Department Environmental Toxicology

Chemical pollution threatens biodiversity of ecosystems, yet we know little about different species’ genetic susceptibility to chemical exposures. ⁠Methods to assess genetic susceptibility are limited because lab-based studies to test chemically-induced adverse outcomes in genetically diverse species are impossible for all chemicals and species while field-based methods like biomonitoring cannot inform exposures that have not yet happened. Therefore, computational methods are necessary to determine species’ differential genetic susceptibility to chemicals. With this knowledge, impacts on populations and the corresponding resilience of an ecosystem to chemical exposures can start to be characterized.

For this project, we will use data integration, analysis, and visualization to characterize and predict genetic susceptibility to chemicals in different species. As a starting point, this project will focus on pesticide-induced neurotoxicity for zebrafish and C. elegans. We will develop a data integration protocol to link existing data on chemical activity, genetic variability, and neurotoxicity endpoints. From these linkages, quantitative risk values will be developed to characterize the likelihood that pesticide-induced neurotoxicity varies in genetically diverse populations, and these risk values and data linkages will be used to analyze similarities and differences in genetic susceptibility across species and pesticide modes of action.