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As a dynamic, interdisciplinary field, behavior genetics and its evolution are being followed closely by scientists across the psychological and medical domains. The discoveries surrounding the human genome and the advancement in molecular genetic technologies have ?led to studies becoming increasingly sophisticated and yielding ?yet more conclusive and useful results. This is certainly the case in the area of child and adult psychopathology.?Behavior Genetics of Psychopathology summarizes the state of the field, examiningthe role of genes and environment as they affect common neurodevelopmental and psychiatric conditions. Emphasizing key research areas (comorbidities, twin studies, the integration of methods), the book assesses the current literature, offers up-to-date findings, sorts through lingering controversies, and identifies a clear future agenda for the field. Expertly-written chapters focus on issues of both general salience that shape behavior genetics of psychopathology, to specific disorders of major clinical importance, among them:?ADHD: the view from quantitative genetic research.Autism spectrum disorders and their complex heterogeneityGenetic influences on anxiety and depression in childhood and adolescence.Evidence for etiologically-defined subgroups within the construct of antisocial behavior.Sleep and psychopathology: the reasons for their co-occurrence. Behavioral genetic approaches to the etiology of comorbidity.Epigenetics of psychopathology.?This combination of timeliness and depth of coverage make Behavior Genetics of Psychopathology a frontline resource for behavior geneticists, psychologists, psychiatrists, and neuroscientists, and is perfectly suited to graduate students looking to join these fields.

Criminology is in need of conceptual revival, and behavior genetics can provide the concepts and research design to accomplish this. Behavior genetics is a biologically‐friendly environmental discipline that often tells us more about environmental effects on individual traits than about genetic effects. Anomie/strain theory is used to illustrate the usefulness of behavior genetics to criminological theories. Behavior genetics examines the individual differences that sort people into different modes of adaptation and that lead them to cope constructively or destructively with strain. Behavior genetics and other biosocial perspectives have the potential to help illuminate Agnew's (1997) extension of General Strain Theory (GST) into the developmental realm.

AbstractThe present study explores whether genetic factors explain variation in the levels of apostasy — defined as a disengagement from religious belief, identity and/or practice — in a US-based sample during the transition from adolescence to early adulthood. I posit that genetic factors at least partially explain the variance of three measures of apostasy: disengagement from religious institutions, cessation of prayer and religious disaffiliation. I argue that genetic factors associated with risk-taking behaviors, externalizing behaviors and/or correlates of apostasy may all influence the likelihood of becoming an apostate during the transition from adolescence to early adulthood in the USA. Results reveal that genetic factors explain approximately 34% of the variance in cessation of prayer and 75% of the variance in religious disaffiliation. However, genetic factors do not influence disengagement from religious institutions. This study advances our knowledge of the etiology of apostasy and highlights the need to incorporate genetic data into social scientific research.

I develop "personalized social policy" as a speculative exercise to examine the possibility that policy makers and service providers, making decisions under conditions of uncertainty, could use genetic information to divide clients into "treatment groups" receiving differential service goods to better meet individual needs or achieve efficiency. Using real and hypothetical examples from genetics and social services, I show how personalized social policy might work and discuss its implications for the practical organization and theoretical justification of social policy. The analysis suggests that behavior genetics could dramatically impact social policy, not by sowing fatalism about change, but by offering specific, practical tools that would reorganize the institutional and professional composition of social services delivery and bolster functionalist rationales for social welfare. Policy analysts should focus on specific ways genetic information might affect policy decisions to prepare proactive responses should the prospect of personalized social policy become a reality.

Animal models of human disorders are a ubiquitous feature of contemporary biomedical research, but how is their value and role in understanding human disorders established? This article examines the dynamics of building up (and sometimes knocking down) claims about what a model can demonstrate in the field of animal behavior genetics. Drawing on long-standing analogies that describe scientific knowledge production as a process of construction, I introduce the metaphor of an 'epistemic scaffold' to illuminate how scientists create and contest claims about the utility of animal models. The flexible, temporary nature of scaffolding draws attention to the processes of building up claims to increasingly risky heights and reconfiguring the evidence supporting particular models by including or excluding particular facts and claims. As researchers include or exclude observations from epistemic scaffolds, to contest or build up different links, they gradually frame human disorders. Negotiations over how much to claim about the utility of animal models also reflect larger tensions in the discipline concerning what animal studies reveal about human disorders.

ObjectivesThis study explains how behavior genetic analysis using a twin design can help us assess the validity of our measures.MethodsWe test multiple indicators of response propensity, a measure used by survey researchers to better understand the similarities and differences between survey respondents and nonrespondents. The response propensity indicators evaluated include response to follow‐up surveys and subsequent waves of a panel and the completion of a sensitive recontact information sheet to aid subsequent recontact efforts.ResultsA classical and the newly proposed method of validation all point to insufficient validity of our response propensity measures. Construct validation using data from the National Survey of Midlife Development in the United States exhibited little correlation between indicators. Genetic analysis suggests that the success of subsequent data‐collection efforts is predominantly driven by additive genetic effects, while nonresponse to inquiries for recontact information is influenced predominantly by familial environmental predictors.ConclusionOur results indicate that different underlying constructs drive the response propensity indicators, suggesting that nonresponse is, at minimum, multidimensional.

This study explains how behavior genetic analysis using a twin design can help us assess the validity of our measures. We test multiple indicators of response propensity, a measure used by survey researchers to better understand the similarities and differences between survey respondents and nonrespondents. The response propensity indicators evaluated include response to follow-up surveys and subsequent waves of a panel and the completion of a sensitive recontact information sheet to aid subsequent recontact efforts. A classical and the newly proposed method of validation all point to insufficient validity of our response propensity measures. Construct validation using data from the National Survey of Midlife Development in the United States exhibited little correlation between indicators. Genetic analysis suggests that the success of subsequent data-collection efforts is predominantly driven by additive genetic effects, while nonresponse to inquiries for recontact information is influenced predominantly by familial environmental predictors. Our results indicate that different underlying constructs drive the response propensity indicators, suggesting that nonresponse is, at minimum, multidimensional. Adapted from the source document.