Principal Investigator: Dr Han Chen
University of Texas (UT Health) USATags: 42646, Complex Traits, efficient algorithm, gene-environment interaction, genetic association studies, large-scale, robust inference
The aims of our proposed research project are to develop software programs that can be applied to gene-environment interaction studies in UK Biobank. Complex human diseases often have both genetic and environmental risk factors, and these environmental risk factors may have different effects on individuals with different genetic backgrounds. For example, although smoking is a known risk factor for many lung diseases, its effects are different for individuals carrying certain genetic mutations, and some smokers are more susceptible than the other smokers to lung diseases. Identifying such gene-environment interactions is important for understanding the etiology of complex human diseases. However, there are statistical and computational bottlenecks that have limited gene-environment interaction studies in large cohorts such as the UK Biobank. During the proposed 3-year research period, we will develop and implement efficient algorithms for conducting gene-environment interaction tests in up to millions of samples. We will demonstrate our method using data from the UK Biobank, and our analytical tools can be applied to analyses of various complex traits in the UK Biobank and beyond, facilitating gene-environment interaction research and improving public knowledge on risk factors of complex human diseases.