Steven Schrodi

My research synthesizes disease gene mapping approaches and new statistical and computational methods with the aim of identifying variants that underlie immune system and metabolic pathologies. The ultimate goals of my research are to develop widely applicable genetic and biomarker statistical methods, gain an understanding of the molecular pathogenesis of complex diseases and provide results useful for predicting disease susceptibility. Our diabetes research has focused on two areas: 1) the discovery of novel genetic mechanisms driving susceptibility to type 1 and type 2 diabetes through using genome-wide scans for loss-of-function effects and shared inherited chromosomal segments in related individuals with diabetes; and 2) the early prediction of metabolic dysfunction using machine learning approaches that combine genetics, metabolite signatures, metabolic and inflammatory proteins.