This application is being submitted in response to NOT-HG-20-030. We will use data-driven network and hierarchical modeling techniques enabled by Cytoscape ecosystem tools and resources to build models of SARS-CoV-2 infection and will use the models to propose mechanisms of variance in host response based on the analysis of population genetic and COVID-19 disease outcome data. These data-driven models will inform research in population risk stratification and potential COVID-19 therapies. Our models, analysis results, and toolset will be made available to the research community via a website and data repository. The models will be derived from molecular and genetic interaction data and will be used to analyze population data on SARS-CoV-2 infection, comorbidities, and clinical outcomes currently being collected by the UK BioBank. The analysis will be updated periodically with each release of data, maintaining an up-to-date resource for the research community. The modeling tools and pipelines will be user-friendly and well-documented, enabling researchers to build alternative models or to analyze other population data.
We will identify modules of genes significantly associated with variance in the clinical response to SARS-CoV-2 infection using hierarchical models of SARS-CoV-2 replication derived from protein and genetic interaction networks. The hierarchical modeling process will discover communities of interacting proteins relevant to SARS-CoV-2 infection and the association of those communities with clinical COVID-19 disease outcomes will be tested for significance using pathway-boosted GWAS analysis. Our models, analysis results, and toolset will be made available to the research community via a website and data repository to inform research in population risk stratification and potential COVID-19 therapies.
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