摘要：Interactions between environmental factors and genetics underlie the majority of chronic human diseases. Chemical exposures are likely an underestimated contributor, yet gene-environment (GxE) interaction studies rarely assess their modifying effects. Here, we describe a novel method to profile the human genome and identify regions associated with differential population susceptibility to chemical exposures. Single nucleotide polymorphisms (SNPs) implicated in enriched chemical-disease intersections were identified and validated for three chemical classes with expected GxE interaction potential (neuroactive, hepatoactive, and cardioactive compounds). The same approach was then used to characterize consumer product classes with unknown risk for GxE interactions (washing products, cosmetics, and adhesives). Additionally, high-risk variant sets that may confer differential population susceptibility were identified for these consumer product groups through frequent itemset mining and pathway analysis. A dataset of 2454 consumer product chemical-disease linkages, with risk values, SNPs, and pathways for each association was developed, describing the interplay between environmental factors and genetics in human disease progression. We found that genetic hotspots implicated in GxE interactions differ across chemical classes (e.g., washing products had high-risk SNPs implicated in nervous system disease) and illustrate how this approach can discover new associations (e.g., washing product n-butoxyethanol implicated SNPs in the PI3K-Akt signaling pathway for Alzheimer’s disease). Hence, our approach can predict high-risk genetic regions for differential population susceptibility to chemical exposures and characterize chemical modifying factors in specific diseases. These methods show promise for describing how chemical exposures can lead to varied health outcomes in a population and for incorporating inter-individual variability into chemical risk assessment.
关键词：Chemical exposures ; Risk assessment ; Gene-environment interactions ; New approach methodologies ; Consumer products ; Data integration
