Dr. Robert Sladek, MD
Associate Professor, Department of Human Genetics
Associate Professor, Department of Medicine, Division of Experimental Medicine
MD, University of Toronto, 1985
Clinical fellowship (Endocrinology), University of Toronto, 1990
Rob Sladek is Associate Professor of Human Genetics and Medicine (Endocrinology) at the 苹果淫院 and G茅nome Qu茅bec Innovation Centre. He completed undergraduate degrees in Engineering Science and Medicine and a clinical fellowship in Endocrinology, all at the University of Toronto. His postdoctoral training explored the role of the Estrogen-related receptors in the regulation of fat metabolism (Err-alpha) and trophoblast formation (Err-beta); as well as and the application of high-throughput genomics technologies to study complex traits. He leads the Diabetes Gene Discovery Group 鈥� a project to identify risk loci for Type 2 diabetes that was sponsored by Genome Canada and G茅nome Qu茅bec. Rob's current research centers on developing and applying new technologies to study gene transcription networks and protein function in living cells in order to understand how genetic risk loci influencing T2D risk or the host response to tumors exert their effects.
Research in the Sladek lab focuses on learning how genetic mutations cause diabetes and other complex diseases. To do this, his group has developed new approaches to identify genetic changes across the whole human genome that are associated with Type 2 Diabetes and also to identify the effects of genetic variation on RNA splicing and gene activation in mouse strains and human populations. Current projects in the lab center on developing new experimental techniques to learn how genetic changes within individual genes and proteins can impair cell metabolism and cause diabetes. Reflecting the increasing impact of diabetes and obesity on global health, much of the group's research in the genetics of diabetes takes place as part of research teams that involve scientists in America, Europe and Asia.
1. Functional studies of type 2 diabetes risk loci identified by genome-wide association studies (using 'omics approaches, cell and mouse models).
2. Development of single cell assays to identify transcription factor networks implicated in metabolic diseases.
3. Genome-wide approaches to identify and characterize genetic variants that improve metabolic fitness in individuals and communities.
