Development of a Trans-Ancestry Genetic Risk Score for Prostate Cancer

When and Where

Friday, May 06, 2022 12:00 pm to 1:00 pm


David Conti


Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. I will present a recent multi-ancestry meta-analysis of prostate cancer genome-wide association studies and the methodological issues surrounding the construction of a genetic risk score (GRS) that is effective in multiple ancestry groups. The top GRS decile is associated with a 4-fold increase in risk for men of European, African, and Asian ancestry and for Latino men. These risks, combined with population-specific incidence rates lead to a 26-38% lifetime risk of prostate cancer across populations. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering a tool for risk stratification.

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About David Conti

David Conti received his B.S. and M.S. in Earth Systems at Stanford University and then trained in Genetic Epidemiology at Case Western Reserve University. Since then, he has been at the University of Southern California and is currently a Professor of Population and Public Health, Division of Biostatistics. In addition, he is the Associate Director for Data Science Integration for the Norris Comprehensive Cancer Center at USC and the Kenneth T. Norris, Jr. Chair in Cancer Prevention. His research covers both applied genetic and environmental epidemiology and statistical methods development, predominantly in cancer research. Dr. Conti has been PI of several projects to develop statistical methods for high dimensional interactions, for the integration of genetic and omic data, and to develop polygenic risk scores that are effective across diverse populations. He is committed to collaborative and team science. He is currently Director of the Data Science Core for a large program project investigating aggressive prostate cancer in African American men integrating the built environment, germline and somatic genetic profiles, gene expression, and tumor microenvironment data.

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