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e-Article

Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk
Document Type
article
Source
Translational Psychiatry. 12(1)
Subject
Biological Psychology
Biomedical and Clinical Sciences
Psychology
Human Genome
Genetics
Underage Drinking
Prevention
Substance Misuse
Brain Disorders
Alcoholism
Alcohol Use and Health
Mental Health
Pediatric
2.1 Biological and endogenous factors
Aetiology
Mental health
Cardiovascular
Oral and gastrointestinal
Good Health and Well Being
Alcohol Drinking
Genetic Loci
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Multifactorial Inheritance
Substance-Related Disorders
COGA Collaborators
Clinical Sciences
Public Health and Health Services
Clinical sciences
Neurosciences
Biological psychology
Language
Abstract
Genome-wide association studies (GWAS) identify genetic variants associated with a trait, regardless of how those variants are associated with the outcome. Characterizing whether variants for psychiatric outcomes operate via specific versus general pathways provides more informative measures of genetic risk. In the current analysis, we used multivariate GWAS to tease apart variants associated with problematic alcohol use (ALCP-total) through either a shared risk for externalizing (EXT) or a problematic alcohol use-specific risk (ALCP-specific). SNPs associated with ALCP-specific were primarily related to alcohol metabolism. Genetic correlations showed ALCP-specific was predominantly associated with alcohol use and other forms of psychopathology, but not other forms of substance use. Polygenic scores for ALCP-total were associated with multiple forms of substance use, but polygenic scores for ALCP-specific were only associated with alcohol phenotypes. Polygenic scores for both ALCP-specific and EXT show different patterns of associations with alcohol misuse across development. Our results demonstrate that focusing on both shared and specific risk can better characterize pathways of risk for substance use disorders. Parsing risk pathways will become increasingly relevant as genetic information is incorporated into clinical practice.