mechanisms for complex disease
Genome-wide association studies (GWAS) have identified thousands of genomic regions that correlate with disease risk. To date, most of the disease-associated variants fall in non-coding regions which makes interpretation challenging. Our lab develops computational tools that integrate GWAS with molecular phenotypes such as gene expression, alternatively-spliced isoforms, protein abundance, and chromatin markers to identify the mechanisms responsible for observed disease risk.
genetic architecture and natural selection
The landscape of the genetic variation that contributes to complex disease can be characterized by how the effect-size distribution changes along with allele frequencies. Natural selection has a large effect in shaping this landscape by coupling the magnitude of effects with selective pressure. Our lab aims to describe this landscape with fast inference procedures using genome-wide association studies and sequencing data.