State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event
Journal article, 2022
The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 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 in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.
Exposome
Environmental exposures
Multiple exposures
Statistical models
Multi-omics
Author
Léa Maitre
Laboratoire d'Informatique en Image et Systèmes d'Information
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
University of Barcelona
University Pompeu Fabra
Jean Baptiste Guimbaud
Laboratoire d'Informatique en Image et Systèmes d'Information
University Pompeu Fabra
University of Barcelona
Charline Warembourg
Inserm Transfert
Nuria Güil-Oumrait
University of Barcelona
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
University Pompeu Fabra
Paula Marcela Petrone
University of Barcelona
Marc Chadeau-Hyam
MRC Centre for Environment and Health
St Mary's Hospital
Martine Vrijheid
University of Barcelona
University Pompeu Fabra
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
Xavier Basagaña
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
University of Barcelona
University Pompeu Fabra
Juan R. Gonzalez
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública
University Pompeu Fabra
University of Barcelona
Rossella Alfano
Universiteit Hasselt
Sanjib Basu
University of Illinois
Jaime Benavides
Columbia University
Lucile Broséus
Grenoble Alpes University
Carl Brunius
Chalmers, Biology and Biological Engineering, Food and Nutrition Science
Alejandro Caceres
University of Barcelona
Matthew Carli
Virginia Commonwealth University
Rémy Cazabet
University of Barcelona
Laboratoire d'Informatique en Image et Systèmes d'Information
Shounak Chattopadhyay
Duke University
Yun Hua Chen
University of Illinois
Lawrence Chillrud
Columbia University
David Conti
USC Norris Comprehensive Cancer Center
Chris Gennings
Icahn School of Medicine at Mount Sinai
Ramkiran Gouripeddi
University of Utah
S. Hari Iyer
Harvard University
Paulina Jedynak
Grenoble Alpes University
Huichu Li
Harvard University
Glen McGee
University of Waterloo
Vishal Midya
Icahn School of Medicine at Mount Sinai
Sejal Mistry
University of Utah
Chiara Moccia
University of Turin
S. Daniel Mork
Duke University
L. John Pearce
Medical University of South Carolina
Michele Peruzzi
Duke University
Jimenez Marcia Pescador
Harvard University
Brigitte Reimann
Universiteit Hasselt
J. Charlotte Roscoe
Harvard University
Xiaotao Shen
Stanford University
Nikos Stratakis
University of Southern California
Ziyue Wang
National Institute of Environmental Health Sciences
Congrong Wang
Universiteit Hasselt
David Wheeler
Virginia Commonwealth University
Ander Wilson
Colorado State University
Qiong Wu
University of Maryland
Miao Yu
Icahn School of Medicine at Mount Sinai
Yinqi Zhao
Keck School of Medicine of USC
Fei Zou
The University of North Carolina System
Daniela Zugna
University of Turin
Ruizhe Chen
University of Illinois
Yu Che Chung
University of Illinois
Jiyeong Jang
University of Illinois
Mary Turyk
University of Illinois
Environment International
0160-4120 (ISSN) 1873-6750 (eISSN)
Vol. 168 107422Subject Categories
Other Computer and Information Science
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1016/j.envint.2022.107422
PubMed
36058017