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Approved research

Integrate summary level data from UK Biobank into the Knowledge Portal Network (KPN), an infrastructure that integrates, interprets, and presents genetic data to spark insights into complex diseases.

Principal Investigator: Dr Jason Flannick
Approved Research ID: 41189
Approval date: July 31st 2019

Lay summary

The Knowledge Portal Network (KPN) builds human genetic knowledge portals that focus on complex diseases, such as T2D, MI, and stroke. The knowledge portals are publicly available to anyone with a google login who is willing to abide by the terms of our data use. KPN proposes running genetic analysis on the UK Biobank data using best practices and making the analyses results publicly available to view in the context of other disease specific data sets. We are specifically focusing on T2D, MI, stroke, and related traits. UK Biobank results will be available on the following disease portals over time: T2D (, MI (, and stroke ( No individual level data will be made available through the knowledge portals. This project will last 3 years. We are proposing the following aims for our application: 1) Securely store, harmonize, perform QC, and single variant analysis on the T2D, MI, stroke, and related traits of interest using the UK Biobank resource. 2) Load the summary level results to the T2D, MI, and stroke portals from the precomputed analyses performed in aim 1. The results can be accessed by users to look at results from the analysis and view these results in the context of other disease specific datasets. 3) Work with the portal community to determine other phenotypes of interest and add them to the data in the knowledge portal. We aspire to assist researchers in their work to advance knowledge of complex diseases by using available human genetic datasets to guide their research. In addition to adding new datasets to the knowledge portals, we also are developing and implementing new tools based on feedback from our community of users. The tools are key for allowing portal users to explore their hypotheses in the context of other datasets for the disease of interest.