American Statistical Association
It has been widely recognized that genes play a central role in the initiation and progression of biological traits. Genetic mapping with molecular markers has proven to be useful for the characterization of genes or quantitative trait loci (QTL) for complex traits, but its practical application may be limited in the situation where traits present a dynamic process. We have proposed a dynamic model, called functional mapping, for identifying temporal QTLs by organizing the developmental mechanisms for trait formation into a mapping framework. I will present a newly developed statistical model that incorporates a system of differential equations into functional mapping, aimed at testing and detecting specific QTLs that control key biological and dynamic pathways of trait formation. By manipulating such QTLs, trait development can be directed to maximize resource use efficiency and minimize adverse effects. A real example for biomass partitioning in soybeans was used to validate the usefulness of the new model. In this example, specific QTLs were detected to determine the global allocation rules for dynamic patterns of biomass partitioning into the stem, leaves and roots. I will discuss how to extend the new model to map complex dynamic systems with genome-wide association studies.
Dr. Wu's research focuses on the development of statistical and computational methods necessary to unravel the genetic machinery of complex traits, diseases, and life processes. He is particularly interested in integrating the idea and principle of systems biology into statistical genetic research, ultimately elucidating a comprehensive atlas of the genetic control network of complex biology.
He is sensitive to concrete genetic or developmental problems fundamentally meaningful to biology and biomedicine and exceptionally good at the design of statistical models and algorithms to solve these problems.
Currently, Dr. Wu directs the Center for Statistical Genetics at Pennsylvania State University. The missions of the Center are: (i) to develop powerful statistical algorithms for identifying genes that control complex traits and integrate biological principles into statistical models, allowing the generation and test of new hypotheses about interplay between gene and development; (ii) to collaborate with applied geneticists to analyze real-world data, facilitating the utilization of our statistical models as well as the discovery of new genes from practical data sets; and (iii) to educate students and post-docs in statistical genetics and genomics.
|Date:||Thursday, November 12, 2009|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York