Grishin et al. Nature Genetics. 2022
Predicting which patients will respond to treatment or develop harmful side-effects is a fundamental goal of precision medicine. We aim to integrate genetics and large-scale electronic health record data to identify meaningful biomarkers of treatment consequences and develop algorithmic models to support clinical decision-making.
Cancer is a disease influenced by both germline (host) and somatic (tumor) variation, but these influences have largely been studied in isolation. We are interested in understanding how somatic mutations and tumor evolution are impacted by germline risk for cancer & related traits, genetic ancestry, and individual genetic variants. We empower these studies using large-scale clinical sequencing from tens of thousands of tumors.
Genetic data has been collected from millions of individuals across many populations. We are interested in using this data to broaden our understanding of the genetic relationships within and across populations.