Statistical models to explore the exposome: From OMICs profiling to 'Mechanome' characterization

Abstract

Over the past decade, high-resolution molecular profiles using OMICS technologies have accumulated and have given rise to an unprecedented source of information to explore the effective biological effects of external stressors and to detect drivers of subsequent disease risk. Although the volume, dimensionality, and complexity of OMICs data are constantly increasing, several methods enabling their analysis are now available. The exploration of these data relies on statistical approaches including univariate models coupled with multiple testing correction, dimensionality reduction techniques, and variable selection approaches. While these methods are established, their application in an exposome context is raising specific methodological challenges. In addition, the isolated exploration of an OMIC profile offers the possibility to capture stressor-induced biological/biochemical alterations, potentially impacting individual risk profiles, but this may only yield a fractional picture of the complex molecular events involved, therefore limiting our understanding of the effective mechanisms mediating the effect of the exposome. Despite efficient developments over systems biological approaches, such integrations remain at best data-specific, usually disease-specific, and more systematically restricted to the exploration of (few) predefined hypotheses. The challenging task of exploring the 'mechanome' as defined by the ensemble of stressor-induced molecular mechanisms occurring throughout the life course and determining the individual's risk of developing adverse conditions can be decomposed in three interdependent streams focusing on (1) OMICs profiling, (2) OMICs data integration, and (3) the exploration of molecular mechanisms involved in the exposure effect mediation towards (chronic) disease development.