American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

Background: Psychopathy is a personality construct characterized by affective and interpersonal features coupled with severe antisocial behavior. While strongly associated with extreme behaviors such as violent crime, empirical work suggests that psychopathy exists as a dimensional trait in the general population. However, our understanding of the genetic structure of psychopathy remains limited, with genome-wide association studies (GWAS) having been restricted to broad measures of antisocial behavior. Methods: We perform the first large-scale GWAS of psychopathy, using total and factor scores from the Self-Report Psychopathy scale (SRP) in up to 10,558 participants enriched for mood disorder cases from the AFFECT study. LD score regression (LDSC) was used to characterize the heritability of SRP scores and estimate their genetic correlation (rG) with several externally-measured traits, using the Benjamini-Hochberg approach to perform multiple testing correction. Results: Psychopathy scores showed significant heritability, with 12-14% of phenotypic variance explained by common genetic variants. However, GWAS did not identify any genome-wide significant loci in this sample. The LDSC-estimated genetic correlation with antisocial behavior was only 0.54 (95%CI: 0.21-0.81), in line with prior twin research. Significant genetic correlations were found with a wide range of psychiatric disorders (rG's: 0.21 - 0.72), with the highest observed for borderline personality disorder (95% CI: 0.41-0.72) and substance use disorders (95% CI: 0.43-0.63). Psychopathy was also genetically correlated with lower life-satisfaction, poorer health outcomes, worse cognitive functioning, and lower educational attainment. Conclusions: In the first large-scale GWAS of self-reported psychopathy, we found significant SNP heritability and genetic overlap with risk for psychiatric disorders, and poorer behavioral, cognitive, and physical health outcomes. These findings provide important genetic support for differentiating psychopathy from antisocial behavior. Further, they inform our understanding of how genetic factors may contribute to poor health outcomes in psychopathy, and shed insight into the comorbidity between psychopathy and other disorders.

Substance use and psychiatric disorders frequently co-occur, but establishing causality of their relationships is difficult given the multifactorial nature of these traits. To assess these relationships, we applied two genetically informed methods using the largest available European-ancestry genome-wide association studies of six substance use behaviors and twelve psychiatric and personality phenotypes. Using Genomic Structural Equation Modelling, we modelled the latent genetic architecture of the psychiatric and personality traits and examined these factors' correlations with substance use traits. A structure with three factors fit the data best: F1 (PTSD, MDD, GAD, ADHD, agreeableness), F2 (BD, SCZ, OCD, openness), and F3 (OCD, GAD, extraversion, neuroticism), which showed differential correlations with substance use. Smoking correlated significantly which each factor, however, strongest with F1, alcohol use showed weak but significant correlations with F1 and F2, and cannabis use showed moderate correlations with F1 and F2, but strongest with F2. Using Mendelian Randomization, we examined bidirectional causal relationships. We found strong evidence that smoking continuation was causally associated with increased bipolar disorder risk, cigarettes per day with increased schizophrenia, and smoking initiation with increased extraversion and decreased agreeableness. Genetic liability to schizophrenia and PTSD were causally associated with increased risk of cannabis use, and schizophrenia with increased alcohol use. Liability to neuroticism, openness and agreeableness was causally linked to smoking behaviors, and extraversion to cannabis use. Most remaining associations showed weak causal evidence, largely due to heterogeneity and pleiotropy. Together, these findings provide evidence for both shared genetic liability and, for a subset of relationships, direct causal effects between substance use behaviors, psychiatric disorders, and personality traits, which can help inform future intervention and prevention research.

Background: Bipolar disorder (BIP) and obsessive-compulsive disorder (OCD) frequently co-occur and show evidence of genetic overlap, yet the specific pleiotropic loci and their functional mechanisms remain unclear. Methods: We conducted large-scale genetic analyses using GWAS summary statistics for BIP and OCD, excluding 23andMe data. We applied conjunctional FDR analysis to identify pleiotropic variants jointly associated with BIP and OCD, followed by integrative annotation through transcriptomic (eQTL, sQTL), epigenomic (mQTL, haQTL), and proteomic (pQTL, histone PTM) data. SMR analysis was used to prioritize putative regulatory effects, while AlphaGenome predictions and targeted histone proteomics were employed to evaluate allele-specific chromatin changes. Results: We observed a significant genetic correlation (rg = 0.38, P = 3.8 x 10-29) and extensive polygenic overlap between BIP and OCD. Bidirectional MR supported causal effects in both directions, with stronger evidence for BIP influencing OCD risk. ConjFDR analysis revealed 2,143 pleiotropic SNPs jointly associated with BIP and OCD, with convergent signals at the ITIH3/ITIH4 locus. Summary-data-based Mendelian randomization (SMR) and colocalization with multi-omic QTLs (eQTL, pQTL, mQTL, and haQTL) further prioritized the ITIH3/4 locus, where multiple SNPs (e.g., rs3774364) colocalized with H3K27ac histone acetylation QTLs in the prefrontal cortex (PP_H4 > 0.5). Integrated PBMC RNA-seq and complementary histone mass spectrometry linked immune--ECM transcriptional activity to exploratory global histone acetylation changes in BIP and OCS-BIP, with suggestive alterations in H3K27ac-containing peptides. Conclusions: Our multi-omic analysis highlights ITIH3/ITIH4 as a prioritized pleiotropic locus for BIP and OCD. Epigenetic regulation, particularly through histone acetylation, may underlie shared susceptibility and offers a novel mechanistic link between these psychiatric disorders.

Background: Across phenotypic and genotypic studies, executive functioning (EF) has been shown to be associated with externalizing behaviors, including attention-deficit/hyperactivity disorder (ADHD), aggression, and substance use, and is a proposed underlying mechanism of these behaviors. However, there is limited research on the shared biological mechanisms underpinning EF and externalizing behaviors, including their overlap at the level of genomic regions. Methods: The current study used genome-wide association study summary statistics of EF, ADHD, aggressive behavior, alcohol use, and alcohol use disorder to examine whether the heritability of these traits or the co-heritability between them is enriched for genes expressed in particular tissues or stratified by other annotation types. Additionally, the study examined the genomic regions contributing to the covariation between EF and the externalizing traits to identify genetic variants and genes that may be underlying their overlap. Results: First, across all phenotypes, there was evidence for enrichment of genetic signals in DNase hypersensitive sites and H3K4me1 regions, and significant enrichment among genes expressed in the anterior cingulate cortex and frontal cortex. Second, when combining results across regional methods, there was evidence for associations between EF and each of the externalizing behaviors in regions on chromosomes 1, 11, and 15. Conclusions: These findings highlight biological pathways that may link the relations between EF and externalizing behaviors and provides additional evidence of EF as an underlying mechanism of externalizing behavior.

Post-traumatic stress disorder (PTSD) has a significant genetic component (h2 = 24-72%). While the majority of prior studies have utilized common variants, recent research suggests rare variants also contribute to PTSD liability. Here, we examined the role of rare copy number variants (rCNVs; MAF < 1%) from multi-ancestry whole-genome sequencing data in the All of Us Research Program, using an electronic health record-defined PTSD phenotype. Genome-wide burden analyses utilizing all rCNV lengths revealed significant associations for deletion count across European-like (EUR-like; beta = 0.005, SE = 0.001, P = 5.99 x 10-6, FDR P = 3.59 x 10-5 ) and African-like (AFR-like; beta = 0.003, SE = 0.001, P = 0.005, FDR P =0.02) ancestries. We conducted rCNV burden analyses within and across EUR-like (N case = 1,179, N control = 33,689), AFR-like (N case = 504, N control = 12,216), and Admixed American (AMR-like) (N case = 418, N control = 11,935) ancestries using three separate domains of gene-sets previously implicated in neuropsychiatric disorders: neurodevelopmental disorders (NDD; N = 53), abnormal behavior mouse mutant-derived (N = 146), and PTSD (N = 5). Within-ancestry analyses across all three domains yielded one significant brain-expressed gene-set within the EUR-like cohort. Meta-analyzing across ancestries identified 11 NDD gene-sets enriched for rCNV count (FDR P < 0.05). rCNVs with lengths greater than 10kb were examined, but observed no significant signals. Future research using larger samples and detailed PTSD phenotypes, integrated with functional genomic data and brain-region-specific expression profiles, is needed to fully map the disorder's genetic landscape.

Schizophrenia (SZ) is a highly heritable psychiatric disorder with neurodevelopmental origins and a marked impact on cognition. Although alterations in the ubiquitin system have been reported in SZ, the contribution of common genetic variation within this system remains unclear. Using polygenic scores (PGS) analysis, we assessed the contribution of common SZ-associated variation within ubiquitin system genes (USG) to the disorder susceptibility and whether this contribution varies according to USG spatiotemporal brain expression patterns. We further explored the association of these PGS with cognitive performance. We defined a Gene Ontology-based panel of 1,450 autosomal USG (global USG panel; gUSG) and tested its enrichment for SZ-associated variation. We calculated the PGS of this panel in 183 individuals with SZ and 127 healthy controls (HC). BrainSpan data were used to stratify the gUSG into different panels by developmental stage (prenatal or postnatal) and brain region (prefrontal cortex and cerebellum). Cognitive evaluation was based on premorbid and current intelligence quotient (IQ), memory and executive function tests. USG were enriched for SZ-associated variation, and individuals with the disorder showed a higher polygenic burden within this system. The strongest associations involved USG expressed during prenatal development, particularly in the prefrontal cortex. Within SZ, the gUSG-PGS was associated with lower premorbid and current IQ, whereas the prenatal-prefrontal PGS was associated with poorer memory. Together, these findings support a role for USGs in the genetic architecture of SZ and suggest that common variation within this system may link genetic susceptibility to neurodevelopmental processes and cognitive heterogeneity in SZ. Keywords: Schizophrenia, Ubiquitin system, Polygenic scores, Cognition

Background: Biological risk scores provide objective markers of bipolar disorder (BD) liability. Large-scale genome-wide association studies (GWASs) have led to polygenic risk scores (PRSs) that capture genetic liability to BD at the individual level. We lack a comparable large-scale effort to leverage a brain-wide association study (BWAS) to create an imaging risk score (IRS) that captures brain structural liability to BD at the individual-level. Here, we conduct the largest multivariate BWAS of BD and develop a BD IRS. We compare the BD IRS to the PRS and assess their combined predictive performance in multimodal risk score (MRS) models. Methods: Our multivariate BD BWAS used 153 structural neuroimaging features from 3902 cases and 6855 controls (mean age (sd)=35.47 years (13.42), 56.70% female) in a cross-validated machine learning approach. Using the resulting model coefficients, we constructed an IRS. We then evaluated the out-of-sample performance of the IRS, PRS derived from the largest GWAS to date, and combined MRS model using logistic regression in a subset of individuals with imaging and genetic data (nBD=486, ncontrols=1701, age (sd)=33.10 (12.02), 56.47% female). We also performed stratified analyses for demographic and BD clinical groups. Prediction performance was quantified using the area under the receiver operating characteristic curve (AUC). Subsequent analyses examined associations between each risk score and BD relevant symptoms in developing youth. Outcomes: In predicting case status, the BD BWAS model achieved a mean AUCBD of 0.62 (sdBD=0.08) and in stratified analyses, had higher prediction than subgroup-specific BWAS models. The BD type II BWAS was the only exception (AUCBDII=0.60, sdBDII=0.07 vs AUCBD=0.58, sdBD=0.09). The BD PRS had an AUCBD of 0.65 (CI95%=0.63-0.68) and a higher AUC than subtype-specific PRS in stratified analyses. The PRS typically attained a higher AUC than the IRS. Notably, the IRS and PRS were uncorrelated (r=0.03, p=0.11). MRS models outperformed the prediction of individual PRS and IRS with the strongest prediction for BD type I (AUCBD=0.74, CI95%=0.70-0.78). The IRS had the strongest associations with BD-related symptoms in developing youth including increased energy (OR=1.21, pfdr=0.03) and distress from psychotic-like experiences (OR=1.23, pfdr=0.04). Interpretation: Our BD IRS is a novel biological indicator of BD risk. The lack of correlation between the IRS and PRS along with improved MRS prediction suggests the two risk scores capture some orthogonal liability to BD. We show that integrating imaging and genetics is useful for biological characterization of BD and should be considered for the development of clinically relevant prediction models.

Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with perinatal and genetic risk factors, including prenatal maternal smoking, pre-pregnancy BMI, gestational age, birth weight, and common genetic variants. These risk factors, as well as ADHD symptoms themselves, have previously been linked to cord blood DNA methylation (DNAm). We tested the hypothesis that cord blood DNAm mediates the effects of these risk factors on ADHD symptoms. Methods: Participants were drawn from two large European population-based cohorts: the Generation R Study and Avon Longitudinal Study of Parents and Children (n=3087). Cord blood DNAm was assessed using Illumina 450k and EPIC v1 arrays. ADHD symptoms were repeatedly measured with parent-based questionnaires between the ages 6 and 10 years. A high-dimensional mediational model based on DNAm principal components mediation analysis (PCMA) estimated the global mediation effect of all tested DNAm sites. Mediation via single principal components and individual DNAm sites was also evaluated using structural equation modeling and Divide-Aggregate Composite-null Test (DACT). Results: DNAm globally mediated the relationships of maternal smoking, low birth weight, and an ADHD polygenic score (PGS) with ADHD symptoms. Specifically, DNAm explained 62% of the total effect for maternal smoking, 56% for birth weight, and 35% for the ADHD-PGS. No association with individual principal components or single DNAm sites survived multiple testing correction. Evidence for mediation was absent for pre-pregnancy BMI and inconsistent for gestational age. Conclusions: In this first epigenome-wide mediation study of ADHD, we demonstrate a role of DNAm at birth in mediating the association of maternal smoking, birth weight and ADHD-related genetic variants with ADHD symptoms. However, lack of individual site-specific findings and the observational design limit causal biological interpretations. We therefore encourage further research of epigenetic pathways for these three risk factors.

While medication for attention deficit hyperactivity disorder (ADHD) is effective, predictors of treatment response remain poorly understood. Given the use of polygenic scores (PGS) in psychiatric conditions, we investigate their potential to predict ADHD medication response. In this work, we recruited 309 participants aged 6-17 years from the Swedish ADHD medication and predictors of treatment outcome (ADAPT) study (ClinicalTrials.gov: NCT02136147; June 2015), and calculated their PGS. Compared with a reference cohort (N=780), ADAPT showed higher PGS for ADHD (p<2.22e-16), and lower PGS for autism (p=0.0051), educational attainment (EA) (p=8e-10), and intelligence (p=7e-08). To assess the association between genetic effects and changes in Swanson, Nolan, and Pelham ADHD Rating Scale-version IV (SNAP-IV) scores, linear mixed-effects models tested the interaction between each PGS and time (baseline, 3-months). PGS for ADHD was associated with higher SNAP-IV scores overall ({beta}=0.1419, p=0.0078, FDR=0.0293) but not with a change at follow-up; whereas, PGS for EA was associated with lower overall scores ({beta}=-0.1161, p=0.0256, FDR=0.0679) and smaller reductions at follow-up ({beta}=0.0946, p=0.0453, FDR=0.1130). Subgroup analyses indicated that these effects were more prominent in adolescents ([&ge;]13 years) and individuals classified as responders to treatment. We further explored machine learning models combining clinical and genetic information across 14 models with 49 features. Although models could not empirically generalize, estimated information-theoretic upper bounds for classification accuracy were 72% with PGS, 84% with clinical and demographic variables, and 92% when combined. This study highlights the potential of using PGS and combining genetic and clinical data to improve treatment stratification in ADHD.

Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.

Schizophrenia, bipolar disorder, and major depression vary in their age of onset, cognitive impacts, degree of impairment, and clinical course. However, since psychosis is a transdiagnostic feature that occurs across all three of these disorders, they can be represented as a spectrum. We leveraged genomic data to explore different functionally distinct components of the psychosis spectrum. We used Genomic Structural Equation Modelling (Genomic SEM) to define four factors indexing specific aspects of psychosis-related symptoms shared across depression, schizophrenia, and bipolar disorder: a general Psychosis factor with low Cognitive functionality (gPsychosisCognition), a specific Psychosis factor independent of cognition (sPsychosis), a specific Affective factor independent of psychosis (sAffective) and a specific Mania factor independent of depression (sMania). We then used Stratified Genomic SEM and transcriptomic analyses to characterize biological functions and temporal trends associated with these factors. sPsychosis, sAffective, and sMania exhibited differential genetic correlations with personality, with sPsychosis and sAffective showing positive correlations with Openness, while Neuroticism showed a strong positive correlation with sAffective and a negative correlation for sMania. GABAergic, cortical, and CA1-CA3 enrichments were significant across the continuum with evidence for factor specific enrichment: dentate gyrus, CA4 and cerebellum were non-significant only for sAffective and oligodendrocytes enriched only in gPsychosisCognition. Transcriptomic analyses revealed different temporal expression trends across factors, identifying a mostly prenatal expression for sMania and sAffective, and more consistent expression across development for sPsychosis. We demonstrated that a clinically informed genomic model can shed light on the functional heterogeneity of psychosis, identify both unique and shared biological components and highlight its partial neurodevelopmental origin.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that remains challenging to treat effectively. Difficulty predicting who will develop the disorder and which individuals will respond to treatment is most likely due to our limited mechanistic understanding and the lack of reliable biomarkers. The current literature suggests that lipid profiles differ in MDD; however, most studies do not integrate other omics layers to elucidate their potential causal relationship at the molecular level. Using phenotypic, genetic, epigenetic and mass-spectrometry lipid data from a Scottish-based cohort of approximately 1,000 participants, we examined associations with individual lipid species. We identified 77 common genetic variant associations ({beta} range = -0.46 to 0.69, p < 3.1x10-10) and 82 genome-wide DNA methylation associations ({beta} range = -1.88 to 1.39, p < 2.2x10-10). We identified 23 lipid species associated with lifetime MDD ({beta} range = -0.23 to 0.2, p < 0.05), including lipid classes such as lysophosphatidylcholine (LPC), diacylglycerol (DG) and phosphatidylcholine (PC). Our integration analysis showed that genetic variants and DNA methylation at sites annotated to the FADS gene cluster were linked with lipid species that were also associated with lifetime MDD status, including LPC(14:1) ({beta} = 0.15, p = 0.03) and PC(19:0)/(20:4) ({beta} = -0.14, p < 0.05). Our findings provide further support for the role of the FADS gene cluster in lipid metabolism and its association with MDD, while introducing DNA methylation as an additional molecular layer supporting this link.

Background. Cord blood DNA methylation profile scores (MPSs) based on genetic and pre-/perinatal risk factors for neurodevelopmental conditions (NDCs) may capture downstream biological effects and help understand how combined exposure signals contribute to NDC risk. Methods. Using data from two longitudinal birth cohorts, Generation R (N-train = 1856, N-test = 476) and ALSPAC (N-validation= 832), we developed cord blood MPSs based on genetic and pre-/perinatal NDC risk factors. We assessed individual and combined predictive performance of risk factors and MPSs for eight childhood psychiatric outcomes (four broad, four specific), measured between ages 5 and 14 years. We also evaluated if the MPSs could be combined into a composite "transmission load" MPS. Results. We validated four novel MPSs: maternal age, birthweight, and genetic liability for ADHD and schizophrenia (r range = 0.08 to 0.29) and included two previously validated MPSs: maternal smoking and gestational age (r range = 0.42 to 0.63). Jointly modeling the six MPSs with their corresponding risk factors explained on average 3.3% of variance in outcomes, higher than that explained by risk factors (1.8%) or MPSs alone (1.6%), indicating complementary sources of risk. The "transmission load" MPS did not replicate due to heterogeneous contributions of the predictors across cohorts. Conclusions. The four novel MPSs based on genetic and pre-/perinatal risk factors can serve as valuable tools for future research. Integrating genetic and prenatal risk factors with DNA methylation at birth can provide insights into their individual and joint contributions to early psychiatric risk and may improve prediction.

Attention Deficit Hyperactivity Disorder (ADHD) frequently co-occurs with distinct personality profiles, but whether ADHD shapes personality (scar model) or personality influences the development of ADHD (vulnerability model) remains unclear. Leveraging genome-wide association study (GWAS) data, we examined directional associations between ADHD and the Big Five personality traits (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). We estimated genetic correlations and conducted bidirectional two-sample Mendelian Randomization (MR) analyses between ADHD and the Big Five. Robustness was evaluated using multiple sensitivity tests, including MR-CAUSE, which accounts for correlated and uncorrelated pleiotropy, and colocalization analyses on fine-mapped variants. Higher genetic liability to Neuroticism and Extraversion, alongside lower liability to Conscientiousness, increased ADHD risk, consistent with the vulnerability model. Conversely, ADHD genetic liability was associated with lower Agreeableness, supporting the scar model. Associations were generally robust across sensitivity analyses, with MR-CAUSE supporting the Neuroticism and Agreeableness associations and colocalization providing variant-level support for associations between ADHD and Neuroticism, Conscientiousness, and Agreeableness. These findings provide early, population-level insight into personality-related vulnerability to ADHD and motivate further work on the role of personality traits in ADHD risk.

Importance. Suicide is a leading cause of mortality, yet its prevention remains a challenge. Suicide attempters exhibit disrupted decision-making, but the specific cognitive processes driving this impairment and their utility for risk stratification are unclear. Objective. To determine which computational decision-making mechanisms are disrupted in high-risk suicide attempters, and whether they distinguish attempters from depressed and healthy controls. Design. This cross-sectional study fit reinforcement-learning models to Iowa Gambling Task performance across two independent studies: SPAD (August 2017 to May 2020); SUICIDE-DECIDE (March 2021 to September 2023). Data was analyzed between January 2025 and July 2026. Setting. SPAD was conducted in Nimes (France). SUICIDE-DECIDE was a muticenter study across Jena (Germany) and Nimes. Clinical groups were recruited from academic inpatient psychiatry units and outpatient clinics, and healthy controls through local advertisement. Participants. SPAD included adults aged 60 years or older; SUICIDE-DECIDE middle-aged adults stratified by attempt means (violent/nonviolent). Consecutive participants were recruited into three groups: suicide attempters, patient controls, healthy controls. Main outcomes and measures. The primary outcomes were group differences on parameters reflecting learning from rewards and losses, sensitivity to win frequency, choice perseverance, and forgetting of past choices. We trained a multinomial logistic regression model in the SUICIDE-DECIDE study using self-reported depression and parameters that differentiated attempters from controls across studies, and evaluated its performance in the SPAD sample. Results. SPAD included 57 participants (40 females [70.2%]; mean [SD] age, 70.1 [8.2]). SUICIDE-DECIDE included 124 participants (77 females [62.1%]; 32.1 [11.0]). Suicide attempters forgot past choices faster than controls (SPAD: F(2,36) = 13.5; P < .001; SUICIDE-DECIDE: F(3,57) = 46.6; P < .001). No other parameter specifically differed in attempters across studies. In SUICIDE-DECIDE, greater forgetting of past choices was associated with attempter status only (RRR = 4.2, 95% CI [1.5, 11.5]). This parameter paired with depression severity correctly classified 87.2% of participants in SPAD (95% CI [74.3%, 95.2%], 41/47). Conclusions and relevance. Short-sighted decision-making may represent a cognitive phenotype associated with suicide in high-risk populations and a potential target for prevention and treatment.

Objective: The Genetic Risk for Serious Mental Illness and Development ("Paisa Kids") Study is establishing a longitudinal cohort of 3,000 youth enriched transdiagnostically for risk for serious mental illnesses (SMI; i.e., schizophrenia, bipolar disorder, and severe depression) from the Paisa region of Colombia. Its objective is to clarify the early clinical, neurobehavioral, genetic, and environmental profiles associated with later SMI and distinguish transdiagnostic from disorder-specific pathways. This paper describes the study infrastructure, recruitment streams, and baseline assessment battery, and characterizes the first two-thirds of the cohort. Method: Youth aged 6-15 years are being recruited. Youth recruited for high SMI risk either have a first or second degree adult relative with a SMI diagnosis or a psychiatric diagnosis themselves identified through electronic medical records (EMRs) associated with later SMI. Lower-risk youth have a relative without a SMI diagnosis. Baseline measures include psychiatric and developmental history, neurobehavioral functioning, environmental exposures, and genome-wide genotyping. Psychiatric EMRs provide longitudinal follow-up. Results: Among 2,384 participants enrolled thus far (mean age=11.2 years, 45.0% female), 2,211 were recruited for high SMI-risk and 173 for lower SMI-risk. Overall, 73.2% met criteria for a history of one or more psychiatric disorders. Logistic regression comparing rates of diagnoses and ACEs between recruitment streams showed elevated rates of depressive and anxiety disorders, attention-deficit/hyperactivity disorder, oppositional defiant disorder, and several ACEs among high versus lower SMI-risk youth. Conclusion: Integrating multidomain phenotyping, genomic data, and longitudinal EMR follow-up, the Paisa Kids cohort provides a unique resource for investigating pathways of risk and resilience to SMI.

BackgroundSchizophrenia liability reflects both the aggregate effects of many common genetic variants and rare structural variation at recurrent neurodevelopmental copy-number variant (ND-CNV) loci. Although both components of genetic liability have been associated with schizophrenia and related phenotypes, it remains unclear whether they exhibit similar neuropsychiatric association profiles or show evidence of interaction when examined jointly. MethodsWe analyzed 71,992 participants from the All of Us Research Program Controlled Tier version 8 dataset, including 2,115 participants carrying structural variants overlapping curated recurrent ND-CNV loci. We integrated binary ND-CNV carrier status, an ancestry-adjusted and standardized reduced schizophrenia polygenic risk score (PRS), and longitudinal electronic health record-derived psychiatric and neurodevelopmental phenotypes. Logistic regression models evaluated pooled, deletion-overlapping, and duplication-overlapping carrier associations, schizophrenia PRS effects, and PRS x ND-CNV interactions. Dimensional analyses, confirmatory factor analysis (CFA), and structural equation modeling (SEM) provided complementary summaries of phenotype covariance and assessed the robustness of primary associations across alternative representations of the phenotype space. ResultsSchizophrenia PRS and ND-CNV carrier status showed distributed association profiles extending beyond schizophrenia diagnosis across psychosis-spectrum, neurodevelopmental, cognitive, and internalizing phenotypes. ND-CNV carriers had higher estimated odds of schizophrenia diagnosis than non-carriers (OR = 1.49, 95% CI 1.09-2.03), while each 1-SD increase in schizophrenia PRS was associated with higher odds of a broad psychosis-spectrum phenotype (OR per SD = 1.07, 95% CI 1.03-1.10). Deletion-overlapping carriers exhibited generally larger and more consistently positive association estimates than duplication-overlapping carriers. Psychosis-spectrum prevalence increased from 5.4% in the lowest PRS decile to 6.7% in the highest, while ND-CNV carriers maintained consistently higher prevalence than non-carriers across the PRS distribution. PRS x ND-CNV interaction estimates remained near the null, providing little evidence of widespread multiplicative interaction. Among tested covariance structures, bifactor models showed the lowest residual misfit and were therefore carried forward for SEM analyses. ConclusionsSchizophrenia-associated polygenic liability and structural variation overlapping recurrent ND-CNV loci exhibit distributed neuropsychiatric association profiles with limited evidence of widespread multiplicative interaction. Their joint evaluation in the large, ancestrally diverse All of Us cohort demonstrates that distinct forms of genomic liability can be examined within a shared, real-world phenotype space extending beyond schizophrenia diagnosis alone.

Objective: To evaluate the psychometric properties, factor structure, validity, and age- and sex-specific norms of the 21- and 10-item parent-report Child Mania Rating Scale (CMRS-P) in a nationally representative U.S. sample of youth aged 6-18 years. Method: Parents of 2,229 youth aged 6-18 years [weighted M(SD)=12.0(3.3) years; 45.9% female] completed the CMRS-P and related measures between July 2025 and March 2026. Data were weighted to represent U.S. demographics. A clinical comparison sample included parents of 522 youth seeking outpatient mental health services. Complete-case analyses evaluated reliability, factor structure, item response theory, measurement equivalence, retest stability, and convergent and discriminant validity. GAMLSS estimated age- and sex-specific norms. Results: Weighted mean scores were 8.5(SD=9.0) for CMRS-P-21 and 4.0(4.3) for CMRS-P-10. Internal consistency was high ({omega}=.97/.93), and forms correlated .95. A general severity factor explained most reliable variance ({omega}H=.94). Three items differed in response thresholds across samples, with an average impact below one point on expected total scores. Both forms correlated strongly with attention problems, suicidality, depression, externalizing symptoms, and emotion dysregulation (r=.51-.63 for CMRS-P-21) and negligibly with pubertal status. Reliable-change thresholds were 14.5 (CMRS-P-21) and 6.8 (CMRS-P-10) points. Weighted GAMLSS CMRS-P-21 95th percentiles declined with age in males (30-23) and increased in females (22-30), with wide bootstrap intervals at age extremes; the female trend was not replicated without weights. Conclusions: Findings support both CMRS-P forms for assessing youth manic symptoms. The first nationally representative U.S. norms provide age- and sex-adjusted benchmarks for interpreting scores and informing further clinical assessment.

Background: Neurodevelopmental copy number variants (ND-CNVs) are associated with diverse neuropsychiatric outcomes, but most evidence derives from clinically ascertained cohorts enriched for severe disease. Whether these associations generalize to large, heterogeneous population biobanks, and whether they reflect locus-specific or distributed genetic effects, remains unclear. Methods: We developed a scalable analytic pipeline within the All of Us Research Program to identify ND-CNV carriers from structural variant callsets and evaluate associations across neuropsychiatric phenotypes derived from electronic health records (EHRs). Logistic regression models estimated associations between carrier status and individual phenotypes, adjusting for age, healthcare utilization, observation time, and genetic ancestry principal components. Sensitivity analyses included exclusion of mosaic chromosomal alteration (mCA)-suspected events and leave-one-locus-out (LOLO) models. Phenotypes were further grouped into Research Domain Criteria (RDoC) domains to assess domain-level structure and cross-domain burden. Results: ND-CNV carrier status was associated with modest but consistent enrichment across neuropsychiatric phenotypes spanning affective, psychotic, neurodevelopmental, and trauma-related domains. Although no individual phenotype survived false discovery rate correction, global permutation analyses demonstrated directional enrichment exceeding expectations under randomized exposure assignment. Effects were distributed rather than driven by a single phenotype or locus, with deletion-overlapping carriers showing stronger associations than duplication-overlapping carriers. Associations remained stable after mCA exclusion and LOLO analyses. Domain-level analyses revealed correlated enrichment across RDoC systems, while cross-domain burden measures demonstrated strong interdependence but limited discriminative separation. Conclusions: ND-CNV carriers exhibit distributed, pleiotropic neuropsychiatric risk in a large biobank. These findings provide empirical support for a distributed model of ND-CNV-associated neuropsychiatric liability in which numerous modest, directionally concordant phenotype associations collectively contribute to pleiotropic risk across neuropsychiatric domains.

Schizophrenia (SCZ) frequently co-occurs with substance use disorders (SUDs), yet the genetic basis of this comorbidity remains unclear. Using the latest European-ancestry genome-wide association studies (GWAS) for SCZ, cannabis use disorder (CanUD), opioid use disorder (OUD), problematic alcohol use (PAU), tobacco use disorder (TUD), and a general addiction factor (AF), together with two SCZ and one SUD case-control samples with individual-level genotype data, we applied multiple complementary genomic approaches to characterize their shared genetic architecture. Significant positive genome-wide genetic correlations were observed across all SCZ-SUD pairs. Local genetic correlation analyses identified multiple genomic regions contributing to this shared architecture, with both positive and negative correlations, and evidence of genomic regions shared across multiple SCZ-SUD pairs. Polygenic overlap analyses indicated substantial sharing (25-50%) of trait-associated variants between SCZ and SUDs. Genomic structural equation modelling supported a common latent factor underlying SCZ and all SUDs, accounting for approximately 23% of SCZ variance. Cross-trait polygenic risk score (PRS) analyses showed bidirectional associations between SCZ and SUD genetic liability. Mendelian randomization analyses provided evidence for a bidirectional causal relationship between SCZ and CanUD. Horizontal pleiotropy analyses identified numerous loci with concordant and discordant effects across traits, including loci shared among multiple SCZ-SUD pairs. Gene mapping and enrichment analyses indicated pathways related to neuroplasticity, synaptic transmission, immune system, metabolism and proteolysis, including both shared and SCZ-SUD specific biological processes. Overall, these findings suggest that part of SCZ liability reflects genetic susceptibility to SUDs with potential implications for patient stratification and clinical management.