With the availability of the high-through sequencing technology, the scientific community is now able to investigate complex phenotypes at both organismal and molecular levels. Nevertheless, it is still considerably difficult to perform controlled experiments and randomized trials to investigate the causal relationships between phenotypes at different levels. It is therefore critically important to perform causal inference based on the observational data. In this project, we will develop computational methods to facilitate systematic investigation of causal molecular mechanisms underlying complex disease process. Specifically, we will target three outstanding scientific issues: i) casual inference of molecular mechanisms of complex diseases; ii) analytic approaches for risk prediction utilizing genomic information and causal molecular mechanisms, and iii) statistical assessment of reproducibility in high-throughput genomic experiments. Finally, we will build user-friendly computational software packages and make them available to the broad community of biological and medical scientists.
Public health relevance: This is project will develop and apply computational tools for analyzing large-scale data from genomic and complex disease studies to gain understanding the molecular mechanisms of complex diseases, Additionally, this project will develop novel risk prediction models to advance preventive medicine for complex diseases. Finally, the project will develop computational tools to aid improving reproducibility of biomedical research. All three aspects are fundamental in complex disease studies, where the knowledge will eventually lead to new and reliable treatment strategies.
