Community Approaches for Integrating Environmental Exposures into Human Models of Disease,Environmental Health Perspectives

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Community Approaches for Integrating Environmental Exposures into Human Models of Disease
Environmental Health Perspectives ( IF 10.4 ) Pub Date : 2020-12-28 , DOI: 10.1289/ehp7215
Anne E. Thessen _{1,

2} , Cynthia J. Grondin ₃ , Resham D. Kulkarni ₄ , Susanne Brander ₁ , Lisa Truong ₁ , Nicole A. Vasilevsky _{5,

6} , Tiffany J. Callahan _{7,

8} , Lauren E. Chan ₉ , Brian Westra ₁₀ , Mary Willis ₁₁ , Sarah E. Rothenberg ₁₁ , Annie M. Jarabek ₁₂ , Lyle Burgoon ₁₃ , Susan A. Korrick ₁₄ , Melissa A. Haendel ₁

Affiliation

Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA
Ronin Institute for Independent Scholarship, Montclair, New Jersey, USA
Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, Oregon, USA
Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, USA
Department of Pharmacology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, USA
Nutrition, Oregon State University, Corvallis, Oregon, USA
University Libraries, University of Iowa, Iowa City, Iowa, USA
School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA
Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
U.S. Army Engineering Research and Development Center, Vicksburg, Mississippi, USA
Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

Abstract

Background:

A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.

Objectives:

Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.

Discussion:

There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215

中文翻译：

将环境暴露纳入人类疾病模型的社区方法

摘要

背景：

基因组医学的关键挑战是确定疾病的遗传和环境危险因素。当前，可用的数据将大多数已知的编码人类基因与表型相关联，但是人类疾病的环境成分在这些链接的数据集中的代表性极低。没有环境暴露信息，即使有了现代基因组学的承诺，我们实现精确健康的能力也会受到限制。实现基因，表型和环境的整合将需要将数据广泛转换为标准，可计算的形式，并扩展现有基因/表型数据模型。当前不存在实现此集成所需的数据标准和模型。