The ASPIRE Path in Molecular Medicine

My proposed thesis project will investigate celiac disease (CeD), a complex intestinal disorder that remains undiagnosed in the majority of patients. This will involve genome, transcriptome, and phenome wide association studies using BioVU as an initial data set. A parallel arm of the project will utilize machine learning to identify potentially undiagnosed CeD patients. Both approaches will then be applied to other biobanks for validation, such as All of Us and the UK Biobank. This data will be used to develop prediction algorithms with the goal of decreasing diagnostic delay and improving the understanding of CeD and associated comorbidities. Furthermore, the only “treatment” for CeD is a gluten-free diet, which can be difficult to maintain completely. Investigating the relationship between nutrition and CeD could provide insight into consequences of this restrictive diet and potentially identify avenues for treatment development. I believe that my project aligns with the APMM goals by leveraging existing data from human subjects to inform the diagnosis, understanding, and treatment of a chronic disease. By including data from various disciplines and perspectives, I think that biomedical research has the potential to be more effective and efficient in ways that benefit all involved.