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PubMed2026

Single-cell expression quantitative trait locus Mendelian randomization reveals immune cell-specific causal regulatory networks and actionable targets in polycystic ovary syndrome.

ObjectiveTo systematically investigate whether the pathogenesis of polycystic ovary syndrome (PCOS) is causally related to dysregulated gene expression in specific immune cell subsets, and to evaluate the potential of these causal genes as actionable drug targets.MethodsThis study employed a two-sample Mendelian randomization (MR) framework using publicly available genome-wide association study (GWAS) summary statistics. The participant data included 797 PCOS cases and 140,558 controls (no direct patient recruitment was involved). Instrumental variables were derived from high-resolution immune cell-specific single-cell expression quantitative trait locus (sc-eQTL) data (OneK1K project) across 14 immune cell types. Primary analyses utilized the inverse-variance weighted (IVW) method. Shared causal variants were validated using Bayesian colocalization. Phenome-wide association analysis (PheWAS), external transcriptomic dataset validation (GSE8157), and DrugBank database screening were conducted for pleiotropy assessment and drug repositioning.ResultsMR analysis revealed genome-wide significant causal associations for GLIPR1 in non-classical monocytes (Mono NC) and XBP1 in CD4+ effector memory T cells (CD4 ET) with PCOS risk. Higher GLIPR1 expression was associated with a decreased PCOS risk (OR = 0.669, P = 4.34×10-6), whereas higher XBP1 expression was associated with an increased risk (OR = 1.406, P = 9.53×10-8). Colocalization analysis confirmed that GLIPR1 shares a causal variant with PCOS (PP.H4 = 96.73%). PheWAS and external validation confirmed the safety profile and significant upregulation (P = 0.03) of GLIPR1. Drug repositioning identified SOT-107, a Phase III protein therapy drug, as a potential interacting agent for GLIPR1.ConclusionsThis sc-eQTL MR study reveals immune cell-specific causal regulatory networks in PCOS. GLIPR1 in non-classical monocytes represents a high-confidence protective target, while XBP1 provides suggestive evidence for immune-mediated pathogenesis. The candidate drug SOT-107 highlights theoretical repositioning opportunities, though rigorous preclinical validation remains required.

Female↗

PubMed2025

From genetic causality to druggable targets: A multiomics framework identifies ZSCAN16 in gout pathogenesis.

ObjectiveGout is a prevalent form of inflammatory arthritis in which many patients respond suboptimally to current therapies. Drug development is hampered by a lack of genetically validated targets, leading to high clinical trial attrition. This study aimed to systematically identify and prioritize novel, druggable targets for gout via a multilayered genetic and functional genomics approach.MethodsWe performed two-sample Mendelian randomization (MR) using cis-expression quantitative trait locus (cis-eQTL) data and dual independent gout genome-wide association study (GWAS) cohorts (openGWAS and FinnGen). The candidate genes were subjected to a rigorous validation pipeline including Bayesian colocalization, phenome-wide association studies (PheWASs) to assess pleiotropy and on-target safety, and single-cell RNA sequencing (scRNA-seq) to delineate the cellular context. Molecular docking was used to evaluate the structural druggability of prioritized targets.ResultsMR analysis revealed 15 genes causally associated with gout. Colocalization analysis (PPH4 > 0.8) prioritized two targets: ZSCAN16 (risk-increasing, OR = 1.04, 95% CI [1.02-1.06]) and TRIM10 (protective, OR = 0.96, 95% CI [0.94-0.98]). Crucially, PheWAS revealed that ZSCAN16 is highly specific to gout, whereas TRIM10 exhibited extensive pleiotropy with hematological and cardiometabolic traits, indicating significant safety risks. Single-cell analysis provided orthogonal validation, demonstrating flare-specific upregulation of ZSCAN16 in cytotoxic T/NK cells. Molecular docking confirmed ZSCAN16 as a structurally druggable target, showing high-affinity binding with known compounds (e.g. digoxin, binding energy = -9.6 kcal/mol).ConclusionsOur study identifies ZSCAN16 as a high-potential, druggable therapeutic target for gout, highlighting its genetic influence on specific immune cell activities during acute flares. Conversely, TRIM10 was deprioritized owing to substantial pleiotropic liabilities and poor chemical tractability. These findings suggest that ZSCAN16 could play a crucial role in the pathogenesis of gout and may provide a valuable lead for future drug discovery efforts.

Humans↗

PubMed2026

Single-Cell Transcriptome-Wide Mendelian Randomization and Colocalization Uncover Potential Immunocytes-Related Therapeutic Targets for Obesity.

Weight-loss treatment is crucial for individuals with obesity to prevent various complications. The role of Immune cells in obesity has been recently recognized, whereas its translation into therapy requires identifying key target genes. We performed Mendelian randomization (MR) analysis to assess causal relationships between expression quantitative trait loci (eQTL) of 14 immune cells and obesity-related traits (obesity, body mass index and body fat percentage), and validated the results in colocalization analysis. For the putative causal genes identified by the MR and colocalization analyses, we conducted pathway enrichment, differential expressed gene (DEG) analysis and search of druggable evidence, and utilized a Tier system to prioritize drug targets for obesity. MR and colocalization evidence was observed for 1630 genes associated with one or more obesity-related traits, mainly expressed in CD4+ naive/central memory T cells and enriched in antigen processing and presentation pathways. Forty-one genes showed causal relationship with all three outcomes, among which 19 genes have not been reported for obesity previously. DEG analysis using single-cell RNA sequencing data of blood or adipose tissue indicated that the differential expression of UBE2Z in monocytes, ZCCHC7 in T cells, and FNBP4 in B cells between lean and obese individuals were consistent with the MR results. By searching drug-gene interaction databases, we found targeted drugs for PYGB and PRUNE1, and PYGB was the top gene ranked in the Tier system. This study provides evidence for the involvement of immune cells in obesity, and the potential cell-specific, immune-related targets for obesity treatment.

Obesity↗

PubMed2026

Integrated multi-omics strategies for identifying novel therapies in psoriasis.

MOTIVATION: Psoriasis is a chronic, immune-mediated disorder with an unmet need for effective treatments. To systematically prioritize therapeutic targets, we integrated proteome-wide Mendelian randomization (MR) with expression validation in blood/skin, genetic susceptibility analysis, differential gene expression (DGE) from bulk and single-cell RNA sequencing (scRNA-seq), colocalization, pathway enrichment, and protein-protein interaction analyses. RESULTS: Proteome-wide MR identified 29 candidate protein targets (Bonferroni-corrected), all replicated in independent datasets. Fifteen targets showed significant expression associations in blood or skin. Eleven proteins-UBLCP1, IL23A, ASF1A, RARRES2, ICAM1, PRSS53, ICAM5, GCA, IL2RA, DBI, and NFKB1-exhibited consistent directional effects with their genes. Genetic susceptibility analysis confirmed 20 target-specific polygenic scores for psoriasis and five for psoriatic arthritis. DGE analysis identified 13 targets in bulk and 13 in scRNA-seq-primarily in keratinocytes and immune cells-with IL2RA, COMP, and A2ML1 dysregulated across both. Colocalization analysis implicated shared causal variants for psoriasis in ASF1A, CD8A, CTF1, IL7R, MMP12, RARRES2, XCL2, DBI, IL23A, IL2RA, SGSH, and TIMD4. Enrichment analyses highlighted involvement in cytotoxicity, immune regulation, and JAK-STAT signaling. Eighteen targets interacted with approved anti-psoriasis drugs. Notably, drugs targeting IL2RA, IL7R, CTF1, ICAM1, MMP12, NFKB1, CD8A, DDX58, IL12A, SGSH, and FAP are approved or in trials for other diseases, suggesting repurposing potential. Our integrative multi-omics approach prioritized 29 high-confidence targets, including 13 novel candidates (RARRES2, ASF1A, CTF1, DBI, B3GNT2, CD8A, TIMD4, CRTAM, SGSH, XCL2, DAPK2, A2ML1, and FAP). Several high-priority targets-such as IL2RA, IL23, MMP12, RARRES2, IL7R, and ICAM1-were supported across analytical layers. These findings provide a robust foundation for psoriasis drug development. AVAILABILITY AND IMPLEMENTATION: The code used for the analyses in this manuscript has been archived in Zenodo at [DOI: 10.5281/zenodo.19692128].

Psoriasis↗

PubMed2026

Genetically Predicted Gene Expression and Circulating Metabolites Associated with Cervical High-Grade Squamous Intraepithelial Lesion: A Mendelian Randomization Study.

BACKGROUND: High-grade squamous intraepithelial lesion (HSIL) is a precancerous condition of the cervix. Identifying risk factors associated with HSIL and understanding their potential mechanisms may inform prevention strategies. This study aimed to investigate the associations of genetically predicted gene expression and circulating metabolites with HSIL risk using Mendelian randomization (MR). METHODS: We performed two-sample MR analysis to evaluate the associations of genetically predicted gene expression (eQTLGen consortium, N=31,684) and circulating metabolites (genome-wide association study [GWAS], N=8,299) with HSIL risk (FinnGen R12, N=293,218; 8,291 cases). Mediation analysis was conducted to explore whether metabolites might mediate the associations between genes and HSIL. Sensitivity analyses, including Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), leave-one-out, and colocalization, were performed to assess the robustness of the findings. All GWAS data used in this study were derived from European-ancestry populations. RESULTS: Eleven genes showed significant associations with HSIL after false discovery rate (FDR) correction (q<0.05), including VWA7, PAX8, GUSBP1, IKZF3, PAX8-AS1, NFKBIL1 (interpret with caution due to an influential single nucleotide polymorphism [SNP]), ERBB2, COL11A2, SKIV2L, TCF19, and PGAP3. Eleven circulating metabolites were also significantly associated with HSIL. Mediation analysis suggested that two phospholipid metabolites (GCST90200685 and GCST90200692) might mediate a small proportion of the total protective association of COL11A2 with HSIL (1.46% and 1.45%, respectively), indicating that the protective association of COL11A2 is largely independent of these circulating metabolites. Colocalization analysis showed strong evidence of shared causal variants for eight genes (PP.H4>0.98), while COL11A2 showed weak evidence of colocalization (PP.H4=1.58×10-15). Functional enrichment analysis indicated that COL11A2-related genes were enriched in extracellular matrix (ECM)-receptor interaction and PI3K-Akt signaling pathways. CONCLUSION: This MR study identified 11 genes and 11 circulating metabolites associated with HSIL risk. Among these, COL11A2 showed a protective association that appeared to be largely independent of circulating phospholipid metabolites, suggesting potential local mechanisms. These findings provide genetic and metabolic clues for future studies on HSIL etiology.

COL11A2↗

PubMed2026

Prioritizing Parkinson's disease risk-associated mitochondrial candidate genes via multi-omics integrative analysis.

BACKGROUND: Mitochondrial dysfunction has been implicated in Parkinson's disease (PD), but the genetically regulated mitochondrial genes associated with PD risk remain incompletely defined. METHODS: We conducted a summary-data-based genetic epidemiology study integrating summary-based Mendelian randomization (SMR), Heterogeneity in dependent instruments (HEIDI) filtering, and Bayesian colocalization to prioritize mitochondrial-related molecular features associated with PD risk. Mitochondrial-related genes were defined using MitoCarta3.0. Genetically predicted gene expression and plasma protein abundance were evaluated using expression quantitative trait loci (eQTL) data from eQTLGen and GTEx v8, and protein quantitative trait loci (pQTL) data was assessed using International Parkinson's Disease Genomics Consortium (IPDGC) as the discovery genome-wide association study (GWAS) and FinnGen as the replication dataset. Prespecified QTL analyses were interpreted using FDR correction, HEIDI filtering, and colocalization support. DNA methylation QTL analysis, mitochondrial phenotype MR, and single-nucleus RNA-seq analysis were performed as complementary analyses. RESULTS: In the primary eQTL analysis, higher genetically predicted TTC19 expression was associated with lower PD risk (OR = 0.80, 95% CI: 0.74-0.87, PPH4 = 0.80), whereas higher MALSU1 expression was associated with increased PD risk (OR = 2.21, 95% CI: 1.59-3.06, PPH4 = 0.96). Both associations survived FDR correction, passed HEIDI filtering, and showed colocalization support. GTEx whole-blood data supported the direction of the TTC19 association. No mitochondrial protein reached significance after FDR correction and colocalization filtering in the primary pQTL analysis. Complementary methylation analysis highlighted cg06270993 as an exploratory regulatory signal for MALSU1. CONCLUSIONS: This MR-colocalization study prioritizes TTC19 and MALSU1 as genetically supported mitochondrial-related candidate genes associated with PD risk. Further validation is required to define their functional roles in PD pathogenesis.

Humans↗

PubMed2026

Multi-omics integration and colocalization analyses prioritize candidate molecular loci associated with hypothermia.

BACKGROUND: Hypothermia is a life-threatening condition lacking specific pharmacological treatments. This study aimed to prioritize genetically supported molecular loci associated with hypothermia and to explore their pharmacological tractability using multi-omics data. METHODS: Initially, 2532 druggable genes were curated from the Drug-Gene Interaction Database and established literature. These were cross-referenced with cis-eQTL and cis-pQTL datasets, encompassing 870,655 and 114,281 SNPs for blood, respectively, alongside 2379 shared SNPs across adipose, skeletal muscle, and heart tissues. Matched instrumental variables were integrated with hypothermia GWAS summary statistics for two-sample Mendelian randomization (MR) and Bayesian colocalization. Transcriptomic differential expression analysis (DEA) was subsequently conducted as an exploratory analysis of cold-exposure-associated expression changes. Database-derived compound annotations were systematically re-evaluated according to target specificity, established pharmacological mechanism, and concordance with the direction of the MR estimates. RESULTS: Among 671 gene-level MR tests, 36 genes reached nominal significance, whereas only ABCC8 remained significant after FDR correction. Colocalization was evaluable for 8 of these 36 genes, and 4 loci (COL18A1, SLC1A7, ADIPOQ, and MERTK) met the prespecified PP.H4>0.90 threshold. The remaining 28 loci were not evaluable because sufficient overlapping regional variants were unavailable after harmonization. Transcriptomic analysis identified altered expression of SLC1A3 and SLCO4A1 under cold exposure, although these findings did not directly validate the colocalization-supported loci. Re-evaluation of database-derived compound annotations did not identify any direct, selective, and directionally concordant drug-repurposing candidate for hypothermia. CONCLUSIONS: COL18A1, SLC1A7, ADIPOQ, and MERTK showed colocalization support among the 8 evaluable nominal MR-associated loci. Because colocalization coverage was limited, these genes should be regarded as preliminary candidate loci rather than established therapeutic targets. The pharmacological annotations were indirect, non-selective, unsupported, or directionally inconsistent and should be interpreted solely as hypothesis-generating information.

Bayesian colocalization↗

PubMed2026

Shared genetic architecture of obesity and gastroesophageal reflux disease.

Obesity is identified as a risk factor of gastroesophageal reflux disease (GERD). This study aims to elucidate the shared genetic architecture of obesity-related phenotypes and GERD. Based on the publicly available genome-wide association studies' datasets, this genome-wide pleiotropic association study was conducted with various genetic approaches (including linkage disequilibrium score regression, high-definition likelihood inference for genetic correlations, pleiotropic analysis under composite null hypothesis, Functional Mapping and Annotation, Bayesian colocalization, summary-based Mendelian randomization, and multi-marker analysis of genomic annotation analysis) sequentially to unravel the genetic associations from single-nucleotide polymorphism to gene levels, and to reveal the underlying shared genetic architecture between obesity-related phenotypes and GERD. This study discovered shared genetic mechanisms between GERD and several obesity-related phenotypes, including arm fat percentage (left), arm fat percentage (right), leg fat percentage (left), leg fat percentage (right), trunk fat percentage, waist-to-hip ratio, and body mass index. Significant genetic correlations were observed by linkage disequilibrium score regression and high-definition likelihood inference for genetic correlations, with multiple associated pleiotropic loci and their mapped genes identified by pleiotropic analysis under composite null hypothesis, Functional Mapping and Annotation, Bayesian colocalization, summary-based Mendelian randomization, and multi-marker analysis of genomic annotation analysis. Additionally, several brain tissues were identified to be linked to both obesity and GERD by multi-marker analysis of genomic annotation. This research provided strong evidence of genetic correlations and brought novel insights into the underlying genetic connections and shared genetic architectures of obesity and GERD.

Humans↗

PubMed2026

Cell Type-Resolved Causal Inference and Spatial Transcriptomic Integration Reveal Immune-Specific Genetic Drivers of Autoimmune and Malignant Thyroid Disease.

BACKGROUND: Thyroid diseases, including autoimmune thyroid disease (AITD) and thyroid cancer, are characterized by immune dysregulation, yet the cell type-specific genetic mechanisms underlying these conditions remain poorly understood. Most genome-wide association studies (GWAS) have relied on bulk tissue expression quantitative trait loci (eQTL), which cannot resolve the heterogeneity of immune cell populations. METHODS: We performed two-sample Mendelian randomization (MR) analyses using single-cell cis-eQTLs from 14 immune cell subtypes (OneK1K cohort) as instrumental variables against GWAS summary statistics for four thyroid outcomes: autoimmune hyperthyroidism, autoimmune hypothyroidism, thyroid cancer and autoimmune thyroiditis. Causal associations were validated through Bayesian colocalization, phenome-wide association analysis (PheWAS) and multi-layered transcriptomic validation encompassing spatial transcriptomics of AITD tissue (GSE248205), bulk RNA-seq of thyroid cancer (GSE3678) and single-cell RNA-seq of thyroid tumours (GSE250521). gsMap spatial LD score regression was applied to map disease heritability onto spatial tissue architecture. RESULTS: We identified six Bonferroni-significant causal gene-cell type pairs for autoimmune hyperthyroidism, including protective effects of ABHD16A in naïve/immature B cells (OR = 0.440), HIST1H3H in CD8 NC T cells (OR = 0.324), HMGN4 in NK recruiting cells (OR = 0.556) and ZKSCAN4 in CD8 S100B T cells (OR = 0.427), with five pairs showing strong colocalization (PP.H4 ≥ 86%). Three pairs reached significance for autoimmune hypothyroidism, including a risk association of HLA-F in CD4 NC T cells (OR = 1.139). For autoimmune thyroiditis, FAM134B/RETREG1 showed consistent suggestive protective associations across both CD4 and CD8 NC T cells (PP.H4 ≥ 90% for both), suggesting a possible involvement of ER phagy regulation in thyroiditis susceptibility. Thyroid cancer showed a suggestive association with HLA-G in classical monocytes (OR = 1.899, PP.H4 = 53%). Spatial transcriptomic validation demonstrated progressive immune infiltration from control tissue to Graves' disease to Hashimoto's thyroiditis (7.7%-15.7%, 46.1%-54.1%, respectively) and strong spatial correlation between target gene expression and corresponding cell type enrichment (e.g., plasma cell-HLA-DQB1: r = 0.491, p < 10-300). HLA-G was independently validated in thyroid cancer bulk (log2fc = 0.542, p = 9.51 × 10-3, AUC = 0.857) and single-cell datasets. PheWAS revealed no significant associations detected for the core candidates. gsMap identified significant enrichment of autoimmune hypothyroidism heritability in gastrointestinal tract, adrenal gland and adipose tissue (all Bonferroni p < 0.002). CONCLUSIONS: This study establishes a multi-scale analytical framework integrating cell type-resolved genetic inference with spatial tissue validation, revealing distinct immunogenetic architectures underlying autoimmune versus malignant thyroid disease. Protective genetic programs in autoimmune hyperthyroidism converge on chromatin remodelling (HIST1H3H, HMGN4, ZKSCAN4) and lipid metabolism (ABHD16A) across lymphocyte subsets, whereas thyroid cancer risk involves immune escape mediated by HLA-G in myeloid cells. The ER-phagy receptor RETREG1 represents a candidate pathway warranting further investigation in autoimmune thyroiditis. These findings provide genetically supported, cell type-specific therapeutic targets and demonstrate a generalizable strategy for dissecting the immune-mediated mechanisms of complex thyroid diseases.

Mendelian randomization↗

PubMed2026

Biochemical analysis of the TPS-b subfamily reveals a cineole-centered monoterpene biosynthetic module in Medicago truncatula.

Terpenoids constitute one of the largest and most structurally diverse classes of plant specialized metabolites, with diversity generated by terpene synthases (TPSs) and downstream tailoring enzymes. In Medicago truncatula, the TPS-b subfamily comprises five putative synthases, two of which are embedded within a previously uncharacterized genomic locus containing a cytochrome P450 (CYP) and a BAHD-type acyltransferase. Here, we present a comprehensive biochemical analysis of the M. truncatula TPS-b subfamily and define a cineole-centered monoterpene biosynthetic module. Heterologous expression and in vitro assays with multiple prenyl diphosphate substrates revealed three catalytically active TPS-b enzymes with distinct substrate preferences and product profiles. MtTPS4 functions as a dedicated (E)-β-ocimene synthase, whereas MtTPS15 exhibits substrate-dependent bifunctionality, producing (E)-β-ocimene from geranyl diphosphate and α-farnesene from farnesyl diphosphate. MtTPS36 generates 1,8-cineole as the predominant product alongside α-terpineol from geranyl and neryl diphosphate. Genome analysis revealed that MtTPS36 is colocalized with a cytochrome P450 belonging to the CYP736 family. Biochemical characterization of this CYP identified a previously undescribed plant cineole hydroxylase that catalyzes oxidation of 1,8-cineole to yield 2α-hydroxy-1,8-cineole (also known as 2-exo-hydroxy-1,8-cineole), establishing a TPS-CYP biosynthetic module. These results define the gene-to-metabolite relationships within the TPS-b subfamily in M. truncatula and expand the known enzymatic biosynthetic capacity underlying oxygenated monoterpene biosynthesis in plants.

1,8-Cineole↗

PubMed2026

Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterized by clinical and genetic heterogeneity. Increasing use of genomics and other omics approaches has driven reliance on publicly available AF datasets to advance biological discovery. Thus, this systematic review aimed to identify freely available genomic AF datasets through Mendeley Data and its interconnected repositories, and to characterize the most common analyses performed on these data. The search was conducted in adherence to the PRISMA 2020 guideline. Nineteen freely available genomic AF datasets were identified: Summary statistics for 'Biobank-driven genomic discovery yields new insight into atrial fibrillation biology', hum0014.v8.58qt.v1, AF GWAS in UK Biobank, UK Biobank (Publication 9659), GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis, GSE115574, GSE128188, GSE14975, GSE2240, GSE238242, GSE254133, GSE261170, GSE271748, GSE271839, GSE293813, GSE294456, GSE31821, GSE41177, and GSE79768. The GEO datasets were further examined using differential gene expression, functional enrichment, protein-protein interaction networks, hub gene analysis, microRNA target prediction, and gene clustering, as well as, for the more recently deposited datasets, eQTL colocalization, single-cell/single-nucleus clustering, cell-cell communication analysis, and gene-dosage-dependent transcriptional and electrophysiological profiling. These analyses show some consistency but also considerable heterogeneity in initial conditions, data normalization, and analytical methodological settings. In conclusion, only a limited number of datasets are freely available, so additional, well-characterized and standardized datasets are needed to provide a complete picture of the AF pathology.

Mendeley Data↗

PubMed2025

Genetic pleiotropy underlying obesity and autoimmune disorders: a large-scale cross-trait gwas analysis in European ancestry populations.

BACKGROUND: Obesity and autoimmune disorders represent a significant comorbidity burden, yet their shared genetic architecture is not fully understood. Elucidating the pleiotropic genetic basis underlying both conditions is crucial for unraveling the mechanisms driving their co-occurrence and advancing therapeutic strategies. METHODS: We conducted a large-scale cross-trait analysis integrating genome-wide association study (GWAS) summary data for obesity and 17 autoimmune diseases. Genetic correlations were assessed using LD score regression and high-definition likelihood. Cross-trait pleiotropic analysis was performed using Stratified Pleiotropic Locus Mapping (PLACO) to identify shared loci, followed by Bayesian colocalization to confirm shared causal variants. Gene-level and tissue-specific heritability analyses were conducted, and drug targets were prioritized via summary-based Mendelian randomization (SMR). Finally, immune co-localization and bidirectional Mendelian randomization were employed to elucidate immunological mechanisms and causal relationships. RESULTS: Our analysis identified eight autoimmune diseases with significant genetic correlations to obesity. We discovered 10,324 pleiotropic SNPs, which mapped to 52 independent risk loci, with nine loci confirmed as shared causal variants by colocalization. Gene-level analysis revealed 133 unique pleiotropic genes, including CLN3, SH2B1, and MMEL1, enriched in pathways of hematopoietic cell differentiation and immune homeostasis. Tissue-specific heritability was most prominent in the spleen, whole blood, and EBV-transformed lymphocytes. Immuno-co-localization implicated six IgD+ CD38- %B cell-related traits as key pathological conduits. Bidirectional Mendelian randomization established a causal role of obesity in hypothyroidism, psoriasis, and multiple sclerosis, while revealing an inverse causal association of type 1 diabetes with obesity risk. CONCLUSIONS: This study demonstrates a robust shared genetic foundation between obesity and multiple autoimmune diseases, pinpointing specific pleiotropic loci, genes, and immune cell subsets. Our findings provide a mechanistic framework for their comorbidity and highlight potential targets for therapeutic intervention.

Humans↗

PubMed2026

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans↗

PubMed2025

Identifying canopy wilting QTLs and evaluating remote sensing approaches for selecting drought-tolerant soybean.

Drought is the most damaging abiotic stress for soybean yield; cultivars with improved drought tolerance are needed to sustain and increase crop production. PI 603535 previously was identified as an ultra-slow canopy wilting (CW) line in a genome-wide association study but the quantitative trait loci (QTLs) underlying this phenotype have not been determined. In this study, a recombinant inbred line (RIL) population derived from Benning × PI 603535 was evaluated for three years under rain-fed conditions. CW was rated following extended periods of drought when CW variation was present. Aerial multispectral and thermal imagery was also captured in conjunction with visual ratings to explore the feasibility of implementing remote sensing to improve the efficiency and objectivity of drought evaluations. The normalized difference vegetation index (NDVI) and green-based NDVI (GNDVI) exhibited strong, significant correlations (|r|= 0.42-0.44) with CW across years. CW scores and the remote sensing traits were used as phenotypes for QTL mapping. Seven CW QTLs were identified across six chromosomes in the combined analysis, with NDVI and GNDVI QTLs generally colocalizing with the CW QTLs with the highest percentage of variation explained (PVE). The QTLs were not consistently identified among individual years, highlighting the complex genetics and gene expression of drought tolerance. The instability and low additive effect estimates of individual QTLs imply challenges of improving drought tolerance through the selection of a few QTLs. However, the slow CW RILs developed in this study can serve as valuable breeding stocks for future drought improvement breeding efforts and genetic studies.

Quantitative Trait Loci↗

PubMed2026

GWAS Meta-analysis Identifies Novel Associated Loci and Points to Causal Tissues in Central Serous Chorioretinopathy.

OBJECTIVE: To define CSC genetic architecture and identify implicated ocular tissues, cell types, genes, and circulating proteins. DATA SOURCES: Genome-wide data were assembled from FinnGen, All of Us, Mass General Brigham Biobank, Million Veteran Program, and a Dutch chronic CSC cohort. Serum protein quantitative trait loci, human single-cell ocular atlases, and UK Biobank macular optical coherence tomography (OCT) imaging were used for downstream analyses. STUDY SELECTION: Five European-ancestry cohorts with genome-wide data and cohort-specific CSC case-control definitions were included, comprising 2,584 cases and 1,044,455 controls. Variants present in at least 2 cohorts were meta-analyzed. DATA EXTRACTION AND SYNTHESIS: Cohort-level GWASs were adjusted for age, age squared, sex, genotyping array or batch, and 10 genetic principal components, then combined using fixed-effects inverse-variance meta-analysis. Post-GWAS analyses included gene prioritization, colocalization, Mendelian randomization, single-cell disease-relevance scoring, and testing of a CSC genetic risk score in UK Biobank OCT images. MAIN OUTCOMES AND MEASURES: Genome-wide significant CSC loci, effector genes and proteins, tissue and cell-type enrichment, and CSC-relevant OCT abnormalities. RESULTS: Across 11,068,938 variants, 10 loci reached genome-wide significance (P < 5 × 10-8), including 3 novel loci near TGFB1, LINC00551, and LOC105375630 and 7 replicated loci near CFH, CD46, NOTCH4, PREX1, PTPRB, GATA5, and TNFRSF10A. Integrative analyses prioritized 10 candidate effector genes. Colocalization and Mendelian randomization implicated circulating TNFRSF10A, TGFB1, and CASP10 levels. Single-cell analyses localized genetic risk to sclera (P = 2.0 × 10-4) and vascular endothelial cells (P = 4.0 × 10-4), with fibroblast enrichment. In UK Biobank, OCT abnormalities were more frequent in the top vs bottom 1% of CSC genetic risk (18 of 109 [16.5%] vs 8 of 134 [6.0%]; odds ratio, 4.05; 95% CI, 1.65-10.87; P = .002). CONCLUSIONS AND RELEVANCE: In this GWAS meta-analysis, CSC susceptibility localized predominantly to scleral and vascular biology rather than primary retinal pigment epithelial dysfunction. These findings support CSC as a sclerovascular disorder and nominate complement regulation, endothelial signaling, and extracellular matrix pathways for future study.

Journal Article↗

PubMed2026

Comparative genomic analysis of Streptococcus parasuis and Streptococcus suis reveals mobile element-associated enrichment of antimicrobial resistance and lack of detectable same-MGE colocalization with virulence-associated genes within stable species boundaries.

Streptococcus suis is a major porcine pathogen and a zoonotic agent that causes meningitis and septicemia in humans. Streptococcus parasuis, a recently recognized close relative, remains poorly characterized with regard to its clinical significance and genomic features. In this study, we generated a single-contig closed genome assembly with genome-wide DNA methylation profiles for S. parasuis strain A1, isolated from a diseased pig in Xinjiang, China, and complemented in silico genomic predictions with isolate-level experimental validation of antimicrobial resistance (AMR) genotypes, virulence genotypes, and phenotypic susceptibility for this reference strain. Using this high-quality genome as a reference anchor, we performed comparative genomic analyses across 195 streptococcal genomes, comprising 15 S. parasuis and 180 S. suis strains, to distinguish genome-level co-occurrence of resistance and virulence determinants from their physical colocalization on the same mobile genetic element (MGE).Species boundaries remained clearly delineated at the genomic level, with a median interspecies average nucleotide identity (ANI) of approximately 86.0%, compared with intraspecies ANI medians of 97.5% for S. parasuis and 96.2% for S. suis. Pangenome analysis identified 12,693 gene clusters, of which 1086 were core clusters, and functional annotation revealed significant differences in accessory gene repertoires between the two species. Within this stable genomic framework, S. parasuis genomes carried a higher AMR gene burden; strain A1 harbored 10 AMR genes, multiple virulence-associated genes, three genomic islands, and eight prophage regions. For strain A1, PCR validation confirmed six AMR genes and six virulence genes, and disk diffusion testing demonstrated a multidrug-resistant phenotype consistent with the genotypic profile.Among 235 predicted mobile elements, 19 harbored AMR genes and seven carried Virulence Factor Database (VFDB) homologs, but none carried both categories simultaneously. This finding reflects a lack of detectable same-MGE colocalization under the applied annotation and assembly framework; it should not be interpreted as evidence of biological physical decoupling. Under a random-placement model, the expected number of co-carrying regions was only 0.57, and the probability of observing zero co-carrying regions was P = 0.55. This negative result should be interpreted with caution, given the limited number of cargo-bearing regions and the predominantly draft status of most genomes. Furthermore, the A1 genome contained multiple restriction-modification systems, showed depletion of several methylation motif families in mobile regions, and had limited CRISPR spacer matching evidence, suggesting prior exposure to the relevant sequence space. None of the genomes met our predefined criteria for whole-genome convergence.Collectively, our results support a model in which S. parasuis accumulates AMR-related genes in a modular fashion via mobile elements within stable species boundaries, with no detectable same-MGE colocalization of AMR and virulence determinants under our analytical pipeline. These findings imply that AMR surveillance strategies for this species should prioritize tracking mobile genetic elements rather than inferring wholesale genomic convergence toward S. suis.

Streptococcus suis↗

PubMed2026

Shared genetic architecture of smoking dependence and Crohn's disease: A cross-trait analysis of GWAS summary statistics.

INTRODUCTION: Smoking dependence (SD) and Crohn's disease (CD) are epidemiologically associated, but whether this relationship reflects shared genetic susceptibility remains unclear. METHODS: We conducted a cross-trait genetic analysis of SD and CD using publicly available genome-wide association study (GWAS) summary statistics from European-ancestry populations. Genome-wide genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Pleiotropic variants were identified using PLACO and mapped to genomic loci using FUMA. Regional signal sharing was assessed by Bayesian colocalization. Functional analyses included stratified LDSC, Multi-marker Analysis of GenoMic Annotation (MAGMA), GTEx tissue analysis, and Metascape. Expression-linked candidate genes were prioritized using expression quantitative trait locus (eQTL)-based summary-data-based Mendelian randomization (SMR) with heterogeneity in dependent instruments (HEIDI) testing. Genetically informed spatial mapping of cells for complex traits (gsMap) was used for spatial mapping. RESULTS: SD and CD showed positive genetic correlation by LDSC (rg=0.2090, p=0.0008) and HDL (rg=0.3817, p=0.00106). PLACO identified 81 genome-wide significant pleiotropic SNPs, which were mapped by FUMA to three loci at 1p31.3, 5p13.1, and 12q12, represented by rs11209031, rs1395152, and rs17467116, respectively. MAGMA identified 22 FDR-significant genes, four of which remained Bonferroni significant: LRRK2, TNFRSF6B, ZGPAT, and RP4-583P15.15. Cross-trait tissue analysis showed significant enrichment of the shared genetic signal in whole blood and small intestine, while gene-set analysis highlighted inflammatory response (pbon=1.86×10-5) and T-helper 17 cell differentiation (pbon=7.37×10-4). SMR/HEIDI analysis further prioritized RPS6KB1 as a shared expression-linked candidate. Spatial mapping revealed a prominent signal in the embryonic gastrointestinal tract and gene-specific regional patterns involving LRRK2 and SLC2A13 in the adult mouse brain. CONCLUSIONS: SD and CD showed measurable shared genetic susceptibility, with convergent evidence from pleiotropic loci, immune-inflammatory pathway enrichment, tissue-level associations, and spatial transcriptomic mapping.

Crohn's disease↗

PubMed2025

Integrative proteomic analysis provides novel therapeutic insights for etiological subtypes of diabetes.

AIMS: Type 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics. MATERIALS AND METHODS: Summary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malmö Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential. RESULTS: Genetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets. CONCLUSIONS: Our study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.

Humans↗