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John S. Witte, PhD
Research Interests
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 my methodologic work
is on hierarchical modeling and association studies. Over six
years ago Witte initiated a series of prostate cancer genetic
epidemiology studies, which have had numerous successes toward
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, with his colleagues
he has 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 they 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 Witte's 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
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.
Association Studies
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.
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