Primary Appointment

Chih-Chieh Wu, PhD, was trained with Applied Mathematics and Statistics in the State University of New York at Stony Brook and later underwent post-doctoral training in Human Genetics and Genetic Epidemiology at MD Anderson Cancer Center. His primary research is to develop and implement mathematical and statistical methods for human genetic analyses in family and case-control studies. He has developed methods to precisely model the measured susceptibility genotypes for the proband and each relative in a family and incorporate this information into mathematical models. These methods determine the joint effects of individual (genetic and non-genetic) risk factors and their interactions on cancer risk simultaneously. He has applied the methods that he developed to cancer family studies of Li-Fraumeni syndrome. He has developed methods to perform genome-wide detection of deletions associated with common, complex diseases in case-control studies using dense single nucleotide polymorphisms (SNPs). He applied the methods for analyses of whole-genome SNP genotype data of rheumatoid arthritis patients. He also proposed and developed novel statistical tests based on minimum frequencies to detect unusually sparse incidences of disease. He used the theories in combinatorial mathematics to derive exact probability distributions and moments that are particularly desirable in the analyses of rare data on diseases. He extended these methods and applied them to analyses of adolescent suicide data from the U.S. National Center for Health Statistics and childhood Langerhans cell histiocytosis data from Taiwan.

• Methods for Cancer Risk Analyses Relevant to Genetic, Environmental, and Epidemiological Risk Factors Combined in Family Studies
• Methods for Detection of Gene-Gene and Gene-Environment Interactions in Family Studies
• Methods for Genome-Wide Detection of Genomic Deletions Associated with Common, Complex Diseases
• Family-Based Genome-Wide Association Studies
• Combinatorial Probability Distribution Theories and Their Application to Detect Disease Anomalous Patterns in Discrete Time Series

Academic Appointments

Last updated: 2/21/2013