, filter, and prioritize genetically associated variants, credible sets, and tissue-specific epigenomic annotations across a region, providing decision support for the prioritization of variants and genes for further research. We work directly with researchers to create community portals that showcase the results and methods deemed most valuable by each disease research community. In addition, we have created the Bring Your Own Results (BYOR) software service, which offers an auto-generated sortable data table, data filters, and visualizers, and allows individual researchers to easily and rapidly create web pages or portals to share results with reviewers, collaborators, or the public. Collaborations are welcome; please contact help@kp4cd.org.

The top 3 key questions that AMP-CMDKP can answer:

1. What genes are associated with chronic kidney disease?

2. Which tissues are most relevant for my disease or trait of interest?

3. What are the curated effector gene predictions for type 1 diabetes?

Dial-in Information:  https://uchealth.zoom.us/meeting/register/tZEqd-6gpjosE9YJ9zVw5NdUJNtCKGYh_G4c

Date/Time: Friday, November 18, 2022, 11 am - 12 pm PT

Upcoming webinars schedule: https://dknet.org/about/webinar