Imaging genetics links genetic variations to brain structures and functions, but the computational challenges posed by high-dimensional imaging and genetic data are significant. In voxel-level genome-wide association studies, we introduce a Representation learning-based Voxel-level Genetic Analysis (RVGA) framework that reduces computational time and storage burden by over 200 times. RVGA enhances statistical power by denoising images and shares minimal datasets of summary statistics for associations across the whole genome of the entire image for secondary analyses. Additionally, it introduces a unified estimator for voxel heritability, genetic correlations between voxels, and cross-trait genetic correlations between voxels and non-imaging phenotypes. Applying RVGA to hippocampus shape and white matter microstructure in the UK Biobank (n = 53,454) reveals 39 and 275 novel loci, respectively. We identify heterogeneity in heritability within images and subregions that share genetic bases with 14 brain-related phenotypes, such as the genetic correlation between the hippocampus and educational attainment, and between the anterior corona radiata and schizophrenia. RVGA replicates known genetic associations and uncovers new discoveries.

Bipolar disorder's (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder-and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic-GABAergic gradient along the psychotic factor. BD's genetic architecture appears hierarchical-a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis -regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

Heterotypic cell-cell interactions are critical to governing cellular physiology, disease progression, and responses to the environment and pharmacologic interventions. For example, neurons and astrocytes engage in intricate interactions that are essential for brain development and function1-3. However, the transformation of these extracellular signals into epigenomic regulation that governs cell function is poorly understood. Here, we report that weeks of co-culture between human induced pluripotent stem cell (hiPSC)-derived neurons and mouse cortical astrocytes extensively reprograms gene expression and the chromatin accessibility landscape in neurons, affecting thousands of genes and putative gene regulatory elements (REs), including many transcription factors (TFs). These genes are enriched for functions implicated in neuronal differentiation and maturation, and tend to be impacted in schizophrenia, and autosomal dominant Alzheimer's disease. Through complementary CRISPR interference and activation screens, we recapitulated hundreds of astrocyte-induced transcriptional and chromatin remodeling events in mono-cultured neurons at both promoters and distal regulatory elements (REs) of TF genes. We discovered functional REs for ~50 astrocyte-responsive TF genes, providing a map of gene regulatory network control. Astrocyte-responsive TF genes fall into groups that exert independent or counter-balancing transcriptional effects, highlighting the complex coordination of the neuronal response to astrocytes. Functional effects of specific TFs, including POU3F2 and TFAP2E, on neurite morphology and neuronal electrophysiology are consistent with transcriptional effects, demonstrating the capacity of direct epigenetic control to mimic heterotypic cellular signals. This work illuminates the regulation of neurodevelopment- and disease-relevant gene modules by neuron-astrocyte interactions, and provides a blueprint for applying modern functional genomics to uncover the links between cell microenvironment and epigenomic programming.

Schizophrenia is a complex psychiatric disorder with significant genetic and clinical heterogeneity. Although numerous rare copy number variations (CNVs) with high risk for schizophrenia have been identified, they show no obvious overlap in gene content or function. We hypothesized that the downstream effects of schizophrenia-associated CNVs converge on shared molecular pathways. To test this, we profiled the prefrontal cortex of five schizophrenia-associated CNV mouse models — 15q13.3del, 3q29del, 1q21.1del, 22q11.2del, and 16p11.2dup — using single-cell RNA sequencing across two developmental stages: adolescence and adulthood. From 292,943 high-quality single-cell transcriptomes, we identified distinct age- and cell type-specific patterns of differential gene expression and biological pathway perturbations in each model. Rather than converging on a shared molecular mechanism, each CNV affected unique cellular pathways in a developmentally dynamic manner. Notably, genes dysregulated in deep-layer corticothalamic projection neurons from 15q13.3del and 16p11.2dup models, and intratelencephalic neurons from adult 22q11.2del mice, showed enrichment for schizophrenia-SNP heritability. These results support a model in which rare CNVs contribute to schizophrenia genetic risk through developmentally dynamic, distinct pathways rather than through a shared molecular mechanism.

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal (trans) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans-acting variants within gene coexpression networks. Integrating these models with conventional cis-based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia (PFDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Astrocytes are increasingly implicated in the pathophysiology of schizophrenia (SCZ), yet how astrocytic dysfunction contributes to disease-relevant neuronal abnormalities remains unclear. Here, we used mass spectrometry-based proteomics to profile lysates (proteome) and secreted proteins (secretome) from iPSC-derived astrocytes originating from 9 SCZ patients and 8 healthy controls. Compartment-specific analyses showed that lysates were enriched for mitochondrial and nuclear pathways, whereas astrocyte-conditioned media (ACM) were enriched for extracellular matrix (ECM) and vesicle-associated proteins. Differential expression analysis revealed minimal overlap between dysregulated proteins in lysates and ACM, suggesting modality-specific effects of SCZ-associated donor background. Interestingly, ECM proteins and key secreted cues involved in synaptic development, including MFGE8 and SEMA3C, were selectively reduced in SCZ ACM, whereas RNA-processing proteins were aberrantly increased. This is in line with previously reported microRNA enrichment in extracellular vesicles (EV) derived from SCZ patients. Gene set analyses further identified the alteration in secretion and nuclear processes as well as the potential involvement of autophagy-dependent release mechanism in SCZ astrocytes. Together, these findings suggest disrupted astrocytic protein homeostasis and extracellular signalling in SCZ iPSC-derived astrocytes, providing mechanistic insight into astrocyte-mediated contributions to synaptic and circuit deficits in the disorder.

More than 20% of individuals with schizophrenia show minimal or no response to antipsychotic medications and little is known about genetic contributions to more severe forms of illness. This study sought to explore if cases with continuously-hospitalized, treatment-resistant schizophrenia (CH-TRS) carry a higher burden of common genetic variants compared to less severe forms. CH-TRS cases were recruited from Pennsylvania state psychiatric hospitals in the USA, with ≥ 5 years of continuous hospitalization, active treatment, and non-response to ≥ 3 antipsychotic medications. Three comparator groups were obtained from cohorts in the USA, Sweden, and the UK including TRS, general schizophrenia and non-psychiatric controls. Polygenic scores (PGS) of schizophrenia and cognitive ability were generated in individuals of European and African ancestry. Logistic regression assessed the association of PGS and CH-TRS cases (vs. comparators), with sex differences and sensitivity analyses excluding individuals with other diagnoses conducted to assess robustness. We included 18,571 individuals of European ancestry (346 CH-TRS, 10,757 TRS, 1148 general schizophrenia, 6320 non-psychiatric controls) and exploratory analyses of 476 individuals of African ancestry (78 CH-TRS, 398 non-psychiatric controls). For each standard deviation increase in the schizophrenia PGS, the odds of CH-TRS among individuals of European ancestry increased by 40-80% compared to general schizophrenia and TRS. Results in African ancestry participants mirrored those of European ancestry albeit with reduced levels of significance. Sex interactions and sensitivity analyses did not materially alter the estimates. This study demonstrates a greater burden of common genetic variants is associated with more severe forms of illness in schizophrenia.

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis -eQTLs. Here, we introduce INGENE and MODULE, trans -eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans -eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis -based model and to EpiXcan, the leading benchmark in cis -model performance. Integration of cis - and trans -predictions significantly improves gene-level expression imputation (MLE α= 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans -heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates. ### Competing Interest Statement A. Bertolino received consulting fees from Biogen and lecture fees from Otsuka, Janssen, and Lundbeck. D. Weinberger serves on the scientific advisory boards of Sage Therapeutics and Pasithea Therapeutics. G. Pergola and G. C. Kikidis received lecture fees from Lundbeck. A. K. Malhotra is a consultant to Genomind, InformedDNA and Concert Pharmaceuticals. M. C. O Donovan, M. J. Owen, and J. T. R. Walters are supported by collaborative research grants from Takeda Pharmaceuticals. O. A. Andreassen is a consultant for HealthLytix and received speaker s honoraria from Lundbeck. C. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. Research Projects of National Relevance 2020 (PRIN 2020; 2020WSCSLZ) Research Projects of National Relevance 2022 (PRIN 2022; 2022KXJYJA) Research Projects Of National Relevance PNRR 2022 (P2022HNBJX) The LIBD funded the collection and analysis of postmortem brain tissue

Understanding the pathophysiological substrates of schizophrenia is a major challenge for current neuropsychiatric research. As part of a set of multi-omics experiments, we performed an extensive case-control proteomics study on 192 post-mortem tissue sections from prefrontal cortex from 96 individuals, including 47 cases with schizophrenia and 49 healthy controls. Using two independently measured cortical datasets, we identified 387 proteins differentially expressed between schizophrenia cases and controls at a 5% FDR threshold. This significantly regulated set of proteins contains genes located in GWAS-identified schizophrenia loci and proteins identified by pQTL analysis. Gene ontology analysis using GOAT provided evidence for regulation of several major protein categories, emphasizing downregulation of mitochondrial oxidative respiration, ribosomes and the proteasome, upregulation of kinases and (small) GTPases. SynGO analysis supports the notion of synaptic dysfunction in schizophrenia, with major regulators of pre- and postsynaptic function compromised. Our findings highlight the complex molecular dysregulation in schizophrenia, with mitochondrial function downregulated versus signaling and trafficking upregulated, and synapse function disrupted; in combination with prior avenues of research, these finding support a role for energy deficits compromising highly ATP dependent neuronal function as a target for therapeutic interventions.

Background The Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only. Results We identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects. Conclusions Across heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations.

Eating disorders—including anorexia nervosa (AN), bulimia nervosa and binge-eating disorder—are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. Here we conducted a genomic meta-analysis of case–control studies of binge-eating behavior (BE; 39,279 cases, 1,227,436 controls), alongside analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six BE-associated loci, including loci associated with a higher body mass index and impulse-control behaviors. AN genome-wide association studies yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry studies. BE and AN exhibited similar positive genetic correlations with psychiatric disorders but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with body mass index. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries. This research identified six areas in the genome that are associated with binge eating, and eight areas that are associated with anorexia nervosa, in people of European ancestry. Binge eating has both shared and distinct genetic features compared with anorexia nervosa.

Copy number variations (CNVs) are a major contributor to the etiology and phenotypic variability in schizophrenia. CNVs are also independently associated with certain neurological, cognitive, and behavioral conditions, yet their role in early neurodevelopment in the context of treatment-resistant schizophrenia remain largely unexplored. This study aimed to investigate the contribution of neurodevelopmental CNVs to early development among individuals with treatment-resistant schizophrenia (TRS). We conducted a structured, systematic, retrospective record review within a case-control analytic framework. Our cases were the group of neurodevelopmental disorder (NDD) CNV carriers (N = 25) identified in the Pennsylvania State Hospital (PASH) cohort of individuals with treatment resistant psychotic symptoms; non-CNV controls were demographically matched individuals with treatment resistant psychotic symptoms (N = 24) from the PASH cohort. We examined the differences in early NDD phenotypes between cases and controls. CNV carriers had a higher total NDD burden score compared to non-CNV controls (z = 2.20, unadjusted p = 0.03, adjusted p = 0.08). Learning disabilities were significantly more prevalent among CNV carriers compared to non-CNV controls (χ2 = 13.437, df = 1, unadjusted p = 0.0002, adjusted p = 0.0015), with 88% of CNV carriers having a history of learning disabilities relative to 38% of non-CNV controls. CNVs carriers with TRS had a higher prevalence of early NDDs compared to non-CNV controls, particularly in learning disabilities. Prospective studies are needed to elucidate the mechanisms linking CNVs, neurodevelopmental phenotypes, and disease progression in treatment resistant schizophrenia.

Background Psychotic major depressive disorder (MDD), a subtype of MDD characterised by psychotic symptoms that occur exclusively during mood episode, is clinically significant yet underexplored genetically due to its rarity. This study comprehensively examines the genetic basis of psychotic MDD and elucidates its position within the mood- psychotic spectrum. Methods This population-based cohort study used Swedish and Danish registry data for over 5.1 M individuals born between 1958 and 1993/1996. Specialist-diagnosed psychotic MDD was defined using ICD-10 sub-codes of MDD, F32.2/F32.3. We estimated familial aggregation/coaggregation using generalised estimating equations, heritability and genetic correlations using structural equation modelling. We also analysed similar to 30,000 genotyped MDD cases from the UK Biobank and a Swedish cohort to explore which polygenic risk score (PRS) may predispose individuals to psychotic MDD. Findings With over 10,000 psychotic MDD identified from the two nationwide patient registers, this study highlights the familial aggregation of psychotic MDD, co-aggregation with mood and psychotic disorders, and its stronger genetic correlation with schizophrenia compared to non-psychotic MDD. The familial risks increased with closer biological relatedness, suggesting genetic influence. Pedigree-heritability of psychotic MDD was 30.17% (95% CI 23.53-36.80%). While the genetic correlation between psychotic and non-psychotic MDD was high (0.82, 95% CI 0.73-0.92), the psychotic subgroup showed a higher genetic correlation with schizophrenia than non-psychotic MDD (0.67 vs 0.46, p-value 7.55*10-4). Within 30,000 genotyped MDD cases, individuals with psychotic MDD had higher mean PRS for schizophrenia and BD but a lower MDD PRS than non-psychotic MDD. PRS for BD type-I was associated with increased odds of psychotic MDD, while BD type-II PRS showed no significant association with psychotic MDD. Interpretation This study provides evidence for the genetic basis of psychotic MDD, underscoring its unique position bridging the spectrum of mood and psychotic disorders. These findings advance our understanding of the aetiology of psychotic MDD and contribute to the limited body of evidence on this phenotype by utilising large-scale population-based data. Copyright (c) 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Schizophrenia is an often devastating disorder characterized by persistent and idiopathic cognitive deficits, delusions and hallucinations. Schizophrenia has been associated with impaired nervous system development and an excitation/inhibition imbalance in the prefrontal cortex. On a molecular level, schizophrenia is moderately heritable and genetically complex. Hundreds of risk genes have been identified, spanning a heterogeneous landscape dominated by loci that confer relatively small risk. Bioinformatic analyses of genetic associations point to a limited set of neurons, mainly excitatory cortical neurons, but other analyses suggest the importance of astrocytes and microglia. To understand different cell type roles in schizophrenia and reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia, our study integrated genetic analysis with single nucleus RNA-seq of 536,618 nuclei from postmortem samples of dorsal prefrontal cortex (Brodmann Area 8/9) of 43 cases with schizophrenia and 42 neurotypical controls. We found no significant difference in cell type abundance. Gene expression in excitatory layer 2-3 intra-telencephalic neurons had the greatest number of differentially expressed transcripts and, together with excitatory deep layer intra-telencephalic neurons, conferred most of the genetic risk for schizophrenia. Most differential expression of genes was found in specific cell types and was dominated by down-regulated transcripts. Down-regulated transcripts were enriched in gene sets including transmembrane transport, mitochondrial function, protein folding, and cell-cell signaling whereas up-regulated transcripts were enriched in gene sets related to RNA processing, including RNA splicing in neurons. Co-regulation network analysis identified 40 schizophrenia-relevant programs across 13 cell types. A gene program largely shared between neuronal subtypes, astrocytes, and oligodendrocytes was significantly enriched for schizophrenia risk, supporting an aetiological role for perturbed protein modification, ion transport, and mitochondrial function. These results were largely consistent with cell-type expression quantitative trait locus and transcriptome-wide association analyses. Moreover, single-cell RNA sequencing results, most prominently mitochondrial dysfunction, had multiple points of convergence with proteomic and long-read RNA sequencing results from samples from the same donors. Our study integrates genetic analysis with transcriptomics to reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia.

Research by the Psychiatric Genomics Consortium (PGC) has advanced the discovery of common and rare genetic variations that contribute to the susceptibility to many psychiatric disorders and neurodevelopmental conditions. This Review reflects on major findings from the past 5 years of research by the PGC in five priority areas: discovery of common variants using genome-wide association studies; rare variation and its interplay with polygenic risk; using genetics to go beyond diagnostic boundaries; ascribing functional attributes to genomic discoveries; and developing and implementing processes for data sharing, outreach to various communities, and training. The insights gained in these domains frame the agenda for the next phase of PGC research. In addition to accelerating integrative findings of common and rare variants within, and across, multiple psychiatric disorders and neurodevelopmental conditions, the next phase will use multiple populations to elucidate genetic causes, integrate results with rapidly accumulating multimodal functional genomics data to gain mechanistic understanding, convert genetic findings to clinically actionable phenotypes, such as treatment response, and address the emerging use of polygenic scores. Together, these next steps will highlight the biological underpinnings of psychiatric disorders and neurodevelopmental conditions, which continue to contribute to global morbidity and mortality.

The 16p13.11 duplication is a rare copy number variation (CNV) that appears to increase risk for multimorbidity across the life span. Initial reports implicated the duplication in autism spectrum disorder, intellectual disability, and various congenital anomalies; however, it was also observed in “phenotypically normal” individuals suggesting incomplete penetrance or non-pathogenicity. Recent studies suggest that duplication carriers often present with multimorbidity, but more data are needed to elucidate the full range of associated phenotypes. Lifetime multimorbidity remains unclear, and no reviews summarizing this literature currently exist. We report a systematic literature review and meta-analysis of published phenotypic characteristics. Speech delays, developmental delays, intellectual disability, learning disability, and autistic symptoms were reported in >30 % of cases. Musculoskeletal abnormalities and cardiovascular disorders were commonly reported. Included is a lifespan case report of a 71-year-old female with a history of behavioral disturbance and treatment-resistant schizophrenia, identified as being a 16p13.11 duplication carrier.

Eating disorders -including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

Somatic symptom and related disorders (SSRD) are characterized by a mixture of neurological and psychiatric features and include functional neurological (FND) and somatic symptom disorders (SomD). While these complex neuropsychiatric disorders show evidence of genetic susceptibility, there are no genome-wide association studies (GWAS) of SSRD, and the heritability is unknown. We did a GWAS of a total of 22,203 patients with SSRD, and 1,831,107 controls of European ancestry. We identified one genome-wide significant locus (chromosome 8:65565084) in SSRD, and one additional locus (chromosome 16:49074278) in the SomD subgroup (n cases = 18,536). The observed-scale SNP heritability was estimated to be 7.3 % for SSRD, 15.7 % for FND and 7.7 % for SomD. FND and SomD were strongly genetically correlated (rg=0.94, SE=0.11, p=3.9E-18). SSRD showed significant genetic correlation with psychiatric disorders (highest with anxiety, post-traumatic stress disorders, depression, rg=0.3- 0.8), neurological disorders (migraine, chronic pain, rg=0.4-0.6) and immune-related diseases (rg=0.2-0.3). Functional follow-up analysis of SSRD loci implicated the genes CYP7B1, BHLHE22, and CBLN1, which are involved in metabolic and brain-related processes, suggesting common underlying pathways. We identified genomic loci associations with SSRD and showed strong genetic correlation between FND and SomD and with neurological and psychiatric disorders, as well as immune-related diseases. The current findings highlight shared underlying pathophysiological processes between SSRD diagnostic categories.

IMPORTANCE:Individuals with psychiatric disorders have increased risk of cardiometabolic diseases (CMDs). Evaluating how psychiatric genetic liability relates to CMD may clarify mechanisms. OBJECTIVE:Identify genetic overlap between psychiatric disorders and CMDs independent of cross-disorder pleiotropy, BMI, and smoking. DESIGN SETTING AND PARTICIPANTS:Three Northern European cohorts (the Swedish Twin Registry, the Estonian Biobank, and the Norwegian Mother, Father and Child Cohort Study [MoBa]) totaling 355,159 individuals. Associations with CMDs were estimated as adjusted odds ratios (AORs) from logistic models mutually adjusted for all psychiatric PRSs and in models additionally adjusting for body mass index (BMI) and smoking. Cohort-specific AORs were pooled by inverse-variance weighting. MAIN OUTCOMES AND MEASURES:Exposures were PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety disorder, posttraumatic stress disorder (PTSD), bipolar disorder, and schizophrenia. Outcomes were diagnoses of CMDs (hyperlipidemia, obesity, type 2 diabetes, hypertensive diseases, arteriosclerosis, ischemic heart disease, heart failure, thromboembolic disease, cerebrovascular disease, and arrhythmias), ascertained from electronic health records. RESULTS:The MDD PRS was associated with increased risk of all CMDs across analyses (AORs ranged from 1.13 [95% CI, 1.10-1.15] for heart failure to 1.02 [95% CI, 1.00-1.05] for arrhythmias). The ADHD PRS was associated with increased risk of all CMDs (AOR ranged from 1.11 [95% CI, 1.09-1.12] for obesity to 1.02 [95% CI, 1.01-1.03] for hyperlipidemia), however associations where attenuated when adjusting for BMI and smoking (lifestyle adjusted AOR for obesity: 1.03 [95% CI, 1.02-1.05]). When not mutually adjusting for all psychiatric PRSs, anxiety disorder and PTSD PRSs were associated with all CMDs; these associations diminished after adjustment. The bipolar and schizophrenia PRSs were inversely associated with most CMDs (AOR for schizophrenia PRS and obesity, 0.93 [95% CI, 0.92-0.94]). CONCLUSIONS AND RELEVANCE:Associations between psychiatric PRSs and CMDs diverged: ADHD, MDD, anxiety disorder, and PTSD PRSs were positively associated with CMDs, whereas bipolar and schizophrenia PRSs were inversely associated. Genetic liability to MDD showed robust associations with CMDs independent of cross-disorder pleiotropy, BMI, and smoking status, whereas associations between the ADHD PRS and CMDs were largely attenuated after adjustment for BMI and smoking.