Over the past decade, high-resolution molecular profiles using OMICS technologies have accumulated and given rise to an unprecedented source of information to explore the effective biological effects of external stressors and to detect drivers of subsequent risk drivers. Although the volume, dimensionality and complexity of OMICs data are constantly increasing, several robust methods enabling their analysis are now established.
In parallel to these technological advance a novel concept, the Exposome, has emerged as a necessary complement to the genome to better understand the risk of chronic diseases. The exposome is defined as all external stressors (i.e. non-genetic) factors potentially impacting human health from conception onwards. This concept has attracted a lot of attention, as its characterization may be facilitated by the exploration of OMICs data. EXPOSOMICS is a one of the two first large scale EU-funded project to study the exposome and includes several sets of OMICs data: DNA methylation, gene expression, metabolomics and targeted proteomics.
The exploration of these data relies on established statistical approaches which will introduced during the lecture. These include 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 that we will also be presented.
In particular, the isolated exploration of an OMIC profile offers the possibility to capture of 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 biology approaches, such integrations remain at best data-specific, usually disease-specific, and more systematically restricted to the exploration of (few) pre-defined 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 an adverse condition can be decomposed in three interdependent streams focusing on (i) OMICs data integration, (ii) the exploration of molecular mechanisms involved in the exposure effect mediation towards (chronic) disease development. The focus of this lecture is to describe methods (and their extensions) to enable such ambitious explorations of OMICs data and ultimately unlock their full potential to provide novel insights into exposome-triggered molecular mechanisms affecting health.