Skip to navigation Skip to main content Skip to footer

Approved Research

Elucidating the Genetic Architecture of Externalizing Disorders in the UK Biobank

Principal Investigator: Professor Danielle Dick
Approved Research ID: 54225
Approval date: May 13th 2021

Lay summary

Twin and family studies consistently demonstrate a common genetic influence across a variety of traits characterized by impulsivity and difficulty with behavioral control. This common genetic influence is highly heritable and is generally referred to as the externalizing spectrum. Externalizing encompasses multiple clinical diagnoses (e.g. attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, antisocial personality disorder, and substance use disorders), non-clinical behavioral problems (e.g. antisocial behavior, risky sexual behaviors, and aggression), and personality characteristics (e.g. sensation seeking, impulsivity). Using the genetic overlap between these disorders and traits will allow us to useĀ  new multivariate methods for genome-wide association studies (GWAS) to identify genes involved in an underlying liability to externalizing.

First, we will run GWAS of various externalizing behaviors in UK Biobank. Second, we will combine these GWAS results with summary statistics from existing GWAS in a multivariate GWAS to identify variants associated with the underlying externalizing trait. Finally, we will use results from these multivariate GWAS to create aggregate measures of genetic risk (in the form of polygenic scores) in independent samples. Polygenic scores will allow us to better understand how genetic risk for externalizing relates to alcohol misuse, illicit drug use, antisocial behaviors, and other risk taking across the life course. Our approach maximizes our chances of discovering variants associated with externalizing related traits because we are combining information across multiple correlated GWAS. The increased sample size should also result in polygenic scores having increased predictive power. Information on genetic risk derived from these analyses could lead to better screening and early intervention efforts aimed at identifying people who are predisposed to a variety of risky health behaviors.