The Importance of Gene—Environment Interaction

Author:

North Kari E.1,Martin Lisa J.2

Affiliation:

1. University of North Carolina at Chapel Hill,

2. Cincinnati Children's Medical Hospital and the University of Cincinnati School of Medicine, Ohio

Abstract

Given recent genetic advances, it is not surprising that genetics information is increasingly being used to improve health care. Thousands of conditions caused by single genes (Mendelian diseases) have been identified over the last century. However, Mendelian diseases are rare; thus, few individuals directly benefit from gene identification. In contrast, common complex diseases, such as obesity, breast cancer, and depression, directly affect many more individuals. Common complex diseases are caused by multiple genes, environmental factors, and/or interaction of genetic and environmental factors. This article provides a framework for the successful conduct of gene—environment studies. To accomplish this goal, the basic study designs and procedures of implementation for gene—environment interaction are described. Next, examples of gene—environment interaction in obesity epidemiology are reviewed. Last, the authors review reasons why epidemiological studies that incorporate gene—environment interaction have been unable to demonstrate statistically significant interactions and why conflicting results are reported.

Publisher

SAGE Publications

Subject

Sociology and Political Science,Social Sciences (miscellaneous)

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The Biosocial Approach to Human Development, Behavior, and Health Across the Life Course;RSF: The Russell Sage Foundation Journal of the Social Sciences;2018-01-01

2. An Overview of Literature Topics Related to Current Concepts, Methods, Tools, and Applications for Cumulative Risk Assessment (2007–2016);International Journal of Environmental Research and Public Health;2017-04-07

3. Identifying Gene–Environment Interactions Associated with Prognosis Using Penalized Quantile Regression;Contributions to Statistics;2017

4. Identifying Gene-Environment Interactions with a Least Relative Error Approach;Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics;2016

5. A Penalized Robust Method for Identifying Gene-Environment Interactions;Genetic Epidemiology;2014-02-24

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