Phenome-wide analysis of genetically imputed neuroimaging phenotypes reveals associations with psychiatric traits in a multi-ancestry cohort
Chihoub, L.; Wiers, C. E.; Gelernter, J.; Zhao, B.; Davis, C. N.; Kranzler, H. R.
Show abstract
BackgroundUnderstanding how variation in brain structure and function contributes to psychiatric and behavioral phenotypes remains a key challenge. The absence of neuroimaging data in many study samples limits this effort. MethodsWe used genome-wide association study (GWAS) summary statistics from the UK Biobank to impute 301 brain imaging-derived phenotype (IDP) genetic scores (IGS) in the Yale-Penn cohort, which is enriched for substance use disorders (n = 10,275; 52.8% European-like [EUR] and 47.2% African-like [AFR] genetic ancestry). The brain IDPs include white matter microstructure, regional volume, and resting-state functional connectivity measures, for which we generated IGS in the Yale-Penn participants. We then conducted a brain-wide phenome-wide association study (pheWAS) of the 301 IGS across 692 behavioral, psychiatric, and environmental traits. ResultsAmong EUR individuals, we identified 19 IGS with significant associations that survived within-trait corrections for multiple testing. These included links between genetically predicted white matter integrity and sedative abuse, tobacco withdrawal, attention deficit hyperactivity disorder (ADHD); structural brain volumes and cocaine dependence, ADHD, and conduct disorder; and functional connectivity with substance-related symptoms and social phobia. Among AFR individuals, we identified 15 IDPs with significant associations, including associations between genetically predicted white matter integrity and stimulant use disorder, regional brain volumes and opioid withdrawal/dependence, and functional connectivity and cocaine craving. ConclusionsGenetically imputed brain features capture biological variation associated with psychiatric traits. This work provides a framework for leveraging genetic data to link neuroimaging measures to substance use and mental health outcomes in samples that lack imaging data.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth 94%
- Prenatal Selective Serotonin Reuptake Inhibitor Exposure, Depression and Brain Morphology in Middle Childhood: Results from the ABCD Study 94%
- Altered physiological, affective, and functional connectivity responses to acute stress in patients with alcohol use disorder 94%
Similar papers in this journal
- Neural Correlates of Positive and Negative Valence System Dysfunction in Adolescents Revealed by Data-Driven Parcellation and Resting-State Network Modeling 96%
- Change in Striatal Functional Connectivity Networks Across Two Years Due to Stimulant Exposure in Childhood ADHD: Results from the ABCD Sample 96%
- Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure 95%
Similar papers in this journal
- Structural brain alterations associated with suicidal thoughts and behaviors in young people: results across 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium 95%
- Replicability of Structural Brain Alterations Associated with General Psychopathology: Evidence from a Population-Representative Birth Cohort 95%
- Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin 94%
Similar papers in this journal
- Reduced responsiveness of the reward system underlies tolerance to cannabis impairment in chronic users 94%
- Neural Response To Threat And Reward Among Young Adults At Risk For Alcohol Use Disorder 94%
- Common and separable neurofunctional dysregulations characterize obsessive compulsive, substance use, and gaming disorders - evidence from an activation likelihood meta-analysis of functional imaging studies 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.