From toxicogenomics to predictive toxicology and exposomics: defining the next decade of gene-environment research

Over the past 15 years, genetics has undergone a profound transformation driven by advances in next-generation sequencing, multi-omics technologies, single-cell analysis, genome editing, and artificial intelligence. Within this broader evolution, the field of toxicogenomics has expanded from studying genomic responses to environmental stressors toward a more comprehensive framework that integrates lifelong environmental exposures with genetic susceptibility. This shift is reflected in the evolution of Frontiers in Toxicogenomics into Frontiers in Predictive Toxicology and Exposomics. Modern exposomics seeks to characterize the totality of endogenous and exogenous exposures across the life course, while predictive toxicology aims to transform exposure and molecular data into actionable forecasts of disease risk and health outcomes. Emerging longitudinal cohorts, wearable sensors, high-throughput functional genomics, and machine-learning approaches are enabling unprecedented insights into dynamic gene-environment interactions. For example, large longitudinal cohorts such as the Environmental Influences on Child Health Outcomes (ECHO) program now integrate environmental, genomic, and developmental data across the life course. However, major challenges remain, including exposure measurement, data harmonization, causal inference, reproducibility, and translation into clinical and regulatory practice. Here, I discuss the scientific advances that have enabled this transition, identify the key barriers that continue to limit progress, and propose priorities for the next decade. I argue that the integration of genomics, exposomics, functional validation, and predictive analytics will redefine environmental health research and accelerate the emergence of precision prevention as a major goal of twenty-first century genetics.