Colloquium: Mining Human Genomes for Genetic Interactions Underlying Disease
Abstract: The recent availability of genome sequences has enabled genome-wide association studies, which attempt to link specific genetic variants to disease. While these studies have produced a number of new candidate genetic loci, most still fail to explain the large majority of heritability associated with common diseases. One explanation is the presence of genetic interactions, or instances where multiple variants combine to cause disease. I will describe our recent efforts to develop computational methods to address this problem. Our work leverages a decade of experiments and data mining of the yeast model organism, where genome editing on a massive scale has been possible for many years. I will describe our efforts to translate insights about genetic interactions from this model system to develop new approaches for interpreting human genomes.
Bio: Chad Myers received his Ph.D. from the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics at Princeton University in 2007. He is currently an Associate Professor in the Department of Computer Science and Engineering at the University of Minnesota. Dr. Myers’s research focuses on computational methods for analysis and interpretation of large-scale genetic interaction networks and methods for integration of diverse genomic data to predict gene function or infer biological networks. His lab is developing approaches for analyzing and leveraging interaction networks to answer biological questions in a variety of systems from yeast to humans.