The primary goal of our research is the development of computational methods to detect genetic risk factors of common, complex traits in human populations.  As the field of human genetics increasingly accepts a complex model of phenotypic development that involving many genetic and environment factors, it is increasingly important to develop analytical strategies that incorporates this complexity.  Data collected from different physiological compartments that represent biological flux across time and space (such as genetic, proteomic, and environmental data) will need to be incorporated to gain a fuller understanding of the biological mechanism underlying complex phenotypes. Our research is focused on the development of methods to detect such complex predictive models in high-throughput genomic data.