Expressive

rs1110320 - DENND1A

Magnitude 2.2 · 2 studies on file

Reported associations

  • Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction - Unknown journal (n.d.) · Unknown authors · PubMed 34446935

    ABSTRACT: Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior, and ADHD, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scor

  • The Genetic and Neural Substrates of Externalizing Behavior - Unknown journal (n.d.) · Unknown authors · PubMed 36324656

    ABSTRACT: Background To gain more insight into the biological factors that mediate vulnerability to display externalizing behaviors, we leveraged genome-wide association study summary statistics on 13 externalizing phenotypes. Methods After data classification based on genetic resemblance, we performed multivariate genome-wide association meta-analyses and conducted extensive bioinformatic analyses, including genetic correlation assessment with other traits, Mendelian randomization, and gene set and gene expression analyses. Results The genetic data could be categorized into disruptive behavior (DB) and risk-taking behavior (RTB) factors, and subsequent genome-wide association meta-analyses provided association statistics for DB and RTB (Neff = 523,150 and 1,506,537, respectively), yieldi

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.