With over 300 registered international participatns, the event was a total success! This initiative was funded by the ISGlobal Severo Ochoa Programme, the ISGlobal Exposome Huband ATHLETE project

Click here to access the Agenda with videos and links to the presentation slides

In this working event, participants were offered an opportunity to test their statistical methods of choice on a real case scenario exposome dataset and later exhibit their findings at the workshop. The dataset included multiple health outcomes (continuous and categorical), multiple exposures-omics and additional non-exposure variables (e.g., potential confounders).

The Exposome dataset represented a real case scenario of exposome dataset (based on the HELIX project database) with multiple correlated variables (N>100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes. The population was drawn from a multi-center study which represented the main confounding structure in the dataset.

A committee had assessed the abstracts. Based on these abstracts, subsets of individuals were invited to present their approach and statistical model(s) at the meeting. Young investigators were solicited as well as well established experts. Prizes and diplomas were awarded.

The workshop will result in a comprehensive document for publication that summarizes the findings from the workshop and outlines the most useful approaches and computational/conceptual/statistical models for determining or predicting health effects of high dimensional exposome datasets in collaboration with the event committee and the selected participants. We discussed advantages and disadvantages of different techniques.

Proposed Themes

  • Exposome-health association studies
  • Omics data integration in exposome-health studies (multi-omics, pathway analysis, mediation…)
  • Causal inference techniques (inferring/validating causal structure from high-dimensional heterogeneous data, techniques to obtain causal estimates with high-dimensional data…)
  • High-dimensional data mediation analysis
  • Mixture, combined or “cocktail” effects
  • Hierarchical modelling
  • Non-linear effects and high-order interactions
  • Machine learning techniques
  • Data visualization/summary measures (new plots, new metrics…)