John S. Witte, PhD
John S. Witte, PhD
John S. Witte’s initial faculty appointment was at Case Western Reserve University in Cleveland, Ohio, where he worked as an assistant and associate professor of Epidemiology and Biostatistics and was awarded tenure and named a Glennan Fellow for innovation in education. Witte was recruited to UCSF in 2003 as a full tenured professor. He is the Head of the Division of Genetic and Cancer Epidemiology, the Vice Chair of the Department of Epidemiology and Biostatistics, and the co-Leader of the Cancer Genetics Program within the UCSF Comprehensive Cancer Center. He directs the National Cancer Institute (NCI) supported UCSF post-doctoral training program in the genetic and molecular epidemiology of cancer and recently received the Stephen B. Hulley Award for excellence in teaching. He is a senior editor of the journal Cancer Epidemiology, Biomarkers & Prevention and has been a member (and chair) of multiple National Institutes of Health study sections. He has published over 200 scientific articles and I am an internationally recognized expert in genetic and cancer epidemiology.
Our research program encompasses a synthesis of methodological and applied genetic epidemiology, with the overall aim of deciphering the mechanisms underlying complex diseases and traits (Witte, Visscher & Wray, Nature Reviews Genetics 2014). Our methods work is focused on the design and statistical analysis of next-generation sequencing and genetic association studies. We are applying these methods to studies of prostate and other cancers.
We have developed extensive methods and software for studying rare genetic variants, pathways, interactions, pleiotropy and genome-wide association studies. To better characterize genetic variants that influence more than one trait or disease (i.e. pleiotropy), we have have developed allele-level tests for evaluating association with multiple phenotypes (Majumdar, Witte & Ghosh, Genetic Epidemiology 2015) and evaluated different approaches for determining the particular phenotypes associated with pleiotropic variants (Majumdar, Haldar & Witte, Genetic Epidemiology 2016). With regard to GWAS, we have determined the remaining heritability across all major traits, and the optimal approach for assessing this (right: Lindquist, Jorgenson, Hoffmann & Witte, Genetic Epidemiology 2013). For analyzing rare variants, we have developed novel Bayesian (Cardin, Mefford & Witte, Genetic Epidemiology 2012) and ‘Step-up’ empirical methods (Hoffmann, Marini & Witte, PLOS One 2010).
Inherited Genetics of Complex Traits
Inherited genetics are known to contribute substantially to disease risk for many complex traits. In collaboration with the Kaiser Permanente Division of Research, we are investigating the genetic effects of inherited factors on prostate cancer and related urological traits and outcomes. Using data from tens of thousands of participants in the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH), ProHealth, and California Men’s Health Studies, we are using statistical modeling to determine how aberrations in genetics and gene expression influence the development, diagnosis, and progression of prostate cancer. Prior results of this work include the discovery of a prostate cancer indel mutation at 6q25.3 (Hoffmann et al., Cancer Discovery 2015) and the characterization of a high-penetrance prostate cancer mutation in the HOXB13 gene (Witte et al., CEBP 2013; Hoffmann et al., PLOS Genetics 2015). Furthermore, we have undertaken the study of breast cancer genetics through the NCI Up for a Challenge (U4C) competition and we are extending our work to assess pleiotropy across multiple different cancers (Sakoda, Jorgenson and Witte, 2013).
Analyzing Somatic Tumor Variants in the Blood
Cell free DNA (cfDNA), a form of ‘liquid biopsy’, has recently emerged as a promising technology to screen, diagnose, and monitor many types of disease. In the context of cancer, cfDNA has several benefits over traditional biopsy in examining the genetic structure of a tumor, including simple and non-invasive sampling methodology and the potential ability to simultaneously detect sub-clonal variants of a tumor (i.e., tumor heterogeneity). Working with the UCSF Medical Center’s Department of Urology, our lab is exploring how cfDNA can be used to more effectively answer clinical questions surrounding the genetics of prostate cancer and is developing new technology to better detect tumor variants within the blood. These variants are often found at very dilute levels, so our approach has been to simultaneously improve the signal with cutting edge sequencing technology while also prioritizing the variants of greatest importance using machine learning.