Exposome analytics: methodological developments and needs

Speaker: Pr. Marc Chadeau-Hyam, Imperial College London

The Exposome concept has been developed as a necessary complement to the genome to better understand the determinants of health and of the risk of chronic diseases. The external exposome combines a large range of external stressors (i.e. non-genetic) factors potentially impacting human health from conception onwards. These external exposures (i) are heterogeneous in nature, scale, and variability, (ii) feature complex correlation  patterns and (iii) may operate as mixtures. The internal exposome can be defined as the way th ese exposures are embodied and its exploration relies on the screening and integration of high-re solution molecular data. While methods for omics data analyses are established, their applicati on in an exposome context is raising specific methodological challenges including the analysis of complex and correlated exposures. Furthermore, 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. 

Taking examples from real-world exposome projects we will illustrate the use of statistical and machine learning techniques to accommodate co-occurring exposures contributing to population stratification, explore the links between these and health outcomes, and investigate the (multi)-omic response to these sets of exposures.