John Witte, Ph.D.
Dept. of Epidemiology & Biostatistics

Contact Information:
Contact Information: wittej@humgen.ucsf.edu Tel: (415) 502-6882
Fax:(415) 514-8109

185 Berry Street
Suite 6600
UCSF
San Francisco, CA 94143-0981

Links:
Epidemiology & Biostatistics

Program in Cancer Genetics

BMS Graduate Program

Complete list of publications

Genetic Epidemiology of Cancer and other Complex Diseases

Background
Our lab is focused on applied and statistical genetic epidemiology, with the overall aim of deciphering the mechanisms underlying complex diseases. At present, our applied work is primarily focused on prostate cancer, while much of our methodological work is on hierarchical modeling and association studies.

Genetic Epidemiology of Prostate Cancer
We have had numerous successes toward sorting out the genetic basis of prostate cancer. 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, we 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; here we detected strong linkages on chromosomes 5, 7, and 19, and have further narrowed these three candidate regions and isolated potentially causal genes. Another important result from our research is determining that a common mutation in the candidate gene RNASEL may be involved with up to 13 percent of prostate cancer cases.

Hierarchical Modeling
This applied work helps motivate our methodological research, which mostly involves issues surrounding the design and analysis of genetic epidemiologic studies. A key aspect is the further development of hierarchical modeling-a potentially valuable analytic approach. We have provided an extensive application of hierarchical modeling in analyzing case-control data on gene-environment interactions. This work has led to the growing use of hierarchical modeling, and the development of additional tools for such analyses. We have shown how this approach can be used to incorporate genotype- and haplotype-level information in linkage disequilibrium mapping.

Association Studies
Another key area of my lab's research is focused on the use of case-control ("association") studies in genetic epidemiology. For example, we have 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, we have investigated the impact of incorporating genetic information into the design and analysis of clinical trials. Our work indicates how one can drastically reduce clinical trial size and duration by pre-genotyping potential study subjects.


Selected Publications:
Witte et al. Bias and efficiency in case-control studies of candidate genes and gene-environment interactions: Basic family designs. Am J Epidemiol 1999;149:693-705.

Witte et al. Genome-wide scan for prostate cancer aggressiveness loci. Am J Hum Genet 2000;67:92-99.
Casey … Witte. RNASEL R462Q variant is implicated in up to 13% of prostate cancer cases. Nat Genet 2002;32:581-583.

Conti and Witte. Hierarchical modeling of linkage disequilibrium: genetic structure and spatial relations. Am J Hum Genet 2003;72:351-363.

Hung … Witte. Using hierarchical modeling in genetic association studies with multiple markers: application to a case-control study of bladder cancer. Cancer Epidemiology, Biomarkers & Prevention 2004; 13:1013-1021.

Singer JB, Hill AE, Burrage LC, Olszens KR, Song J, Justice M, O'Brien WE, Conti DV, Witte JS, Lander ES, Nadeau JH. Genetic dissection of complex traits with chromosome substitution strains of mice. Science 2004;304:445-448.