Identification of Transdiagnostic Childhood Externalizing Pathology Within an Electronic Medical Records Database and Application to the Analysis of Rare Copy Number Variation.

Externalizing traits and behaviors are broadly defined by impairments in self-regulation and impulse control that typically begin in childhood and adolescence. Externalizing behaviors, traits, and symptoms span a range of traditional psychiatric diagnostic categories. In this study, we sought to generate an algorithm that could reliably identify transdiagnostic childhood-onset externalizing cases and controls within a university hospital electronic health record (EHR) database.

Genomic structural equation modeling of reward-related traits: exploring the genetic factor structure and its relationship with psychopathology.

Reward sensitivity has a partial genetic background, and extreme levels may increase vulnerability to psychopathology. This study explores the genetic factor structure underlying reward-related traits and examines how genetic variance links to psychopathology. We modeled GWAS data from ten reward-related traits: risk tolerance (N = 975,353), extraversion (N = 122,886), sensation seeking (N = 132,395), (lack of) premeditation (N = 132,667), (lack of) perseverance (N = 133,517), positive urgency (N = 132,132), negative urgency (N = 132,559), attentional impulsivity (N = 124,739), motor impulsivity (N = 124,104), and nonplanning impulsivity (N = 123,509) to derive their genetic factor structure.

Advancing Gene Discovery for Substance Use Disorders Using Additional Traits Related to Behavioral Disinhibition.

Importance: Substance use disorders (SUDs) frequently co-occur with each other and with other traits related to behavioral disinhibition, a spectrum of outcomes referred to as externalizing. Nevertheless, genome-wide association studies (GWAS) typically study individual SUDs separately. This single-disorder approach ignores genetic covariance between SUDs and other traits and may contribute to the relatively limited genetic discoveries to date.

Multi-omic network analysis identifies dysregulated neurobiological pathways in opioid addiction.

Background: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. Yet, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.