Chemical pollution is recognized as a driver of biodiversity loss. Gene - environment (GxE) interactions for humans are increasingly well-characterized using predictive approaches, but comparable methods remain underdeveloped for non-human species. Here, we demonstrate how a computational framework previously developed for human health characterization of GxE interactions in chemical-induced diseases can be restructured and expanded to predict GxE interactions in ecologically-relevant species using Mus musculus as a proof-of-concept. We integrated publicly available datasets (e.g., Chemical - Gene interactions from the Comparative Toxicogenomics Database, pathway data from REACTOME) with two sets of mouse genetic variants: one dataset from the literature for wild mice (>50,000 variants, representing wild variability, but for a subset of genome) and one dataset from lab mice (>15,000,000 variants, covering the whole genome, but not necessarily variability in wild populations). Through this, we built predictions of Chemical - Pathway - Gene - Variant - Phenotype associations in mice that may describe the toxicity mechanisms underlying GxE interactions for chemical pollution. By analyzing these novel linkages for three sets of common environmental contaminants (pesticides, cosmetics, and pharmaceuticals), we predict pathways and genes implicated across different chemicals and highlight genes and pathways with the highest variability, suggesting that these may represent important mechanisms underlying susceptibility or adaptation to chemical pollution in the wild. The genes and pathways we highlight can serve as a starting point to characterize GxE interactions in wild mouse populations and our framework can be expanded to other ecologically-relevant species to characterize chemical impacts on biodiversity.

Co-authors: Daniel Guignard, Maria Büttner, Tiffany Scholier