Identify genetic causes of human diseases by integrative analysis of genetics variants with functional genomics rooted in biological mechanisms. To improve the ability to identify risk variants and genes, we develop new computational methods to integrate gene expression and epigenomic data with genetic data. These methods are usually based on machine learning models with heuristics developed from biological mechanisms, such as haploinsufficiency. We apply these methods in genetic studies of human diseases in collaboration with Dr. Wendy Chung's group. In the last few years, we have been focusing on developmental disorders, such as congenital diaphragmatic hernia, congenital heart disease, and autism.
Develop computational methods to identify and interpret genetic variations from genome sequencing data. Ongoing projects include detecting copy number variants and structural variants from exome or genome sequencing data, accurate estimation of genotype likelihood from sequencing data using deep learning, predicting genetic effect of missense or noncoding variants.
Coverage Tradeoffs and Power Estimation in the Design of Whole-Genome Sequencing Experiments for Detecting Association
A SNP discovery method to assess variant allele probability from next generation resequencing data
Computational analysis and mathematical modeling to understand dynamics of immune cells in human.
Analyzing T cell repertoire diversity by high-throughput sequencing