Department Environmental Toxicology

There have been thousands of genome-wide association studies (GWAS) in the past couple decades, which have identified many genetic variants contributing to diseases. However, the etiology of most chronic human diseases involves interactions between environmental factors (e.g., pesticides) and genetics, but gene-environment (GxE) interactions are difficult to characterize in populations. This project aims to harness existing data on human genetic variability in Parkinson disease and chemical activity data to predict differential population susceptibility to pesticide-induced Parkinson disease.

The adverse outcome pathway (AOP) approach is a knowledge-organization method to describe stressor non-specific pathways between exposures and adverse effects. This approach starts from a molecular initiating event (MIE) and follows a sequential chain of intermediate key events at different levels of biological organization (e.g., cell, tissue) through to an adverse outcome. For this project, we will use data integration and network analysis to computationally predict AOPs for pesticide-induced Parkinson disease. We will then use this framework to predict relevant genetic variants and characterize potential mechanisms for GxE interactions in pesticide-induced Parkinson disease.