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Community Approaches for Integrating Environmental Exposures into Human Models of Disease.

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Title: Community Approaches for Integrating Environmental Exposures into Human Models of Disease.
Authors: Thessen, Anne E.1,2 annethessen@gmail.com, Grondin, Cynthia J.3, Kulkarni, Resham D.4, Brander, Susanne1, Truong, Lisa1, Vasilevsky, Nicole A.5,6, Callahan, Tiffany J.7,8, Chan, Lauren E.9, Westra, Brian10, Willis, Mary11, Rothenberg, Sarah E.11, Jarabek, Annie M.12, Burgoon, Lyle13, Korrick, Susan A.14, Haendel, Melissa A.1
Source: Environmental Health Perspectives. Dec2020, Vol. 128 Issue 12, p125002-1-125002-6. 6p. 2 Diagrams, 1 Chart.
Subjects: Database management standards, Algorithms, Biological models, Community health services, Computer simulation, Diseases, Semantics, Environmental exposure, Statistical models, Genotypes
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. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Community Approaches for Integrating Environmental Exposures into Human Models of Disease.
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  Data: <searchLink fieldCode="AR" term="%22Thessen%2C+Anne+E%2E%22">Thessen, Anne E.</searchLink><relatesTo>1,2</relatesTo><i> annethessen@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Grondin%2C+Cynthia+J%2E%22">Grondin, Cynthia J.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Kulkarni%2C+Resham+D%2E%22">Kulkarni, Resham D.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Brander%2C+Susanne%22">Brander, Susanne</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Truong%2C+Lisa%22">Truong, Lisa</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vasilevsky%2C+Nicole+A%2E%22">Vasilevsky, Nicole A.</searchLink><relatesTo>5,6</relatesTo><br /><searchLink fieldCode="AR" term="%22Callahan%2C+Tiffany+J%2E%22">Callahan, Tiffany J.</searchLink><relatesTo>7,8</relatesTo><br /><searchLink fieldCode="AR" term="%22Chan%2C+Lauren+E%2E%22">Chan, Lauren E.</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Westra%2C+Brian%22">Westra, Brian</searchLink><relatesTo>10</relatesTo><br /><searchLink fieldCode="AR" term="%22Willis%2C+Mary%22">Willis, Mary</searchLink><relatesTo>11</relatesTo><br /><searchLink fieldCode="AR" term="%22Rothenberg%2C+Sarah+E%2E%22">Rothenberg, Sarah E.</searchLink><relatesTo>11</relatesTo><br /><searchLink fieldCode="AR" term="%22Jarabek%2C+Annie+M%2E%22">Jarabek, Annie M.</searchLink><relatesTo>12</relatesTo><br /><searchLink fieldCode="AR" term="%22Burgoon%2C+Lyle%22">Burgoon, Lyle</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Korrick%2C+Susan+A%2E%22">Korrick, Susan A.</searchLink><relatesTo>14</relatesTo><br /><searchLink fieldCode="AR" term="%22Haendel%2C+Melissa+A%2E%22">Haendel, Melissa A.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. Dec2020, Vol. 128 Issue 12, p125002-1-125002-6. 6p. 2 Diagrams, 1 Chart.
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  Data: <searchLink fieldCode="DE" term="%22Database+management+standards%22">Database management standards</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+models%22">Biological models</searchLink><br /><searchLink fieldCode="DE" term="%22Community+health+services%22">Community health services</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Diseases%22">Diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Genotypes%22">Genotypes</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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