Approved research

Novel statistical methods for information-aggregative and causality-inferring data analysis in systems genetics

Lay summary

This project aims at developing and testing novel statistical strategies for analyzing big genetic data from a homeostatic perspective. Genetics has evolved into a new era; high-throughput data at various biological scales are now available for dissecting the genetic mechanism of complex traits. One major challenge in cutting-edge genetical studies is to utilize the big data from heterogenous sources in an incorporative way for improving statistical power and revealing causal genetic factors and biological pathways. Since many factors are involved in the process of genetics of complex traits, a systems perspective is better than the traditional data analysis of isolated inquiries. Through statistical innovation in global hypothesis testing, we propose novel strategies that allow flexible information aggregation, high-dimensional interaction study, and causal inference. They will incorporate heterogeneous data sources for detecting meaningful genetic signals that may be hard to be detected by itself. The project is expected to last for 36 months. The success of this project would provide scientists advanced new tools to better understand genetic etiology of complex human diseases, and to accelerate the translation of genetic studies to clinical diagnosis, intervention and prevention.