American Journal of Psychiatry

Objective: Autistic individuals may face elevated risk for PTSD, yet the degree to which this risk differs by sex remains unknown. We examined the association between autism and incident PTSD, characterized sex differences in risk, identified high-risk subgroups, and described post-diagnosis clinical trajectories. Method: We conducted a population-based matched cohort study using Swedish national registers. Individuals born 1990 through 2010 were followed from age 6 years through December 31, 2017. Autistic individuals (N=42,862) were matched 1:10 to controls (N=412,251) on sex and birth year. Cox proportional hazards regression estimated hazard ratios (HRs) for incident PTSD. Among those who developed PTSD, we compared care utilization, hospitalization rates, and persistence of care contacts. Results: During mean follow-up of 5.1 years, 401 autistic individuals (0.9%) and 903 controls (0.2%) developed PTSD (incidence rates: 18.3 vs 4.2 per 10,000 person-years). Autism was associated with 4.4-fold increased PTSD risk (HR=4.37; 95% CI, 3.93-4.86). Risk was higher among females (HR=4.79) than males (HR=3.39; P interaction=.006). Among autistic individuals, comorbid ADHD conferred additional risk (HR=1.38; 95% CI, 1.14-1.68). Ten-year cumulative incidence reached 6.0% among autistic females with ADHD. Autistic individuals with PTSD had higher care utilization (mean visits: 5.0 vs 3.9; P<.001), more psychiatric hospitalizations (27.9% vs 19.8%; P=.002), and more persistent courses (24.8% vs 12.3% with contacts in all 3 post-diagnosis years; P=.001). Conclusion: Autism is associated with substantially elevated PTSD risk, particularly among females with comorbid ADHD. When PTSD occurs, autistic individuals experience more severe and persistent clinical courses, supporting targeted screening and sustained follow-up.

Obsessive compulsive disorder (OCD) is significantly heritable, but only a fraction of the contributory genetic variation has been identified, and the molecular etiology involved remains obscure. Identifying rare contributory variants of large effect would be an important milestone in helping to elucidate the mechanisms involved. Analysis of densely affected pedigrees is a potentially useful strategy to bypass the sample size challenges of standard case-control approaches. Here we performed whole genome sequencing (WGS) of 25 individuals across two multiplex OCD pedigrees. We prioritised rare variants using a Bayesian inference approach which incorporates variant pathogenicity and co-segregation with OCD. In the first pedigree, we identified a highly deleterious missense variant in NPY5R, carried by the majority of affected individuals. This gene is brain-expressed and has previously been implicated in panic disorder and internet addiction GWAS studies. In the second pedigree, we identified a large deletion of DLGAP1 and a missense variant in MAPK8IP3, that perfectly co-segregated in a specific branch of the family: both genes have previously been implicated in OCD and autism. Both genes contribute to a protein interaction network including ERBB4 and RAPGEF1 which we had previously identified in a large Tourette Syndrome pedigree. Our analysis suggests that both energy homeostasis and downstream signalling from the post-synaptic density may both be important avenues for future research.

Polygenic risk scores (PRS) have shown increasing utility for risk stratification across complex diseases, but for psychiatric disorders such as bipolar disorder (BD), current PRS explain only a fraction of disorder liability (~1-9%), with predictive performance further diminished in non-European populations and real-world clinical cohorts. To explore the potential of integrating social and environmental risk factors alongside genetic liability to improve risk prediction, we evaluated the relationship between a PRS for BD (PRSBD) and six social risk measures - perceived stress, discrimination in medical settings, neighborhood social cohesion, perceived neighborhood disorder, cost-related medication nonadherence, and adverse childhood experiences - to BD case status in 115,275 participants (7,000 cases; 108,275 controls) from the All of Us Research Program. PRSBD was associated with BD case status across ancestry groups, though liability-scale variance explained was attenuated relative to what has been reported for curated research cohorts (R2 = 1.86% in European, 0.60% in African, 1.65% in Latino/Admixed American ancestries). Each social risk factor tested exhibited a larger effect size than PRSBD, with perceived stress (OR = 2.05 per SD) and adverse childhood experiences (OR = 2.68 for [&ge;]4 ACEs) demonstrating the strongest associations. Individuals in the lowest genetic risk decile with high social burden exhibited BD prevalence comparable to or exceeding those in the highest genetic risk decile with low social burden. These findings demonstrate the substantial explanatory power of social risk factors and support the development of integrated genetic-social risk frameworks for more accurate and equitable psychiatric risk prediction.

Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.

Importance: Suicide is a leading cause of death in the United States with risk strongly influenced by Interpersonal trauma, contributing to treatment resistance and clinical complexity. Objective: To assess clinical and genetic factors in individuals who died from suicide, with and without interpersonal trauma exposure. Design: Individuals who died from suicide with and without trauma were compared in a retrospective case-case design. Prevalence of 19 broad clinical categories was assessed between groups. Results directed selection of 42 clinical subcategories, and 40 polygenic scores (PGS) for further assessment. Multivariable logistic regression models, adjusted for critical covariates and multiple tests, were formulated. Models were also stratified by age group (<26yo and >=26yo), sex, and age/sex. Setting: A population-based evaluation of comorbidity and polygenic scoring in two suicide death subgroups. Participants: A total of 8 738 Utah Suicide Mortality Research Study individuals (23.9% female, average age = 42.6 yo) who died from suicide were evaluated, divided into trauma (N = 1 091) and non-trauma exposed (N = 7 647) individuals. A subset of unrelated European genotyped individuals was also assessed in PGS analyses (Trauma N = 491; Non-trauma N = 3 233). Exposures: Trauma is here defined as interpersonal trauma exposure, including abuse, assault, and neglect from International Classification of Disease coding. Main Outcomes and Measures: Prevalence of comorbid clinical sub/categories and PGS enrichment in trauma versus non-trauma exposed suicide deaths. Results: Overall, trauma-exposed individuals died from suicide earlier (mean age of 38.1 yo versus 43.3 yo; P <0.0001) and were disproportionately female (38% versus 21%, OR = 3.3, CI = 2.9-3.8). Prevalence of asphyxiation and overdose methods, prior suicidality, psychiatric diagnoses, and substance use (OR range = 1.3-3.7) were elevated in trauma exposed individuals who died from suicide. Genetic PGS were also elevated in trauma-exposed individuals who died from suicide for depression, bipolar disorder, cannabis use, PTSD, insomnia, and schizophrenia (OR range = 1.1-1.4) with ADHD and opioid use showing uniquely elevated PGS in trauma exposed males (OR range = 1.2-1.4). Conclusions and Relevance: Results demonstrated multiple convergent lines of age- and sex-specific evidence differentiating trauma-exposed from non-trauma exposed suicide death. Such findings suggest unique biological backgrounds and may refine identification and treatment of this high-risk group.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

Substance use disorders run in families, yet the mechanisms underlying intergenerational transmission remain unclear. We investigated indirect genetic effects, pathways through which parental genotypes influence offspring phenotypes via the family environment, for alcohol use disorder (AUD), nicotine dependence (ND), and related quantitative outcomes, and aimed to identify family environmental factors through which such effects may operate. Using transmitted and non-transmitted polygenic scores (PGS) constructed for problematic alcohol use, tobacco use disorder, and general addiction liability, we analyzed 5972 European-ancestry adult offspring with at least one genotyped parent from the population-based Lifelines cohort (Netherlands). Offspring outcomes included lifetime DSM-5 AUD diagnosis, AUD symptom count, maximum drinks in 24 hours, Fagerstrom Test for Nicotine Dependence score, and cigarettes per day. AUD findings were meta-analyzed with data from the Brisbane Longitudinal Twin Study (N = 1368; Australia). We also examined parent-of-origin effects and mediation by parental substance use and socioeconomic status using structural equation modeling. Transmitted PGS robustly predicted all AUD and ND outcomes ({beta} = 0.07-0.16; OR = 1.20 for AUD diagnosis). Non-transmitted PGS, indexing indirect genetic effects, were negligible for all clinical syndrome outcomes. The only significant indirect genetic effect was on cigarettes per day ({beta} = 0.03, p = 0.01), mediated by parental smoking behavior but not socioeconomic status. These findings indicate that intergenerational transmission of risk for AUD and ND is driven primarily by direct genetic effects, with modest indirect genetic effects on smoking quantity. Larger samples and cross-trait analyses are needed to further elucidate these mechanisms.

Background: Differences in age of psychosis onset (AOO) in schizophrenia (SCZ) are associated with different illness trajectories. Determining whether AOO differences can be explained by genome-wide or pathway-partitioned polygenic risk for SCZ (SCZ-PRS) may elucidate mechanisms underlying clinical variability. This study examined relationships between AOO, genome-wide SCZ-PRS, and pathway-partitioned SCZ-PRS in a harmonized, multi-ancestry North American dataset (SCZ-NA) and in UK Biobank (SCZ-UKBB). Methods: For each cohort, we computed one genome-wide SCZ-PRS and 18 mutually-exclusive pathway-based PRS derived from previous published and validated neurodevelopmental gene-sets. We evaluated 13 SNP-to-gene mapping strategies, including comparing non-coding SNP-to-gene mappings informed by functional annotations versus distance-based windows. SCZ case-control prediction and AOO associations were tested using logistic and linear mixed models, respectively, controlling for sex, ancestry principal components, and genetic relatedness. Results: Genome-wide SCZ-PRS robustly predicted SCZ case-control status in both cohorts but not AOO. In contrast, pathway-based analyses identified AOO associations for a fetal angiogenesis and a postnatal synaptic signaling and plasticity gene-set across both cohorts (p < .05), alongside nominal cohort-specific associations in other gene-sets. Associations depended on SNP-to-gene mapping definitions; experimentally informed strategies, particularly those incorporating brain expression Quantitative Trait Locus (eQTL) annotations performed best. Conclusion: Findings suggest that neurovascular and postnatal synaptic signaling and refinement mechanisms contribute to AOO variation in SCZ, and that pathway-informed PRS, especially with brain-specific non-coding SNP-to-gene mappings, can help identify mechanisms contributing to variability in AOO. Replication in larger, prospectively phenotyped cohorts with harmonized AOO definitions will further clarify genetic mechanisms underlying clinical variability in SCZ.

The etiology of autism is influenced by genetic and non-genetic factors, with observational studies suggesting associations between early maternal health diagnoses and offspring autism. However, these associations may partly reflect shared familial genetic liability rather than direct causal effects. Using comprehensive national health registers and individual-level genetic data from the iPSYCH cohort (N=117,542), we examined whether maternal health diagnoses are associated with offspring polygenic scores (PGS) for autism. Such associations between maternal health and offspring autism would indicate shared genetic factors and the possibility of genetic confounding in the observational associations. We also tested such associations with PGSs for other neuropsychiatric and neurodevelopmental conditions that are genetically correlated with autism, but with better-powered PGS (due to larger GWAS sample sizes and likely more polygenic genetic architecture), as well as height, a negative control. Several maternal diagnoses were nominally associated with autism PGS in the child, including, e.g., certain obstetric complications, asthma, and obesity. After adjustment for multiple testing, the only statistically significant results included those between maternal diagnoses, predominantly psychiatric, and other neuropsychiatric and neurodevelopmental PGSs in the child. Sensitivity analyses confirmed the robustness of our results across exposure windows, diagnostic settings, and socioeconomic adjustments. These findings indicate that maternal diagnoses associated with autism partially reflect shared genetic liabilities between mothers and their children. However, such genetic effects, as captured by child PGS do not fully explain the observed associations, suggesting additional factors, including e.g., non-genetic familial factors, rare variants, and indirect effects.

Progress in pharmacological treatment development for neurodevelopmental disorders is hindered by a misalignment between targeted mechanisms, outcome measures, and trial designs. This study was initiated as a post-trial access pathway for bumetanide and later expanded with treatment-naive participants. Within this framework, we implemented a parent-cocreated sensory outcome measure set (PROMset) in an unmasked, multiple-baseline single-case experimental design with randomized baseline periods of 2-12 weeks, followed by 6 months of bumetanide treatment (up to 1.5 mg twice daily). Participants (7-19 years) had atypical sensory reactivity and a diagnosis of ASD, ADHD, epilepsy, or TSC. The primary outcome was a PROMset comprising seven PROMIS item banks assessing anxiety, depressive symptoms, sleep disturbance, fatigue, sleep-related impairment, cognitive function, and peer relationships. Secondary outcomes included SSP, SRS-2, RBS-R, and ABC. Of 113 enrolled participants (mean age 13.2 [SD 2.7], 64% male), 102 completed the trial and 95 had analyzable PROMsets. At baseline, PROMset scores showed substantial impairment across domains (mean deviation =9.0 T-score points, p<.001) and correlated with sensory reactivity (SSP; r=-0.40, p<.001). Individual-level analyses showed improvement in 24-41% of participants per PROM domain, most frequently in anxiety and depressive symptoms (41% and 38%; mean across-case Cohen's d=-1). Overall, 83% improved on at least one domain. Group-level analyses showed improvement across all secondary outcomes (p<.001), with superiority over historic placebo for RBS-R and SSP. Integrating PROMsets with individualized trial designs can reveal clinically meaningful changes, supporting a more sensitive and patient-centered framework for treatment evaluation in heterogeneous populations.

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P) remains limited, constraining preventive care. The effectiveness of the CHR-P state is constrained, in part due to clinical assessments requiring specialist interpretation of narrative interviews, limiting scalability. Here, we evaluate whether large language models (LLMs; deep learning models trained on large text corpora to process and generate language) can extract clinically meaningful information from such interviews to support psychosis risk assessment. We assessed 11 open-weight LLMs on 678 PSYCHS interview transcripts from 373 participants (77.7% CHR-P). Models inferred CHR-P status and estimated severity and frequency across 15 symptom domains, benchmarked against researcher-rated scores. Larger models achieved the strongest classification performance (Llama-3.3-70B: accuracy = 0.80, sensitivity = 0.93, specificity = 0.58). LLM-generated symptom scores showed good correlations with researcher-rated scores (ICCsev = 0.74, ICCfreq = 0.75). Performance disparities were minimal across most demographic groups but varied across sites. Generated summaries were largely faithful to source transcripts, with low rates of clinically relevant confabulation (3%). Errors primarily reflected over-pathologisation of non-clinical experiences. While accuracy scaled with model size, smaller models achieved competitive performance with substantially lower computational cost. These findings demonstrate that open-weight LLMs can assess psychosis risk from clinical interview transcripts, supporting scalable, human-in-the-loop approaches to early detection.

Catatonia is a severe psychomotor syndrome that occurs across psychiatric diagnoses and is increasingly conceptualized as reflecting neurodevelopmental vulnerability. The anterior cingulate cortex (ACC) plays a central role in motor initiation and cognitive-affective integration and displays substantial interindividual variability in its sulcal morphology, which is established prenatally and remains stable across life. In this MRI study, we examined whether ACC sulcal patterns represent a structural trait marker of catatonia. We analyzed high-resolution T1-weighted images from a hospital-based cohort comprising patients with catatonia (N = 109), psychiatric patients without catatonia (N = 323), and healthy controls (N = 91). The presence of the paracingulate sulcus (PCS) in each hemisphere was determined through blinded visual inspection, and regression analyses tested associations with diagnostic group, adjusting for age, sex, scanner type, intracranial volume, and benzodiazepine and antipsychotic exposure. Patients with catatonia exhibited a significantly reduced prevalence of the left PCS and diminished hemispheric asymmetry compared with both non-catatonic patients and healthy controls. These effects were independent of whether catatonia occurred within psychotic or mood disorders. PCS size did not differ across groups, and sulcal pattern did not correlate with catatonia severity among affected individuals. The findings demonstrate that ACC sulcal deviations are specifically associated with catatonia across diagnostic categories, supporting a neurodevelopmental etiology and reinforcing ACC involvement in its pathophysiology. Early-determined sulcal morphology may represent a trait-level marker contributing to vulnerability for catatonia, with implications for early identification, risk stratification, and targeted intervention strategies.

Importance: Conventional repetitive transcranial magnetic stimulation (rTMS) can be ineffective in individuals who have previously failed brain stimulation, ketamine and/or multiple lines of therapies. Modern accelerated rTMS protocols using image-guided targets have not been systematically investigated in these individuals. The goal of this study was to assess the feasibility and efficacy of personalized, connectivity-guided, accelerated intermittent theta-burst stimulation (iTBS) in patients with treatment-resistant depression (TRD) of varying refractoriness. Objective: To assess whether connectivity-guided, accelerated iTBS produces significant reductions in depression severity, and to what extent this benefit extends to ultra treatment-resistant depression (UTRD). Design: This was an open-label feasibility trial of connectivity-guided, accelerated iTBS in patients with TRD. Two distinct groups of participants were recruited from a neurosurgical-psychiatry clinic with UTRD and an interventional psychiatry clinic with TRD. Patients were stratified into a priori treatment-resistance subgroups. Patients received five days of open-label treatment. Outcome measures were collected immediately prior to and after treatment, as well as at 4- and 12-weeks post-treatment. Setting: This trial (NCT05813093) was conducted between November 2023 and July 2025 at Sunnybrook Health Sciences Centre in Toronto, Ontario, Canada. Participants: Patients with major depressive disorder. A total of 96 participants were screened, with 73 meeting eligibility criteria (UTRD=30, TRD=43). One withdrew due to inability to tolerate the baseline MRI, and the other withdrew voluntarily prior to treatment. Intervention: Participants underwent a neuronavigated accelerated iTBS (600 pulses) protocol using personalized left dorsolateral prefrontal cortex (dlPFC) targets derived from functional magnetic resonance imaging (fMRI), comprising eight daily treatments, repeated over five days. Main Outcomes: Primary outcomes were i) change in Hamilton Depression Rating Scale (HAM-D17) from baseline to the end of the fifth day of treatment, and ii) the difference in change in HAM-D17 between UTRD and TRD subgroups. Results: Connectivity-guided fMRI targeting yielded personalized targets clustered around the anterolateral dlPFC. Accelerated iTBS elicited rapid antidepressant effects ({Delta}HAM-D17 -9.01 [SD 6.06], t = -12.45, p < 0.001) regardless of treatment-resistance group ({Delta}HAM-D17 -9.64 [SD 5.94] vs -8.10 [SD 6.12], t = -1.05, p = 0.299), which were sustained up to 12 weeks after treatment. Overall response and remission rates at the end of treatment were 40.8% and 16.9%. Self-report scales revealed broad symptomatic relief outside of core depressive symptoms. Conclusions & Relevance: This study demonstrated that fMRI connectivity-guided, accelerated iTBS induces sustained antidepressant effects and broader psychiatric benefits in patients across the spectrum of TRD. In a cohort unlikely to respond to most antidepressant therapies, connectivity-guided, accelerated iTBS offers a safe, well-tolerated option that can achieve benefit, or when ineffective, allow patients to expeditiously proceed with subsequent therapies than conventional rTMS. Trial Registration: This clinical trial was registered at clinicaltrials.gov with NCT05813093.

Schizophrenia spectrum disorders (SSDs) are clinically and neurobiologically heterogeneous. Normative modeling addresses heterogeneity of structural brain alterations by focusing on individual-level deviations, but their clinical relevance in SSDs remains controversial. We mapped the relationship between individual gray matter volume (GMV) deviations and schizophrenia diagnosis and symptoms. Normative models of GMV were established using cross-sectional, T1-weighted magnetic resonance imaging data from a large, multi-site, healthy reference cohort (N = 7957). Deviations were derived for SSD patients (n = 379) and healthy controls (n =149). Patients showed a significantly more negative average deviation compared to controls and regional deviations predicted diagnostic status with adequate performance (AUC = 0.79). A more negative deviation was associated with higher symptom severity and lower cognitive functioning in SSD. Negative deviations were scattered across the brain, with the largest alterations in the salience network. Our findings strengthen the potential of normative modeling to disentangle the heterogeneous underpinnings of SSD and provide further evidence for individualized structural deviations, particularly in the salience network, as promising markers of illness severity in SSDs.

BackgroundNeurodevelopmental disorders, including autism spectrum disorder, involve widespread transcriptional dysregulation. Copy number variations at 16p11.2 are among the strongest genetic risk factors for autism spectrum disorder, yet the molecular mechanisms by which these copy number variations contribute to neurodevelopmental pathology remain unclear. ResultsWe identify significant genetic associations between autism spectrum disorder susceptibility and the HIST1 histone gene cluster through genome-wide analysis. Transcriptomic profiling across post-mortem brain tissue, patient-derived neural progenitor cells, neurons, and cerebral organoids reveals consistent upregulation of linker histone variants H1.2 and H1.5 in idiopathic autism spectrum disorder and 16p11.2 hemi-deletion carriers, but not in schizophrenia or bipolar disorder. Functional assays demonstrate that dysregulated H1 expression disrupts gene networks involved in synaptic signaling, chromatin remodeling, and neural differentiation. Mechanistically, we link H1 upregulation to MAZ, a transcription factor encoded within the 16p11.2 locus. MAZ binds the promoter regions of H1 genes and represses their transcription. Knockdown of MAZ leads to H1 overexpression. H1 upregulation alone is sufficient to alter the expression of autism spectrum disorder-associated genes. ConclusionsOur findings define a MAZ-dependent regulation of H1 dosage as a critical chromatin-mediated mechanism contributing to transcriptional pathology in 16p11.2-associated autism spectrum disorder.

BackgroundThe severity of positive psychotic symptoms largely defines emerging psychosis syndromes. However, depressive and negative symptoms are strongly psychologically and biologically interlinked. A transdiagnostic exploration of symptom severity across early illness syndromes could enhance the understanding of shared common factors and future trajectories of mental illness. We aimed to identify subgroups based on the severity of positive, negative, and depressive symptoms and assess relationships with: 1) premorbid functioning, 2) longitudinal illness course, 3) genetic risk, and 4) brain volume differences. MethodsWe analysed 749 participants from a multisite, naturalistic, longitudinal (18 months) cohort study of: clinical high risk for psychosis (n=147), recent onset psychosis (n=161), and healthy controls (n=286), and recent onset depression (n=155). Participants were stratified into subgroups based on severity of baseline positive, negative, and depression symptoms. Baseline and longitudinal differences between groups for clinical, functioning, and polygenic risk scores (schizophrenia, depression, cross-disorder) were assessed with ANOVAs and linear mixed models. Voxel-based morphometry was used to examine whole-brain grey matter volume differences. Discovery findings were replicated in a held-out sample (n=610). ResultsParticipants were stratified into no (n=241), mild (n=50), moderate (n=182), and severe symptom (n=254) subgroups. The mean (SD) age was 25.3 (6.0) and 344 (47.3%) were male. Symptom severity was associated with poorer premorbid functioning and illness trajectory, greater genetic risk, and lower brain volume. Findings were not confounded by the original study groups or symptoms and were largely replicated. Conclusions and relevanceTransdiagnostic symptom severity is linked to shared aetiologies, prognoses, and biological markers across diagnoses and illness stages. Such commonalities could guide therapeutic selection and future research aiming to detect unique contributions to specific psychopathologies.

Prenatal depression is a substantial contributor to maternal morbidity, and screening is an entry point to psychiatric assessment and treatment during pregnancy. Following updated guidelines and quality metrics for prenatal depression screening, we evaluated whether screening uptake differed by preferred language within a large U.S. healthcare system. We used electronic health record data to identify a retrospective cohort of deliveries at or beyond 20 weeks gestation in 2019-2024. We used logistic regression with a language-year interaction to estimate the adjusted marginal probabilities of screening by language preference. Among 99,526 pregnancies (82,632 individuals), screening increased substantially over time but increases differed across language groups (p<0.001). In 2019, screening probabilities were similar (English 0.50; Spanish 0.48; Another Language 0.50). By 2024, probabilities diverged (English 0.81; Spanish 0.66; Another Language 0.71). Unequal screening uptake can systematically under-identify prenatal depression among patients with non-English language preference, with implications for equitable access to psychiatric care.

BackgroundExternalizing liability is a strong risk factor for early substance initiation, but the neurobiological pathways linking polygenic risk to initiation remain incompletely characterized. MethodsUsing the ABCD Study, we implemented a four-stage framework linking an externalizing polygenic risk score (extPRS) to baseline multimodal neuroimaging-derived phenotypes (IDPs) and longitudinal substance initiation (alcohol [primary], nicotine, cannabis, and any substance). First, we screened extPRS-IDP associations using covariate-adjusted linear models (age, sex, ancestry principal components, site/scanner variables; modality-specific covariates where applicable) and controlled multiple testing using false discovery rate (FDR) procedures. Second, we estimated direct extPRS associations with time-to-initiation using Cox proportional hazards models. Third, we fit joint Cox models including extPRS and each discovery-significant IDP, retaining outcome-IDP associations after within-outcome FDR correction. Fourth, we conducted mediation analyses for prioritized outcome-IDP pairs using an extPRS [-&gt;] IDP mediator model and an initiation model including both extPRS and IDP, estimating indirect (ACME) and direct (ADE) effects via bootstrap with multiple-testing control. ResultsAmong 10,608 participants, higher extPRS was associated with earlier initiation across outcomes, with the largest effects observed for nicotine and cannabis and a modest but significant effect for alcohol. Stage 1 identified thousands of extPRS-associated IDPs that were highly concordant across robustness specifications. Stage 3 prioritized outcome-specific IDPs associated with initiation beyond extPRS, with the number of retained IDPs varying across sensitivity settings (site-clustered vs. HC3 standard errors; SES covariates on/off) but showing a replicated core set across models. In Stage 4, mediation analyses showed that indirect effects of extPRS through IDPs were small in magnitude (ACME {approx} 10-4) and accounted for less than 2% of the total effect, while direct effects (ADE {approx} 0.02-0.05) remained strong across outcomes. FDR-significant mediation signals were observed only for alcohol and any-substance initiation, whereas no mediation effects survived multiple testing correction for cannabis or nicotine. Across outcomes, direct genetic effects were substantially larger than mediated effects, indicating that genetic liability operates primarily through direct pathways rather than through baseline brain measures. ConclusionsExternalizing polygenic liability is broadly associated with substance initiation, with robust and consistent direct effects across substances. Although specific frontal structural and microstructural phenotypes show statistically significant mediation signals, their contribution is small, suggesting that baseline brain measures explain only a limited proportion of genetic risk. This framework provides a scalable approach to prioritize neurobiological pathways linking genetic liability to early substance initiation while highlighting the dominant role of direct genetic effects.

Severe mental disorders (SMDs), including bipolar disorder, schizophrenia, and major depressive disorder, are highly complex conditions associated with a substantial clinical burden and an increased suicide risk. Here, we present the Madrid Manic Cohort (MadManic), a large-scale initiative from Spain designed to integrate genomic, multi-omics, clinical, and digital phenotyping data to investigate the biological basis and clinical heterogeneity of SMDs. The cohort is still expanding and currently includes over 4,400 participants (~2,300 psychiatric patients and ~2,100 controls) and >11,000 biospecimens. Genotyping, transcriptomic and epigenetic data are available for different subsets of the cohort. By establishing the MadManic cohort we aim to integrate molecular data with detailed clinical and longitudinal digital information, allowing a more precise characterization of patient subgroups based on biological and phenotypic profiles. The MadManic cohort is well positioned to contribute to major international efforts in psychiatric genetics by enhancing the representation of Southern European populations, and advancing the identification of genetic risk, clinical predictors, and pharmacogenomic markers of treatment response. This cohort represents a valuable resource for advancing precision psychiatry, with the potential to improve risk prediction and guide personalized interventions in SMDs.

Mental health conditions, including perinatal suicidality, remains a significant health burden representing a leading cause of maternal mortality in the United States. Although the etiology of perinatal suicidal ideation (SI) is not well understood, DNA methylation may provide meaningful mechanistic insights and/or serve as clinical biomarkers during the peripartum period. Using data provided by the Swedish BASIC cohort, we performed a retrospective analysis of DNA methylation changes associated with perinatal SI at three perinatal timepoints (17- and 38-weeks gestation and 8 weeks post-partum) through a targeted and genome-wide approach. Targeted analysis of a priori genes revealed 1, 10, and 4 significantly differentially methylated probes at each timepoint and implicated genes associated with the hypothalamic-pituitary-adrenal axis. Genome-wide results identified 465, 2,880, and 510 differentially methylated probes and 7, 25, and 12 differentially methylated regions at each timepoint. Pathway analysis at 38-weeks gestation identified vitamin digestion and absorption as the top term differentially methylated in perinatal SI. Additionally, genes implicated in estrogen and oxytocin signaling were also significantly differentially methylated. Post-partum ideation-risk was successfully predicted using the top ten genome-wide differentially methylated probes at 17 weeks (AUC=66.9%), with prediction accuracy highest when DNA methylation and depression severity were combined (AUC=93.2%). Furthermore, the prediction accuracy for identifying novel SI in the post-partum period increased to 86.2% with 17-week biomarkers. Our results deliver novel insights regarding the role of DNA methylation and perinatal SI, with biomarkers providing both mechanistic insights and clinical usefulness, contributing to the field of perinatal psychiatry and epigenetics.