Although several associations reached nominal significance, none remained significant after correction for multiple comparisons and the precision of the estimates was constrained by the available imaging GWAS sample size, uncertainty in SNP-heritability estimates, and the large number of regional comparisons.
Abstract
Adolescent externalizing behavior is a major risk factor for later substance use and other psychiatric outcomes. Understanding its genetic architecture and its relationship with brain imaging phenotypes requires scalable genome-wide methods applied to youth cohorts. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we implemented a pipeline for genome-wide association studies (GWAS) of longitudinally measured externalizing traits and multimodal neuroimaging-derived phenotypes (IDPs). We performed quality-controlled genotype processing and constructed harmonized phenotype and covariate datasets. GWAS analyses were conducted using REGENIE in a two-step framework, with Step 1 ridge regression models trained on LD-pruned variants and Step 2 association testing performed genome-wide. Externalizing traits measured at baseline and summarized as longitudinal means and slopes, together with approximately 200 IDPs measured at baseline and summarized as longitudinal means and slopes, were analyzed. We further constructed a custom linkage disequilibrium (LD) reference panel using unrelated individuals and computed LD scores using LDSC. Genetic correlations between externalizing traits and imaging phenotypes were estimated using LD Score Regression. This exploratory study systematically evaluated genome-wide genetic correlations between regional cortical morphology and externalizing phenotypes in adolescence. Although several associations reached nominal significance, none remained significant after correction for multiple comparisons. These findings should not be interpreted as demonstrating an absence of shared genetic architecture. Rather, the precision of the estimates was constrained by the available imaging GWAS sample size, uncertainty in SNP-heritability estimates, and the large number of regional comparisons. Larger imaging-genetics samples and independent replication will be required to determine whether modest or regionally specific genetic correlations exist.
An atlas of brain IDPs associated with bipolar disorder is established, by integrating epidemiological and genetic evidence, and candidate IDPs that were consistently associated with BD are highlighted across complementary analyses.
Wenzhuo Yang, Lin Pan, Haoqun Xie et al.· BMC Medicine· 0 citations
No statistically robust evidence of global genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found and small, localized, mixed-direction, or developmentally specific genetic effects remain possible.
The findings support a model of shared genetic liability across diverse substance use behaviors, mediated by specific gene expression patterns in the mesolimbic dopamine system and frontal cortex, and provides critical insights into the tissue-specific neurobiological pathways underlying addiction vulnerability.
Jiahua Zhou, An-Phuc Ta, Catherine Yang et al.· Biomedicines· 0 citations
Externalizing and internalizing behaviours capture heritable, transdiagnostic dimensions of psychopathology, but their genetic architecture and links to societal outcomes remain incompletely understood. This study applied a multivariate framework to population-based, non-clinical traits to explore simultaneous genetic associations across externalizing and internalizing behaviours to elucidate their biological underpinnings and genetic relation to clinical diagnoses. Using genomic structural equation modelling, we built a two-factor model for externalizing (EXT) and internalizing (INT) behaviour utilizing 13 genome-wide association study (GWAS) summary statistics from population-based cohorts (n=64,000-1,200,000). We tested genetic correlations of EXT and INT with 12 psychiatric diagnoses (n=14,000-2,000,000) using linkage-disequilibrium score regression, and we studied genetic relation to emotional well-being with polygenic score analyses (PRS-CS) in an independent cohort (n= 4,565). Multivariate GWAS revealed 184 genome-wide significant loci for EXT and 31 for INT. Gene-level analyses identified 52 novel genes for EXT and 18 for INT. Enrichment analyses using single-cell-RNA-sequencing data showed significant enrichment in neuronal and non-neuronal populations, with strongest signals in hippocampal CA4 for EXT and amygdalar excitatory neurons for INT. EXT correlated with attention-deficit/hyperactivity disorder and substance-use disorders, INT showed significant genetic correlations with obsessive-compulsive disorder and anorexia nervosa. EXT and INT also exhibited overlap in genetic correlations with posttraumatic stress disorder, major depressive disorder, schizophrenia, and anxiety disorder. PRS-CS demonstrated association of INT liability with emotional well-being. Overall, these findings advance our understanding of the genetic architecture underlying transdiagnostic externalizing and internalizing behaviours and underscore their relevance for both psychiatric risk and emotional well-being.
B. Šakić, M. Klein, N. Roth Mota et al.· European Neuropsychopharmaco...· 0 citations
The findings highlight genetic NDDs as genome-informed yet exposure-sensitive disorders and several external factors that both represent markers of epilepsy severity and may support early, genetically informed seizure management and careful stewardship of treatment exposures.
C. Boßelmann, Natasha N. Ludwig, C. Holingue et al.· Epilepsia· 0 citations
Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional linear frameworks attempting to model synergistic risk. While various loci are identified as contributing to neurocognitive disparities, the emergent phenotypic expression and associated predictive value relative to standard clinical baselines require further investigation. To facilitate dimensional reduction of complex genetic data into identifiable phenotypes, Generalized Low Rank Modeling (GLRM) and K-means clustering are applied to participant data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The utility of these derived archetypes and clusters is assessed, stratifying variance for Mini-Mental State Examination (MMSE) performance. Utilizing generalized additive modeling (GAM) and partial eta squared (p2) effect size, the derived genetic features are compared with other predictors including age, educational attainment, gender, and raw, genetic variant carriage dimensions. Results & Conclusion: In accordance with the hypothesized empirical regularity, age and education persist as primary predictors of MMSE performance. The unsupervised machine learning pipeline successfully identified a composite genetic cluster that emerged as an influential predictor in terms of relative magnitude (p2). Centroid analysis of the GLRM subspace indicated that a particular sub-population (Cluster 2) - defined by a substantial weighting on the EPHA1 target - demonstrated a statistically significant association with MMSE scores, relative to cluster 3. These results suggest that data-driven genetic feature engineering provides an interpretable basis for inference regarding variance in global cognition. By discovering multivariate genetic architecture, this modeling approach captures complexity often missed by individual clinical variable modeling. Such findings implicate the utility of interpretable machine learning for translational dementia research and predictive clinical stratification.
M. Espero· medRxiv· 0 citations
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