State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event
Artikel i vetenskaplig tidskrift, 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

Författare

Léa Maitre

Laboratoire d'Informatique en Image et Systèmes d'Information

Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública

Universitat de Barcelona

Universitat Pompeu Fabra

Jean Baptiste Guimbaud

Laboratoire d'Informatique en Image et Systèmes d'Information

Universitat Pompeu Fabra

Universitat de Barcelona

Charline Warembourg

Inserm Transfert

Nuria Güil-Oumrait

Universitat de Barcelona

Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública

Universitat Pompeu Fabra

Paula Marcela Petrone

Universitat de Barcelona

Marc Chadeau-Hyam

MRC Centre for Environment and Health

St Mary's Hospital

Martine Vrijheid

Universitat de Barcelona

Universitat 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

Universitat de Barcelona

Universitat Pompeu Fabra

Juan R. Gonzalez

Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública

Universitat Pompeu Fabra

Universitat de Barcelona

Rossella Alfano

Universiteit Hasselt

Sanjib Basu

University of Illinois

Jaime Benavides

Columbia University

Lucile Broséus

Université Grenoble Alpes

Carl Brunius

Chalmers, Biologi och bioteknik, Livsmedelsvetenskap

Alejandro Caceres

Universitat de Barcelona

Matthew Carli

Virginia Commonwealth University

Rémy Cazabet

Universitat de 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

Université Grenoble Alpes

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

Universita degli Studi di Torino

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

Universita degli Studi di Torino

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 107422

Ämneskategorier

Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1016/j.envint.2022.107422

PubMed

36058017

Mer information

Senast uppdaterat

2024-03-06