American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

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: 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.

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: 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.

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.

Polygenic risk scores (PRS) are rapidly developing, enabling estimates of genetic risk for developing psychiatric disorders. Psychiatric PRS are also increasingly entering consumer and clinical settings. Yet, little is known about how adults with psychiatric disorders perceive their potential use. Here, we conducted semi-structured interviews with 29 adults with psychiatric disorders to examine their perspectives on hypothetically receiving psychiatric PRS for themselves and their current or future children. Key themes emerged around perceived utility, motivations and deterrents to genetic testing, interpretation of PRS, and anticipated responses to results. In particular, participants expressed hope that PRS could support self-understanding, inform better psychiatric care, and enable earlier recognition of difficulties for their children. Participants however raised multiple concerns, including those of privacy, stigma, discrimination, eugenics, and the risk of self-fulling prophecies. Together, these findings offer critical empirical evidence to inform guidance on the responsible use of psychiatric PRS in clinical and consumer settings.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics

Objective: Brief measures of depressive symptoms and family psychiatry history may support the identification of youth who warrant further assessment, but their interpretation requires population-based measurement evidence. This study evaluated the Very Quick Inventory of Depressive Symptomatology (VQIDS) and Family Index of Risk for Mood (FIRM) in the BENCHMARK 2025 national survey. Method: The sample consisted of 2,252 parent reports for children aged 6-18 years and 1,008 linked adolescent self-reports aged 12-18 years. Analyses examined VQIDS dimensionality, reliability, item functioning, age- and sex-based norms, and informant agreement, alongside FIRM score distributions, alternative scoring approaches, and associations with depressive symptoms. Results: VQIDS findings supported a single dimension and good internal consistency, with greatest IRT precision at elevated symptom levels and no practically meaningful differential item functioning across age or sex. Two-week stability was moderate, and parent-adolescent agreement was lower within more symptomatic subsets. FIRM totals showed stronger cross-informant agreement and retest stability. Family history scores were associated with higher VQIDS percentiles and discriminated scores at or above the 90th percentile across informants (AUROC = .670-.698). FIRMs associations were strongest with internalizing symptoms, although evidence was mixed for mood specificity. Conclusions: These findings provide a measurement and normative foundation for brief depressive symptom assessment and support family history as a complementary correlate of symptom elevation. Diagnostic and prospective prediction utility require further evaluation.

Brief assessments reduce clinician and patient burden and increase utilization. Recently, five core depression symptoms (low mood, poor concentration, low self-worth, loss of interest, low energy) have been shown to be particularly sensitive to treatment effects in adults. How these core depressive symptom domains perform for screening or diagnostic purposes in youth is unknown. To address this question, we extracted five analogous symptom items from four widely used symptom assessment tools for children and adolescents (Child Behavior Checklist [CBCL], Youth Self Report [YSR], Parent General Behavior Inventory [P-GBI], and Adolescent General Behavior Inventory [A-GBI]) to create 5-item short forms. We evaluated their psychometric properties and diagnostic utility in a large (N=1185), racially diverse sample (48% Black youth) across community and academic outpatient mental health settings, with diagnoses established through LEAD consensus. All short forms correlated highly with full-length measures (r=.74-.92) and demonstrated comparable inter-item correlations (r =.19-.46). The short forms did not differ meaningfully from the full measures in correlations with psychiatric diagnoses, demographics, or diagnostic interviews. Area under the curve (AUC) values for identifying depression and any mood disorder diagnosis (determined by semi-structured diagnostic interviews) were fair to good (AUC=0.60-0.79), which were not worse than full measures, except for the P-GBI. The CBCL-5, YSR-5, P-GBI-5, and A-GBI-5 may be useful brief depression assessments in youth. How these five symptom domains perform as outcome measures in treatment deserves study.

Background: Genetic and observational studies have identified metabolic factors potentially contributing to the risk of Major Depression (MD). However, studies to date have concentrated on common genetic variants, many of which are in linkage disequilibrium. This makes it difficult to localise variants to a single gene. To overcome this, we used short read whole-genome sequenced datasets (srWGS) to conduct a targeted rare variant association analysis of the FADS region, which has previously been implicated in metabolic effects on MD. Methods: Using srWGS data from 740,373 individuals from All of Us (AoU) and the UK Biobank (UKB) we tested for associations between rare variants in the FADS region and MD. Additionally we conducted association tests between rare FADS variants and five blood-based metabolites implicated by previous studies of MD. Finally, we applied Mendelian randomisation to identify causal effects of metabolites on MD using rare-variants. Results: We found FADS rare variants were significantly associated with all tested metabolites, but not with MD. Using Wald-Ratio MR we found a putative causal effect of Docosahexaenoic acid (DHA) on MD using a rare-variant instrument in the FADS2 gene. Conclusion: Our study represents the largest rare-variant analysis of MD to date. However, the lack of associations between FADS rare-variants and MD suggests our analysis is underpowered, or that genetic effects on MD are conferred substantially by higher frequency variants. Rare-variant MR suggested a causal effect of DHA on MD, and implicates the FADS2 gene.

Objective: Emotion dysregulation and irritability occur across pediatric disorders, complicating distinctions between general symptom burden and specific clinical presentations. Person-centered analyses can look at profiles of scores to identify groups with similar presentations. We examined whether modeling shared and domain-specific symptom variance identified differentiated profiles in youth. Method: Caregivers of 2,187 US youth ages 6-18 completed 87 items assessing emotion dysregulation, depression, mania-related symptoms, sleep-related impairment, and attention and behavioral problems. An ordinal bifactor model estimated one general and 13 specific factors. Latent profile analyses used standardized scores on all 14 factors, with an expanded raw-score analysis as a comparator. We examined caregiver-reported diagnoses and functioning and approximately two-week classification agreement among 166 recontacted caregivers. Results: The general factor accounted for 62% of common variance ({omega}H=.94), although model fit was mixed (CFI=.884; RMSEA=.058; SRMR=.092). A provisional four-class solution distinguished population-modal (67.4%), internalizing (11.8%), attention/hyperactivity (11.0%), and unusual-thought/elevated-mood (9.8%) profiles. Entropy was .83, but model enumeration and initialization checks did not establish a unique solution. Profiles showed overlapping diagnostic and functional correlates. Recontact agreement was 78.9% ({kappa}=.60). Irritability-specific reliability was limited, constraining interpretation of its weak profile differentiation. Conclusions: General symptom burden coexisted with differentiated residual symptom patterns. These findings support evaluating severity alongside symptom configuration, without equating profiles with diagnoses or weak residual irritability signals with limited clinical relevance. Measurement refinement, model-selection verification, and confirmation of classification-error-adjusted associations are needed before clinical application, but profiles could simplify clinical interpretation of sets of scales.

Motivation: Genetically supported drug targets are an estimated 2.6 times more likely to succeed clinically, yet a disease association alone does not show which protein to target, in which direction, or what other effects may follow. This currently requires chaining separate tools. Results: drugMR is a Nextflow pipeline that, from GWAS and pQTL summary statistics, runs cis-Mendelian randomisation, colocalisation, SMR-HEIDI with bulk and/or single-cell QTLs, three-trait colocalisation and a phenome-wide screen for adverse effects and repurposing. Datasets are added through a manifest, every step runs in containers on a laptop, cluster or cloud, and results are explored in a dashboard. Applied to schizophrenia with brain pQTLs, drugMR tested 2,019 proteins in 4 h 19 min and prioritised 58 proteogenomic targets, 22 with multi-omic support; 4 had potential adverse effects. Availability and implementation: drugMR: https://github.com/guillermocomesanacimadevila/drugMR; documentation: https://guillermocomesanacimadevila.github.io/drugMR/. Demo data: https://doi.org/10.5281/zenodo.22917384.

Objective: Parent-adolescent disagreement in ratings of attention-deficit/hyperactivity disorder (ADHD) symptoms can complicate clinical assessment. This study examined parent - adolescent agreement in ADHD symptom ratings and characteristics associated with disagreement. Method: Participants were 1,008 parent-adolescent dyads recruited from a nationally stratified U.S. community sample. Adolescents were 12--18 years old (49.6% female) and along with their parents completed measures of ADHD and depressive symptoms. Agreement on Inattention (IN), Hyperactivity/Impulsivity (HI), and Total ADHD scores was examined. Item-level agreement was assessed using weighted kappa, Gwet 's AC2, percent agreement, and polychoric correlations. Associations between age, sex, and severity of ADHD and depression with levels of agreement were analyzed. Results: Agreement for ADHD factor and total symptom scores was moderate (intraclass correlations = 0.67--0.71), with moderate-to-strong rank-order correspondence. Adolescents reported slightly higher ADHD symptom levels than parents, although the mean difference was small. Item-level agreement was moderate to high (Gwet 's AC2 = 0.79--0.95). Several HI items demonstrated the kappa's paradox, with relatively low kappa values despite high Gwet's AC2 and percent agreement. Q-correlations revealed low agreement on relative strengths and weaknesses. Age and sex were not associated with disagreement and parent-reported ADHD severity and adolescent-reported depressive symptoms were associated with disagreement. Conclusions: Parent and adolescent ratings showed moderate agreement across boys and girls ages 12-- 18; however, disagreement related to relative strengths and weaknesses. Disagreement was associated with ADHD and depressive symptom severity. Findings suggest that ratings from parents and adolescents offer unique perspectives on the assessment of ADHD symptoms.

Abstract Background: Glycosylated RNAs (GlycoRNAs) participate in immune recognition and intercellular communication, but their relationship with brain cell-type transcriptional signatures in major depressive disorder (MDD) remains unclear. We examined GlycoRNA-related transcriptional coupling with microglial and other cellular signatures in the dorsolateral prefrontal cortex (DLPFC). Method: Postmortem DLPFC transcriptomic data from 22 controls and 26 MDD patients (GSE102556) were analyzed. GlycoRNA-related pathway enrichment was quantified using gene set variation analysis (GSVA), whereas microglial, neuronal, astrocytic, and oligodendrocytic signatures were derived using single-sample gene set enrichment analysis (ssGSEA). Pearson correlations, linear regression, and generalized estimating equations (GEE) evaluated cell-type-specific associations and diagnostic interactions. Exploratory GEE results underwent false discovery rate (FDR) correction. Results: No differentially expressed genes survived FDR correction, and GlycoRNA-related and cell-type signatures did not differ significantly between groups. In controls, GlycoRNA enrichment was positively associated with the microglial signature (beta = 0.447, FDR-adjusted P = 0.006), with significantly weaker associations for neurons, astrocytes, and oligodendrocytes (all FDR-adjusted P < 0.05). The two-way GlycoRNA x is_microglia interaction was nominally significant (beta = 0.210, P = 0.048), confirming that GlycoRNA transcriptional coupling is substantially stronger for microglia than for the other three cell types combined. The microglial association was numerically attenuated in MDD (beta difference = -0.273, P = 0.167), but diagnostic interactions were nonsignificant. Conclusions: GlycoRNA pathway transcriptional enrichment exhibits preferential microglial association in control DLPFC. Its possible attenuation in MDD remains to be validated. keyword: Major depressive disorder; GlycoRNA; microglia; dorsolateral prefrontal cortex; generalized estimating equation; ssGSEA; bioinformatics

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Objective: To provide national norms, percentiles, and results of factor analyses for the ADHD symptoms and performance items on the parent-report and self-report versions of the Vanderbilt Assessment Scale (VAS) for youth ages 6 to 17. We hypothesized that symptom items would demonstrate separate inattention and hyperactivity/impulsivity factors, performance items would demonstrate interpretable latent dimensions, measurement invariance would be supported across age and sex, and symptom ratings would vary systematically by age and sex. Method: In a nationally stratified survey conducted in July 2025-March 2026, 2,230 parents of youth aged 6-17 years and 996 adolescents aged 12-17 years completed Vanderbilt Assessment Scale items. Both versions asked about the frequency of 18 ADHD symptoms and performance in 8 domains. To evaluate the psychometric properties and age-related normative curves of the VAS across parent- and youth-report forms, we conducted exploratory factor analysis, confirmatory factor analysis, and measurement invariance testing. We also generated age and sex-specific norms. Results: Factor analyses produced the expected two symptom factors and two performance factors. Developmental and sex-specific patterns of ratings of inattention and hyperactivity/impulsivity symptoms indicated that after increasing slightly during the preadolescent years, parent ratings of inattention symptoms declined and remained stable. In contrast, ratings of hyperactivity/impulsivity declined steadily with age. Normative data were generated using GAMLSS estimated centiles from fitted probability distributions. Conclusions: This study provides norms for the parent-report and adolescent self-report VAS. Findings underscore the importance of using sex- and age-based norms to better inform estimates of severity and diagnostic decision making.