Proteogenomic analyses of tissue-specific networks to study human disease mediated by adipocytes (360G-Wellcome-224894_Z_21_Z)

Fat distribution has been closely linked with a variety of metabolic complications, cardiovascular disease and obesity. Thus, studying genes linked to fat distribution, could lead to important insights that would benefit our understanding of multiple diseases. I am interested in combining genetic studies and proteomic experiments to explore the role of protein-protein interactions in the context of this trait. I will use large-scale genetic studies to nominate genes/proteins involved in fat distribution and subsequently temporally characterize their protein-protein interaction partners throughout fat cell differentiation. Tracking the expression of risk genes and their interaction partners will allow us to build protein-protein interaction networks that represent biological circuits that are functionally important for disease etiology. To understand the consequences of perturbing these networks, I will then use UK-Biobank’s electronic health records to link mutations of individuals within networks to phenotypes. Finally, we will develop a model that incorporates network and individual-level genetic information to predict disease liability. Together, this work will link statistical genetics and proteomic analysis to enable a deeper understanding of the genetic architecture of complex diseases that is driven by fat cell biology.