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PubMed2025

Saturating the eQTL map in Drosophila: Genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populations.

Most genetic polymorphisms associated with complex traits are found in non-coding regions of the genome. Characterizing their effect presents a formidable challenge, and expression quantitative trait locus (eQTLs) mapping has been a key approach to do so. As comprehensive eQTL maps are available only for a few species, here we developed the Drosophila outbred synthetic population (Dros-OSP) and used it to characterize the landscape of transcriptional regulation in Drosophila melanogaster. We collected head and body transcriptomes and genomes from 1,286 outbred flies and mapped local and distant eQTLs for 98% of the genes. We characterized the network organization of the transcriptome across tissues and described the properties of local and distal eQTLs in terms of genetic diversity, heritability, connectivity, and pleiotropy. These results provide new insights into the genetic basis of transcriptional regulation in the fruit fly and offer a new mapping resource that will expand the possibilities currently available for the Drosophila community.

Animals↗

PubMed2026

Exploring genomic regions regulating the liver transcriptome and energy homeostasis in pigs.

In pigs, energy homeostasis has an impact on meat quality and health. In a Duroc pig population, 30 quantitative trait locus (QTL) regions associated with fatty acid (FA) composition in adipose tissue, plasma, liver and muscle were previously identified. Mapping of expression quantitative trait locus (eQTL) regions will provide a molecular hypothesis for genotype-phenotype interactions and may allow the identification of shared causal variants, key to increasing our understanding of the genetic regulation of FA composition and energy homeostasis. However, gene expression is impacted by environmental factors, while individual-level allelic imbalance (AI) can be more reliable and can be surveyed via allelic-specific expression (ASE) analysis. Furthermore, treatment of ASE as a quantitative trait allows the identification of allele-specific expression quantitative trait loci (aseQTLs), which are variants whose heterozygosity is linked to the AI of a nearby single-nucleotide polymorphism (SNP), pointing to regulatory elements. In this study, liver was selected as a key metabolic hub with an important role in the regulation of energy homeostasis, and 310 liver RNA sequencing samples were analysed using a combination of (1) eQTL mapping, (2) ASE analysis, and (3) aseQTL mapping methods. A total of 2 188 eQTL regions were identified, mostly cis-eQTL regions (73.17%). ASE analysis reported 1 964 ASE SNPs, associated with 633 genes. Finally, aseQTL mapping reported 64 172 aseQTL, associated with the AI of 31 genes. Colocalisation analysis combined with ASE analysis showed that the expression of FADS1 and FADS2 genes is associated with the polyunsaturated FA composition in several tissues, where microRNA regulation may be present. Finally, in the DGAT2 gene, annotated in a QTL region associated with multiple FAs in adipose tissue, ASE revealed allelic imbalance in the 3' untranslated region (UTR) of this gene. Allelic differential expression can be caused by a 13-bp insertion affecting messenger RNA stability, previously described, exemplifying how allelic imbalance is caused by a post-transcriptional regulatory mechanism undetectable by eQTL mapping. Furthermore, aseQTLs were associated with this gene, linked to a previously identified copy number variant not yet associated with DGAT2 expression. These results demonstrate how ASE analysis and aseQTL mapping can complement eQTL mapping, as they resolved a complex region affected by allelic heterogeneity, a main confounding effect of QTL mapping. In conclusion, the combination of eQTL mapping, ASE analysis and aseQTL mapping allowed the characterisation of the regulation of liver gene expression, improving our understanding of the genetic determinism of energy homeostasis.

Allele-specific expression↗

PubMed2025

Integrative omics of the genetic basis for wheat WUE and drought resilience reveal the function of TaMYB7-A1.

Improving wheat drought resilience and water use efficiency (WUE) is critical for sustaining productivity under increasing water scarcity. Here, we integrate genome-wide association study (GWAS), expression quantitative trait locus (eQTL) mapping, population-transcriptome analysis, and summary-data-based mendelian randomization (SMR), followed by functional validation using indexed EMS mutants and transgenic lines, to systematically identify key WUE regulators. GWAS across water conditions in 228 accessions identifies 73 quantitative trait loci (QTLs) for WUE-traits. Transcriptome profiling of 110 diverse accessions reveals 28 drought-responsive modules. eQTL mapping uncovers 146,966 regulatory variants, including condition-specific hotspots associated with key drought-related pathways. Integrative analysis underscores 85 high-confidence candidate genes, notably TaMYB7-A1. Overexpression of TaMYB7-A1 enhances photosynthesis, WUE, root development, and grain yield under drought condition by activating TaPIP2;2-B1 (water transport), TaRD20-D1 (stomatal regulation), and TaABCB4-B1 (root growth), reflecting reduced water loss and improved physiological resilience. Our study presents a comprehensive regulatory map and robust targets for wheat drought adaptation and resilient cultivar breeding.

Triticum↗

PubMed2025

Exploring genetic mapping and co-expression patterns to illuminate significance of Tbx20 in cardiac biology.

The transcription factor Tbx20 is integral to heart development and plays a significant role in various cardiac diseases. Despite its established importance, the regulatory mechanisms and functional significance of Tbx20 remain incompletely understood. To elucidate these mechanisms, we initially conducted eQTL mapping to identify genetic loci associated with Tbx20 expression in heart tissue from BXD mice. Co-expression and enrichment analyses revealed pathways linked to Tbx20, including dilated cardiomyopathy, hypertrophic cardiomyopathy, and FoxO signaling. Additionally, protein-protein interaction studies identified essential cardiac proteins, such as Myl2 and Myl7, along with upstream regulators like Mef2c. To validate our bioinformatic findings, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to assess the relative mRNA expression levels of TBX20 and Mef2c in the heart tissues of BXD mice compared to their parental strains (B6 and D2). Our results demonstrated significant up-regulation of both TBX20 and Mef2c in the BXD group relative to the parental strains. Conversely, both genes were down-regulated in B6, D2, Control, and Treatment groups when compared to BXD mice. These findings confirm the predicted regulatory roles of TBX20 and Mef2c in cardiac development as suggested by our initial analyses.This study not only reinforces the critical role of Tbx20 in cardiac gene regulation but also highlights its potential as a therapeutic target for cardiovascular disorders. Further investigations into Tbx20 and its interactions will enhance our understanding of heart biology and contribute to the development of targeted therapies for heart diseases.

Animals↗

PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed2026

Integrating Genetics and Environment to Find Causal Mechanisms for Multiple Sclerosis.

Genome-wide association studies (GWAS) have identified hundreds of risk loci for multiple sclerosis (MS), but we have limited knowledge of the mechanisms through which genetic variants mediate risk. Similarly, epidemiological studies implicate numerous environmental risk factors in MS risk, but these cannot identify specific causal mechanisms. We review our current knowledge of genetic mechanisms in MS, including the critical role of expression quantitative trait locus (eQTL) mapping in translating genetic risk loci into causal mechanisms. Molecular and functional context has emerged as an important missing component of these studies, and we discuss how environmental risk factors can be modelled in a quantitative genetic context to identify disease mechanisms. In parallel, we highlight recent advances in which quantitative genetic methods establish a causal role for low vitamin D and obesity in MS, and to dissect the mechanisms through which these operate. As genetic, transcriptional, and epigenetic studies continue to expand, further mechanistic insights for MS are likely to come from the integration of genetic and environmental data.

Humans↗

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

Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.

MOTIVATION: Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. RESULTS: We present a new approach, MetaFR, to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. AVAILABILITY AND IMPLEMENTATION: MetaFR is available under https://github.com/SchulzLab/MetaFR.

Single-Cell Analysis↗

PubMed2026

SwinePan for pig graph-based pangenome and multiomics data mining.

Pigs are one of the most important livestock species worldwide. Although multiple high-quality reference genomes exist, reliance on a single linear reference limits the detection of structural variants (SVs) and the characterization of population-specific genetic diversity. To address this limitation, we developed SwinePan, a comprehensive and integrated multiomics database for pigs built on a graph-based pangenome framework. SwinePan incorporates a variome derived from the graph-based pangenome, covering 2,598 individuals across 35 breeds, including 185,759 SVs, 117 million SNPs, and 6.8 million indels. The database also integrates transcriptomic data from liver, loin muscle, abdominal fat, and backfat, along with over 150,000 phenotypic records. The online toolkit deployed in SwinePan enables genome-wide association studies (GWAS), expression quantitative trait locus (eQTL) mapping, and colocalization, while interactive modules visualize population structure and multiomics associations, streamlining candidate gene and variant exploration. Additionally, two proof-of-concept analyses demonstrate how SwinePan pinpoints trait-associated loci and deciphers their potential regulatory mechanisms.

Journal Article↗

PubMed2025

New insights into genetic comorbidity mechanisms: type 2 diabetes and primary open-angle glaucoma.

AIMS: To investigate the shared genetic mechanisms between type 2 diabetes (T2D) and primary open-angle glaucoma (POAG). Using large-scale genome-wide association study (GWAS) data, we performed single nucleotide polymorphism (SNP) level analysis to detect pleiotropic variants and loci, paired eQTL mapping analysis and gene-level analysis to identify candidate pleiotropic genes. In addition, Mendelian randomisation (MR) analysis was performed to assess causal associations. MATERIALS AND METHODS: We used POAG GWAS data from Finngen (9565 cases and 430 250 controls) and T2D GWAS data from 55 555 European ancestry samples. We used Linkage Disequilibrium SCore (LDSC) regression to assess the genetic association between T2D and POAG and further used PLeiotropic Analysis under the COmposite null hypothesis (PLACO) to identify shared genetic variants between paired traits. Finally, we further used MR analysis to explore the causal association between T2D and POAG at the genetic level. RESULTS: The LDSC results and MR analysis revealed that the T2D effect was significantly higher than that of the POAG (OR=1.09, 95% CI 1.03 to 1.14, p=1.50×10-3). The PLACO property analysis determined that the T2D sum POAG shared 178 individual SNPs, separate localisation of 79 individual causes. The five most popular choices are based on the effectiveness of CCND2, SVEP1, ST6GAL1, TCF7L2 and HMGA2. expression quantitative trait loci mapping further revealed 36 genes with regulatory roles in optic nerve-related brain tissues. Functional enrichment analyses indicated that these pleiotropic genes are involved in neurodevelopmental, neuroprotective and metabolic pathways, with tissue-specific enrichment observed in neural, pancreatic, adipose and retinal tissues. It is possible to present the main comorbid mechanisms of T2D and POAG. CONCLUSIONS: Our study provides new insights into the aetiology and pathogenesis of T2D and POAG at the genetic level.

Humans↗

PubMed2026

Multi-omic characterization of the Hispanic/Latino blood lipidome reveals an additional locus and attenuated genetic prediction.

While lipids have been extensively investigated, genetic regulation of the circulating lipidome in diverse populations remains poorly understood. We conducted a lipidome-wide genome-wide association study (GWAS) of 830 lipid species in 2,287 Hispanic/Latino participants and performed predictive modeling across omics layers. We identified 7,593 genome-wide significant SNPs mapping to 208 genes. Conditional analysis disentangled the long-range linkage disequilibrium artifacts from the pleiotropic FADS1/2/3 cluster. Separately, we discovered an association at the GPLD1 locus for a circulating ceramide. Colocalization revealed shared genetic architecture with conventional lipids alongside distinct, species-specific pathways. Incorporating Native/Indigenous American expression quantitative trait loci (eQTLs) within a multi-omic framework uncovered 62 likely regulatory genes missed by European-centric gene expression models. Finally, genetically regulated predictive models demonstrated performance declining from transcriptomics to proteomics to lipidomics, reflecting increased distance from gene action along the molecular cascade. Our study provides a genetic landscape of lipid metabolism in a highly burdened population and highlights the challenges in predicting lipid abundance.

Hispanic/Latino population↗

PubMed2026

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying intensified care. The Esketamine multi-omic biomarker evaluation in MDD (EMBER-MDD) is a non-interventional, investigator-initiated, in-vitro study within the EU Psych-STRATA programme, analyzing biospecimens collected in the randomized INTENSIFY study and the mirror OBS-TR cohort after participants complete treatment. EMBER-MDD aims to discover individual-omic and integrated multi-omic (hypothesis-free) biomarkers and signatures associated with TR risk, and molecular correlates of clinical response to esketamine nasal spray versus treatment as usual (TAU). Biomaterials will derive from approximately 420 adults with MDD (estimated n = 210 esketamine; n = 210 TAU) and include whole blood, RNA-stabilized whole blood, plasma and serum, sampled at baseline and, when feasible, during and after treatment (up to ~ 5,040 aliquots stored at - 80 °C). Genomics will use baseline DNA genotyping on Illumina Infinium GSA v3.0+MD arrays; epigenomics will profile genome-wide DNA methylation across time points using MethylationEPIC v2.0; transcriptomics will employ mRNA-seq (NovaSeq X/ X Plus); and proteomics/ metabolomics will be generated using high-throughput Olink and/ or Biocrates platforms. Each layer will undergo state-of-the-art preprocessing and analyses (e.g., GWAS/ PRS, EWAS, differential expression, WGCNA, pathway and network analyses), followed by integrative strategies including QTL mapping (meQTL/ eQTL/ pQTL/ mQTL) and intermediate-fusion machine learning with nested cross-validation, explainable AI (SHAP/ LIME) and treatment-effect modelling. All outputs are research-only and will not support individual efficacy, tolerability, or clinical decision-making. The study will deliver robust biosignatures and mechanistic hypotheses to guide future validation and inform stratified, molecularly guided intervention strategies in subsequent prospective trials. Trial registration number: 2023-506617-21-00 and 2025-178-f-S.

Humans↗

PubMed2026

Mapping the regulatory architecture of circadian clock adaptation: A genome-wide eQTL analysis in Drosophila melanogaster.

The circadian clock enables organisms to align internal daily rhythms with environmental cues, with major consequences for survival and fitness. Although the molecular framework of this system in Drosophila melanogaster is well characterized through transcription translation feedback loops involving ten core clock genes, the genetic basis of natural variation in their expression remains poorly understood. Here, we used natural expression variation to identify expression quantitative trait loci (eQTLs) through genome-wide association mapping. Using the Drosophila Genetic Reference Panel, we measured relative expression of all core clock genes at a single time point two hours after light onset. We identified 109 significant SNPs and 28 indels associated with expression variation across the clock network. Expression levels varied widely, with Pdp1ε showing the greatest variation (an 86-fold difference between extreme lines) and cyc the least (11.3-fold). Only three significant SNPs were located within clock genes themselves, all in Clk, whereas most associations represented trans-eQTLs in genes with diverse molecular functions. Candidate regulators included transcription factors such as Abd-B, tai, and E5; RNA binding proteins including Pum, Bru-3, and Mbl; and several long noncoding and antisense RNAs. Variants were also detected in gbb and the BMP pathway transcription factor Mad. Consistent with this, Mad knockdown reduced vri expression. Together, these results reveal a complex regulatory architecture underlying natural variation in circadian gene expression.

Journal Article↗

PubMed2025

Endogenous fine-mapping and prioritization of functional regulatory elements in complex genetic loci.

Most genetic loci linked to polygenic traits are in non-coding regions, with complex regulation and linkage disequilibrium (LD), complicating causal variant and gene prioritization. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait loci (eQTLs) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one-third of the causal CREs lack classical epigenetic markers prior to perturbation, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms underlying molecular trait regulation and disease development.

Humans↗

PubMed2026

Genetic dissection of cardiac iron regulation using transcriptome network analysis and systems genetics in BXD mice.

Cardiac iron homeostasis is essential for myocardial energy metabolism and contractile function, yet the genetic and molecular mechanisms governing iron levels within the heart remain poorly understood. We used a systems genetics approach to dissect the transcriptional regulation of cardiac iron homeostasis. Myocardial iron level varies substantially across BXD strains (40-112 μg/g) and is under heritable genetic control (H2 = 0.38). Elevated cardiac iron is associated with reduced ventricular mass, increased ventricular ectopy, and prolonged atrioventricular conduction in the BXD population. Weighted gene co-expression network analysis of the BXD heart transcriptome identified a co-expression module that was significantly and negatively correlated with cardiac iron levels in both young and old BXD mice and enriched for pathways related to metabolic regulation, cyclic AMP (cAMP) signaling, circadian entrainment, and cardiovascular physiology. The module showed substantial overlap with a curated cardiac iron gene set, and cross-species enrichment analysis confirmed its conservation in human cardiomyopathy differentially expressed genes (enrichment ratio = 1.49; false discovery rate [FDR] = 0.0342). Quantitative trait locus (QTL) mapping of the first principal component of the overlapping module iron genes (n = 38), corroborated by individual gene mapping, identified trans-eQTL hotspots on multiple chromosomes, implicating Fcho2, Gcc2, and Rmdn1 as candidate upstream regulators operating through sequential steps of intracellular iron trafficking. Together, these findings establish a systems-level map of cardiac iron gene regulation, identify candidate genetic regulators, and provide a molecular framework linking disruption of iron-related transcriptional networks to structural and electrical cardiac dysfunction with implications for iron-related heart diseases.

BXD mouse population↗

PubMed2017

Functional mapping and annotation of genetic associations with FUMA.

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Chromatin↗

PubMed2026

Multi-omics Mendelian Randomization Prioritizes Neutrophil Extracellular Trap-related Genes Associated with Atrial Fibrillation Risk.

BACKGROUND: Neutrophil extracellular traps (NETs) participate in thrombosis, inflammation, and cardiovascular remodeling, yet whether NET-related genes (NRGs) are associated with atrial fibrillation (AF) risk across multiple molecular layers remains unclear. This study used a multiomics Mendelian randomization framework to prioritize NRGs supported by methylation, expression, and protein quantitative trait loci (QTL) data. METHODS: Genome-wide significant cis instruments (P < 5 × 10-8) were obtained for 90 methylation QTLs (mQTLs), 100 expression QTLs (eQTLs), and 38 protein QTLs (pQTLs) mapped to 137 literature- curated NRG entries. Summary-data-based Mendelian randomization (SMR) coupled with the heterogeneity in dependent instruments (HEIDI) test was applied using whole-blood mQTL data (n = 1,980), eQTLGen blood eQTL data (n = 31,684), and deCODE plasma pQTL data (n = 35,559). AF outcome data were obtained from a meta-analysis including 60,620 cases and 970,216 controls of European ancestry. RESULTS: At the methylation level, 21 CpG-feature associations across 13 genes remained significant after HEIDI filtering and false discovery rate (FDR) correction. Expression-level analysis identified eight significant gene-AF associations, whereas protein-level analysis identified seven significant features representing five unique proteins. Cross-omics integration prioritized C3, MAPK3, and STAT3 as Tier 1 genes, CTSC, LPAR3, and THBD as Tier 2 genes, and fourteen additional genes as Tier 3 candidates. C3 showed risk-increasing protein-level associations together with multiple significant CpG signals, whereas MAPK3 and STAT3 showed directionally protective expression/protein or methylation/protein patterns. DISCUSSION: The cross-omics convergence on C3, MAPK3, and STAT3 is consistent with complement activation, immune-fibrotic signaling, and cytokine-regulatory pathways implicated in AF biology, but the findings should be interpreted as genetic prioritization rather than definitive intervention-ready causality. CpG-level heterogeneity at the C3 locus and the blood/plasma origin of the QTL resources further support a cautious interpretation. Modest colocalization support and the unresolved possibility of pQTL sample overlap further support this cautious, hypothesis-generating interpretation. CONCLUSION: Multi-omics SMR prioritizes C3, MAPK3, and STAT3 as the most consistently supported NET-related genes associated with AF risk. These findings provide a framework for atrialtissue replication and mechanistic validation of NET-related pathways in AF.

Atrial fibrillation↗