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Drug and Alcohol DependenceGenetic & Genomic MedicinePeer-reviewed journal article

Genomic relationships across psychiatric disorders including substance use disorders

Abdel AbdellaouiiD, Dirk J. A. SmitiD, Wim van den BrinkiD, Damiaan A J P DenysiD, Karin J. H. VerweijiD

PDFDOIPreprint

Abstract

Background and aimsA recent study investigated the genetic associations and latent genetic structure among eight psychiatric disorders using findings from genome-wide association studies (GWASs). No data from substance use disorders were included, while these represent an important category of mental disorders and could influence the latent genetic structure. We extended the original paper by recreating the genetic relationship matrix, graph, and latent genetic factor structure, including additional data from substance use disorders. MethodsWe used GWAS summary statistics of 11 psychiatric disorders, including alcohol dependence, nicotine dependence, and cannabis use disorder. We estimated genetic correlations between all traits in Linkage Disequilibrium-score regression. A graph was created to illustrate the network of genetic correlations. We then used the genetic relationships to model a latent genetic factor structure. ResultsAlcohol and nicotine dependence showed significant genetic correlations with several other psychiatric disorders, including ADHD, schizophrenia, and major depression. Cannabis use disorder was only significantly associated with ADHD. The addition of substance use disorders resulted in some changes in the latent structure of the factor model when compared to the original model including eight disorders. All substance use disorders contributed mostly to Factor 3, a heterogeneous factor with also loadings from ADHD, major depression, Autism Spectrum disorders, and Tourette Syndrome. ConclusionsAlcohol and nicotine dependence show widespread genetic correlations with other psychiatric disorders. Including substance use disorders in the factor analysis results in some changes in the underlying genetic factor structure. Given the instability of such models, identified structures should be interpreted with caution.

Citations
61
FWCI
6.07
Field percentile
top 2%

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