A data-centric perspective on exposomics data analysis

Abstract Exposomics represents a systematic approach to investigate the etiology of diseases by formally integrating individuals’ entire environmental exposures and associated biological responses into the traditional genotype-phenotype framework. The field is largely enabled by various omics technologies which offer practical means to comprehensively measure key components in exposomics. The bottleneck in exposomics has gradually shifted from data collection to data analysis. Effective and easy-to-use bioinformatics tools and computational workflows are urgently needed to help obtain robust associations and to derive actionable insights from the observational, heterogenous, and multi-omics datasets collected in exposomics studies. This data-centric perspective starts with an overview of the main components and common analysis workflows in exposomics. We then introduce six computational approaches that have proven effective in addressing some key analytical challenges, including linear modeling with covariate adjustment, dimensionality reduction for covariance detection, neural networks for identification of complex interactions, network visual analytics for organizing and interpreting multi-omics results, Mendelian randomization for causal inference, and cause-effect validation by coupling effect-directed analysis with dose-response assessment. Finally, we present a series of well-designed web-based tools, and briefly discuss how they can be used for exposomics data analysis.