American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

In the past two decades, the focus on genome-wide association studies in large samples of unrelated patients has overshadowed family genetic studies. Therefore, little is still known about the levels and effects of the transmission of polygenic risk scores (PRS) among familial cases of schizophrenia (SZ) or bipolar disorder (BD) and their unaffected relatives. Prior research has shown that PRS are elevated in both patients and young individuals at familial risk for BD and SZ. We sought to study the transmission of PRS in affected multigenerational families and non-affected adult relatives (NAARs) with or without other non-mood nonpsychotic DSM-IV diagnoses and unrelated non-affected individuals from the same population. We genotyped 1,117 participants divided in 48 families from the Eastern Quebec Schizophrenia and Bipolar Disorder Kindreds. PRSs for both SZ and BD were computed using Multivariate Lassosum. For both SZ PRS and BD PRS, SZ and BD cases present higher PRS compared to controls, replicating previous findings. Regardless of a diagnosis of other non-psychotic and non-mood conditions, NAARs presented higher PRS than the unrelated cohort. Crucially, a subset of families presented consistently low PRS transmission profiles across generations, falling below expectations from our polygenic inheritance model. When the effect of individual PRs is accounted for, we observed sex-specific associations between familial PRS and patients' symptom dimensions. Our results clearly demonstrate that polygenic inheritance alone does not adequately explain disease transmission in families. Such an approach may also clarify why some families exhibit dense clustering of cases despite minimal polygenic burden.

Objective: To develop and validate clinically relevant polygenic scores (PGS) for alcohol (AUD), cannabis (CanUD), opioid (OUD), tobacco (TUD), and polysubstance use disorders (polySUD) across African (AA), European (EA), and Latinx (LA) ancestry populations. Methods: Using multiple genome-wide association study summary statistics and PGS methods, substance use disorder PGS were developed and evaluated in Indiana Biobank samples (IB, N: 1,356-24,989), then top-performing PGS were validated in All of Us Research Program samples (AOU, N: 62,389-209,952). Case and controls were defined using ICD-9/10 codes. All participants were aged 18 years or older (>=21 years for AUD controls). Clinical relevance was defined as an odds ratio (OR) >=2 for individuals with the highest PGS determined based on disorder prevalence compared to everyone else. Results: In EA and LA, all PGS achieved clinically relevant performance in both IB and AOU (ORs: 2.00-9.10; P <= 3.87E-4). In AA, PGS met this threshold in IB (ORs: 2.02-2.71; P <= 2.20E-4) but not in AOU (ORs: 1.28-1.56; P <=0.03). Overall, OUD PGS showed the strongest associations in most analyses, followed by CanUD and polySUD. Generally, compared to female PGS, male PGS had higher or comparable ORs, but the differences were not significant except AUD PGS in AOU LA. Conclusions: PGS demonstrated clinically meaningful risk prediction for substance use disorders in EA and LA, supporting the feasibility of future clinical implementation for population-level screening. However, reduced performance in AA underscores the urgent need for more genetic studies in that population.

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.

Genetic instrumental variable studies can provide stronger insights into the causal relationships between smoking and mental illness than conventional observational studies because they are less susceptible to confounding and reverse causation. However, they still rely on genetic instruments that partly capture genetic influences shared with other substance use and socioeconomic status (SES), potentially biasing estimates of smoking-specific effects. We therefore aim to (1) develop a more specific genetic instrument for smoking that minimizes these shared influences and (2) use this instrument to examine the causal effects of smoking on psychiatric disorders. We applied Genomic Structural Equation Modelling to 19 European-ancestry GWAS summary statistics (7 smoking, 8 substance use and 4 SES phenotypes), deriving a novel smoking-specific genetic factor representing liability to smoking independent of shared substance-use and SES influences. We used this factor as an instrument in Mendelian Randomization analyses to test causal effects of smoking on eight psychiatric disorders. The smoking-specific factor was associated with 52 independent genome-wide significant loci. Genetically predicted smoking-specific liability was significantly causally associated with seven psychiatric disorders. These findings support a causal role of smoking in increasing the risk of multiple psychiatric disorders beyond influences shared with other substance use and SES, providing stronger evidence for smoking-specific effects and informing targeted smoking prevention and intervention strategies. More broadly, our findings highlight that the validity of Mendelian Randomization depends on the specificity of its genetic instruments. Future studies should strive to develop instruments that better isolate the exposure of interest from shared genetic influences, enabling more accurate identification of causal mechanisms.

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

Importance: Avoidant/restrictive food intake disorder (ARFID) is a feeding and eating disorder characterized by extremely restricted dietary variety and/or quantity resulting in significant physical health impairment and psychosocial dysfunction. ARFID frequently co-occurs with neurodevelopmental conditions, yet the extent to which this co-occurrence reflects shared genetic or environmental influences remains largely unknown, as few twin or genetic studies of ARFID have been conducted. Objective: To examine the extent to which genetic and environmental influences contribute to the association between a broad ARFID phenotype and neurodevelopmental traits. Design, Setting, and Participants: Population-based twin study using data from the Child and Adolescent Twin Study in Sweden, including 30,374 twins born 1992-2008. Main Outcomes and Measures: A broad ARFID phenotype was identified using a composite measure derived from parent reports and national health registers between ages 6 and 12 years. Parents completed measures of neurodevelopmental traits at age 9 or 12 years, including autism (subdomains: social communication problems and restricted/repetitive behaviors), attention-deficit/hyperactivity disorder (ADHD, subdomains: inattention and impulsivity/hyperactivity), tic disorders, learning disorders, oppositional defiant disorder, conduct disorder, obsessive-compulsive disorder (OCD), sensory perception problems, and sleep problems. Phenotypic associations were estimated using polyserial correlations. Bivariate twin models decomposed variance and covariance into genetic and environmental components. Results: Phenotypic correlations with the broad ARFID phenotype ranged from 0.18 (95% CI: 0.15-0.21) for OCD to 0.36 (95% CI: 0.33-0.38) for autism. Broad genetic correlations (rH; additive plus dominant genetic influences) ranged from 0.27 (95% CI: 0.21-0.33) for conduct disorder to 0.52 (95% CI: 0.44-0.60) for autism-restricted/repetitive behaviors. Genetic factors explained 77% to 95% of all phenotypic correlations. Non-shared environmental correlations were minimal to small, with the largest observed for autism (0.17; 95% CI: 0.08-0.26). Conclusions and Relevance: The broad ARFID phenotype shares substantial genetic influences with a number of neurodevelopmental traits. These findings suggest that the frequent co-occurrence of ARFID with neurodevelopmental traits largely reflects shared genetic influences rather than overlapping environmental influences, supporting the conceptualization of ARFID within a broader neurodevelopmental framework.

Insulin signalling contributes to neurodevelopment and brain function, and insulin resistance (IR)-related traits are associated with cognitive performance. However, the genetic architecture shared across specific cognitive domains and IR-related phenotypes remains insufficiently defined. We analysed large-scale genome-wide association study summary statistics for 11 IR-related traits (N=53,334-933,970) and 10 cognitive measures (N=28,156-436,853) to quantify global and local genetic correlations, fine-map shared association signals, and annotate implicated genes and drug-gene interactions. Pairwise global and local genetic correlations were estimated, and shared high-confidence variants were prioritised using the multivariate Sum of Single Effects model. Positional and expression quantitative trait locus mapping was performed, and implicated genes were examined through functional annotation, tissue enrichment, and drug-gene interaction analyses. Low-to-moderate genetic correlations were observed between six IR-related traits and seven cognitive measures (|rg|=0.08-0.34), with predominantly opposite directions, except for correlations involving visual declarative short-term memory. Local genetic correlations showed mixed effect directions across most trait pairs, and multivariate fine-mapping prioritised 696 shared likely causal variants with high posterior support. Gene annotation indicated enrichment in several pathways, including immune-related, signal transduction, neurogenesis, neurotransmitter metabolism, receptor regulation, and lipid and cholesterol metabolism regulation. Implicated genes were expressed across various brain regions and showed prior associations with neuropsychiatric and cardiometabolic conditions. Several drug-gene interactions were identified, involving immunomodulatory and anti-inflammatory compounds. These findings indicate widespread heterogeneous genetic overlap between IR-related traits, particularly body mass index and waist-to-hip ratio, and cognitive measures of general intelligence, processing speed, and short-term visual declarative memory. The findings prioritise apolipoprotein-related lipid transport and inflammatory and oxidative stress pathways as candidate mechanisms linking cognitive, cardiometabolic, and neuropsychiatric phenotypes.

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.

Large genome-wide association studies (GWASs) have generated extensive summary-statistics resources across psychiatric disorders, ancestries, and sex strata. These resources create an opportunity to compare genetic architectures across related datasets, but practical tools for identifying both shared and divergent association signals remain limited. We developed an AI-assisted workflow for local comparative analysis and visualization of multiple psychiatric GWAS summary-statistics datasets. The workflow harmonizes input GWASs, computes pairwise differential association statistics, prioritizes shared loci with concordant evidence across paired datasets, and renders genome-wide and locus-level visualizations with nearby gene context. To improve accessibility and reproducibility, the same analytical workflow can be executed either directly from the command line or through AI-assisted natural-language workflows, while detailed implementation steps remain transparent and locally controlled. We demonstrate the workflow using sex- and ancestry-stratified Psychiatric Genomics Consortium schizophrenia GWAS summary statistics. In the demonstration analysis, the differential workflow highlighted a novel candidate sex-divergent locus at rs185665940 showing protective effect to European females in an intergenic region close to CYP26B1 and EXOC6B, with another independent SNP rs10166057 close to rs185665940 (a risk SNP to schizophrenia and also an brain eQTL of CYP26B1) showing female-specific risk association with schizophrenia in both European and Asian female but not male populations. These results show that the workflow can recover biologically credible shared association signals while also identifying candidate subgroup-differential loci for downstream investigation. The pipeline provides a practical bridge between comparative GWAS analysis, publication-style visualization, and AI-assisted reproducible execution under local user control.

Neuropsychiatric polygenic indices (NPGIs) are used as genetic predictors of poor mental health. However, NPGIs are also associated with environmental factors which could affect mental health in adulthood, including the rearing environment. Hence, their "genetic" effects are both direct and environmentally mediated. There is a need to identify alternative genetic predictors without environmental signal. Endophenotype-based polygenic indices (EPGIs) trained on brain structure and function are under-studied alternatives which, due to their relative biological proximity, may exhibit associations with mental health outcomes which are less environmentally mediated than those of NPGIs. Using four representative UK samples (Understanding Society; UKHLS, NCDS, BCS70 and MCS) we employ sex-stratified path models to estimate the direct and environmentally mediated effects of eleven NPGIs and 30 EPGIs on adult mental health, focussing on the rearing environment. The depression NPGI is consistently associated with mental health symptoms across most sex-stratified sub-samples (best meta-analysis beta = 0.091, p 0.001) but demonstrates 1.6 - 24.5% environmental mediation. Seven other NPGIs and three EPGIs show sample- and sex-specific associations with mental health symptoms. NPGIs for attention deficit hyperactivity disorder, depression and substance use disorder are robustly associated with measures of the rearing environment, which in turn are frequently associated with mental health symptoms. Sensitivity analyses find that NPGI associations vary substantially depending on who is included in the sample. In conclusion, the rearing environment likely mediates a substantial portion of NPGIs' so-called "genetic" effects on mental health symptoms, but EPGIs are not currently powerful enough to replace them.

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.

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contributing to model decisions. Method: We analyzed participants from the Oregon ADHD-1000 cohort split into training, validation, and test subsets. We trained modular neural network models classifying ADHD case-control status using SNP-level genotype data with biological annotations, polygenic scores, demographics, parenting and family conflict, stress and trauma, geocoded measures, cognitive task measures, temperament factor scores, and missingness indicators. Hyperparameter optimization selected model architecture and feature block inclusion. We evaluated model performance using AUC, precision-recall curves, calibration analyses, prediction certainty analyses, and decision curve analysis. We used integrated gradients to quantify block-level, feature-level, and individualized feature importance. Results: The best model using temperament features had an AUC of 0.97 in the held-out test subset, with high accuracy, sensitivity, and specificity and a Brier score of 0.06. The best model excluding temperament had an AUC of 0.75. Feature importance analyses highlighted temperament, demographic, and cognitive domains in the temperament-inclusive model. Individualized explanations showed that prediction drivers varied across participants and could help reveal conflicting or supporting evidence across domains. Conclusion: Explainable, multi-modal classification models can integrate heterogeneous ADHD-relevant information and identify features that contribute to individual predictions. These types of models may advance ADHD risk modeling research and clinician-led decision support, especially in complex or diagnostically uncertain cases.

Background: Suicide prediction models in psychiatry often rely on purely data-driven feature selection, which can produce unstable and clinically opaque predictor sets in modest-sized samples. We developed Evidence-Based AI LASSO (EBAL), an evidence-guided regularization framework that incorporates curated clinical evidence into feature-specific penalty factors for interpretable prediction. Methods: Baseline data from 136 youth with confirmed bipolar spectrum disorder in the Greater Houston Area Bipolar Registry were analyzed using 20 candidate clinical predictors. Forty higher-level evidence documents on suicidality and related predictor domains were curated through a structured evidence synthesis workflow and indexed as an auditable evidence corpus. An open-weight large language model assigned feature-specific penalty factors using a prespecified scoring rubric, and these penalties were used to fit a weighted LASSO model. EBAL was compared with a standard evidence-agnostic LASSO using nested leave-one-out cross-validation. Results: For suicidal ideation, EBAL achieved an AUROC of 0.768, balanced accuracy of 0.757, sensitivity of 0.758, and specificity of 0.757. The standard LASSO achieved an AUROC of 0.760 and balanced accuracy of 0.715. EBAL improved balanced accuracy (+0.042, p=0.010) and Matthews correlation coefficient (+0.079, p=0.010), while retaining fewer stable predictors than standard LASSO (11/20 vs 18/20). The strongest positive predictors were current depressed mood, duration of mood disorder illness, and comorbid generalized anxiety disorder. For suicidal behavior, both models performed near chance and retained all candidate predictors. Limitations: The study was cross-sectional, single-site, and modest in sample size, with no external validation cohort. Conclusions: EBAL produced a sparser and more clinically coherent model for suicidal ideation in pediatric bipolar disorder, but did not improve prediction of suicidal behavior. These findings support evidence-guided regularization as a transparent strategy for aligning psychiatric prediction models with prior clinical knowledge while preserving interpretability.

Schizophrenia and bipolar disorder are diagnostically distinct categories that overlap substantially in clinical features and genetic aetiology. Understanding genetic variants that contribute liability specifically to each disorder can offer insights into biological processes that differentiate them. Here we used Case-Case GWAS (CC-GWAS) to identify common genetic variants differentially associated with schizophrenia and bipolar disorder, analysing 67,390 schizophrenia cases and 41,917 bipolar disorder cases. We identified 19 genome-wide significant loci, of which 16 (84%) demonstrated divergent genetic effects with risk alleles showing opposite directions of association between disorders. The CC-GWAS summary statistics had detectable disorder-differentiating heritability (10.27%, SE=0.01) and showed genetic correlations indicating that SCZ-differentiating alleles were associated with lower educational attainment, lower cognitive performance, and increased risk of ADHD, anorexia, autism, BD1 (though not BD2), cannabis use disorder, and OCD. Four loci showed divergent effects despite not reaching genome-wide significance in either individual disorder GWAS, demonstrating enhanced power to detect opposite-direction effects. Functional annotation identified 102 mapped genes significantly enriched for expression across all 13 tested brain regions, with no significant enrichment in peripheral tissues, and gene set enrichment analysis implicated neuronal projection and synaptic compartments as the strongest biological themes differentiating the two disorders. Polygenic risk scores derived from these disorder-differentiating variants were associated with earlier age at onset and more severe negative symptoms in schizophrenia, consistent with these variants marking neurodevelopmental dimensions of illness. Our findings provide targets for understanding pathogenic differences between schizophrenia and bipolar disorder and demonstrate that genuine divergent genetic effects exist beyond the substantial shared liability.

Background: Cannabis is one of the most widely used psychoactive substances worldwide. {Delta}-tetrahydrocannabinol ({Delta}-THC) is the main contributor to cannabis-induced effects such as euphoria, anxiety, and psychotomimetic effects, and is metabolized by several hepatic enzymes, including CYP3A4. There are interindividual differences in how cannabis affects users, which have substantial genetic contributors. Methods: We examined how real-time effects of {Delta}-THC on psychotomimetic measures and on subjective effects of "high", sadness and anxiety in 188 healthy volunteers in a laboratory infusion paradigm, relate to polygenic risk scores (PRS) for cannabis lifetime use (CanLU), cannabis use disorder (CanUD), and CYP3A4 expression. Results: CYP3A4 expression PRS was significantly associated with {Delta}-THC-induced psychotomimetic effects. Genetic liability to use and misuse cannabis is potentially associated with lower {Delta}-THC-induced psychotomimetic symptoms. CanLU PRS nominally predicted enhanced {Delta}-THC-induced "high", while CanUD PRS predicted it to be lower. Conclusions: Our findings suggest that genetic liability to produce more CYP3A4 enzyme may be associated with faster {Delta}-THC degradation and the consequential diminution of the latter's effects. Nominal effects suggest that aversive outcomes may reduce cannabis use and use disorder genetic liability, and that CanUD subjects may need higher {Delta}-THC doses to experience euphoria ("high"). In total, this study provides novel insights regarding some of the specific genetic factors that influence interindividual variability in {Delta}-THC effects, mainly via {Delta}-THC metabolism.

Transdiagnostic psychiatric research on reward-guided learning has largely focused on simple associative processes, leaving it unclear whether or how higher-level processes are disrupted. Here, we studied how abstraction, the ability to extract relevant features from complex information, and metacognition, the ability to monitor and evaluate one's own mental processes, map onto specific transdiagnostic dimensions. Using an online sample (N = 249), we examined associations between these processes and three cross-culturally robust transdiagnostic dimensions derived from a large existing dataset (N = 19,505): Compulsive hypersensitivity, Social withdrawal, and Addictive behaviours. Computational modelling of an abstract representation learning task with confidence judgments revealed that Compulsive hypersensitivity was negatively associated with both abstraction ability (pboot = 0.003) and metacognitive sensitivity (pboot = 0.005), while Social withdrawal was positively associated with metacognitive sensitivity alone (pboot = 0.002). Moreover, transdiagnostic dimensions revealed more coherent associations with higher-order cognition than symptom-level analyses, highlighting the added value of examining psychopathology at the factor rather than the symptom level. These findings portray a hierarchical view of cognitive dysfunctions in psychopathology and point to representational and metacognitive processes as potential targets for transdiagnostic intervention.

Cocaine Use Disorder (CUD) poses a significant public health and socioeconomic challenge. Determining the genetic basis of predisposition for development of CUD is challenging in human populations but can be studied in Drosophila. We assessed cocaine consumption and cocaine preference of 74,875 flies from 598 sequenced, wild-derived, inbred lines from the expanded Drosophila melanogaster Genetic Reference Panel (DGRP3). We found significant genetic variation, sexual dimorphism, and genetic variation in sexual dimorphism for these traits. Whereas most lines showed cocaine avoidance, ~10% of the lines showed innate cocaine preference in at least one sex. Genome-wide association analyses for cocaine consumption, preference, and micro-environmental variance of these traits identified 2,155 polymorphisms in/near 866 genes that were enriched for Gene Ontology terms associated with neurogenesis, development, and behavior. Many of the associated genes had human orthologs with known associations with CUD and other substance use disorders as well as psychiatric and behavioral traits. We confirmed causal associations with cocaine preference for three polymorphisms with large effect sizes by assessing their effects in DGRP3 lines not included in the initial association analyses. Pairwise associations between these polymorphisms exhibited suppressing epistasis. These polymorphisms are in genes with human orthologs that fulfill essential functions in the nervous system, including the glucose transporter SLC2A8; KCNC2, a subunit of the voltage gated potassium channel; and CHRNA7, a nicotinic cholinergic receptor subunit. Thus, studies on Drosophila can provide insights into the genetic and neural mechanisms of CUD.

Studies of resemblance for disorders and other traits measured at the binary (yes/no) level between relatives frequently contain individuals who are currently in the negative category but who will become positive in future. For example, a 10-year-old may develop depression in the future, but is as yet unaffected. Such censoring can substantially bias estimates of correlation between relatives. To overcome this problem we develop a model for the association between liability to a disorder, and its age at onset. The model is designed for data from pairs of relatives to enable estimation of the correlation between an individuals' liability to disorder and their age at onset. Usually, such information is not available at the individual level, because age at onset is uniquely available when onset has occurred. Lacking variation in disorder status, data from non-related persons cannot estimate the covariance between liability and age at onset. Data from relatives can resolve this issue when there is a correlation in liability between the relatives, because different age at onset distributions would be expected in concordant vs. discordant pairs of relatives. Greater severity and worse outcomes are often observed among those with earlier onset, so a correlation between disorder liability and age at onset seems likely in many cases. In this article we present the basic theory of the model, implemented as a mixture distribution, and an application to cannabis use in a Virginia Twin Study of Adolescent Behavioral Development. A negative association of (-.212) between age at onset an liability was found, with confidence intervals of -.263 to -.152, which do not cross zero. The method contrasts with Cox Proportional Hazards, in which disorder liability and onset timing are treated as a single dimension.

Background Physical activity levels are altered across neuropsychiatric disorders. While these traits are heritable, the genetic overlap between normal variation in activity levels and neuropsychiatric disorders that involve motor dysfunction such as schizophrenia and Parkinson's disease (PD) remains unexplored. Objectives To investigate the genetic overlap between physical activity, schizophrenia, and PD. Methods Multi-Trait Analysis of genome-wide association studies (GWAS) was used to boost the GWAS power for objectively measured physical activity (n=89,683) by leveraging three GWAS of self-reported activity (n=124,842-377,234). Genetic overlap between the activity, schizophrenia and PD was characterized using linkage disequilibrium score regression, causal mixture modeling, and local genetic correlations. Pleiotropic variants were identified using the conjunctional false discovery rate, annotated to genes, and investigated for enrichment of biological processes, tissue types and association with GWAS-catalog traits. Results Genetic correlations of physical activity with schizophrenia and PD were negligible (rg=-0.02-0.02, p>0.05), but polygenic overlap was substantial, reflecting mixed effect directions. We identified 32 independent variants shared with schizophrenia and 11 with PD, including CRHR1, MAPT and KANSL1 within the 17q21.31 region. Schizophrenia-shared variants mapped to genes differentially expressed in subcortical regions, especially amygdala and basal ganglia. Gene-set analyses revealed enrichment for mental health and cognitive-behavioural traits (schizophrenia-shared genes) versus structural brain phenotypes and neurodegenerative disorders (PD-shared genes). Conclusions Despite negligible genetic correlations, physical activity shares substantial genetic architecture with schizophrenia and PD. Shared genes implicated brain regions and traits spanning motor and cognitive-affective function, consistent with the psychomotor nature of physical activity.

Suicidal behavior results from complex behavioral and biological changes. Previous cross-sectional studies indicate that proinflammatory immunobiological factors are often increased in close temporal proximity to a suicide attempt. Suicidal individuals may also exhibit a biological trait vulnerability to stress and inflammation, due to persistent epigenetic modifications. We enrolled 130 individuals with major depressive disorder (MDD), 83 with suicidal behavior at intake, and followed them for 12 months with up to eight clinical assessments. Quantification of plasma inflammatory markers and metabolites was performed by high-sensitivity electrochemiluminescence and Ultra High-Performance-Liquid-Mass Spectrometry (UPLC-MS), respectively. Epigenetic changes were identified using Illumina EPIC arrays. We identified 15 genes with altered DNA-methylation associated with suicidal behavior and attempts at baseline. Childhood trauma predicted lifetime suicide attempts and was associated with altered methylation of seven genes. Increased neutrophils and lower plasma serotonin at baseline predicted future suicide attempts over the following year (neutrophil estimate = 0.42, P = 0.016; serotonin OR = 0.58, 95% CI: 0.39-1.13). Utilizing biomarkers from baseline and epigenetic data from the genes with highest predictive values (STBD1 ,PRDM8, and TRIM15), we achieved an area under the curve (AUC) of 0.84 for suicide attempts over the year. Suicidal behavior in MDD was associated with specific epigenetic signatures. Several of the identified genes, such as MAD1L1, have been implicated in psychiatric disease, suicidal behavior and the immune response. These findings support the usefulness of epigenetic and immunometabolic blood markers for identifying suicidal individuals in clinical settings, potentially enhancing preventative efforts.