Modeling Genetic Complexity
Recent progress in genome-wide association studies promises to accelerate the development of personalized medicine by linking genetic variation with health outcomes. Dozens of alleles have been identified for a number of prevalent diseases, offering a useful tool for assessing individual health risk and providing clues to the biology that underlies the disease. We are developing computational methods to understand how trait genes combine to affect biological processes in model organisms. By studying the complexities of pleiotropy and genetic interaction, we hope to reveal networks of gene-to-gene information flow and gain the ability to predict the effects of novel allelic combinations. The ultimate goals of our research are to use genetic network models to better understand fundamental biology and devise methods that may someday be used to model the effect of genetic variation in populations.