摘要：The integration of genetics and the social sciences will lead to a more complex understanding of the articulation between social and biological processes, although the empirical difficulties inherent in this integration are large. One key challenge is the implications of moving “outside the lab” and away from the experimental tools available for research with model organisms. Social science research methods used to examine human behavior in nonexperimental, real-world settings to date have not been fully taken advantage of during this disciplinary integration, especially in the form of gene–environment interaction research. This article outlines and provides examples of several prominent research designs that should be used in gene–environment research and highlights a key benefit to geneticists of working with social scientists. SINCE THE PUBLICATION IN Science of empirical evidence of gene–environment (G×E) interaction, there has been growing interesting in integrating biological and social science approaches, data, and models. The original results by Caspi et al. 1 suggested an important, genetic source of heterogeneity in responses to early life insults, attempting to partially answer the question of why some individuals are resilient to stressors, whereas others experience deleterious psychological sequelae. Although these studies created substantial interest in potential gene-by-environment interactions, they also needed to be replicated and extended by other researchers using alternative data. There are now competing meta-analyses suggesting either that the original results linking differential response to stress by the serotonin transporter gene (5-HTT) is robust 2 or lacks consistent supporting replication. 3 The discussion generated by this line of research in the biological and social science communities has been valuable in highlighting the shortcoming of the research design by Caspi et al. A key concern that has been the subject of much debate is whether the study (and studies like it) is adequately powered. 4,5 We point to another concern that is the subject of less inquiry. Even with highly powered studies (many current collaborative groups have amassed data sets that include tens of thousands of individuals), an important conceptual (and statistical) issue is the likelihood that the measured environments may be correlated with unmeasured genetic variation, and thus, may be acting as proxy for a gene-by-gene interaction rather than a G×E interaction. As sample sizes continue to get larger, a shift in focus should be from the statistical issue of power to the conceptual issue of modeling interactions between variables that are not themselves correlated (gene–environment correlation [rGE]). Although for studies aiming to detect main effects of genotypes, approaches that try to control for population stratification—such as genomic control, 6 principal components, 7 or family-based analysis 8 —may be adequate to account for rGE, when trying to model G×E interaction effects, the added burden of obtaining exogenous environmental variation is present, lest models become misspecified. In light of this uncertainty, many researchers have turned to examinations of model organisms to be able to control—through random assignment—the environment as well as the genotype of animal subjects. Because human research focusing on genetic and environmental interactions will be unable to use truly experimental research designs in the near future, this leaves G×E research in a precarious position. On the one hand, results from animal models, where both the genetic and environmental contributions of phenotype can be experimentally altered, will no doubt continue to be used to suggest likely mechanisms involved in similar human phenotypes. However, it is often difficult for social scientists, and others, to fully disregard the difficulty in translating results from animal models to human populations. On the other hand, research on humans often has little leverage in experimentally altering genotype and social environment (outside of laboratories) to facilitate causal inference in G×E research. Although there is active involvement in enrolling individuals in randomized controlled trials and examining genetic heterogeneity of causal effects, this is only a small area of, typically pharmacological, research and likely does not have the capacity to answer many important G×E questions of broader relevance to public health. Because many public health interventions occur on a large scale, such as state soda taxation, federal alcohol access policies (e.g., the minimum legal drinking age of 21 years), and federal guidelines for clinical care, only large epidemiological and social science data and methods, combined with genetic and biomarker measures, will be able to examine issues related to broad public health questions. In this essay, we suggest a way forward in G×E research in humans, which is for social scientists to utilize their training in methods of causal inference using nonexperimental data and collaborate with biological and genetic scientists to leverage the large advances in social science data that now contain biomarkers and genotype measures. Such an approach represents a path forward that is truly interdisciplinary, where both sides bring important expertise to the table. Social scientists generally lack knowledge of biological functionality important in selecting credible gene targets for examination, whereas geneticists have not been trained in advanced econometric methods. The bread and butter of large sections of modern empirical economics, political science, and sociology is leveraging so-called “natural experiments” and institutional quirks that, under reasonable assumptions, can allow causal inference using observational data. We outline specific methods and examples in this essay and also suggest new approaches.
