The Huang lab develops cutting-edge computational methods to tackle problems arising from the human genetics research. Despite that we often draw inspirations from our work on the genetics of inflammatory bowel diseases and psychiatric disorders, methods that we develop can be applied broadly to most human complex disorders and traits. The Huang lab developed the Bayesian fine-mapping method to resolve known genetic associations to variants with high causal probabilities (Huang et al., Nature, 2017) and extended this method to model the diversity in linkage disequilibrium across ancestries to more precisely isolate causal alleles (Lam and Chen et. al. Nature Genetics 2019). Working with the Ge lab at the Massachusetts General Hospital, we are developing methods to address critical challenges in studies using multiple ancestral populations, including methods to improve the polygenic risk prediction across ancestries, to further leverage genomic diversity to improve the resolution of fine-mapping, and to accurately characterize the cross-ancestry genetic correlations. Other method development activities in the Huang lab include methods for admixture populations and for rare variant association studies using large-scale sequencing data with population structure.