摘要：In this article, we make the case that social epidemiology provides a useful framework to define the environment within gene–environment (G×E) research. We describe the environment in a multilevel, multidomain, longitudinal framework that accounts for upstream processes influencing health outcomes. We then illustrate the utility of this approach by describing how intermediate levels of social organization, such as neighborhoods or schools, are key environmental components of G×E research. We discuss different models of G×E research and encourage public health researchers to consider the value of including genetic information from their study participants. We also encourage researchers interested in G×E interplay to consider the merits of the social epidemiology model when defining the environment. Inquiry into the complex relationships between genetic and environmental influences on behavioral traits has increased substantially in the past decade, 1,2 and this trend is particularly pronounced in health research. 3–6 A PubMed search yielded 42 articles published in 2000 that contained the expression “gene–environment interaction” in the title, abstract, or keywords, and this number increased to 704 by 2012. Although new and important findings have emerged from this body of work, there are also strong criticisms of the existing gene–environment (G×E) interaction studies from researchers across health, psychological, and social sciences. 2,7–10 There has been a weak replication record for “established” G×E interaction results, 11,12 there are concerns about statistical power for G×E associations, 8 and few researchers articulate plausible biological pathways for G×E associations. 7 Each of these factors has reduced the potential impact of many candidate G×E studies. To date, however, there has been very little discussion about one of the key shortcomings in the existing G×E research. Specifically, there is no real consensus about the nature and scope of the environment within G×E studies. 13 Because the “E” is one half of the G×E framework, it is critical to define the environment in a manner that maximizes the contributions from both social and biological sciences and improves our understanding about the health of populations. This need for cross-disciplinary discussions is echoed in the current efforts of the National Coalition for Health Profession Education in Genetics ( http://www.nchpeg.org/bssr ). This group, with support from the Office of Behavioral and Social Science Research with the National Institutes of Health (NIH), has developed a project entitled “Genetics and Social Science” with the explicit goal to “create an educational program that will improve social scientists’ genetics literacy.” This project points to a variety of collaboration opportunities within the area of G×E interplay and states that “geneticists may be less familiar with measures used to quantify the observable external environments, and can benefit from the guidance of social and behavioral researchers.” 14 The goal of this article is to address this comment by offering guidance for operationalizing and measuring the social environment in G×E studies. Consensus regarding the definition of the social environment will help to guide future work and locate G×E evidence in a more coherent framework. We make 3 contributions toward this goal. First, we discuss the importance of existing social epidemiological and sociological theory for understanding the environment in a multilevel, multidomain, longitudinal framework that accounts for upstream processes influencing health outcomes. In particular, this approach draws a sharp distinction between individual and family attributes and the broader social contexts in and through which they arise. Second, and relatedly, we emphasize the potentially important role that characteristics of intermediate levels of social organization, such as neighborhoods, schools, and the workplace, have to play in a more thoughtful account of the environment in G×E interplay research. Finally, we discuss different forms and models of G×E interplay with frequent reference to previous published research.
