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This chapter discusses the study of gene-environment and gene-gene interactions in genetic epidemiology. It starts with a review of the definition of statistical gene-environment interaction, which is what is typically measured and reported in genetic association studies, and contrasts this with specific theoretical and general intuitive models for biologic interaction. It then describes how these concepts can be extended to the study of gene-gene interactions, and reviews available study designs, with particular emphasis on estimating joint gene-environment and gene-gene effects.

This chapter discusses genetic modifiers of cancer risk. Topics covered include rationale for the study of low-penetrance genes, the role of low-penetrance genes in cancer susceptibility, methodological issues, gene selection in population studies, overview of candidate genes, overview of cancer-specific associations, and gene-environment interaction.

Scientists have been debating the interaction of nature and nurture for nearly a century. Over those 100 years, two radically different visions of interaction have emerged—one side understanding it to be a nuisance to biological explanation and rare in nature, and the other side understanding it to be common in nature and vitally important to biological explanation. This chapter surveys that 100 years of empirical research on the interaction of nature and nurture to offer up an integrative analysis of interaction that rises above the divisive debate. That new analysis is then applied to the contemporary debate over the serotonin transporter gene, exposure to stressful life events, and the development of depression.

Defining Vulnerabilities opens with a description of the history of childhood lead poisoning in the United States, which frames the puzzle at the center of this book: why and how did environmental health scientists, whose defining focus is on the effects of the environment on human health, make “gene-environment interaction” their mantra?How do we understand how scientists deeply committed to protecting public health decided to shift their research practices inside the human body and to the molecular level? The introduction provides a brief overview of the central analytic categories of the book, including fields, institutions, socially skilled actors, and biopolitics.

In 2003, Terrie Moffitt and Avshalom Caspi published a groundbreaking study examining how the serotonin transporter gene and stressful life events interact to contribute to the risk of developing depression. When dozens of research teams around the globe attempted to replicate that original result, a peculiar thing emerged—some of the studies supported the original finding, but many came back negative. Faced with this dilemma, scientists performed meta-analyses of the replications; however, the meta-analyses only created their own puzzle—one came back supportive of the original finding, while several came back in conflict with it. Scientists studying the nature and nurture of depression were thus unable to agree whether the original study held up to the scrutiny or fell into disrepute, and unable to agree whether research on gene-environment interaction or research on genome wide association studies was the way forward for human genetics. This episode can be understood as the most recent instantiation of a long-standing dispute about gene-environment interaction. This chapter displays how contemporary scientists debating the nature and nurture of depression have repeated arguments for and against interaction that can be traced back through nearly a century of scientific debate.

The gene-environment interplay perspective is a relatively new and exciting area in the social sciences. It has garnered a great deal of attention in the study of physical and mental health and it has begun to shape the work of demographers and sociologists. This chapter describes the gene-environment interplay perspective and reviews the limited work that has been done in this area thus far. The discussions cover the gene-environment correlation; gene-environment interaction; and gene-environment interplay and political science.

Depending on which gene a child has, being sent to daycare can either increase or decrease a child’s chances of developing allergies. Likewise, children with one form of a gene respond better to an empathy-oriented approach to discipline, while children with the other form of the gene respond better to a punishment-oriented approach to discipline. Research on these cases of gene-environment interaction raises the prospect of a “genetic guide to parenting”—the idea being that information about the child’s genome would inform parental decisions about everything from whether or not to send a child to daycare, to how to respond to the next temper tantrum. As calls for the whole genome sequencing of newborns increase and the goal of a $1000 genome grow near, the reality of a genetic guide to parenting becomes more likely. This chapter considers what a genetic guide to parenting might look like, evaluating the promises and perils of this new technology as they relate to concerns about medicalization and genetic determinism.

The ultimate goal of epidemiology is to uncover the determinants of health and disease. In the absence of experimental designs, we must rely upon observational designs, which necessitate caution in interpretation. Reproductive and perinatal epidemiology faces many unique study design challenges when evaluating etiologic or predictive questions. This chapter reviews a description of select novel study designs with relevancy for reproductive and perinatal epidemiology, viz., case-cohort study, case-crossover study, case-time-control study, a hybrid design for studies of biomarkers, case-only studies of gene-environment interactions, the case-parent-triad design, a hybrid design for genetic studies, and time-to-pregnancy and current duration approaches. Strengths and limitations of each study design are noted, as well as the appropriate setting for application along with examples for each design. While these designs hold promise for application to many areas of reproductive and perinatal epidemiology and provide a valuable set of tools, many unresolved issues remain and support the need for even more design options.

Chapter 5 critically analyzes the biomedical and lifestyle approaches dominating epidemiologic theorizing and research since the mid-20th century. Tracing the origins of the term “biomedicine,” it explicates the core assumptions of the “biomedical model” and its reduction of explanations of disease occurrence to disease mechanisms within individual organisms. It next discusses the complex history of “lifestyle” theorizing, as linked to the rise of “consumer” society and also 20th c methodological individualism, including its reduction of social phenomena to individual attributes. Together with the newly coined construct of “risk factor” (first employed by the Framingham study for research on cardiovascular and other chronic diseases), these frameworks coalesced into mainstream epidemiology's theoretical foundation, exemplified by the widely-adopted spiderless web of causation, whose features are explicated. New 21^st century variants are also discussed, as per the frameworks of “gene-environment interaction,” ‘evolutionary medicine,” and the “developmental origins of health and disease” (DOHaD).

The field of psychology has a long history of debating to what degree psychological traits are determined by genetic predispositions versus environmental factors. According to quantitative behavioral genetics studies, psychological well-being has a significant heritable genetic component. However, environmental factors consistently tend to explain more variance than genetic ones. While these findings imply that genes and environment have independent and distinctly quantifiable effects, a biological perspective suggests that any developmental outcome is the function of the dynamic and bidirectional interplay between genetic and environmental factors. This view is supported by recent molecular genetics studies on gene–environment interaction and epigenetics. Consequently, psychological well-being reflects the combined additive and multiplicative effect of multiple factors, including genes, environment, their interaction, as well as stochastic processes. Given this complex relationship between genes and environment, current methods and approaches are not yet able to precisely disentangle genetic and environmental contributions to psychological well-being.

This chapter presents set-based approaches that focus on identifying G X E interactions rather than set-based approaches that are based primarily on detecting G main effects (e.g., via marginal effects). The author reviews both his own research and the development of his Set Based Gene EnviRonment InterAction test (SBERIA), as well as another set-based G X E approach referred to as GESAT. GESAT extends the variance component test of the SNP-set Kernel Association Test (SKAT) to evaluate G x E effects while incorporating the main SNP effects as covariates. While both of these approaches (SBERIA and GESAT) have outperformed other benchmark methods (e.g., likelihood ratio test) and have been demonstrated to retain the appropriate Type 1 error rate, in this chapter the author conducts simulation studies to compare findings for SBERIA and GESAT approaches, and identifies associated strengths and limitations of the respective methods.

Chapter 3 introduces the six core neurodevelopmental disorders and their genetic origins. The genetics of four disorders (Fragile X syndrome, Down syndrome, Williams syndrome and 22q11 deletion syndrome) are now well documented but the genetic origins of autism and ADHD, although generally assumed are as yet unclear. However, establishing the causal links between the genetic, cognitive and behavioral levels of analysis is a complex process. Similarities in behaviors across differing disorders can arrive through different cognitive mechanisms that have their roots in specific genetic-brain interactions. Current research endeavors represent exciting new directions in teasing out the genetic contribution to the attention problems that characterize many neurodevelopmental disorders.

Family history studies are now used for a much wider range of purposes than in the past. This chapter discusses their usefulness for refining phenotype definitions, targeting preventive interventions, characterizing genetic effects, and exploring gene-environment interaction. These are in addition to their original role in genetic research, which was to examine whether diseases aggregate in families. The chapter also elaborates on their relationship to the risk factor designs described in previous chapters.

This chapter presents a detailed discussion of the dynamics between genetic and environmental influences on behavior. It discusses how serious, persistent offenders (SPOs) differ from the general population and why their numbers are few. It proposes that genetic variability can account for some of the likelihood that an individual will develop persistent patterns of behavior and we discuss how some individuals are genetically more susceptible to criminogenic environmental influences. In the discussion, gene × environment interactions and gene × environment correlations are explored in detail. The chapter concludes with an example of genetically influenced susceptibility to abuse, and suggestions for interventions that should be explored.

This chapter examines the application of the empirical method to the study of causality by focusing on the biological sciences. In particular, it considers the nature-nurture debate and gene-environment interactions, the application of causal knowledge to the goal of eradicating specific infectious diseases such as plague and influenza, and the emerging discipline of ecology to illustrate the application of the empirical method and its interaction with Facet 2's level of analysis and Facet 1's causative models. The chapter also provides examples of how genetic variation can influence vulnerability to disease and concludes with a discussion of the directionality of time and the concept of chance, along with generalizations about causality in biology.

The aim of the RELIEF algorithm is to filter out features (e.g., genes, environmental factors) that are relevant to a trait of interest, starting from a set of that may include thousands of irrelevant features. Though widely used in many fields, its application to the study of gene-environment interaction studies has been limited thus far. We provide here an overview of this machine learning algorithm and some of its variants. Using simulated data, we then compare of the performance of RELIEF to that of logistic regression for screening for gene-environment interactions in SNP data. Even though performance degrades in larger sets of markers, RELIEF remains a competitive alternative to logistic regression, and shows clear promise as a tool for the study of gene-environment interactions. Areas for further improvements of the algorithm are then suggested.

Gene–environment interaction (G×E) research in humans seeks to answer how specific genetic variation contributes to marked individual differences in responding to life experiences, primarily in regard to psychological functioning. In this chapter, we highlight theoretical models underlying G×E research, aspects of its history and controversies, the current state of G×E knowledge, and emerging and future directions for G×E research. Throughout this discussion, we show how this work has emerged across multiple units or levels of analyses, ranging from those closer to the biological functioning of the genes involved, such as neural activity in functional imaging, to more distal outcomes such as diagnoses of psychopathology. Important future directions for G×E research are transitioning from single variant to multiple variant approaches, and more carefully conceptualizing and measuring risk environments while also boosting sample sizes. Ultimately, by attending to these issues, G×E research can not only contribute to early detection of individuals with risky genetic and environmental profiles, but can also aid in revealing etiological pathways, thereby elucidating novel treatment approaches to mental illnesses.

Francis Galton first envisioned a science of “nature versus nurture”—a science devoted to understanding whether nature or nurture contributes more to human traits and a science devoted to using that understanding to intervene on those traits with eugenics. Soon after Galton’s introduction of nature versus nurture, scientists moved beyond versus and began considering how nature and nurture interact (now understood as gene-environment interaction). Beyond Versus explores famous episodes from the nature/nurture debate regarding eugenics, race and IQ, and the causes of depression; in so doing, it tells the story of the past, takes stock of the present, and considers the future of research on the interaction of nature and nurture—research that continues to make headlines and raise controversy.

This chapter summarizes key quantitative aspects of criminal career studies, including such parameters as initiation, duration, and termination, and in between, frequency, seriousness of the offenses, and crime mix including specialization. It stresses that despite advances, there is a great need to build a more complete and comprehensive knowledge base of delinquency career characteristics using official and self-reported delinquency records based on longitudinal data from multiple sites and from multiple countries. It emphasizes the importance of trajectory research and research on co-offending, that future studies should include measures of genetics and brain functioning, and that the study of gene-environment interactions will become more important than it is now. It makes the case that more research is needed to examine the impact of different justice policies and practices on criminal career parameters.