My lab uses computational approaches to study the genetics of human diseases, including cancer. A primary focus of our research is to develop novel tools for mapping risk genes of complex diseases from genome wide association studies (GWAS), sequencing studies or somatic mutations in the case of cancer. These tools are often been used in close collaboration with experimental biologists. A key feature of our strategy is the integration of multiple genomic datasets, such as transcriptome data, epigenetic data, and biological networks. This integrated approach could combine signals in different datasets to increase the power of studies, and shed light on the mechanism connecting genetic changes to phenotypes.

We are also interested in computational questions in regulatory genomics. How do cis-regulatory sequences interpret the information in cellular environments to drive spatial-temporal gene expression patterns? How do variations of regulatory sequences shape phenotypic variation and evolution? We believe a better understanding of these questions will also help the study of human genetics, specifically by improving our ability to interpret variations in non-coding sequences.