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PubMed2025

Circulating inflammatory proteins as causal drivers and therapeutic targets in asthma: insights from genetic and pathway-based analyses.

OBJECTIVE: To identify circulating inflammatory proteins with potential causal roles in asthma development through integrated genetic and pathway-based analyses, and to evaluate their potential as therapeutic targets. METHODS: We used genetically anchored instrumental variables from 180 protein quantitative trait loci (pQTLs) to assess the causal effects of 91 circulating inflammatory proteins on asthma risk, using large-scale GWAS datasets. Analytical robustness was evaluated through pleiotropy and heterogeneity testing. Functional enrichment and literature-based pathway analyses were performed to support biological plausibility and validate findings. RESULTS: Four proteins showed significant causal effects on asthma: CCL19 and LIFR were protective (OR = 0.89 and 0.91, p ≤ 6.8E-03), while ARTN and IL6 were associated with increased risk (OR = 1.15 and 1.18, p ≤ 1.1E-04). We also identified reverse causal effects of asthma on 11 cytokines, including MMP10, TGFB1, IL33, and IL18R1. Most of these proteins were enriched in pathways related to cytokine signaling and immune response (p < 0.001). All identified proteins had prior literature support linking them to asthma or airway inflammation. CONCLUSIONS: Our findings highlight a subset of circulating inflammatory proteins that are likely causal in asthma pathogenesis and may serve as promising targets for therapeutic intervention. These results offer novel insights into the immunological mechanisms underlying asthma and support the utility of genetic causal inference in target prioritization.

Asthma↗

PubMed2024

Multiple sclerosis and abnormal spermatozoa: A bidirectional two-sample mendelian randomization study.

OBJECTIVE: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, and previous observational epidemiological studies have suggested an association between MS and male infertility; male infertility due to sperm abnormalities may result from a number of aetiological factors, such as genetics, autoimmune factors, etc., and there are currently no studies to assess whether MS is associated with sperm abnormalities in men. Therefore, we performed a Mendelian randomization (MR) analysis to assess the causal relationship between MS and abnormal spermatozoa. METHODS: In this study, independent single nucleotide polymorphisms (SNPs) strongly associated with multiple sclerosis (MS) were identified by mining public genome-wide association study repositories and used as instrumental variables to explore causality. The causal effect of MS on sperm abnormalities was systematically assessed using two-sample Mendelian randomization (MR) techniques, and various analytical models such as inverse variance weighting (IVW), MR-Egger and MR-PRESSO were implemented to dissect the association. In addition, a wide range of sensitivity tests, including Cochran's Q test to detect heterogeneity, MR-Egger intercept analysis to assess bias, leave-one-out to test model robustness, and funnel plot analysis to detect potential publication bias, were implemented to ensure the robustness and reliability of the causal inference results. RESULTS: There was a significant causal relationship between MS and abnormal sperm (OR 1.090, 95% CI [1.017-1.168], p = 0.014); The accuracy and robustness of the results were confirmed by sensitivity analysis. CONCLUSION: Here we show that there appears to be a causal relationship between multiple sclerosis and abnormal spermatozoa. MS as a chronic disease has a higher risk of concomitant sperm abnormalities in its male patients, and reproductive and fertility issues in men with MS should receive special attention from clinicians.

Humans↗

PubMed2025

Genotype by Environment Interactions in Gene Regulation Underlie the Response to Soil Drying in the Model Grass Brachypodium distachyon.

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called genotype and environment (G × E). Understanding the mechanisms driving G × E is fundamental for ensuring stable crop performance across environments and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, yet the interactions between genotype and environment in gene regulation are rarely considered, particularly at the genome scale. Current frameworks and experimental designs often lack power to explicitly test network rewiring or to systematically compare regulatory networks. Here, we leverage a highly replicated RNA-sequencing dataset to model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We first identified genotypic, environmental, and G × E effects on physiological, metabolic, and gene expression traits. We identify patterns of conservation-or variation-in gene coexpression networks and link these coexpression features to physiological traits. We further develop predictions of gene-gene interactions using causal inference and screen for interactions specific to-or with higher affinity in-a single genotype, treatment, or their interaction, G × E. Our analyses identify variation in candidate gene regulatory networks that may shape the evolution of environmental response in B. distachyon. We highlight the environmentally dependent regulatory control of several metabolic traits shown previously to play a role in drought acclimation. The framework presented here provides a scalable approach for more complex comparisons, particularly with the growing availability of large datasets from technologies such as single-cell transcriptomics.

Brachypodium↗

PubMed2026

Comparing genome-wide significant and chemosensory variants as instruments for dietary patterns in Mendelian randomization.

BACKGROUND: Diet is a modifiable risk factor for cardiometabolic disease, yet establishing causality remains challenging. Mendelian randomization (MR) leverages genetic variants as instrumental variables (IVs) to enable causal inference. METHOD: Using two-sample MR, we assessed the causal effects of four principal component-derived dietary patterns (DPs)-Unhealthy, Healthy, Meat-based, Pescatarian-on cardiometabolic outcomes including body mass index, coronary artery disease, blood lipids, blood pressures, type 2 diabetes, fasting glucose and insulin, and glycated haemoglobin. Two sets of IVs were employed: conventional genome-wide significant variants associated with each DP, filtered for pleiotropy and directionality; and biologically informed variants in chemosensory receptor genes, given the role of taste and smell perception in food choice. RESULTS: Using conventional IVs, the Pescatarian DP was associated with reduced fasting insulin (βIVW = -0.10 pmol/L per SD increase in the Pescatarian DP score, 95% confidence interval -0.15, -0.04; P = 1.19 × 10-3), surviving multiple sensitivity analyses. Associations between the Unhealthy DP and elevated blood pressure and glycated haemoglobin should be interpreted cautiously; one of the two filtered IVs was strongly associated with caffeine intake, limiting the attribution of these findings to the DP itself. Chemosensory Receptor IVs yielded null findings, reflecting insufficient power. CONCLUSION: Evidence for causal effects of DPs on cardiometabolic traits was limited, with the strongest support for a protective effect of the Pescatarian DP on fasting insulin. Chemosensory IVs demonstrated limited utility for DPs, likely reflecting the heterogeneous and complex sensory profiles of overall diets. Future efforts should consider guideline-based dietary indices to facilitate interpretability and translation.

Humans↗

PubMed2026

Modelling time-varying genetic effects on binary disease risk via functional Mendelian randomization.

MOTIVATION: Genome-wide association studies have identified thousands of genetic variants associated with complex traits, establishing Mendelian randomization (MR) as a powerful framework for causal inference using variants as natural experiments. However, existing MR methods treat causal effects as static, relying on cross-sectional exposure measurements and ignoring how genetic predispositions to disease operate dynamically across the life course. Recovering age-specific causal effect functions from longitudinal data requires combining functional data representations of exposure trajectories with instrumental variable estimation strategies suitable for binary disease endpoints, a methodological gap that has remained unaddressed. RESULTS: We develop a functional MR framework for binary outcomes that integrates functional principal component analysis with two-stage residual inclusion (2SRI), ensuring consistent estimation under the nonlinear logistic link function that renders standard instrumental variable estimators inconsistent. Simulations across different causal effect trajectory shapes, varying measurement densities, and varying instrument strengths demonstrate accurate recovery of time-varying genetically predicted effects with minimal bias. Applied to UK Biobank data, the framework identifies an age-specific causal effect of genetically predicted body mass index on type 2 diabetes risk concentrated in early mid-adulthood and progressively attenuating thereafter. Concordance between the proposed 2SRI estimator applied to type 2 diabetes and the established continuous-outcome functional MR estimator applied to the paired glycated haemoglobin marker in the same cohort provides indirect empirical support for the validity of the proposed approach. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package mvfmr, with a full tutorial vignette.

Mendelian Randomization Analysis↗

PubMed2025

Dietary patterns and risk of ischemic stroke: A two-sample Mendelian randomization study.

Diet and nutrition critically influence the development and outcomes of ischemic stroke (IS). However, observational studies often yield inconsistent findings due to confounding and measurement error. Mendelian randomization (MR) provides an alternative approach to strengthen causal inference. We conducted a 2-sample MR analysis to evaluate the causal associations between 22 dietary factors and IS risk. Genetic instruments for dietary exposures were derived from the UK Biobank genome-wide association study, and outcome data were obtained from the MEGASTROKE consortium. Inverse-variance weighted analysis served as the primary method, complemented by sensitivity analyses. Consumption of oily fish (β = -0.402, P = .022), cheese (β = -0.364, P = .001), dried fruit (β = -0.710, P = .0002), weekly red wine (β = -0.507, P = .024), and calcium supplements (β = -3.994, P = .015) was associated with reduced risk of IS. Conversely, dietary patterns characterized by high-sugar or high-protein intake showed suggestive associations with increased IS risk, although these did not remain significant after multiple-comparison correction. This 2-sample MR study provides evidence that specific dietary factors, including oily fish, cheese, dried fruit, red wine, and calcium, may reduce IS risk, while high-sugar and high-protein diets may confer increased risk. These findings underscore the importance of dietary management in stroke prevention and highlight the need for further studies to validate the role of potentially harmful dietary patterns.

Humans↗

PubMed2026

Multitarget interactions of bisphenol A in polycystic ovary syndrome: evidence from integrated network toxicology, mendelian randomization, and molecular docking.

OBJECTIVE: To study the potential pathogenic mechanisms of bisphenol A (BPA) in polycystic ovary syndrome (PCOS) using an integrative computational strategy. DESIGN: Integrative computational study combining network toxicology, Mendelian randomization (MR), and molecular docking. SUBJECTS: For MR analysis, genetic data were sourced from large European-ancestry cohorts, including plasma protein quantitative trait loci data and genome-wide association study summary statistics for PCOS (3,045 cases and 267,780 controls). EXPOSURE: In silico exposure to BPA for target prediction; genetically predicted plasma protein levels for causal inference. MAIN OUTCOME MEASURES: Identification of overlapping targets between BPA and PCOS; functional enrichment pathways; causal effects of prioritized proteins on PCOS risk (odds ratios with 95% confidence intervals); binding affinities between BPA and core targets (kcal/mol). RESULTS: Network toxicology identified 310 overlapping targets between BPA and PCOS. Enrichment analyses revealed significant involvement in endocrine signaling, inflammatory pathways (eg, IL-17), and cellular processes. MR demonstrated that genetically elevated levels of RET, CXCL8, HTR6, MMP1, MMP9, NTRK1, and TNNI2 were significantly associated with increased PCOS risk, whereas higher PSAP and SHBG levels were protective. Molecular docking confirmed stable binding between BPA and all nine key targets, with strongest affinity for SHBG (-8.4 kcal/mol), followed by NTRK1, TNNI2, and RET. CONCLUSION: This integrative investigation suggests that BPA may contribute to PCOS pathogenesis through multitarget interactions involving inflammatory mediators, endocrine regulators, and tissue remodeling proteins. The findings provide prioritized targets and mechanistic insights for future experimental validation and environmental risk assessment.

Female↗

PubMed2025

Exact model-free function inference using uniform marginal counts for null population.

MOTIVATION: Recognizing cause-effect relationships is a fundamental inquiry in science. However, current causal inference methods often focus on directionality but not statistical significance. A ramification is chance patterns of uneven marginal distributions achieving a perfect directionality score. RESULTS: To overcome such issues, we design the uniform exact function test with continuity correction (UEFTC) to detect functional dependency between two discrete random variables. The null hypothesis is two variables being statistically independent. Unique from related tests whose null populations use observed marginals, we define the null population by an embedded uniform square. We also present a fast algorithm to accomplish the test. On datasets with ground truth, the UEFTC exhibits accurate directionality, low biases, and robust statistical behavior over alternatives. We found nonmonotonic response by gene TCB2 to beta-estradiol dosage in engineered yeast strains. In the human duodenum with environmental enteric dysfunction, we discovered pathology-dependent anti-co-methylated CpG sites in the vicinity of genes POU2AF1 and LSP1; such activity represents orchestrated methylation and demethylation along the same gene, unreported previously. The UEFTC has much improved effectiveness in exact model-free function inference for data-driven knowledge discovery. AVAILABILITY AND IMPLEMENTATION: An open-source R package "UniExactFunTest" implementing the presented uniform exact function tests is available via CRAN at doi: 10.32614/CRAN.package.UniExactFunTest. Computer code to reproduce figures can be found in supplementary file "UEFTC-main.zip."

Algorithms↗

PubMed2026

Identifying causal genetic variants for high-altitude adaptation through blood eQTL analysis in plateau populations.

A substantial number of genetic variants have been associated with high-altitude adaptation (HAA), yet most of them are located in non-coding genomic regions, leaving their specific functions and underlying mechanisms largely unknown. In this study, we analyze whole-genome and transcriptome sequencing data from a self-established cohort comprising 61 native highlanders (NHs) and 164 acclimatized newcomers (ANs), identifying 6,586 cis- and 34,203 trans-expression quantitative trait loci (eQTLs), along with 130 cell type-specific eQTLs. By further combining these data with a large East Asia (~30% Tibetan) genome-wide association study (GWAS) cohort, we employ colocalization and causal inference analyses to prioritize 85 cis-eQTLs associated with HAA and identify several novel candidate causal genes, including EXOC8, which is experimentally confirmed to regulate erythroid differentiation. Additionally, network analysis of these causal genes uncovers multiple regulatory pathways, mainly involving energy metabolism, autophagy, ubiquitination and inflammation. Our study offers a comprehensive eQTL map and reveals causal chains of "variant-gene-phenotype" for HAA-related traits, which provides new insights into potential regulatory mechanisms and targets for prevention and treatment of altitude sickness.

Quantitative Trait Loci↗

PubMed2026

Causal Effects of Gut Microbiota on Morning Chronotype, Insomnia and Sleep Duration: A Two-Sample Mendelian Randomization Study.

BACKGROUND: The gut microbiota has been shown to be closely associated with brain function; however, whether it exerts a causal influence on sleep traits remains to be further explored. Mendelian randomization (MR) is an emerging epidemiological approach that uses whole-genome sequencing data to infer causal relationships. In this study, we conducted a two-sample MR analysis to investigate the causal effects of gut microbiota on three domains of sleep traits: morning chronotype, insomnia, and sleep duration. METHODS: Single nucleotide polymorphisms strongly associated with 196 gut microbiota taxa were selected as instrumental variables. Morning chronotype, insomnia, and sleep duration were used as outcomes. MR and sensitivity analyses were performed to assess the causal relationships between gut microbiota and sleep traits. RESULTS: Three taxa (Bifidobacteriales, Bifidobacteriaceae, and Bifidobacterium) were negatively associated with morning chronotype, while Tyzzerella 3 showed a positive causal effect on morning chronotype. Oscillibacter was negatively associated with insomnia, whereas four taxa (Negativicutes, Selenomonadales, the Clostridium innocuum group, and Lachnoclostridium) were identified as risk-increasing factors for insomnia. Lentisphaerae and Victivallaceae were positively associated with sleep duration. Actinobacteria and Alistipes had negative effects on long sleep duration, whereas Ruminiclostridium 6 was positively associated with long sleep duration. Four taxa (Victivallales, Anaerofilum, Lentisphaerae, and Lentisphaeria) were negatively associated with short sleep duration. CONCLUSIONS: Our findings suggest that specific gut microbiota taxa may be positively or negatively associated with sleep traits. These results offer new insights into the potential role of gut microbiota in sleep regulation and provide a basis for future studies aimed at understanding whether modulating microbial composition could influence sleep health.

Mendelian randomization↗

PubMed2025

Causal relationship between albumin, total protein, and colorectal cancer risk: A 2-sample Mendelian randomization study.

Albumin (ALB) and total protein (TP) are vital constituents of the blood, and their levels and roles in the risk of colorectal cancer (CRC) are of significance. Previous observational studies have reported correlations among ALB, TP, and CRC. However, the existence of a causal relationship between ALB and CRC in European populations has not been adequately investigated and the causal link between TP and CRC remains unexplored. To address these gaps, we applied Mendelian randomization (MR) to investigate the potential causal relationship between ALB, TP, and CRC. Two-sample MR analysis was used to investigate whether there was a causal relationship between ALB, TP, and CRC. Our exposure data were extracted from genome-wide association study (GWAS) databases sourced from the UK Biobank, containing 315,268 and 314,921 Europeans participants for ALB and TP analyses, respectively. Single nucleotide polymorphisms that were significantly associated with ALB and TP were assessed using GWAS datasets. Our data were derived from the FinnGen Consortium CRC GWAS, which contained 6509 CRC cases and 28,7137 controls. Causal inference between ALB, TP, and CRC was performed using 3 MR methods: inverse variance weighting (IVW), MR-Egger, and weighted median. The IVW analysis showed no significant causal association between ALB and CRC (OR = 1.04, 95% CI = 0.89-1.21, P = .65). In contrast, the IVW analysis for TP and CRC showed a significant causal association (OR = 0.78, 95% CI = 0.66-0.92, P = .003), suggesting a reduced risk of CRC. Through a 2-sample MR study investigating the causal relationship between ALB, TP, and CRC in a European population, our findings revealed a significant causal relationship between TP and a reduced risk of CRC.

Humans↗

PubMed2026

Shared genetic architecture and cellular convergence between female reproductive disorders and pulmonary function: a genome-wide cross-trait analysis.

Female reproductive disorders (FRDs), including polycystic ovary syndrome, endometriosis, uterine leiomyomata, and infertility, have been epidemiologically associated with impaired pulmonary function. However, it remains unclear whether this cross-organ link reflects shared genetic etiology and, if so, which cellular mechanisms mediate it. We performed a systematic genome-wide cross-trait analysis of three FRDs and lung function traits (FEV₁, FVC, FEV₁/FVC) using GWAS summary statistics from individuals of European ancestry, integrating genetic correlation, bidirectional causal inference, pleiotropy mapping, and single-cell enrichment analyses. We identified significant negative genetic correlations between FRDs and lung volume traits, most prominently for FVC (rg range: - 0.077 to - 0.178). Bidirectional causal analyses indicated that FRDs have a detrimental effect on lung volume, with higher FRD genetic liability associated with reduced lung volume. Cross-trait meta-analysis identified 17 pleiotropic variants across 11 loci, with the 19q13.2 (LTBP4) and 12q13.13 (HOXC6/HOXC9) loci showing strong evidence of shared causal variants. Critically, single-cell analyses revealed that shared genetic risk converged on mesenchymal lineages across organs, specifically alveolar adventitial fibroblasts in the lung and stromal/smooth muscle cells in the endometrium. Transcriptome-wide analyses further nominated the estrogen-responsive gene RERG as a convergent gene linking these conditions with lung function. Our study revealed a shared genetic architecture between female reproductive disorders and lung function traits, providing a basis for further mechanistic investigations and potential clinical evaluation. Furthermore, our findings suggest that shared fibroproliferative and hormone-responsive pathways may offer insights into the biological mechanisms underlying these conditions.

Female↗

PubMed2024

Is there a causal relationship between resistin levels and bone mineral density, fracture occurrence? A mendelian randomization study.

BACKGROUND: In a great many of observational studies, whether there is a relevance of resistin levels on bone mineral density (BMD) and fracture occurrence has been inconsistently reported, and the causality is unclear. METHODS: We aim to assess the resistin levels on BMD and fracture occurrence within a Mendelian randomization (MR) analysis. Exposure and outcome data were derived from the Integrative Epidemiology Unit (IEU) Open genome wide association studies (GWAS) database. Screening of instrumental variables (IVs) was performed subject to conditions of relevance, exclusivity, and independence. Inverse variance weighting (IVW) was our primary method for MR analysis based on harmonized data. Weighted median and MR-Egger were chosen to evaluate the robustness of the results of IVW. Simultaneously, heterogeneity and horizontal pleiotropy were also assessed and the direction of potential causality was detected by MR Steiger. Multivariable MR (MVMR) analysis was used to identify whether confounding factors affected the reliability of the results. RESULTS: After Bonferroni correction, the results showed a suggestively positive causality between resistin levels and total body BMD (TB-BMD) in European populations over the age of 60 [β(95%CI): 0.093(0.021, 0.165), P = 0.011]. The weighted median [β(95%CI): 0.111(0.067, 0.213), P = 0.035] and MR-Egger [β(95%CI): 0.162(0.025, 0.2983), P = 0.040] results demonstrate the robustness of the IVW results. No presence of pleiotropy or heterogeneity was detected between them. MR Steiger supports the causal inference result and MVMR suggests its direct effect. CONCLUSIONS: In European population older than 60 years, genetically predicted higher levels of resistin were associated with higher TB-BMD. A significant causality between resistin levels on BMD at different sites, fracture in certain parts of the body, and BMD in four different age groups between 0-60 years of age was not found in our study.

Bone Density↗

PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗

PubMed2025

Integration of multiple omics reveals key targets and cellular mechanisms for intervention in sarcopenia.

BACKGROUND: Sarcopenia, an age-related syndrome characterized by progressive loss of muscle mass, strength, and function, presents a significant global health burden with limited therapeutic interventions. This study integrates genomic causality, multi-tissue omics, and cellular mediation analyses to identify and prioritize mechanistically grounded therapeutic targets. METHODS: A multi-tiered analytical framework was applied, beginning with two-sample Mendelian randomization (MR) to infer causal relationships between 4907 plasma proteins (cis-pQTLs from 35,559 individuals) and sarcopenia traits in Pan-UK Biobank participants. Bayesian colocalization and transcriptomic validation in human sarcopenia muscle biopsies were employed to prioritize targets. Cellular mediation analysis quantified contributions of immune and stromal cell subtypes to protein-trait pathways using transcriptomic deconvolution. RESULTS: MR identified 1237 plasma proteins causally associated with sarcopenia traits, with six targets (HGFAC, GATM, HMOX2, F2, LMAN2L, HPGDS) validated through colocalization, transcriptomic expression, and sarcopenia-related dysregulation. Cellular mediation revealed immune mechanisms underlying HGFAC's effects, with CD4+ regulatory T cells mediating 3.49 % of its impact on sarcopenia traits. Prothrombin exhibited muscle-protective effects independent of coagulation. CONCLUSION: This study establishes a causal map linking plasma proteins to sarcopenia through immune-stromal interactions. The integration of MR, multi-omics validation, and cellular mediation prioritizes six proteins as actionable targets, supporting repurposing of thrombin inhibitors and development of immunometabolic therapies. The framework bridges genomic causality with cellular pathophysiology, advancing precision strategies for age-related muscle decline.

Humans↗

PubMed2026

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↗

PubMed2026

Mendelian Randomization Using a Japanese GWAS Identifies an HLA-Linked Causal Effect of Chronic Hepatitis B on Cholangiocarcinoma Risk.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant cancer that develops in the bile ducts. Its incidence is particularly high in East Asian populations, but the underlying genetic factors remain unclear. To investigate potential risk factors for CCA, we conducted a Mendelian randomization study to infer causality. METHODS: Using large-scale genome-wide association study data from the BioBank Japan resource, we systematically investigated the causal effects of genetic predisposition to seven conditions, chronic hepatitis B (CHB), chronic hepatitis C, autoimmune hepatitis, type 1 diabetes, type 2 diabetes, chronic gastritis, and chronic pancreatitis, on CCA risk. RESULTS: Our analysis reveals a significant association between genetic susceptibility to CHB with a 24% higher likelihood of developing CCA than non-susceptible individuals (Inverse-Variance Weighted Odds Ratio = 1.24, 95% Confidence Interval: 1.08-1.42; p = 0.002). This genetic association is significantly driven by instrumental variables enriched in the immune-regulatory HLA class II region (6p21), suggesting a plausible biological mechanism. For the primary outcome (CCA), statistical significance was assessed across seven exposures at a Bonferroni-corrected threshold (two-sided p<0.0071). Notably, the CHB-CCA association remains significant after correction. This primary finding is strongly supported by comprehensive sensitivity analyses that showed no evidence of confounding by horizontal pleiotropy or heterogeneity. Conversely, no significant causal effects on CCA were identified for the other six conditions. CONCLUSIONS: Our MR analysis supports a causal role of HBV infection in CCA development, highlighting the importance of targeted HBV screening and surveillance.

Female↗

PubMed2026

Integrative Genomic and Functional Investigation of the Multi-Layered Genetic Architecture Between Anorexia Nervosa and Bone Loss.

OBJECTIVE: Bone loss is a severe and often irreversible complication of anorexia nervosa (AN), yet the genetic mechanisms underlying this comorbidity remain underexplored. This study focuses on constructing a comprehensive genetic architecture between AN and estimated calcaneal bone mineral density (eBMD). METHOD: We applied an integrative framework incorporating genetic correlation, pleiotropic association, and causal inference across single-variant, multi-variant, and gene expression levels. Functional validation was conducted in vitro to investigate the biological role of the key candidate gene. RESULTS: Local genetic correlation analysis identified significant signals at 8p21.2 and 10q26.3, despite the lack of significant global correlation. Mendelian randomization analysis pointed to a suggestive negative causal effect of genetically predisposed AN on eBMD. Extensive pleiotropic signals were detected, particularly at 3p21.31 and 10q26.3, loci enriched with genes associated with both traits. Notably, we identified a novel pleiotropic signal near NCAM1 at 11q23.2, which was supported by multi-layered genetic evidence and confirmed through in vitro functional experiments. NCAM1, a well-established neural-associated gene, promoted osteoclastic differentiation and bone resorption when overexpressed in osteoclast precursor cells, indicating that NCAM1 possesses distinct functional roles in both neural and skeletal tissues. DISCUSSION: This study constructs a comprehensive genetic architecture underlying AN and eBMD and highlights NCAM1 as a key pleiotropic gene.

anorexia nervosa↗