With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. For example, UK Biobank is a national and international health resource that collected whole-genome scale genetic data, thousands of complex traits and exposures from ICD billing codes, web surveys, and lab measurements on ∼500,000 individuals. Phenome-wide association studies (PheWAS) utilize large numbers of measured phenotypes and can explore the associations between one genetic variant and the entire phenome. Benefit from the large sample size and extensive traits in analysis, PheWAS in biobanks have the potential to discover novel associations for translational and clinical research, including to construct risk prediction models for complex diseases and phenotypes, to identify the causal effect of exposures and drugs, and to identify drug targets and repurposing.

Our researches focus on big data analysis, statistical genetics, and systems biology. 

1. Propose GWAS algorithm for complex trait including categorical phenotype, time-to-event data, longitudinal data, and imaging data.

2. Propose GWAS algorithm for admixture population and family-relatedness for complex trait.

3. Propose gene-based and region-based assocition approaches for complex trait.

4. Propose computational efficient and powerful gene-environment interaction algorithm for GWAS.