Polygenic overlap of substance use behaviors and disorders with externalizing and internalizing problems independent of genetic correlations
Year: 2025
Type: article
Abstract: BACKGROUND: Externalizing and internalizing pathways may lead to the development of substance use behaviors (SUBs) and substance use disorders (SUDs), which are all heritable phenotypes. Genetic correlation studies have indicated differences in the genetic susceptibility between SUBs and SUDs. We investigated whether these substance use phenotypes are differently related to externalizing and internalizing problems at a genetic level. METHODS: We analyzed data from genome-wide association studies (GWAS) of four SUBs and SUDs, five externalizing traits, and five internalizing traits using the bivariate causal mixture model (MiXeR) to estimate genetic overlap beyond genetic correlation. RESULTS: Two distinct patterns were found. SUBs demonstrated high genetic overlap but low genetic correlation of shared variants with internalizing traits, suggesting a pattern of mixed effect directions of shared genetic variants. Conversely, SUDs and externalizing traits exhibited considerable genetic overlap with moderate to high positive genetic correlation of shared variants, suggesting concordant effect direction of shared risk variants. CONCLUSIONS: These results highlight the importance of the externalizing pathway in SUDs as well as the limited role of the internalizing pathway in SUBs. As MiXeR is not intended for the identification of specific genes, further studies are needed to reveal the underlying shared mechanisms of these traits.
Source: Psychological Medicine
Institutions: Universidade de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago, University of Oslo, Inserm, Université Paris Cité, Optimisation Thérapeutique en Neuropsychopharmacologie, University of Bergen, Complejo Hospitalario Universitario de Santiago, Servicio Gallego de Salud
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
Awards: G1300865376, G1420817852, Modern Signal Processing Methods for Ultra-realistic Light-Field Displays, G292593226, G3433676821, G354670507, G3761261155, G434337789, G4493028280, G4597357768, G5479941350, G5800369618, G6488308326, Predicting comorbid cardiovascular disease in individuals with mental disorder by decoding disease mechanisms, G8130263415, Using real-world big data from eHealth, biobanks and national registries, integrated with clinical trial data to improve outcome of severe mental disorders
OpenAlex ID: W4408998492
