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PubMed2026

Cross-tissue multi-omics integration highlights BPHL and mitochondrial targets in Alzheimer's disease.

BACKGROUND: Mitochondrial dysfunction is a hallmark of Alzheimer's disease (AD), yet specific molecular targets remain to be fully characterized. METHODS: A summary-data-based Mendelian randomization (SMR) framework integrated AD genome-wide association study (GWAS) statistics (39,918 cases) with blood DNA methylation quantitative trait loci (mQTL), gene expression (eQTL), and protein (pQTL) data for 1136 mitochondria-related genes. Associations were assessed using Bayesian colocalization and HEIDI testing. Tissue relevance was evaluated in four brain regions (hippocampus, amygdala, cortex, frontal cortex) using GTEx and external transcriptomic datasets. RESULTS: Screening identified eight candidates supported across blood mQTL and eQTL layers. Stepwise central nervous system (CNS) evaluation singled out biphenyl hydrolase-like (BPHL) as the consistent candidate. Higher genetically predicted BPHL expression was associated with reduced AD risk across the hippocampus (OR=0.920, 95% CI 0.873-0.970), amygdala (OR=0.925, 95%CI 0.880-0.973), cortex (OR=0.943, 95% CI 0.908-0.978), and frontal cortex (OR=0.938, 95%CI 0.901-0.976). These findings aligned with protein-protein interactions connecting BPHL to respiratory complexes and lower BPHL expression in independent AD brains. Functional enrichment converged on oxidative phosphorylation pathways. CONCLUSIONS: By integrating multi-omics data with tissue-specific validation, this study nominates BPHL as a consistent protective candidate in the brain. These findings provide genetic support for mitochondrial molecular perturbations in AD, offering insights for future validation.

Alzheimer Disease↗

PubMed2026

Integrated transcriptomic and functional characterization of Claudin-1 reveals its oncogenic and immunomodulatory roles in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, driven by its invasive nature and lack of effective biomarkers. Disruption of the epithelial barrier, mediated by tight junction components, is a critical yet underexplored contributor to PDAC progression. Claudins, integral regulators of tight junction integrity, display altered expression across cancers, but their prognostic and immunomodulatory roles in PDAC remain unclear. We performed an integrative analysis of 177 RNA-Seq datasets from TCGA and GTEx to characterize Claudin family alterations in PDAC. Differential expression, copy number variation, methylation, and co-expression networks were analyzed alongside clinical and survival data. Prognostic significance was assessed using Kaplan - Meier and Cox regression analyses, while immune cell infiltration was examined using deconvolution algorithms. Functional validation of Claudin-1 was conducted in Capan-1 cells using CRISPR/Cas9 knockout, followed by proliferation, wound-healing, and Western blot assays. Ten Claudin genes were significantly dysregulated, with Claudin-1 and Claudin-4 frequently amplified and associated with advanced stage and poor survival. High Claudin-1 expression correlated with reduced immune infiltration, indicating an immune-excluded phenotype characterized by immune cells retained in the tumor stroma but largely absent from the tumor parenchyma. Claudin-1 knockout markedly inhibited proliferation, migration, and EMT, evidenced by downregulation of Snail and Slug and restoration of E-cadherin expression. This integrative transcriptomic and functional study identifies Claudin-1 as a key driver of PDAC aggressiveness and immune modulation. These findings establish Claudin-1 as a promising prognostic biomarker and therapeutic target for restoring epithelial integrity and counteracting immune evasion in pancreatic cancer.

Humans↗

PubMed2026

AGRN activates GPX4 via the Wnt/beta-catenin signaling pathway to suppress ferroptosis in cervical cancer cells.

Cervical cancer remains a leading cause of cancer-related deaths among women worldwide. Targeting ferroptosis, a regulated form of cell death driven by lipid peroxidation, has emerged as a promising therapeutic strategy. This study aimed to elucidate the mechanisms by which cervical cancer cells acquire resistance to ferroptosis. AGRN expression and its prognostic significance were analyzed in cervical cancer using TCGA and GTEx data. In vitro, AGRN levels were measured in HeLa, CaSki, and Ect1/E6E7 cells via qRT-PCR. mRNA and protein expression levels of AGRN, beta-catenin, and ferroptosis-related markers were evaluated by qRT-PCR and western blot (WB). Cell viability, RSL3 sensitivity (IC50), and lipid ROS were assessed via CCK-8 assay and flow cytometry. In a xenograft model, RSL3's effect on tumor growth was examined. Immunohistochemistry (IHC) and molecular analyses (qRT-PCR, WB, and flow cytometry) were performed on tumor tissues to evaluate AGRN, beta-catenin, Ki-67, ferroptosis-related genes, and lipid ROS. It is found through research that, AGRN was significantly upregulated in cervical cancer tissues and cell lines. Its overexpression activated Wnt/beta-catenin signaling, resulting in elevated GPX4 and SLC7A11 levels and reduced CHAC1 and PTGS2 expression levels. These alterations decreased lipid ROS accumulation and enhanced resistance to RSL3-induced ferroptosis, an effect further confirmed in xenograft models. In short, In short, AGRN promotes ferroptosis resistance in cervical cancer by activating the Wnt/beta-catenin signaling pathway and upregulating GPX4 expression. Targeting the AGRN-Wnt/beta-catenin-GPX4 axis may represent a novel therapeutic approach for cervical cancer.

Humans↗

PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗

PubMed2026

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software↗

PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.

Biosample identifiers↗

PubMed2025

Genetic liability to meniscus degeneration and its comorbidity patterns: a phenome-wide association study in the UK Biobank.

Meniscus degeneration is a common knee pathology causing pain, disability, and osteoarthritis. While mechanical factors are established, the contribution of inherited genetic liability to its systemic disease patterns remains unclear. We applied a polygenic risk score (PRS) for meniscus degeneration, derived from FinnGen genome-wide association study (GWAS) results to 323,999 UK Biobank participants and conducted a phenome-wide association study (PRS-PheWAS) across 962 clinical phenotypes. The PRS-PheWAS revealed significant associations beyond musculoskeletal traits, extending to metabolic, cardiovascular, psychiatric, and gastrointestinal domains, indicating broad shared genetic architecture. To support these findings, linkage disequilibrium score regression confirmed strong genetic correlations with osteoarthritis and related arthropathies, and genotype-tissue expression (GTEx) analysis highlighted tissue-specific expression of lead genes (e.g., GDF5, SOX5, BMP6) in connective and metabolic tissues. The results demonstrate that genetic liability to meniscus degeneration extends beyond the knee, sharing pathways with systemic conditions. This systemic genetic architecture underscores the need for integrative management approaches that combine orthopedic care with metabolic and lifestyle interventions.

Journal Article↗

PubMed2026

Mitochondria-Related Pathogenic Genes in Paediatric Asthma: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is implicated in asthma pathogenesis, but causal roles of mitochondrial-related genes in paediatric asthma remain unclear. We performed a multi-omics Mendelian randomization study integrating GWAS data from paediatric asthma cohorts with blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs) and protein QTLs (pQTLs) datasets. Causal inference was assessed using Summary-data-based Mendelian Randomization (SMR) and HEIDI testing, complemented by colocalization analysis. Findings were validated in independent cohorts and evaluated for tissue specificity using GTEx. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. SMR analysis identified 80 methylation sites spanning 54 genes, 26 gene expressions, and three proteins significantly associated with paediatric asthma. Colocalization analysis confirmed strong evidence for 10 methylation sites (7 genes), the STX17 eQTL (PP.H4 = 0.98) and the UNG pQTL (PP.H4 = 0.84). Tissue-specific eQTL validation replicated the STX17 association. Multi-omics integration associated ALAS1 (cg13241645, cg15698299) and TXNRD1 (cg09884423) with asthma at both methylation and expression levels, with colocalization supporting both ALAS1 associations. Furthermore, integrated mQTL-eQTL analysis suggests that DNA methylation potentially regulates ALAS1 and TXNRD1 expression. Functional enrichment and network analyses revealed that these candidate genes converge on mitochondrial metabolic pathways and identified seven hub genes with potential regulatory significance (SDHB, MFN2, GLDC, PHB2, TXNRD1, ATP5MC1 and PHB). This study provides multi-omics evidence supporting a causal role for mitochondrial-related genes, particularly ALAS1 and TXNRD1, in paediatric asthma, offering new insights into pathogenesis and potential therapeutic targets.

Humans↗

PubMed2026

Integrative Pan-Cancer Characterization of lncRNA UPK1A-AS1 and Its Role in Hypoxia-Associated Sorafenib Resistance in Hepatocellular Carcinoma.

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of tumor initiation and progression through transcriptional and posttranscriptional mechanisms. UPK1A antisense RNA 1 (UPK1A-AS1), a cancer-associated lncRNA, has been reported to participate in oncogenic processes; however, its overall landscape across human malignancies and its biological role in therapy resistance remain poorly understood. Given the increasing importance of identifying functional lncRNAs with prognostic and therapeutic potential, this study presents a comprehensive multiomics characterization of UPK1A-AS1 and its experimental validation in hepatocellular carcinoma (HCC). We integrated datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression Project (GTEx), the cancer immunology data engine (CIDE), and the cBioPortal for cancer genomics (cBioPortal) to systematically assess its expression pattern, genomic alterations, clinical significance, and immunological associations. Our analyses revealed that UPK1A-AS1 is significantly upregulated in multiple tumor types, with copy-number amplification as the predominant genomic alteration driving its overexpression. Elevated UPK1A-AS1 expression was correlated with advanced disease stage, poor differentiation, immune exclusion, and unfavorable prognosis, supporting its potential as a cancer type-dependent biomarker. In parallel, functional studies demonstrated that hypoxia transcriptionally induces UPK1A-AS1 in HCC, where it promotes sorafenib resistance by suppressing apoptosis. Silencing UPK1A-AS1 restored apoptotic and enhanced sorafenib efficacy both in vitro and in vivo. Collectively, our findings suggest that UPK1A-AS1 is a hypoxia-inducible oncogenic lncRNA that plays dual roles in cancer, with cancer type-dependent associations with progression and immune modulation across malignancies and mechanistically mediating hypoxia-associated drug resistance in HCC.

Humans↗

PubMed2026

Human Variation-Informed Prioritization of MPHOSPH6 in Lung Adenocarcinoma: A Source-Aware Multiomics Evidence Framework.

Moving from an association signal to a clinically credible biomarker requires several links that are often conflated: verified variant identity, aligned allelic effects, reproducible gene-level association, relevant cellular expression, and a plausible functional consequence. We developed a source-aware multiomics framework to assess MPHOSPH6 in lung adenocarcinoma (LUAD) while keeping those evidence classes separate. Six prespecified rsIDs were recovered from the harmonized TRICL LUAD dataset, of which five reached p < 5 × 10 - 8. Only rs112333466 and rs76474922 were available with alignable alleles in FinnGen R10, and both showed concordant directions. Fixed-effect estimates were OR = 1.592 for rs112333466-T (95% CI, 1.401-1.809; p = 9.91 × 10 - 13) and OR = 0.819 for rs76474922-C (95% CI, 0.773-0.867; p = 1.03 × 10 - 11). In a prespecified two-variant GTEx v8 lung model, genetically predicted MPHOSPH6 expression was positively associated with LUAD in TRICL (Z = 3.341, p = 8.35 × 10 - 4) and FinnGen (Z = 2.697, p = 0.0070). This gene-level result did not establish colocalization or connect MPHOSPH6 to the six susceptibility rsIDs. Patient-level analysis of 89,241 immune cells from six paired tumor and normal-adjacent lung samples found no significant difference in MPHOSPH6 pseudobulk abundance (exact paired Wilcoxon p = 0.3125). None of 688 lung-lineage pharmacogenomic tests remained significant after false-discovery-rate correction. Ten recorded MPHOSPH6 missense alleles, including five ClinVar variants of uncertain significance, were curated; structural analysis identified I58 at an experimental RNA-exosome interface and defined a focused perturbation series. MPHOSPH6 is therefore supported as a human-variation-informed candidate for functional evaluation, not as a validated LUAD biomarker, pathogenic gene, drug-response predictor, or therapeutic target.

Humans↗

PubMed2026

IL1RAP Is Associated With an Inflammation-Immunity-Related State in Skin Cutaneous Melanoma: Integrative Evidence From Pan-Cancer Data and Melanoma Immunotherapy Cohorts.

BACKGROUND: The crosstalk between inflammation and immunity plays a central role in tumor progression, immune evasion, and therapeutic response. Interleukin-1 receptor accessory protein (IL1RAP) is a key adaptor in inflammatory signaling, yet its immunological relevance and clinical implications in skin cutaneous melanoma (SKCM) remain largely unexplored. METHODS: We performed an integrative analysis combining pan-cancer and melanoma-focused datasets. Bulk transcriptomic, single-cell, spatial transcriptomic, genomic alteration, pharmacogenomic, and clinical survival data were obtained from TCGA, GTEx, GEO, ENA, and other public resources. IL1RAP expression was evaluated across cancer types in relation to diagnostic performance, immune subtypes, survival outcomes, functional pathway activity, immune-genomic states, somatic alterations, and drug-response metrics. Melanoma-focused analyses examined immune infiltration, methylation-derived tumor-infiltrating lymphocyte (MeTIL) scores, and exploratory survival associations in five treatment cohorts; the survival groups were defined using cohort-specific optimal cutoffs rather than median splits. RESULTS: IL1RAP expression differed between tumor and normal tissues in multiple cancers, although the direction and magnitude varied by cancer type. Pan-cancer survival associations were likewise context dependent. Single-cell and spatial transcriptomic resources indicated cell-type and spatial heterogeneity of IL1RAP expression within tumor microenvironments. Pathway, immune-genomic, and pharmacogenomic analyses identified exploratory associations with functional states, genomic features, and drug-response metrics. In SKCM, IL1RAP expression was associated with several immune-infiltration estimates and higher MeTIL scores. Across five melanoma immunotherapy cohorts, the direction and magnitude of the overall survival associations varied substantially. CONCLUSIONS: This retrospective integrative analysis suggests that IL1RAP may mark an inflammation-immunity-related state in SKCM. The heterogeneous associations across cancers and melanoma treatment cohorts support further validation but do not establish IL1RAP as a causal regulator, a treatment-response predictor, or a therapeutic target.

IL1RAP↗

PubMed2025

New Genetic Loci Implicated in Cardiac Morphology and Function Using Three-Dimensional Population Phenotyping.

BACKGROUND: Cardiac remodeling occurs in the mature heart and is a cascade of adaptations in response to stress, which are primed in early life. A key question remains as to the processes that regulate the geometry and motion of the heart and how it adapts to stress. METHODS: We performed spatially resolved phenotyping using machine learning-based analysis of cardiac magnetic resonance imaging in 47 549 UK Biobank participants. We analyzed 16 left ventricular spatial phenotypes, including regional myocardial wall thickness and systolic strain in both circumferential and radial directions. In up to 40 058 participants, genetic associations across the allele frequency spectrum were assessed using genome-wide association studies with imputed genotype participants, and exome-wide association studies and gene-based burden tests using whole-exome sequencing data. We integrated transcriptomic data from the GTEx project and used pathway enrichment analyses to further interpret the biological relevance of identified loci. To investigate causal relationships, we conducted Mendelian randomization analyses to evaluate the effects of blood pressure on regional cardiac traits and the effects of these traits on cardiomyopathy risk. RESULTS: We found 42 loci associated with cardiac structure and contractility, many of which reveal patterns of spatial organization in the heart. Whole-exome sequencing revealed 3 additional variants not captured by the genome-wide association study, including a missense variant in CSRP3 (minor allele frequency 0.5%). The majority of newly discovered loci are found in cardiomyopathy-associated genes, suggesting that they regulate spatially distinct patterns of remodeling in the left ventricle in an adult population. Our causal analysis also found regional modulation of blood pressure on cardiac wall thickness and strain. CONCLUSIONS: These findings provide a comprehensive description of the pathways that orchestrate heart development and cardiac remodeling. These data highlight the role that cardiomyopathy-associated genes have on the regulation of spatial adaptations in those without known disease.

Humans↗

PubMed2026

Integrated transcriptomic and immunogenomic analysis unravels the immunological functions and prognostic landscape of WD repeat domain 76.

BackgroundWD Repeat Domain 76 (WDR76) plays a potential role in cellular regulation; however, its comprehensive landscape across human malignancies and its specific biological function in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsWe conducted a systematic pan-cancer analysis utilizing multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) atabases to evaluate WDR76 expression, subcellular localization, and its correlation with clinicopathologic features, genomic instability, and immune infiltration. Diagnostic and prognostic values were assessed via Receiver operating characteristic (ROC) and Kaplan-Meier analyses. Furthermore, the functional role of WDR76 in HCC was validated in vitro using Hep-3B and Huh7 cell lines through siRNA-mediated knockdown, followed by CCK-8, wound-healing, and transwell assays.ResultsWDR76 was significantly upregulated in the majority of tumor types, including LIHC, LUAD, and COAD, while exhibiting nuclear localization. Elevated WDR76 expression correlated with advanced tumor staging, metastasis, and poor clinical outcomes across multiple cohorts, particularly in ACC, KIRP, and LIHC. ROC analysis highlighted its exceptional diagnostic precision in cancers such as GBM and LIHC. Immunologically, WDR76 expression was intricately linked to immune cell infiltration, immune checkpoint markers, and genomic instability parameters, suggesting a role in shaping the tumor microenvironment. Drug sensitivity profiling revealed that high WDR76 levels correlate with resistance to specific chemotherapeutic agents. Experimentally, silencing WDR76 in HCC cells significantly suppressed cell proliferation, migration, and invasion capabilities.ConclusionOur study establishes WDR76 as a robust pan-cancer prognostic biomarker and a potential immunotherapeutic target. Specifically, we provide experimental evidence that WDR76 functions as an oncogenic driver in liver cancer, promoting malignant phenotypes and offering a novel avenue for targeted therapeutic intervention.

Humans↗

PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗

PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗

PubMed2025

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗

PubMed2026

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate = 1.02, 95%CI = 1.01-1.04, FDR adjusted P = 1.69 × 10-2; OR in putamen = 1.02, 95% CI = 1.01-1.03, PFDR = 3.37 × 10-2, OR in nucleus accumbens = 1.02, 95% CI = 1.01-1.04, PFDR = 3.37 × 10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation↗

PubMed2026

The association between GLP-1R expression and cardiovascular-kidney-metabolic-related diseases in non-diabetic and non-obese population: evidence triangulation using Mendelian randomization, observational and polygenic score association analysis.

BACKGROUND: Glucagon-like peptide-1 receptor (GLP-1R) agonists are emerging as promising therapies for cardiovascular-kidney-metabolic (CKM) related diseases in individuals with type 2 diabetes mellitus (T2DM) or obesity. But their effects in non-obese and non-diabetic individuals are unclear. This study triangulates evidence using Mendelian randomization (MR), polygenic scores (PGS) and observational analyses to estimate the associations of GLP-1R expression with chronic kidney disease (CKD), heart failure (HF) and metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: For the MR analysis, instruments mimicking GLP-1R expression were identified using pancreas-specific cis-expression quantitative trait loci from GTEx (N ≤ 305). MR-Robust method was used as the primary MR approach. PGS and observational analyses were performed both in non-diabetic and non-obese individuals separately. A genome-wide association study (GWAS) for MASLD (14,231 cases and 348,091 controls) was performed in the general population using data from UK Biobank. RESULTS: GLP-1R expression showed robust effects on CKD (odds ratio [OR] 0.96, 95%CI 0.95 to 0.97, q = 1.7 × 10- 10 ), HF (OR = 0.96, 95%CI 0.94 to 0.97, q = 2.5 × 10- 8) and MASLD (OR = 0.96, 95%CI 0.93 to 0.98, q = 1.3 × 10- 3) in the general population. Consistent results were observed in validation analyses. Furthermore, PGS and observational analyses among non-T2DM and non-obese individuals found little evidence to support its association with CKD, HF or MASLD. GWAS analysis identified eight conditionally independent variants associated with MASLD, in which rs563199662 was a new signal located at TFPI region. CONCLUSIONS: This study provides multilayered evidence for GLP-1R expression in mitigating CKD, HF and MASLD risks in the general population, while de-prioritized its effect on CKM-related diseases in non-obese and non-diabetic individuals. Further clinical trials are needed to validate the effects of GLP-1R agonists in relative health population.

Humans↗