Bio: Marc Chadeau-Hyam is a distinguished statistician for his translational research devising reproducible and interpretable methods for the characterisation of the exposome via omics profiling and integration. He has been involved as lead statistician in multiple large-scale projects exploring biological signatures of external insults including air pollution, lifestyle and social factors and investigates their effect on health. He is involved in the Real-time Assessment of Community Transmission (REACT) Study as lead statistician and contributed to the real-time monitoring of the SARS-CoV-2 epidemic in England. His group includes 20 multidisciplinary scientists focusing on the analysis of data from mega-sized and/or deeply phenotyped studies.

Abstract: 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. 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 these exposures are embodied, and its exploration relies on the screening and integration of high-resolution molecular data. While methods for omics data analyses are established, their application 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 explore the embodiment of air pollution and subsequent health effects.