The Wu Lab
- Statistical Models for Studying the Genetic Architecture of Complex Traits, supported by NSF
- Gene Discovery of Plant Growth and Development, supported by NSF
- Dynamic Modeling of Biological System, supported by NIH
- Genetic Mapping of Polyploids, supported by DOE/USDA
- SHARP project (asthma genetics), supported by NIH (through Dr. Chinchilli and Dr. Mauger)
- Other studies supported by Penn State College of Medicine and Penn State Cancer Institute, including statistical cancer genomics, family-based designs for case-control studies, genetic control of IBD diseases, functional GWAS of longitudinal traits, high-order epistatic interactions, and network modeling.
The research interest of our group is to understand the genetic architecture of complex quantitative traits important to modern agriculture, environmental quality, evolutionary biology, and biomedicine. We study fundamental genetic processes that underlie phenotypic variation and evolution across time and space scales and functional signals. The extraordinarily high complexity of such genetic processes has prompted and intrigued us to develop powerful experimental designs and statistical models for dissecting their underlying mechanisms with the aid of analytical tools from other disciplines, such as control theory and game theory.
In nature, no cell or organism can grow in isolation. Instead, they exist with other interacting members in a community or ecosystem. We have incorporated evolutionary game theory to model how such ecological interactions affect the phenotypic formation of any individual in a population. Mathematical equations have been established to quantify the independent growth of an individual (i.e., growth by assuming that it is in isolation) and its interactive growth with other conspecifics in the shared environment. Our group is interested in developing game-theoretic models for mapping ecological QTLs that mediate independent growth vs. interactive growth through competition and collaboration. These models are being generalized to ecological interactions that take place at a wide range of levels of organization from proteins and RNA to cells to complex organisms.
We have always enjoyed generating aggressive ideas for our genetic and genomic research. On one hand, we have developed new theory, designs, and methods simulated from the latest discoveries of quantitative genetics and biology, which are used for next-level hypothesis tests by other researchers. On the other hand, established theoretical ideas are further used to design new experiments and collect new experimental data from which to gain new insight into various genetic processes.
The Liu Lab
Statistical Genetics: We are interested in developing novel statistical methods and computational tools for analyzing large scale genomic datasets. We have developed methods for analyzing rare variant association studies using sequence data, approaches for integrating multiple omics datasets, as well as efficient tools for large scale data analysis. Our methods and tools are being actively applied in hundreds of genetic studies worldwide.
Addition Genetics: We are actively pursuing a better understanding on the genetic basis for nicotine addiction. To do so, we seek to aggregate very large datasets on tobacco use phenotypes (in collaboration with the GSCAN consortium), integrate phenotypes of nicotine metabolites, smoking topography and tobacco use (in collaboration with TCORS at Penn State), and develop powerful and scalable methods that enable these analyses.
Functional Biology of X-inactivation We aim to understand the genetic regulation of X-inactivation using integrative genomics approach and apply these methods to study lupus genetics.