Project 466752

Advances in genetics have increased our understanding of hereditary diseases and their pathologies. New sequencing technology has allowed for the study of different aspects of the human genome, providing researchers with a large range of tools to dissect the underpinnings of disease onset. Despite great successes in research and treatment of diseases governed by one or few genes, there are still many obstacles to overcome in the study of complex diseases such as Type-2 diabetes, which are influenced by many genes. Representing 90 percent of diabetes cases in Canada, and with nearly 500 new diagnoses every day, understanding the genetic causes behind Type-2 diabetes is crucial to finding an effective treatment for the disease. To address this challenge, researchers have developed approaches that can integrate different types of sequencing data to provide more detailed information on the genome. Although these approaches have enabled researchers to uncover novel mechanisms in disease onset, they do not integrate data at the resolution of individual cells (more commonly referred to as ;single cell data), which provides more pertinent information in tissues and diseases that are composed of many cell types, such as the pancreas for Type-2 diabetes. Existing methods also have limited accuracy in identifying specific gene regulatory elements of interest when building the gene networks involved in disease progression. In this project, we aim to expand upon past methods to incorporate single cell data and use more powerful computational methods to identify specific elements of interest that influence disease onset. Our work will provide a better representation of the complex gene networks that are perturbated in Type-2 diabetes, improving drug discovery and disease prevention.