Abstract: Chemical exposures significantly affect individual and public health, yet there is no unifying framework describing how diverse chemical compounds may interfere with biological processes and contribute to disease risk. Here, we use a network-based approach to construct a comprehensive map linking 9887 exposures through their shared genetic effects. This map can be used to define classes of exposures that affect common biomolecular processes, even when they are chemically distinct. We find that exposures target specific modules in the human interactome of protein-protein interactions, and that exposure harmfulness relates to interactome connectivity. Systematically comparing exposure modules with disease modules suggests that their interactome proximity predicts exposure-disease relationships. We validate these predictions by integrating nationwide disease prevalence data with reported environmental exposures, finding higher disease incidence where exposure and disease modules overlap. Together, our study provides a blueprint for the systematic investigation of the pathobiological impact of chemical exposures ranging from the molecular to the population level. Here, the authors introduce a network-based framework to link thousands of chemical exposures through shared genetic effects, revealing how diverse compounds converge on interactome modules and predict disease risk from molecular mechanisms to population-level patterns.
External IDs:doi:10.1038/s41467-026-72402-y
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