Computational standards and tools for exposome-wide association studies linking the human exposome with health outcomes

The human exposome (the cumulative measure of environmental exposures across the life course) offers a critical complement to genomics in deciphering the multifactorial origins of complex diseases. Exposome-wide association studies (ExWAS) represent an emerging class of high-dimensional epidemiological analyses designed to systematically assess associations between diverse environmental exposures and health outcomes. However, ExWAS requires advanced computational standards and tools capable of handling exposure complexity, temporal variability, co-exposure correlation, and multi-omics data integration. This review synthesizes current computational methodologies and platforms for ExWAS, highlighting recent advances in statistical modeling, exposure quantification, and bioinformatics tools. We conducted a PRISMA-ScR-guided scoping review across PubMed, Scopus, and Web of Science (2010-2025), with dual-reviewer screening in Rayyan, standardized data charting, and SWiM-aligned narrative synthesis. We explore multivariable and mixture modeling approaches (e.g., weighted quantile sum regression, Bayesian kernel machine regression), integration of external and internal exposome domains, and the application of longitudinal designs and environmental risk scoring. Key platforms such as the rexposome suite, exposomeShiny, and the integrative INTEGRA framework are examined for their role in operationalizing exposomic analyses at population scale. We also discuss the importance of data standardization, including exposure ontologies, harmonization protocols, and federated data infrastructure supporting cross-cohort analyses. Moreover, we discuss how computational exposomics can elucidate mechanistic pathways linking environmental exposures to disease, particularly when integrated with transcriptomic and metabolomic data. Finally, we outline future directions for the field, including genome-exposome integration, AI-driven causal inference, and translational pipelines for regulatory and clinical implementation. Beyond listing methods, we assess computational maturity and reproducibility (open licensing, containerization, federation readiness) and connect standards + tools to ExWAS workflows and translation. Computationally mature and mechanistically anchored, ExWAS are poised to become central tools in precision environmental health, enhancing the interpretability of genome-environment interactions and the predictive power of integrated omics frameworks.