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C. Charles Gu

Associate Professor

Phone314-362-3642

Emailgc@wustl.edu

Dr. Gu is an Associate Professor of Biostatistics. He received his Ph.D. in Mathematics from Washington University in 1992, and completed his postdoctoral training in Statistical Genetics from 1992 to 1995 in the Department of Psychiatry at WU. In 1995, Dr. Gu joined the Division of Biostatistics as a Research Instructor and has been a Research Assistant Professor since 1997. He serves on the Curriculum and Evaluations Committee of the Genetic Epidemiology Master of Science training program. He is also a member of the Program in Computational Biology. Dr. Gu has taught both introductory and graduate level courses, and is course master of two new courses on bioinformatics, and study design and management. As a teacher, he strives to follow the Confucianist philosophy: pass thee knowledge; vest thee with career; and clear thy doubts (师者,所以传道授业解惑也。韩愈).

Dr. Gu’s research activities encompass a wide spectrum across designing, conducting and analyzing genetic studies of complex diseases. He is an active investigator on several multi-center genetic epidemiological studies of such complex traits as heart disease, genetic response to exercises, and pharmacodynamic factors regulating drug therapy. His research efforts there deal with issues related to mapping complex disease genes by large-scale GE studies. He is also PI of a genetic epidemiological study of endometrial cancer sponsored by the Siteman Cancer Center. Single nucleotide polymorphisms (SNP) in candidate genes are investigated in conjunction with environmental factors for their collective actions on the development of the cancer. Dr. Gu has extensive experience in developing methodologies that are crucial to detecting complex disease genes and characterizing their functions. Some of his published works include combined extreme sibpair test (EDAC) for genetic linkage to quantitative trait loci (QTLs), algorithm for study design optimization using generalized relative risk ratio; and meta-analysis method for pooling nonparametric linkage studies. He also published results using multivariate data-reduction techniques (PCA, clustering analysis, etc.) to extract hidden structure in phenotypic data for refined linkage analysis, and results using multivariate variance components analysis in genetic linkage and association studies.

The overarching interest of Dr. Gu’s research is motivated to translate complex high dimensional data observed in genetic/clinical studies into applicable scientific knowledge. His current research activities include: construction and application of a genome-wide haplotype map; and interpretation of high dimensional microarray data (gene expression, and protein interaction). The former aims at obtaining most efficient designs for genome-scale mapping of disease genes; and the latter will enable us to recognize the most important collective actions of multiple genes, and to understand further the functional role of such interactions in complex biological systems.