Background: There is converging and compelling evidence that mental disorders are more optimally conceptualized in a hierarchical framework that transcends traditional categorical boundaries. However, the majority of this evidence comes from studies that draw upon predominantly Caucasian populations. Whether the hierarchical conceptualization of mental disorders generalizes across racial-ethnic groups, including for African American (AA) youths, is unclear. This research is especially crucial in light of the observed racial-ethnic differences in the prevalence rates of several mental disorders. Methods: We tested multidimensional and bifactor models of 15 DSM-5 diagnoses and psychiatric traits in two groups, including AA (n=3,088) and European American (EA) (n=5,147) youths aged 8-21 from the Philadelphia Neurodevelopmental Cohort (PNC). We also conducted multigroup confirmatory factor analyses to test for factorial invariance between the best fitting AA and EA multidimensional and bifactor models. Results: In the multidimensional model tests, a three-factor model, specifying internalizing, externalizing, and thought dimensions, emerged as the best fitting model for AAs and EAs. In the bifactor model tests, a three-factor model (i.e., internalizing, externalizing, and thought dimensions) that also specified a general factor emerged as the optimal for both AAs and EAs. The general factor accounted for a significant proportion of the covariation between the secondary factors and the individual disorders and traits. Furthermore, both models were factorially invariant, indicating that there was no significant difference in the factor structure of mental disorders between AAs and EAs in PNC. Conclusions: This study provides evidence that INVARIANCE IN THE STRUCTURE OF MENTAL DISORDERS 3 the hierarchical factor structure of mental disorders may be racial-ethnically robust. This finding has implications for etiological and epidemiological studies focused on racial-ethnic subgroup comparisons, particularly with respect to identifying similarities and differences in prevalence rates or sociodemographic risk factors for mental disorders.

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Background Childhood exposure to interpersonal violence (IPV) may be linked to distinct manifestations of mental illness, yet the nature of this change remains poorly understood. Network analysis can provide unique insights by contrasting the interrelatedness of symptoms underlying psychopathology across exposed and non-exposed youth, with potential clinical implications for a treatment-resistant population. We anticipated marked differences in symptom associations among IPV-exposed youth, particularly in terms of ‘hub’ symptoms holding outsized influence over the network, as well as formation and influence of communities of highly interconnected symptoms. Methods Participants from a population-representative sample of youth (n = 4433; ages 11–18 years) completed a comprehensive structured clinical interview assessing mental health symptoms, diagnostic status, and history of violence exposure. Network analytic methods were used to model the pattern of associations between symptoms, quantify differences across diagnosed youth with (IPV+) and without (IPV–) IPV exposure, and identify transdiagnostic ‘bridge’ symptoms linking multiple disorders. Results Symptoms organized into six ‘disorder’ communities (e.g. Intrusive Thoughts/Sensations, Depression, Anxiety), that exhibited considerably greater interconnectivity in IPV+ youth. Five symptoms emerged in IPV+ youth as highly trafficked ‘bridges’ between symptom communities (11 in IPV– youth). Conclusion IPV exposure may alter mutually reinforcing symptom co-occurrence in youth, thus contributing to greater psychiatric comorbidity and treatment resistance. The presence of a condensed and unique set of bridge symptoms suggests trauma-enriched nodes which could be therapeutically targeted to improve outcomes in violence-exposed youth.

Objective: This study explored whether maltreatment moderates the association of polygenic risk for ADHD. Because individuals with low polygenic scores (PGS) for ADHD were previously shown to have better than expected functional outcomes (i.e., cognitive, mental health, social-emotional) than individuals with middle or high ADHD PGS, we hypothesized that low ADHD PGS may possible confer a protective effect against maltreatment in the development of ADHD. Method: Data were from participants with phenotypic and genotypic data in the National Longitudinal Study of Adolescent to Adult Health (Add Health; n=4,722), which was used to examine the effects of ADHD PGS, maltreatment, and their interaction on childhood ADHD symptoms. ADHD PGS were generated from the most recent genome-wide association study on ADHD and categorized into three groups (i.e., low, medium, high) using empirically determined cut-points. A maltreatment factor score was derived from five forms of self-reported maltreatment experiences prior to age 18. Results: ADHD PGS and maltreatment were positively associated with ADHD symptoms, as expected. However, we did not detect an interaction between ADHD PGS and maltreatment on ADHD symptoms. Conclusion: Despite the increase in predictive power afforded by PGS, the lack of an interaction between ADHD PGS and maltreatment on ADHD symptoms converges with an emerging body of psychiatric PGS studies that have also failed to detect polygenic-environment interplay on psychiatric outcomes. We discuss possible reasons for this pattern of results and offer alternative methods for future research in revealing important polygenic-environment interactions for ADHD.

Objective: Polygenic scores (PGS) are widely used in psychiatric genetic associations studies due to their impressive power to predict focal outcomes. However, they lack in discriminatory power, in part due to the high degree of genetic overlap between psychiatric disorders. The lack of prediction specificity limits the clinical utility of psychiatric PGS, particularly for diagnostic applications. The goal of the study was to enhance the discriminatory power of psychiatric PGS for two highly comorbid and genetically correlated neurodevelopmental disorders in ADHD and autism spectrum disorder (ASD). Methods: Genomic structural equation modeling (GenomicSEM) was used to generate novel PGS for ADHD and ASD by accounting for the genetic overlap between these disorders (and eight others) to achieve greater discriminatory power in non-focal outcome predictions. PGS associations were tested in two large independent samples, the Philadelphia Neurodevelopmental Cohort (N=4,789) and the Simons Foundation Powering Autism Research for Knowledge (SPARK) ASD and sibling controls (N=5,045) cohort. Results: PGS from GenomicSEM achieved superior discriminatory power in terms of showing significantly attenuated associations with non-focal outcomes relative to traditionally computed PGS for these disorders. Additionally, genetic correlations between GenomicSEM PGS for ASD and ADHD were significantly attenuated in cross-trait associations with other psychiatric disorders and outcomes. Conclusions: Psychiatric PGS associations are likely inflated by the high degree of genetic overlap between the psychiatric disorders. Methods such as GenomicSEM can be used to refine PGS signals to be more disorder-specific, thereby enhancing their discriminatory power for future diagnostic applications.