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Examining gaps in institutional policies for clinical genomic data sharing: A cross-jurisdictional study.

The sharing of data generated by clinical genetic and genomic testing without explicit consent is important for timely diagnosis and treatment. While many jurisdictions permit the sharing of identifiable data for direct clinical care, institutional policies vary in how clearly they specify key elements, including when sharing is permitted, what data are covered, and what safeguards apply. Greater clarity around these elements may support responsible data sharing while balancing timely care with transparency and appropriate protections. We conducted a mixed-methods content analysis of data-sharing and privacy policies from 33 clinical genomic institutions across 17 countries and regions. Using a predefined analytical framework, we assessed how policies document key governance elements relevant to sharing without explicit consent. Two independent reviewers extracted information about clinical contexts, data types, justifications, and protections. Although 70% of institutions described circumstances permitting data sharing without explicit consent, most policies did not clearly define the scope or governance of such sharing. Policies also rarely distinguished clinical from research or secondary use and inconsistently specified privacy and security safeguards. While sharing was commonly justified for clinical care (78.3%) or testing services (43.5%), data recipient roles and onward-sharing expectations were often left undefined. This uneven documentation could make it difficult for clinical teams and institutional decision-makers to identify and justify decisions about what is permitted and under what conditions. A guidance framework specifying core governance elements and corresponding protections could help institutions communicate their governance choices more clearly and support comparable baseline practices for responsible data sharing.

Information Dissemination

CanVar-UK: A collaborative platform for germline interpretation in cancer susceptibility genes.

Germline variants in cancer susceptibility genes (CSGs) are typically inherited rather than arising de novo. Hence, wide cascade testing of families across geographies is common, meaning consistency in variant classification is particularly critical. Variant interpretation requires collation of variant-level data from diverse sources, as well as assembly of comprehensive clinical data, often necessitating sharing of information between genomic testing centers. Here, we describe CanVar-UK, a freely accessible web platform bespoke designed to support interpretation of germline CSG variants. CanVar-UK contains variant-level data for over 1.1 million single-nucleotide variants (SNVs), comprising all possible coding SNVs in 116 established CSGs. The data sources with which variants are annotated include in silico scores from 11 clinically relevant tools, population allele frequencies from gnomAD v4.1, case counts from multiple cohorts, including National Health Service (NHS) clinical laboratory testing, variant-level readouts from 47 selected functional and splicing datasets across 19 CSGs, genetic epidemiology studies, and live linkage to existing consensus classifications in the ClinVar database. The diagnostic discussion forum is only available to registered diagnostic scientist users. Through this, a variant-tagged email message can be dispatched in real time across the diagnostic forum community of >1,500 users, with all exchanges and classifications captured and stored in the platform. Already widely used by NHS diagnostic clinical scientists in the UK, CanVar-UK has a rapidly growing international diagnostic user base (>800 UK and >600 non-UK registered users). Survey of the NHS diagnostic user community illustrates the wide-ranging utility of CanVar-UK within their clinical workflows for interpretation of germline CSG variants.

Journal Article

289th ENMC international workshop: assessing and managing emerging AAV related toxicities after gene therapy for neuromuscular disorders, 26 - 28 September 2025, Hoofddorp, The Netherlands.

Adeno-associated virus (AAV) mediated gene therapies has emerged as a potentially transformative treatment approaches for neuromuscular disorders, with two FDA-approved products now in widespread clinical use: onasemnogene abeparvovec (Zolgensma) for spinal muscular atrophy and delandistrogene moxeparvovec-rokl (Elevidys) for Duchenne Muscular Dystrophy. However, severe and occasionally fatal adverse events affecting vital organs, including the blood, liver, muscle, and heart, have emerged in both clinical trials and real-world post marketing settings. The 289th European NeuroMuscular Centre (ENMC) workshop convened 38 participants from patient advocacy groups, industry, and preclinical and clinical research groups to collaboratively review these toxicities, their underlying mechanisms, and potential mitigation and monitoring strategies. Discussions addressed the clinical spectrum and biological drivers of these events, the respective roles of innate and adaptive immunity, the contribution of specific vector characteristics as well as of the specific disease and recipient. The application of risk stratification and immunosuppressive regimens for prevention, monitoring, and management were considered. Emerging toxicities, including capillary leak syndrome, endothelial and dorsal root ganglia injuries, were reviewed alongside corresponding preclinical data from non-human primates. Participants agreed on the need to harmonize standard operating procedures, clinical guidelines, and data-sharing practices, and endorsed collaborative initiatives to proactively address critical gaps and unresolved key questions through a patient-centered framework.

Adaptive immune response

NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.

MOTIVATION: Biomedical models improve when trained on data pooled across institutions, but sensitive patient records (e.g. genomics, clinical data, and medical images) are difficult to share due to privacy constraints. Moreover, data collected at different sites often have shifted distributions because of covariate differences (including batch effects), so privacy-preserving sharing alone cannot simply resolve cross-site mismatch. Methods that protect individuals while explicitly aligning distributions are needed to enable reliable multi-institutional analyses. RESULTS: We present NoisyFlow, a three-stage differentially private framework for cross-institutional harmonization under distribution shift. In stage I, each site learns a differentially private flow-based generator of its local labeled distribution. In stage II, it learns a neural optimal transport map to a shared reference distribution. In stage III, a central server composes the released models to generate reference-aligned pseudo-data for downstream analysis without accessing raw records. Across four biomedical settings spanning single-cell genomics, histopathology, neurogenomics, and wearable sensing, NoisyFlow reduces distribution shift while preserving downstream utility under formal differential privacy guarantees. AVAILABILITY AND IMPLEMENTATION: The implementation of NoisyFlow is available at https://github.com/gersteinlab/NoisyFlow.

Information Dissemination

Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's Disease.

INTRODUCTION: Neuroimaging genetics have advanced Alzheimer's disease (AD) research, yet frameworks mechanistically connecting genes to neurological outcomes via functional genomics are needed to elucidate genetic associations. To address this challenge, we assessed relationships between AD-associated variants and disease via their impact on gene expression and neuroimaging phenotypes. METHODS: We mapped established AD genes to neuroimaging traits using NeuroimaGene atlas and predicted transcript-driven AD neurological features by comparing gene-derived neuroimaging features to clinical neuroimaging data. Genetic correlation and covariance analyses characterized shared genetic architecture between AD endophenotypes and neuroimaging features and identified neuroimaging features associated with dementia family history. RESULTS: Our analyses implicate PSMC3 expression as a strong contributor to AD pathophysiology and indicate AD endophenotypes, including dementia family history, linked to frontal cortex thickness, volume, and cerebrospinal fluid volume changes. DISCUSSION: Our findings prioritize AD genes whose regulation is associated with vulnerable brain regions, offering a potential mechanistic framework for downstream functional validation.

Alzheimer’s Disease

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.

Humans

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans

Conference report: the third Bacterial Genome Sequencing Pan-European Network conference.

The third Bacterial Genome Sequencing Pan-European Network conference, held in Engelberg, Switzerland (12-15 January 2026), brought together experts from six European countries to discuss the implementation of bacterial genome sequencing in clinical microbiology and public health. Key themes included regulatory frameworks (In Vitro Diagnostic Regulation, General Data Protection Regulation), standardization, quality control, data sharing, economic evaluation, and the integration of artificial intelligence and long-read sequencing into diagnostic workflows. Across presentations, panel discussions, and workshops, participants emphasized that successful implementation of genome sequencing requires more than technical capacity: it depends on robust validation, sustainable funding, interoperable data standards, ethical governance, and interdisciplinary collaboration. The meeting highlighted that sequencing should remain question-driven and clinically meaningful, balancing cost, turnaround time, and public health impact. Overall, the conference reinforced the need for coordinated European efforts to advance responsible, standardized, and sustainable genomic surveillance and diagnostics.

bacterial genome sequencing

Genomic Characterisation of Carbapenem-Resistant Klebsiella pneumoniae and Enterobacter hormaechei Clinical Isolates from Nigeria: Evidence of Resistance, Virulence, and Putative Plasmid-Mediated Gene Sharing.

The global proliferation of carbapenem-resistant Enterobacterales (CRE) constitutes one of the most urgent public health threats, yet high-resolution genomic data from sub-Saharan Africa remain critically scarce. We applied whole-genome sequencing (WGS) and comparative phylogenomics to characterise antimicrobial resistance determinants, virulence genes, and mobile genetic elements (MGEs) in three carbapenem-resistant clinical isolates originating from three tertiary hospitals (selected from a broader surveillance collection spanning four facilities) in Osun State, southwestern Nigeria. We purposively selected three isolates, two Klebsiella pneumoniae subsp. pneumoniae (K22, ST411; K31, ST17) and one Enterobacter hormaechei subsp. steigerwaltii (K32, ST45) from a broader surveillance collection of 27 carbapenem-non-susceptible Enterobacterales, to represent phenotypically and genotypically divergent lineages. Resistome analysis revealed extensive plasmid-associated β-lactam and aminoglycoside resistance in K31 (including blaCTX-M-15, blaOXA-1, and blaTEM-1). K32 harboured an intrinsic chromosomal blaACT-17 AmpC gene, while IS26 and ISEcp1 insertion sequences, consistent with transposon-mediated mobilisation, flanked its acquired aminoglycoside and sulfonamide resistance cassettes. K22 lacked detected acquired carbapenemase, ESBL, or plasmid-mediated AmpC genes, indicating that its carbapenem-resistant phenotype may involve non-carbapenemase mechanisms such as porin alteration or efflux-mediated reduced susceptibility; however, this mechanism requires confirmation by direct ompK35/ompK36 sequence analysis and/or phenotypic outer membrane protein profiling. Virulome profiling identified a broader repertoire of siderophore, adhesion, and biofilm genes in both K. pneumoniae isolates than in E. hormaechei. Phylogenomic analysis demonstrated that K22 and K31 cluster within the broader K. pneumoniae population framework but represent distinct high-risk lineages (ST411 and ST17) rather than a single clonal outbreak. Analysis also identified a shared plasmid backbone between K31 and K32, supporting interspecies horizontal gene transfer. These descriptive genomic findings identify clinically relevant resistance and virulence determinants in three purposively selected carbapenem-resistant Enterobacterales from Nigerian tertiary-care hospitals. The detection of shared resistance elements between K. pneumoniae and E. hormaechei suggests possible plasmid-mediated gene sharing. Still, larger WGS studies with long-read sequencing and patient-level epidemiological data are required to define transmission and dissemination patterns.

Nigeria

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell

Genetic insight into lung neuroendocrine tumors: Notch and Wnt signaling pathways as potential targets.

BACKGROUND: The molecular landscape of lung neuroendocrine neoplasms is still poorly characterized, making it difficult to develop a molecular classification and personalized therapeutic approaches. Significant clinical heterogeneity of these malignancies has been highlighted among poorly differentiated histotypes and within the subgroup of well-differentiated neuroendocrine tumors (NET). Currently, the main prognostic factors of lung NET include stage, histotype, grade, peripheral location, and demographic parameters. To gain deeper insights into the genomic underpinnings of lung NETs, we conducted a pilot investigation to uncover potential genetic mutations and copy number variations (CNVs) implicated in their pathogenesis. METHODS: Formalin-fixed, paraffin-embedded intraoperative tumor biopsies and matched peripheral blood mononuclear cell samples were collected from six consecutive patients with lung NETs. The whole exome sequencing (WES) was performed to profile germline and somatic mutations, identify novel genetic alterations, and detect CNVs. Clinical and pathological data were systematically documented at diagnosis and during follow-up. RESULTS: The WES analysis identified a subset of mutations shared between germline and somatic; some were of particular clinical interest as they were associated with tumor proliferation and potential therapeutic targets such as the genes KDM5C, ATR, COL7A1, NOTCH4, PTPRS, SMO, SPEN, SPTA1, TAF1. These mutations were predominantly linked to chromatin remodeling and were involved in critical oncogenic pathways such as Notch and Wnt signaling. CONCLUSIONS: This pilot study highlights the potential role of NGS analysis on solid biopsy in the assessment of the mutational profile of lung NET. A comparison of germline and somatic mutations is critical to identifying putative tumor driver mutations. In perspective, the enrichment of a subpopulation of cancer cells in the blood, with one or more specific mutations, is information of enormous clinical relevance, either for prognosis or therapeutic decisions. Translational studies on large prospective series are required to establish the role of liquid biopsy in lung NET.

Humans

A consensus guide to preclinical indirect calorimetry experiments.

Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data repository built on shared standards. This overdue initiative stands to markedly improve the accuracy and depth of efforts to interrogate mammalian metabolism. Data sharing according to established best practices will also accelerate the translation of basic findings into clinical applications for metabolic diseases afflicting global populations.

Calorimetry, Indirect

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers.

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Humans

P2X7 Receptor in Rare Diseases: Shared Molecular Mechanisms and Therapeutic Implications.

Rare diseases (RDs) are individually uncommon but collectively affect a large global population, and the vast majority still lack effective disease-modifying therapies. With advances in genomics and data-sharing platforms, research has increasingly shifted from a single-disease perspective to the search for convergent molecular pathways that might be shared across clinically distinct entities. In this context, the purinergic P2X7 receptor (P2X7R) has emerged as a putative "shared molecular platform" due to its central role in inflammation amplification, cell death and immune regulation. P2X7R is an ATP-gated ion channel with unique structural and functional features: under high extracellular ATP, it not only forms a non-selective cation channel but can also dilate into a "large pore" permeable to macromolecules, thereby triggering Ca2+overload, NLRP3 inflammasome assembly, reactive oxygen species (ROS) production and apoptotic/necrotic-like cell death. This review briefly outlines the epidemiology of RDs and the structural-functional characteristics of P2X7R, then systematically summarizes current evidence linking P2X7R to multiple rare diseases, including Charcot-Marie-Tooth disease, Guillain-Barré syndrome, amyotrophic lateral sclerosis, Huntington's disease, multiple sclerosis, and selected inflammatory and metabolic RDs (CAPS, familial Mediterranean fever, Systemic sclerosis, Dravet syndrome and Gaucher disease). By comparing P2X7R expression and functional alterations, downstream signaling pathways and pharmacological data from animal models across these conditions, we propose that a P2X7R-dependent network centered on a "Ca2+-NLRP3-inflammation/cell death axis" may constitute a common pathogenic backbone for diverse RDs. At the same time, disease-specific spatiotemporal expression patterns of P2X7R in central vs peripheral nervous systems and in immune vs target organ cells confer marked context dependence and "double-edged sword" properties. Finally, we discuss opportunities and challenges for P2X7R-targeted strategies, including the impact of disease stage and sex differences on therapeutic efficacy, and key bottlenecks in translating preclinical findings into clinical benefit. A deeper understanding of both shared and disease-specific roles of P2X7R may provide a conceptual framework and therapeutic entry point for precision stratification and multi-target interventions in rare diseases.

P2X7 receptor

dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.

MOTIVATION: There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations.Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can be used, but no such algorithm exists for GAMLSS. RESULTS: We propose distributed GAMLSS (dGAMLSS), a distributed algorithm that can fit GAMLSS models across multiple sites without sharing patient-level data. This includes specific considerations for the fitting of smooth functions at varying levels of communication efficiency. We demonstrate the effectiveness of dGAMLSS in constructing population reference charts across clinical, genomics, and neuroimaging settings and show that dGAMLSS is able to reproduce pooled reference charts and inference down to numerical differences. AVAILABILITY AND IMPLEMENTATION: An R package providing examples of the dGAMLSS algorithm, as well as functions for sharing and aggregating site-specific parameters, is available at https://github.com/hufengling/dGAMLSS.

Algorithms

Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision. METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder. RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates. CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

Humans