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Presentation 1: Using Protein Quantitative Trait Loci and Polygenic Risk Scores to Identify Novel Therapeutic Targets in Giant Cell Arteritis.
By: Natalie Chaddock
Abstract:This work focuses on the identification of novel therapeutic biomarkers for giant cell arteritis (GCA), a polygenic, medium- and large-vessel vasculitis that occurs in individuals aged > 50 years. Glucocorticoid monotherapy remains the principle treatment for GCA, but toxicity and unpleasant side effects experienced in many on long courses of treatment suggest that new, more targeted therapies are required. Evidence has demonstrated that genetic data can improve success rates of drug approval from clinical trials, but little is currently known about the genetic basis of this complex disease. Here, publicly available summary statistics from protein GWAS’ and GCA case-control genetic data are repurposed using a polygenic risk scoring (PRS) approach to identify novel biomarkers for this disease. The causality of these biomarkers are then assessed through the use of Mendelian randomization, highlighting potential therapeutic targets for future drug repurposing efforts.
Presentation 2: Tools for Reproducible Research.
By: Alex Coleman
Abstract: Reproducibility is a critical part of research but often we don’t start our research projects with ensuring reproducibility in mind (we want to get straight to the research!). Thankfully, there’s a large array of useful tools and techniques for ensuring our work is reproducible which can help super charge our research projects and ensure we have lasting and impactful outcomes. The Research Computing Team at Leeds is here to help researchers implements these tools so come along and hear more!
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