Combining Multiple Types of Genomics Data to Understand Complex Human Traits

Recent availability of large-scale genomic datasets from human cohort studies present new opportunities for deriving molecular mechanisms for complex disease. However, a central challenge in using these genomics data to understand complex traits is to disentangle causal, and hence reproducible and meaningful associations, from merely correlated ones. In this talk, I’ll describe ongoing projects that develop new statistical and computational methods for integrative analysis of genomic data, with the ultimate goal of providing insights into complex human diseases.