Using statistical modelling and analysis, this team aims to discover and characterize genes that influence susceptibility to disease and lead to a better understanding of how genes function, resulting in the development of new approaches to the diagnosis and treatment of common diseases.
Dr. Shelley Bull, University of Toronto
Complex traits, such as susceptibility to diabetes, cancer or tuberculosis, which vary in human and natural populations, are determined by multiple genetic and environmental factors that interact with one another in complicated ways. This interaction depends upon population characteristics as well as characteristics of the individual and the family. Using statistical modelling and analysis, this team aims to discover and characterize genes that influence susceptibility to disease and lead to a better understanding of how genes function, resulting in the development of new approaches to the diagnosis and treatment of common diseases. In the past year, new statistical tools were developed, including a new data mining tool designed to efficiently identify a complex combination of genetic markers that predict variation in quantitative traits such as blood pressure. The team also developed analytic tools for high dimensional microarrays, a technology that can measure thousands of markers simultaneously in a small sample, such as tumour tissue. This tool can detect changes in DNA associated with cancer. By applying these new statistical tools and others, the team has contributed to an understanding of susceptibility to type I diabetes and its complications, as well as other diseases such as rheumatoid and psoriatic arthritis, cystic fibrosis, breast cancer, and cardiovascular disease in Canadian and international populations.