Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

Ming Kei Chung; John S House; Farida S Akhtari; Konstantinos C Makris; Michael A Langston; Khandaker Talat Islam; Philip Holmes; Marc Chadeau-Hyam; Alex I Smirnov; Xiuxia Du; Anne E Thessen; Yuxia Cui; Kai Zhang; Arjun K Manrai; Alison Motsinger-Reif; Chirag J Patel; Members of the Exposomics Consortium

doi:10.1093/exposome/osae001

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

Exposome. 2024 Jan 17;4(1):osae001. doi: 10.1093/exposome/osae001. eCollection 2024.

Authors

Ming Kei Chung^{1

2

3}, John S House⁴, Farida S Akhtari⁴, Konstantinos C Makris⁵, Michael A Langston⁶, Khandaker Talat Islam⁷, Philip Holmes⁸, Marc Chadeau-Hyam⁹, Alex I Smirnov¹⁰, Xiuxia Du¹¹, Anne E Thessen¹², Yuxia Cui¹³, Kai Zhang¹⁴, Arjun K Manrai¹, Alison Motsinger-Reif⁴, Chirag J Patel¹; Members of the Exposomics Consortium

Collaborators

Members of the Exposomics Consortium:
Yau Adamu, Clement Adebamowo, Farida Akhtari, Farida Akhtari, Maria Argos, Saravanan Arunachalam, Brittney Baumert, Emily Beglarian, Kimberly Berger, Jessie Bhutani, William Bisson, Carrie Breton, Lu Cai, Mu-Rong Chao, Anastasia Chrysovalantou Chatziioannou, Qiwen Cheng, Ming Kei Chung, Ming Kei Chung, Ming Kei Chung, Robert Clark, Elaine Cohen Hubal, David Conti, Marcus Cooke, Elizabeth Costello, Yuxia Cui, Erin Dierickx, Dana Dolinoy, Xiuxia Du, Lawrence Engel, Peng Gao, Christopher Gaulke, Ryland T Giebelhaus, Jesse Goodrich, Katerina Grafanaki, Rama Gullapalli, Rima Habre, Ariana Haidari, Homero Harari, Jaime Hart, Jingxuan He, Philip Holmes, Darryl B Hood, John House, Hui Hu, Chiung-Wen Hu, Peter James, Marta Jankowska, Hong Ji, Kannan Srimathi, Corina Konstantinou, Yunjia Lai, Mike Langston, Janine Lasalle, Amy Leang, Donghai Liang, Jiawen Liao, Jiajun Luo, Konstantinos C Makris, Katherine Manz, Gary Miller, Alison Motsinger-Reif, Marion Ouidir, Grier Page, Shudi Pan, Graham Parker, Kimberly Paul, Alina Peluso, Trevor Penning, Brandon Pierce, Nirmala Prajapati, Gail Prins, Penelope J E Quintana, Arcot Raja Rajasekar, Aramandla Ramesh, Douglas Ruden, Blake Rushing, Elizabeth Scholl, Sophia Miryam Schüssler-Fiorenza Rose, Ruchir Shah, Mohammad Shahriar, Ram Siwakoti, Lissa Soares, Ghada Soliman, J Christopher States, Jeanette Stingone, Savannah Sturla Irizarry, Yangbo Sun, Scott Sundseth, Susan Teitelbaum, Anne Thessen, Ai Phuong Tong, Nisha Vijayakumar, Ryan Walker, Yi Wang, Kathryn Whyte, Ander Wilson, Lang Wu, K Lily Wu, Xin/Jean Yuan, Qiong Zhang, Xinyue Zhang, Neil Zhao, Yeyi Zhu

Affiliations

¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
² School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
³ Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China.
⁴ Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
⁵ Cyprus International Institute for Environmental and Public Health, School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus.
⁶ Department of Electrical Engineering and Computer Science, University of TN, Knoxville, TN, USA.
⁷ Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern CA, Los Angeles, CA, USA.
⁸ Department of Physics, Villanova University, Villanova, Philadelphia, USA.
⁹ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
¹⁰ Department of Chemistry, NC State University, Raleigh, NC, USA.
¹¹ Department of Bioinformatics and Genomics, College of Computing and Informatics, University of NC at Charlotte, Charlotte, NC, USA.
¹² Department of Biomedical Informatics, University of CO Anschutz Medical Campus, Aurora, CO, USA.
¹³ Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA.
¹⁴ Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of NY, Rensselaer, NY, USA.

Abstract

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Keywords: Exposome-Wide Association Study (ExWAS); data science; epidemiology; exposome; false discovery rate; phenotype.

Abstract

Grants and funding