Public Health Sciences Division, Fred Hutch
Dr. Burcu Darst’s research is focused on identifying and understanding genetic and multi-omic risk factors of prostate cancer in diverse populations. She has extensive expertise in genetic epidemiology as well as genetic association and metabolomic investigations. Her research focuses on understanding genetic risk of prostate cancer in multi-ancestry populations, particularly using rare variants captured with whole exome sequencing, genome-wide association studies, polygenic risk scores and metabolomics to distinguish aggressive from non-aggressive disease and to understand the stark health disparities that contribute to prostate cancer risk. She has also done research on genetic testing in precision medicine, developmental neurogenetics, and the genetics of complex aging traits and worked to integrate genomic and metabolomic data to understand the underlying mechanisms contributing to complex aging traits and Alzheimer’s disease. Currently, she is leading an effort to improve understanding of the genomic and metabolic mechanisms contributing to prostate cancer risk in men from diverse populations as well as improving the utility of polygenic risk scores and rare genetic variants for the prediction of prostate cancer across patient populations. Dr. Darst has received multiple awards for her work including the Prostate Cancer Foundation Young Investigator Award and the AACR NextGen Star award.
Ph.D., Epidemiology, University of Wisconsin, Madison, 2018
B.S., Cognitive Science, University of California, San Diego, 2009
Genetic basis of prostate cancer risk and its health disparities (genome-wide association studies, whole-exome sequencing studies, polygenic risk scores)
Clinical utility of incorporating genetic information into prostate cancer screening to reduce incidence and mortality rates and the health disparities of this disease
Integrating genetics with other multi-omic data (particularly metabolomics) to better understand the genetic basis of complex traits and comprehensively model risk
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