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Journal Article

Citation

Baselmans B, Hammerschlag AR, Noordijk S, Ip H, van der Zee M, de Geus E, Abdellaoui A, Treur JL, van 't Ent D. Biol. Psychiatry Glob. Open Sci. 2022; 2(4): 389-399.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.bpsgos.2021.09.007

PMID

36324656

PMCID

PMC9616240

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 (N (eff) = 523,150 and 1,506,537, respectively), yielding 50 and 257 independent genetic signals. The statistics of DB, much more than RTB, signaled genetic predisposition to adverse cognitive, mental health, and personality outcomes. We found evidence for bidirectional causal influences between DB and substance use behaviors. Gene set analyses implicated contributions of neuronal cell development (DB/RTB) and synapse formation and transcription (RTB) mechanisms. Gene-brain mapping confirmed involvement of the amygdala and hypothalamus and highlighted other candidate regions (cerebellar dentate, cuneiform nucleus, claustrum, paracentral cortex). At the cell-type level, we noted enrichment of glutamatergic neurons for DB and RTB.

CONCLUSIONS: This bottom-up, data-driven study provides new insights into the genetic signals of externalizing behaviors and indicates that commonalities in genetic architecture contribute to the frequent co-occurrence of different DBs and different RTBs, respectively. Bioinformatic analyses supported the DB versus RTB categorization and indicated relevant biological mechanisms. Generally similar gene-brain mappings indicate that neuroanatomical differences, if any, escaped the resolution of our methods.


Language: en

Keywords

GWAS; Disruptive behavior; Externalizing behavior; Gene set analysis; Genetic correlation; Mendelian randomization; N-GWAMA; Risk-taking behavior; Stratified LD-score regression

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