Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Tonic immobility predicts poorer recovery from posttraumatic stress disorder

Background:  Tonic immobility (TI; a state of motor arrest during threat) and has been found to be associated with the development of psychopathology. It also hindered recovery from posttraumatic stress disorder (PTSD) after pharmacological treatment. The present study investigated the role of TI in recovery from PTSD in a large representative community sample with mixed traumas outside an exclusive treatment context. Methods:  Participants with PTSD from the panel for Longitudinal Internet Studies for the Social Sciences (LISS) completed measures for trauma, PTSD symptoms, and peritraumatic responses (fear, dissociation, and TI) in two subsequent years. Traumatized participants with PTSD were selected for the analyses (N = 262). Results:  TI was a relevant predictor for increased PTSD symptoms in year 2 after controlling for peritraumatic fear, peritraumatic dissociation, and PTSD symptoms in year 1, especially in abuse victims. Peritraumatic fear and dissociation no longer predicted PTSD in year 2 after entering TI in the model. Conclusions:  Our results indicate that TI may indeed hinder recovery from PTSD. TI may thus be a relevant factor to take into account after trauma and in treatment. The effects of TI may be especially negative for abuse victims

Guest Editors’ Introduction to the Special Issue on Causality at Work

Not being able to carry out proper experiments as the true (physical) scientists and even the colleagues from the neighboring psychology department do, sociologists have thrown themselves into the promising arms of the attractive “causal” models. From the 1960s onward, linear structural equation modeling became the norm and standard for the causal analysis of the nonexperimental data, to such an extent that it elicited biting comments from Louis Guttman (1977): “There has been a flowering of causal discoveries in sociology at a pace unheard of in the history of science . . . which undoubtedly put sociology at the forefront of all sciences in terms of frequency of discovery of fundamental relationships” (p. 103). Many others for many different reasons and from many different angles have shared Guttman’s criticisms and worries....

Marginal Models

Presents an overview of the basic principles of marginal modeling and offers a range of possible applications. This book includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a description of how to apply marginal models for a great diversity of research questions