John S. Witte, PhD
John S. Witte, PhD
John Witte, PhD joined the departments of Epidemiology & Biostatistics and Urology from Case Western Reserve University, School of Medicine. Witte earned his master’s degree from the University of California, Berkeley, and a PhD in epidemiology from the University of California, Los Angeles. After receiving his degree in Los Angeles, he was an Assistant Professor of Preventive Medicine at the University of Southern California. At Case, Witte was honored with a teaching award, the 1999-2000 Glennan Fellowship, given for innovation in teaching and education. He was honored again in 2002-2003 with the Visiting Scientist award from the International Agency for Research on Cancer in Lyon, France. Witte’s research program constitutes applied and methodological genetic epidemiology, with the overall aim of deciphering the mechanisms underlying complex diseases. His applied work is focused on prostate cancer, and complements other work done at the UCSF Prostate Cancer Center. Witte’s genetic epidemiology studies examine the genetic basis of prostate cancer, and have included searches across the human genome and work on specific candidate genes. Successes have included the isolation of distinct chromosomal regions that appear to harbor prostate cancer causing genes, and the first genome-wide scan for genes linked to the aggressiveness of prostate cancer.
John Witte's research constitutes applied and methodologic genetic epidemiology, with the overall aim of deciphering the mechanisms underlying complex diseases. At present, his applied work is primarily focused on prostate cancer, while much of his methodologic work is on hierarchical modeling and association studies.
Applied Research in Prostate Cancer
Over six years ago, Witte initiated a series of prostate cancer genetic epidemiology studies, which have helped in sorting out the genetic basis of this disease. These include findings from searches across the human genome and from work on specific candidate genes. In particular, using a novel combination of genome-wide scan and confirmatory allelic imbalance studies, he and his colleagues have isolated distinct chromosomal regions that appear to harbor genes that cause prostate cancer. This work includes the first genome-wide scan looking for genes linked to the aggressiveness of prostate cancer; the UCSF team detected strong linkages on chromosomes 5, 7, and 19, and has further narrowed these three candidate regions and isolated potentially causal genes.
Another important result from Witte's research is determining that a common mutation in the candidate gene RNASEL may be involved in up to 13 percent of prostate cancer cases.
The applied work helps motivate Witte's methodological research, which primarily involves issues surrounding the design and analysis of genetic and epidemiologic studies. For example, a key aspect of his research is the further development of hierarchical modeling, a potentially valuable analytic approach. Witte has provided an extensive application of hierarchical modeling in analyzing case-control data on diet and breast cancer. This work has led to the growing use of hierarchical modeling and the development of additional tools for such analyses. Witte has also undertaken a simulation study showing that hierarchical modeling generally gives more accurate effect estimates than standard analytic techniques. In related work he has shown how this approach can be used to incorporate genotype- and haplotype-level information in linkage disequilibrium mapping.
A final key area of Witte's research is focused on the use of case-control (“association”) studies in genetic epidemiology. For example, he has shown that using as controls some types of family members, such as siblings, can reduce power for detecting main genetic effects but can provide improved power for detecting gene-environment interactions.
Other related work is investigating the use of sets and haplotype tagging single nucleotide polymorphisms (SNPs) for association studies. Finally, Witte has investigated the impact of incorporating genetic information into the design and analysis of clinical trials. His research here indicates how one can drastically reduce clinical trial size and duration by pre-genotyping potential study subjects.