Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

BACKGROUND:The extensive genetic overlap between anxiety disorders (ANX) and major depression (MD) may partly reflect the inclusion of comorbid cases in genome-wide association studies (GWASs). We investigated this genetic relationship between ANX and MD, with and without mutual comorbidity. METHODS:Using the UK Biobank, we performed disorder-specific GWASs for ANX-only (cases/controls = 9980/179,442) and MD-only (cases/controls = 15,301/179,038) and derived polygenic risk scores (PRSs). In the Norwegian Mother, Father, and Child Cohort Study (MoBa), we tested associations between PRS and MD-only (n = 7486), ANX-only (n = 1992), and comorbid ANX and MD (ANX-MD) (n = 3468) cases and controls (n = 85,851). PRS associations with anxiety and depression symptoms were tested in MoBa (n = 54,862). GWASs including comorbid cases (MD-comorbid [MD with comorbid ANX] or ANX-comorbid [ANX with comorbid MD]) were used for comparison. Genetic correlations were compared by comorbidity status, and Mendelian randomization was employed to assess causal relationships. RESULTS:MD-comorbid and ANX-comorbid PRSs showed a stronger association with ANX-MD cases than with their primary disorders, MD-only (z = -2.82, padjusted = .01) and ANX-only (z = -2.36, padjusted = .03), respectively. MD-only PRS was more strongly associated with MD-only than with ANX-only cases (z = 3.63, padjusted = 6.9 × 10-4). The genetic correlation (rg) was lower between ANX-only and MD-only (rg = 0.53, SE = 0.11) than between ANX-comorbid and MD-comorbid (rg = 0.91, SE = 0.01). Bidirectional causal effects observed in comorbidity-inclusive analyses were attenuated to null when comorbid states were excluded. Gene sets of MD-comorbid, ANX-comorbid, and MD-only, but not of ANX-only, were enriched for the immune regulation pathway-interleukin 21 production. CONCLUSIONS:The genetic distinction between ANX and MD becomes more pronounced when comorbid cases are excluded. The findings underscore the importance of disorder-specific genetic studies for advancing precision medicine.

Background:Substance use disorders (SUDs) are highly heritable, but the extent of shared and distinct genetic architecture across different SUDs is unclear. Aims:To compare the genetic architectures of alcohol use disorder (AUD), cannabis use disorder (CUD) and opioid use disorder (OUD) and to identify shared and unique genetic loci. Methods:We analysed large-scale genome-wide association study (GWAS) summary statistics from individuals of European ancestry recruited in Europe and the USA. The mixture model MiXeR was used to estimate the unique genetic architecture characteristics of each SUD, including its polygenicity, single nucleotide polymorphism (SNP)-heritability and discoverability, a measure of the distribution of genetic signal across all causal variants. Pairwise conditional/conjunctional false discovery rate (cond/conjFDR) analyses identified shared loci, followed by biological annotation of implicated genes. Results:AUD demonstrated the highest polygenicity, followed by CUD and OUD. SNP-based heritability was 0.10 for AUD and OUD and 0.01 for CUD. Discoverability was highest for OUD, followed by AUD and CUD. Currently, genome-wide significant SNPs explain 2.0% of AUD, 0.3% of CUD and 0.2% of OUD variance. Cond/conjFDR identified 39 novel loci for AUD, 10 for CUD and 1 for OUD. Of implicated genes, most were expressed in the brain, including several involved in gamma-aminobutyric acid and dopaminergic neurotransmission, opioid neurophysiology, myelination, DNA recombination, apoptosis and ubiquitin-dependent protein catabolism. Conclusions:SUDs have polygenic architectures with many shared loci and are similar with regards to some characteristics. However, the level of polygenicity differs across SUDs, with AUD being considerably more polygenic than OUD, whereas CUD is intermediate in terms of its polygenicity. The novel loci implicate genes primarily expressed in the brain, involving a variety of biological functions. The findings expand our view of the aetiology of these disorders, while supporting the hypothesis of a shared set of pleiotropic SUD genes.

BACKGROUND:Bipolar disorder (BD) is a major mood disorder influenced by both genetic and environmental factors. While DNA methylation from peripheral tissues can reflect both genetic and environmental influences and reveal insights into disease biology, it remains understudied in BD. DNA methylation signatures may complement polygenic scores (PGS) and hold potential as biomarkers. Here, we conducted the largest epigenome-wide association study (EWAS) of BD to date and evaluated the predictive value of polymethylation scores (PMS) in classifying case-control status. METHODS:DNA methylation from peripheral blood of 1729 cases and 1747 controls, comprising twelve cohorts, was obtained. We performed meta-analyses for the total sample, male-only, and female-only analyses. Differentially methylated regions (DMRs) were identified using the comb-p method. Polymethylation scores for BD (BD-PMS) were tested for association with BD, and in combination with PGS. FINDINGS:We identified 47 differentially methylated CpG positions (DMPs) in the total and four in the female-only analysis. Ninety, fourteen and six DMRs were identified in the total sample, female-only, and male-only analyses, respectively. Genes annotated to the top DMPs were enriched for immune activation and phosphorylation pathways. DMRs were annotated to genes relevant to neurotransmission, including GABBR1 and CACNA2D4. BD-PMS explained 2% of the variance in BD case-control status, and improved the variance explained from 7.9 to 8.5% when combined with PGS. For bipolar I disorder, BD-PMS explained 4.9% of the variance, and improved the variance explained by PGS from 15.9 to 18.5%. Association of BD with PMS for schizophrenia and major depression suggests pleiotropic epigenetic effects. INTERPRETATION:DNA methylation signatures of BD are detectable in blood using adequately powered data and may reveal novel BD biology that is not captured by genetic studies. PMS from large cohorts have the potential to facilitate the development of prediction tools to aid clinical decision-making. FUNDING:This investigation was primarily funded by the Research Council of Norway (RCN #250299, #273446, #223273) and the University of Bergen. A complete list of funding organisations is provided in the Acknowledgements.

Abstract Background Khat ( Catha edulis) is a widely consumed natural amphetamine-analog used across East Africa and the Arabian Peninsula. Accurate field-feasible measurement of recent khat use is a prerequisite for large-scale epidemiological research; yet no validated alternatives to laboratory reference methods have been identified in the scientific literature. This nested validation study evaluated the diagnostic accuracy of two point-of-care measures, a commercial amphetamine immunoassay and a Timeline Followback (TLFB) Assisted Self-Report (ASR), against high-performance liquid chromatography (HPLC) quantification of urinary norephedrine (NE), while additionally assessing agreement between the two field measures. Methods A prospective, random sub-sample of 119 male participants aged 18-40 years from the Gilgel Gibe Field Research Center (GGFRC) longitudinal cohort, Ethiopia (validation timepoint T2, 2015), was used. Three index-reference comparisons were conducted: (1) amphetamine immunoassay (nal von minden, Drug-Screen AMP test, 300Öng/mL cutoff) vs.ÖHPLC; (2) binary ASR (past-week use) vs.ÖHPLC; and (3) binary ASR vs.Öimmunoassay. Sensitivity (positive percent agreement, PPA), specificity (negative percent agreement, NPA), positive predictive value (PPV), negative predictive value (NPV), overall accuracy (overall percent agreement, OPA), and Cohen’s kappa were calculated with 95% confidence intervals. Pre-specified secondary analyses applied three pharmacokinetically-informed recall windows (0-2, 3-5, and 6-7 days prior to interview) to ASR. Results Against HPLC (77 positive, 42 negative), the immunoassay showed perfect specificity (1.0 [0.916-1.0]) and PPV (1.0 [0.91-1.0]) but low sensitivity (0.52 [0.40-0.64]), NPV (0.53 [0.42-0.65]), overall accuracy (0.69 [0.60-0.77]), and weak kappa (0.43 [0.34-0.52]). Binary ASR showed high sensitivity (0.96 [0.89-0.99]), specificity of 0.60 [0.433-0.74], PPV (0.81 [0.72-0.89]), NPV (0.89 [0.72-0.98]), with overall accuracy 0.83 [0.75-0.89] and moderate kappa (0.60 [0.51,0.69]). Restricting ASR to use within 0-2 days improved specificity to 0.69 [0.52-0.84], PPV to 0.86 [0.77-0.93], overall accuracy to 0.87 [0.79-0.93], and kappa to 0.69 [0.61-0.78] (moderate), while sensitivity (0.96 [0.89-0.99]) and NPV (0.89 [0.72-0.98]) remained stable. Against the immunoassay, ASR achieved high PPA of (1.0 [0.91-1.0]), NPA of 0.35 [0.25-0.47], OPA of 0.57 [0.48-0.66], and minimal kappa (0.27 [0.19-0.35]). Conclusions Time-stratified ASR (0-2 days) is a valid, scalable alternative to biological testing for recent khat use in resource-limited settings. The immunoassay’s 300 ng/mL cutoff functions as a marker of heavy or recent high-dose khat use rather than any-use detection. Its perfect specificity and PPV make it valuable as a confirmatory test for substantial exposure, while its lower sensitivity reflects calibration to amphetamine rather than to khat-derived cathinone metabolite. Registration Not registered.

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. ### Competing Interest Statement OAA is a consultant to Precision-Health.ai and has received speaker's honoraria from Eli Lilly and Company, Bristol Myers Squibb, H. Lundbeck A/S, Otsuka Pharmaceutical Co., Sunovion Pharmaceuticals Inc., and Janssen Pharmaceuticals, all outside the submitted work. The remaining authors declare no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study exclusively used publicly available, de-identified, summary-level genome-wide association study (GWAS) data. The secondary analyses conducted in this study were approved by the University of Cape Town Faculty of Health Sciences Human Research Ethics Committee (HREC reference 012/2025). Schizophrenia GWAS summary statistics are available from the Psychiatric Genomics Consortium at https://pgc.unc.edu/for-researchers/download-results/. Parkinson's disease GWAS summary statistics are available through the Neurodegenerative Disease Knowledge Portal at https://ndkp.hugeamp.org/research.html?pageid=a2f\_downloads\_280. Physical activity GWAS summary statistics are available from the NHGRI-EBI GWAS Catalog under accession numbers GCST90093322 and GCST006097. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. South African Medical Research Council (SAMRC) Division of Research Capacity Development

BACKGROUND:Severe mental disorders (SMDs) are associated with unhealthy lifestyle, contributing to increased risk of comorbid cardiovascular disease. Genetic factors influence both SMDs and lifestyle behaviours, but their genetic relationships remain unclear. Here, we aimed to unravel the shared genetic architecture of SMDs and lifestyle factors. Additionally, we assessed if genetic propensity to SMDs predicts body mass index (BMI) and lipids through lifestyle factors. METHODS:We analysed genome-wide data on major depression (MD) (N = 480,359), schizophrenia (SCZ) (N = 130,644), bipolar disorder (BIP) (N = 353,889) and self-reported lifestyle factors (N = 266,048-606,820), including food intake, physical activity, sedentary behaviours, and accelerometer-assessed activity from All of Us (N = 30,132) and UK Biobank (N = 91,105) to obtain objective measures for sensitivity analysis. We estimated the shared genetic architecture using bivariate MiXeR. Shared genetic loci were identified using conjunctional false discovery rate and mapped to genes, which were subject to enrichment analyses. We applied structural equation modelling (SEM) to assess if lifestyle mediates the relationship between polygenic risk score for SMDs and BMI and lipids. People with lived experience were involved in the research. FINDINGS:There was extensive genetic overlap between lifestyle factors and SMDs, with different patterns of effects. MD was genetically correlated with less physical activity and more sedentary behaviour. SCZ and BIP displayed opposite patterns with genetic associations with less sedentary behaviour, more physical activity and healthier food intake. This divergent pattern across SMDs was largely consistent using accelerometer-assessed activity. We identified 551 shared loci, implicating biological processes related to neurodevelopment and synaptic and neuronal properties. Further analyses indicated that lifestyle factors partly mediate the relationship between genetic risk for SMDs and BMI and lipids. INTERPRETATION:The results show a genetic propensity towards unhealthier lifestyle behaviours in MD, while SCZ and BIP displayed a divergent pattern. The genetic correlations reflecting mixed effect directions may also imply subgroups with different genetic propensity, which can form the basis for risk stratification and more tailored lifestyle interventions and personalised treatment. FUNDING:Research Council of Norway (grants, 273291, 273446, 300309, 324252, and 326813), South-East Norway Regional Health Authority (grants 2023-031 and 2022-073), NordForsk (University Cooperation Grant 164218, PreciMENT), European Union's Horizon 2020 Research and Innovation Programme (grant 847776, CoMorMent; grant 964874, RealMent), and the National Institutes of Health (grant R01MH125938).

Women with bipolar disorder (BIP) have a higher risk of developing polyendocrine metabolic ovarian syndrome (PMOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PMOS. Still, the mechanism underlying PMOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PMOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PMOS (3609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PMOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PMOS. Among the 10 genes mapped to the locus on chromosome 8:11,444,837-11,463,015, GATA4, NEIL2, and FDFT1 showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499,849-2514,270, CACNA1C, FKBP4, DCP1B, and ITFG2 are expressed in both the ovaries and the brain. Valproate interacts with CACNA1C, and CACNA1C is part of biological pathways that also include other genes interacting with valproate. We identified shared genetic underpinnings of BIP and PMOS and highlighted genes that may potentially contribute to the biological mechanisms underlying their comorbidity and to a hypothesized role of valproate in these mechanisms.

Background Individuals with antisocial behaviour (ASB) exhibit increased risk of substance use disorders, likely linked to shared genetic vulnerabilities and overlapping neural pathways involved in reward processing and impulse control. This susceptibility is further accentuated by the comorbidity of ASB with various psychiatric disorders, suggesting common genetic underpinnings. Despite this, there remains a paucity of work examining the shared genetic etiology of ASB, related neuropsychiatric traits and relevant brain regions. Methods Using GWAS of ASB (n=50,252), surface area and thickness of cortical frontal regions, volumes of the amygdala nuclei, psychiatric and substance use phenotypes, we utilized Linkage Disequilibrium Score Regression (LDSC) to assess the genetic correlation between these phenotypes. We then leveraged genetic overlap to boost discovery of genomic loci associated with ASB, and to identify specific shared loci associated with both ASB and each phenotype, using the conditional/conjunctional false discovery rate (cond/conjFDR) approach. FUMA was used for functional analyses. Results Using LDSC, we identified significant genetic correlations between ASPD and drinks per week (rg= 0.28; p=1.33×10-9), cannabis (rg= 0.37; p= 1.91×10-8), bipolar disorder (BD, rg= 0.20; p=1.95×10-5), major depressive disorder (MDD, rg= 0.53; p=1.09×10-15) and marginally significant correlations with caudal anterior cingulate surface area (rg=-0.17; p=0.04), superior frontal surface area (rg=0.18; p=0.03), and global measures of cortical surface area (rg=-0.15; p=0.02) and thickness (rg=-0.13; p=004) and ICV (rg=-0.2; p=0.01). A total of 54 loci, representing 127 independent SNPs, became significant after conditioning on related psychiatric disorders (8 SCZ, 7 BD, 11 MDD), substance use traits (18, drinks per week; 10, cannabis use) and the corpus callosum (11 anterior, 7 mid anterior, 8 central, 6 mid posterior, 9 total CC). These SNPs have been associated with schizophrenia, disruptive behaviour, risk raking behaviours, substance use disorders and cortical surface area in prior GWAS. Two biological processes were implicated: ethanol oxidation and metabolism. Discussion Our results reveal a complex genetic relationship between substance use, psychiatric traits, and ASB. We provide strong evidence for existence of distinct genetic loci exhibiting pleiotropic effects in both ASB and related traits. Our findings provide convergent evidence to suggest that substance use traits and psychiatric disorders have shared genetic underpinnings with ASB.

Women with bipolar disorder (BIP) have a higher risk of developing polycystic ovary syndrome (PCOS). Shared genetic architecture may underlie this comorbidity. Valproate, a mood-stabilizer commonly used to treat BIP, increases the risk of PCOS. Still, the mechanism underlying PCOS in BIP remains unknown. Here, we aimed to identify genetic variants shared between BIP and PCOS, as well as their interaction with valproate. We used the results of large-scale genome-wide association studies of BIP (41,510 cases and 354,340 controls), and PCOS (3,609 cases and 229,788 controls). Using conditional false discovery rate, we discovered genetic variants jointly associated with BIP and PCOS. Gene mapping of identified variants was performed using the Open Targets platforms. We analyzed the tissue-specific expression, interaction with valproate, and involvement in biological pathways of the mapped genes. We identified two loci shared between BIP and PCOS. Among the 10 genes mapped to the locus on chromosome 8:11455262, GATA4, NEIL2, and FDFT1 showed expression profiles suggesting their role in the observed comorbidity. Mapped to the locus on chromosome 12:2499849, CACNA1C, FKBP4, DCP1B, and ITFG2 are expressed in both the ovaries and the brain. CACNA1C expression is affected by valproate, and CACNA1C plays a role in biological pathways involving other valproate-affected genes. We identified shared genetic underpinnings of BIP and PCOS, and implicated genes which may explain the biological mechanisms of the comorbidity between these disorders and a potential mechanism for the role of valproate.

DNA methylation is an epigenetic modification that can be influenced by a range of factors, including genetic background, environmental exposures, biological variables (such as age and sex), pharmacological treatment, or the physiological effects of a disorder (such as stress). Unlike many other epigenetic modifications, DNA methylation can be measured using DNA extracted from clinical samples, making it particularly suitable for large-scale case-control studies. In recent years, methylation-wide association studies (MWAS) have been conducted across several psychiatric disorders, including schizophrenia, bipolar disorder, post-traumatic stress disorder (PTSD), major depressive disorder, and obsessive-compulsive disorder (OCD).While significant progress has been made in identifying methylation differences associated with disease status, key questions remain. Specifically, it is still unclear to what extent these methylation changes reflect underlying genetic influences, represent independent environmental or disease-related effects, or could complement genetic studies to shed light on disease mechanisms. Importantly, there is growing interest in whether methylation signatures could serve as biomarkers to improve disease prediction, diagnosis, or treatment response.In this presentation, I will review recent findings from large-scale MWAS in psychiatric disorders, discuss their implications for understanding biological mechanisms, explore their potential to translate genetic findings into functional insights, and consider their promise as biomarkers for clinical application.

Bipolar disorder, characterized by variability in its manifestation across individuals, poses challenges to the identification of the underlying genetic factors. The objective was guided by the a priori hypothesis that distinct clinical presentations (subphenotypes) exhibit unique contributions from common genetic variants. We initiated a series of subphenotype-specific genome-wide association studies (GWAS). These initial analyses were subsequently expanded to incorporate additional BD cases for whom detailed subphenotype data were unavailable, thereby increasing the overall statistical power. To further enhance the sample size and leverage existing knowledge, a comprehensive meta-analysis was performed, integrating the results of these subphenotype-specific GWAS with the most recent findings from a large-scale schizophrenia GWAS, utilizing the powerful Multi-Trait-Analysis-of-GWAS (MTAG) methodology. This integrated analysis allowed for a more robust examination of prioritized genetic variants, implicated genes, and the underlying biological processes that are potentially linked to the distinct pathophysiologies of the various BD subphenotypes. The study involved data collected through (semi-)structured clinical interviews for 25,543 cases and 312,788 control individuals across 58 different research cohorts. The main outcomes and measures of the study revealed that the presence of psychosis and the occurrence of comorbid psychiatric conditions were the primary factors explaining much of the variance observed in the clinical subphenotype data. The subphenotypes were differentiated by several key genetic characteristics, including their SNP-based heritability (h²SNP), patterns of global and local genetic correlations, signatures of negative selection in the genome, specific genomic loci, prioritized sets of genes, enrichment in particular cell types within the brain, and patterns of gene expression across different brain tissues. Fifty novel loci not previously associated with the subphenotypes, BD, or schizophrenia were identified. A substantial proportion (85%) of the up to 609 independent single nucleotide polymorphisms (SNPs) located within these genomic loci were found to be shared across the various subphenotypes examined. However, the study also revealed differential gene enrichment across 46 distinct gene sets that are known to be implicated in critical neuronal processes such as synaptic neuroplasticity and signalling. Furthermore, enrichment was observed in 53 specific cell types within the brain, with a notable prevalence of GABAergic interneurons, excitatory pyramidal neurons, and dopamine neurons. Divergent transcriptome-wide associations (TWAS) were detected across 15 human fetal and adult brain tissues, suggesting that the genetic risk for different subphenotypes may exert its effects through distinct patterns of gene expression in specific brain regions and developmental stages. In conclusion, this research has significantly increased our understanding of the specific biological mechanisms that contribute to the pronounced heterogeneity observed in bipolar disorder, a heterogeneity that has historically complicated the process of gene discovery required for the development of novel and more effective treatments. Future fine-mapping studies will be essential to pinpoint causal genetic variants beyond the putative gene associations identified in this study.

BACKGROUND:Adolescent self-reported psychotic experiences are associated with mental illness and could help guide prevention strategies. Youth report substantially more experiences than adults. However, with large societal changes like the digital revolution and COVID-19 pandemic, existing questionnaires may no longer accurately capture youth experiences. We aimed to determine the ability of the CAPE-16 questionnaire in capturing psychotic experiences across contexts (biological sex and COVID-19 response) and generations, thereby validating important psychometric aspects of the tool in modern adolescents. METHODS:We used data from the Norwegian Mother, Father and Child Study (MoBa), a population-based pregnancy cohort. Adolescents responded to the CAPE-16 questionnaire (n = 18,835). For a comparison between age groups, we included adult men from the parent generation who responded to the CAPE-9 (n = 28,793). We investigated the psychometric properties of CAPE-16 through confirmatory factor analyses, measurement invariance testing across biological sex, response before/during the COVID-19 pandemic, and generations (adolescents and fathers), and examined subscale and item-level associations with subsequent registry-based psychiatric diagnoses (average time between CAPE and last registry update: 3.68 ± 1.34 years). RESULTS:Out of 18,835 adolescents, 33.2% reported lifetime psychotic experiences. We confirmed a three-factor structure (paranoia, bizarre thoughts, and hallucinations) and good subscale reliability (ω = .86 and .90). CAPE-16 scores were stable across biological sex and pandemic status. CAPE-9 response patterns were non-invariant across adolescents and adult men, with an item related to digital technology particularly prone to bias. CAPE-16 subscales were associated with subsequent psychiatric diagnoses, especially psychotic disorders. CONCLUSIONS:CAPE-16 is a reliable measure of psychotic experiences across sex and a major societal stressor in adolescents. More frequent and distressing experiences increase the risk of subsequent psychiatric diagnoses. Different response patterns between adults and adolescents for items related to digital technology suggest differences in interpretation. Hence, certain items may benefit from revisions.

Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

Objective: This study sought to determine if the R package LDpred2, designed for polygenic risk score creation for genome-wide association studies using summary statistics, could be adapted for deriving DNA methylation scores from methylome-wide association studies. Recognizing that linkage disequilibrium, used as prior in LDpred2, does not apply to methylation, we explored co-methylated regions and topologically associating domains as alternative structural priors for correlation between methylation sites. A genomic sliding-window approach was also tested. The performance of the LDpred2-based models was evaluated on methylation data from schizophrenia and control samples (N=1,227). Results: LDpred2 models employing topologically associating domains and sliding window clusters as priors performed similarly to existing methods, explaining approximately 3.6% of schizophrenia phenotypic variance. The co-methylated regions model underperformed due to insufficient clustering of probes. The similarity in performance between the model using topologically associating domains and a null model consisting of random clusters suggests that the structural information provided by these domains enhances performance only marginally. In conclusion, while LDpred2 can be adapted for methylation data, it does not substantially enhance methylation score performance over existing methods, and the choice of structural prior may not be a critical factor.

Background Movement abnormalities occur across diverse brain disorders, from schizophrenia (SCZ) (e.g. motor slowing, catatonia, dystonia) to Parkinson’s disease (PD). Yet, the genetic mechanisms underlying these abnormalities remain unclear. Recent GWAS of accelerometry-derived physical activity measures suggest that everyday variation in movement is partially attributable to genetic factors, and implicates a role for genes expressed in the central nervous system. However, the genetic overlap between objectively measured movement and brain disorders has not yet been explored. Methods Linkage Disequilibrium Score Regression (LDSC) was used to assess genetic correlations between accelerometry-derived movement phenotypes (n=91,105), SCZ (n=53,386 cases, 77,258 controls) and PD (n=27,693 cases, 991,367 controls). Local Analysis of [co]Variant Association (LAVA) captured mixed directions of effect at local genomic regions. Pleiotropic loci were identified using the conjunctional false discovery rate (conjFDR) approach. Open Targets Platform was used to map pleiotropic variants to genes, and FUMA for gene set enrichment analysis. A phenome-wide association study (PheWAS) was conducted to determine which of the shared variants are known risk factors for other conditions. Results LDSC identified a significant negative genetic correlation between SCZ and sedentary behavior (rg=-0.13, p=6.01 × 10-6), with a positive genetic correlation noted for walking duration (rg=0.11, p=5 × 10-4) and moderate activity (rg=0.1, p=1.4 × 10-2). However, no genome-wide significant genetic correlations were detected between PD and physical activity. Nevertheless, LAVA identified 12 significantly correlated regions between SCZ and physical activity, and four between PD and physical activity, showing mixed directions of effect. One region on chromosome 7 (27.4-28.9 Mb) in ‘overall activity’ showed opposite relationships, a strong positive correlation with SCZ (rg=0.72, p=0.01) and a negative correlation with PD (rg=-0.7, p=0.01). Using conjFDR, we identified a total of 60 pleiotropic loci shared between SCZ and activity phenotypes, and seven shared between PD and physical activity. Gene set analysis revealed that genes shared between SCZ and activity were predominantly enriched in subcortical motor regions, skeletal muscle, and adipose tissue. In contrast, genes shared between PD and activity showed enrichment primarily in cortical regions and the cerebellum. PheWAS implicated haematological, metabolic and chronotype traits. Discussion The results show considerable genetic overlap between everyday variation in movement, SCZ and PD. Functional annotation of shared genetic loci indicates diverging tissue specificity across disorders, and implicates the involvement of diverse biological systems, from neural motor circuits to metabolic processes and circadian rhythms. Everyday variation in activity may act as a probe into the mechanisms of sensorimotor dysfunction across psychiatric and neurodegenerative diseases.

Multimorbidities are a global health challenge. Accumulating evidence indicates that overlapping genetic architectures underlie comorbid complex human traits and disorders. This can be quantified for a pair of phenotypes using various techniques. Still, the pattern of genetic overlap between three distinct complex phenotypes, which is important for understanding multimorbidities, has not been possible to quantify. Here, we present and validate the novel trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three complex phenotypes using summary statistics from genome-wide association studies. Our simulations show that trivariate MiXeR can reliably reconstruct different patterns of genetic overlap and estimate the proportions of genetic overlap between three phenotypes. We found substantial genetic overlap between gastro-intestinal and brain diseases supporting a genetic basis of the gut-brain axis—the pattern consistent with pairwise analysis. However, the pattern of genetic overlap between three diverse cardiometabolic and renal health indicators and three immune-linked disorders revealed a much larger genomic component shared between all phenotypes than expected from separate pairwise analyses. This suggests the existence of core pathways underlying distinct but related chronic conditions. Overall, trivariate MiXeR offers a novel and efficient tool for investigating patterns of genetic overlap among three complex phenotypes. This contributes to a better understanding of genetic relationships between complex traits and disorders, potentially providing new insights into the mechanisms underlying common multimorbidities. Trivariate MiXeR is freely available at https://github.com/precimed/mix3r .

Background Externalizing and internalizing pathways may lead to the development of substance use behaviors (SUBs) and substance use disorders (SUDs), which are all heritable phenotypes. Genetic correlation studies have indicated differences in the genetic susceptibility between SUBs and SUDs. We investigated whether these substance use phenotypes are differently related to externalizing and internalizing problems at a genetic level. Methods We analyzed data from genome-wide association studies (GWAS) of four SUBs and SUDs, five externalizing traits, and five internalizing traits using the bivariate causal mixture model (MiXeR) to estimate genetic overlap beyond genetic correlation. Results Two distinct patterns were found. SUBs demonstrated high genetic overlap but low genetic correlation of shared variants with internalizing traits, suggesting a pattern of mixed effect directions of shared genetic variants. Conversely, SUDs and externalizing traits exhibited considerable genetic overlap with moderate to high positive genetic correlation of shared variants, suggesting concordant effect direction of shared risk variants. Conclusions These results highlight the importance of the externalizing pathway in SUDs as well as the limited role of the internalizing pathway in SUBs. As MiXeR is not intended for the identification of specific genes, further studies are needed to reveal the underlying shared mechanisms of these traits.

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.