A multi-omics and mediation-based genetic screening approach identifies STX4 as a key link between epigenetic regulation, immune cells, and childhood asthma

Abstract

Background

Childhood asthma presents a multifaceted immune-driven pathology shaped by genetic, epigenetic, and immune regulatory interactions. Despite extensive genome-wide analyses pinpointing multiple susceptibility loci, the precise functional contributors to asthma pathogenesis remain elusive. This study employs a comprehensive multi-omics framework and Mendelian randomization (MR) analysis to systematically identify and validate key genetic determinants implicated in childhood asthma.

Methods

A genome-wide screening of over 19,000 human genes was performed to identify cis-eQTL-regulated genes associated with childhood asthma. Two-sample MR was conducted to assess causality, followed by Summary-based Mendelian Randomization (SMR) to validate findings in independent datasets. Colocalization analysis determined whether gene expression and asthma GWAS signals share a common causal variant. Protein quantitative trait loci (pQTL) analysis further validated gene associations at the protein level. DNA methylation quantitative trait loci (mQTL) MR and mediation analysis explored epigenetic regulatory mechanisms, while linkage disequilibrium score regression (LDSC) quantified genome-wide genetic correlations. Immune cell mediation analysis examined potential immune-driven effects, and Phenome-Wide Association Study (PheWAS) evaluated pleiotropy and therapeutic safety.

Results

Following systematic screening, STX4 emerged as a strong candidate gene for childhood asthma. MR and SMR analyses confirmed its causal role, while colocalization analysis provided robust genetic evidence supporting STX4’s regulatory influence on childhood asthma susceptibility. pQTL validation confirmed that STX4’s effects extend to the protein level, strengthening its biological relevance. DNA methylation analysis revealed key CpG (Cytosine-phosphate-Guanine) sites regulating STX4 expression, with higher methylation levels reducing childhood asthma risk. Immune cell mediation analysis demonstrated that STX4 influences childhood asthma risk via CD4+ and CD8+ T cell subsets. LDSC analysis reinforced a significant genetic correlation between STX4 and childhood asthma, while PheWAS detected no major pleiotropy, suggesting that STX4 is a specific and promising therapeutic target.

Conclusions

This study systematically identifies and validates STX4 as a key genetic regulator in childhood asthma by integrating large-scale genetic, epigenetic, and immune regulatory data. These findings provide strong evidence for STX4’s role in childhood asthma pathogenesis, highlighting STX4 as a potential target for future precision therapies in childhood asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-025-01908-x.

Introduction

Childhood asthma is a prevalent chronic respiratory disease that presents a major global public health challenge due to its high incidence and disease burden [1–3]. Epidemiological studies indicate that its prevalence remains substantial, particularly among younger age groups, where the risk of severe complications is notably higher [4, 5]. While inhaled corticosteroids (ICS) remain the primary treatment strategy for symptom management, a subset of pediatric patients continues to experience uncontrolled symptoms, progressive lung function decline, and persistent airway inflammation [6]. Despite extensive research, the precise pathogenesis of childhood asthma remains incompletely understood. Genetic factors are believed to play a substantial role in disease susceptibility, with evidence suggesting a significant heritable component [7]. This underscores the need for an in-depth investigation into the genetic regulatory mechanisms underlying childhood asthma and the identification of novel pathogenic genes and therapeutic targets to inform precision medicine strategies.

Recent advancements in genome-wide association studies (GWAS) have uncovered several genomic regions linked to asthma susceptibility. Among them, ORMDL3/GSDMB has been strongly linked to childhood asthma, IL33 and IL1RL1 have been associated with atopic asthma, and TSLP (Thymic Stromal Lymphopoietin) plays a key role in promoting Th2 immune responses [8–10]. However, while GWAS has successfully identified associations between genetic variations and disease risk, it does not inherently establish causality or clarify the underlying molecular regulatory mechanisms [11]. Moreover, the majority of variants identified through GWAS are located in non-coding regions, posing challenges for functional interpretation [12]. A key scientific challenge remains: understanding how these genetic variations influence asthma development through gene expression regulation and their downstream biological consequences.

To address the limitations of GWAS, integrative multi-omics analysis has emerged as a powerful strategy for unraveling disease mechanisms [13, 14]. Functional genomic approaches, including expression quantitative trait loci (eQTL), methylation quantitative trait loci (mQTL), and protein quantitative trait loci (pQTL), provide crucial insights into how genetic variations modulate gene expression and protein function [15, 16]. Meanwhile, Mendelian randomization (MR), a causal inference approach leveraging genetic variants as proxies, helps address biases and the issue of reverse causality, which are common limitations in traditional epidemiological studies [17, 18]. MR has been widely applied to elucidate the causal relationships between gene expression and disease risk. Although previous MR studies have investigated associations between inflammatory proteins, metabolic traits, and asthma phenotypes, a systematic multi-omics exploration of the genetic regulatory network and molecular mechanisms underlying childhood asthma remains lacking [19–22].

This study aims to identify key genes associated with childhood asthma by integrating genetic, epigenetic, and immune data. Linkage disequilibrium score regression (LDSC) was used to assess genome-wide genetic correlations, while MR and SMR (Summary-based Mendelian Randomization) determined causal relationships between gene expression and asthma susceptibility. Colocalization analysis refined gene selection, and mQTL data were incorporated to explore epigenetic regulation. pQTL analysis validated functional relevance, and mediation analysis assessed immune cell involvement. Finally, PheWAS evaluated broader clinical implications.

By combining multiple omics layers, this study offers a comprehensive genetic and immune regulatory map of childhood asthma, highlighting promising targets for future precision medicine approaches.

Methods

Study design

This study applied a multi-level genetic analysis framework to systematically identify and validate key genes associated with childhood asthma, while also elucidating their underlying regulatory mechanisms. Initially, genome-wide cis-eQTL (cis-expression quantitative trait loci) data from the eQTLGen consortium were utilized to perform MR analysis, examining whether gene expression contributes to childhood asthma susceptibility. To refine the selection of candidate genes, we employed SMR analysis, complemented by the HEIDI (Heterogeneity in Dependent Instruments) test to exclude associations driven by linkage disequilibrium (LD) rather than causal variants. To further validate these candidate genes at the protein level, pQTL datasets from UKB-PPP and deCODE were analyzed through MR. Colocalization analysis was then applied to confirm whether the genetic signals influencing gene expression were aligned with childhood asthma GWAS loci. Further exploration of epigenetic regulation involved integrating mQTL data to assess how DNA methylation affects gene expression and contributes to childhood asthma susceptibility. Mechanistic pathways were analyzed using mediation analysis, and the immune regulatory aspect was explored by integrating immune cell GWAS data to evaluate how specific immune cell subsets contribute to STX4 (Syntaxin-4)-related childhood asthma risk. Genetic correlation (rg) between childhood asthma and the identified genes was quantified using LDSC, providing an estimate of genome-wide genetic correlations. Finally, a PheWAS was conducted to investigate possible pleiotropy and determine the therapeutic viability of the candidate genes (Fig. 1).

Fig. 1.

All analyses illustrated in this figure were independently designed and performed by the authors using publicly available summary-level datasets. No pre-performed or third-party analyses were used

Data source

This study integrates multi-omic datasets to investigate the interplay between gene expression regulation, protein variation, DNA methylation, and immune cell dynamics in childhood asthma. The eQTL data were obtained from the eQTLGen Consortium (https://eqtlgen.org/), which includes 31,684 individuals of European ancestry across 37 independent studies. This dataset emphasizes cis-eQTL analysis, evaluating genetic variants within 1 Mb of a gene's transcription start site to identify regulatory elements affecting expression levels in PBMCs (peripheral blood and mononuclear cells) [23]. The pQTL data were sourced from two independent proteomic studies: the UKB-PPP (http://ukb-ppp.gwas.eu) and deCODE (https://www.decode.com/) Genetics. The UKB-PPP dataset analyzed 2,923 plasma proteins in 54,219 individuals, identifying 14,287 pQTL associations [24]. Meanwhile, the deCODE study, conducted in 35,559 Icelandic individuals, measured 4,907 proteins and identified 18,084 pQTL associations, providing an extensive resource for protein-level validation [25]. The DNA methylation QTL dataset was sourced from the Genetics of DNA Methylation Consortium (GoDMC, http://mqtldb.godmc.org.uk/). This dataset aggregates methylation profiles from 27,750 individuals of European ancestry, using Illumina 450K and EPIC BeadChip platforms to conduct a meta-GWAS for investigating the genetic basis of DNA methylation variation [26].

Childhood asthma GWAS summary statistics were derived from the FinnGen cohort (Version: R11, https://www.FinnGen.fi/en), comprising 7537 cases and 238,922 controls, providing a robust dataset for disease association analysis. For allergic asthma, the dataset included 11,867 cases and 248,261 controls. The eosinophilic asthma dataset comprised 3104 cases and 401,589 controls. Additionally, the obesity-related asthma analysis was conducted using 13,159 cases and 401,589 controls. Lastly, for non-allergic asthma, the dataset consisted of 8505 cases and 238,922 controls [27]. To explore the genetic determinants of childhood asthma risk factors, we incorporated GWAS data from publicly available datasets (https://gwas.mrcieu.ac.uk/), including, mood swings (UKB dataset: ukb-a-45, 148,601 cases, 180,827 controls), body mass index (BMI) (UKB dataset: ukb-b-19953, total sample size: 461,460), hay fever/allergic rhinitis (UKB dataset: ukb-b-18290, total sample size: 25,486), and eczema/dermatitis (UKB dataset: ukb-a-99, 8,718 cases, 328,441 controls) [28–31].

Additionally, immune cell GWAS data were retrieved from a publicly available dataset (GCST0001391 to GCST0002121), derived from 3757 Sardinian individuals [32]. This dataset encompasses 731 immune phenotypes, including variations in cell counts, fluorescence intensity, and morphological traits, providing insights into immune system regulation in childhood asthma. This study follows the STROBE-MR (Strengthening the Reporting of Mendelian Randomization Studies) guidelines to ensure transparency and robustness in study design, analysis, and interpretation. The completed STROBE-MR checklist is provided as supplementary file 1.

Selection of instrumental variables

The selection of instrumental variables (IVs) followed core MR assumptions to maintain the reliability and robustness of causal inference [33]. These key assumptions include: (1) Relevance Assumption: The selected single nucleotide polymorphisms (SNPs) must be strongly associated with the exposure of interest; (2) Independence Assumption: The selected SNPs must be independent of any potential confounders; (3) Exclusion Restriction Assumption: The selected SNPs should influence the disease only through their effects on the exposure of interest, without alternative pathways.

For cis-eQTL, pQTL, and mQTL data, IVs selection followed these criteria [34, 35]: (1) SNPs were required to reach genome-wide significance (P < 5 × 10⁻⁸); (2) To minimize the impact of LD, SNPs were filtered using LD threshold r² < 0.1 and a clumping distance > 10,000 kb; (3) During data harmonization, all IVs were subjected to F-statistic testing, and SNPs with F-statistic < 10 were excluded to reduce the risk of weak instrument bias.

For immune cell GWAS data, the IVs selection strategy differed slightly [36, 37]: (1) SNPs significantly associated with each immune cell phenotype (P < 1 × 10⁻⁵) were extracted; (2) using the European population as a reference, SNPs were filtered based on LD criteria (r² < 0.001, clumping distance > 10,000 kb) to ensure independence; (3) SNPs with an F-statistic < 10 were removed to prevent bias from weak instruments.

All selected IVs across datasets were required to have MAF > 0.05 to ensure the reliability of association signals and reduce rare variant bias. Additionally, across all analyses, palindromic SNPs were removed when harmonizing with outcome variables to maintain strand alignment and reduce potential mismatches. For reverse MR analysis, identical selection criteria were used for cis-eQTL, pQTL, and mQTL data to explore whether childhood asthma influences the exposure of interest. Applying stringent IV selection minimized confounding and weak instrument bias, strengthening the reliability of causal inference.

MR analysis and sensitivity tests

MR analysis was conducted to assess the causal effects of various exposures on childhood asthma. For exposures with a single SNP IV, the Wald ratio method was applied for effect estimation (R package: “TwoSampleMR”, version: version 0.6.4). When multiple SNPs were available, the IVW (inverse variance weighted) approach was prioritized for its statistical efficiency in causal inference [17, 38]. To enhance result robustness, MR-Egger regression, weighted median, simple mode, and weighted mode were incorporated as complementary analytical approaches. Given its reliability in handling heterogeneous IVs, IVW remained the principal method for effect estimation [17]. To reduce false positives arising from multiple comparisons, FDR (false discovery rate) correction was implemented.

Moreover, various sensitivity analyses were performed to ensure the reliability of MR findings. The MR-Egger intercept test was utilized to assess horizontal pleiotropy, with P < 0.05 indicating potential pleiotropic effects that might bias causal estimates [39]. Cochran’s Q test in IVW and Rucker’s Q test in MR-Egger were used to assess heterogeneity among IVs, with P < 0.05 indicating significant heterogeneity [40].

To enhance result reliability, MR-PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier) analysis identified and adjusted for outliers in the MR estimates [41]. If any outliers were detected, further analyses were performed after excluding these variants to ensure the stability of causal effect estimates. Leave-One-Out Sensitivity Analysis was performed to evaluate the stability of the MR results. In this analysis, each SNP was sequentially excluded, and the causal estimates were recalculated to assess the influence of individual genetic variants on the overall findings. This approach helps identify any SNPs that disproportionately affect the causal relationship between the exposure and outcome [42].

SMR

SMR integrates cis-eQTL data with GWAS summary statistics to determine whether gene expression directly influences childhood asthma risk. We utilized cis-eQTL data from the eQTLGen Consortium, with childhood asthma GWAS summary statistics as the outcome dataset in SMR analysis. The HEIDI test was applied to differentiate true causal effects from LD-driven associations [16, 43]. If a gene exhibited a significant SMR association (P < 0.05) without evidence of heterogeneity in the HEIDI test (P > 0.05), it was considered a likely causal driver of childhood asthma.

Colocalization analysis

Colocalization analysis using the "coloc" R package (version 5.2.3) assessed whether the identified genetic signals regulate both gene expression and childhood asthma risk [44]. This Bayesian framework estimates the probability that both cis-eQTL signals and GWAS associations originate from the same causal variant. The analysis computed five posterior probabilities (PP) [44]: PPH0 (no association with either the gene or the disease), PPH1 (association only with the gene), PPH2 (association only with the disease), PPH3 (independent associations with both the gene and the disease), and PPH4 (colocalization, indicating that both gene expression and disease share a causal variant). We focused on PPH4 > 0.80, indicating a high probability of colocalization. The regional results were visualized using the "ieugwasr" R package (available at https://github.com/MRCIEU/ieugwasr).

PheWAS

We performed a PheWAS analysis utilizing publicly available data from AstraZeneca's PheWAS Portal (https://azphewas.com/) and the PheWeb database (https://pheweb.org/) [45]. This analysis incorporated genetic and phenotypic data from nearly 450,000 UK Biobank participants, encompassing a broad spectrum of binary and continuous phenotypes [46]. Multiple testing correction was implemented with a significance threshold of 5 × 10⁻⁸, aligning with standard settings to minimize false positives. Utilizing this extensive phenotypic dataset enabled a comprehensive assessment of potential trait associations.

LDSC

LDSC (https://github.com/bulik/ldsc) was conducted to assess genome-wide genetic correlations [47]. LDSC measures the genetic correlation between two traits on a scale from − 1 to 1, representing complete negative to complete positive correlation. It assesses potential test statistic inflation due to polygenic architecture or systematic bias by examining GWAS results in relation to LD patterns. Unlike traditional methods, LDSC is not affected by sample overlap, making it a robust approach for estimating genetic associations using GWAS summary statistics.

Results

MR and SMR pinpoint causal genes for childhood asthma

We systematically evaluated the causal effects of cis-eQTL on childhood asthma risk using MR. To ensure robust genetic instruments, we first applied stringent IV selection criteria to the cis-eQTL dataset. Consequently, 15,695 of over 19,000 genes met these standards and advanced to subsequent analysis.

Two-sample MR analysis followed, employing five distinct methods (IVW, Weighted Median, MR-Egger, Simple Mode, and Weighted Mode). To adjust for multiple comparisons, we implemented FDR correction (FDR < 0.05/15695), with additional assessments for horizontal pleiotropy (P > 0.05) and heterogeneity (P > 0.05). After these rigorous controls, 49 genes exhibited a significant causal relationship with childhood asthma (Fig. 2; Supplementary Tables S1, S2).

Fig. 2.

Forest plot of the causal association between cis-eQTL and childhood asthma. IVW, inverse variance weighted; CI, confidence interval; OR, odds ratio; FDR, false discovery rate

To validate our findings, we conducted SMR analysis. Among the 49 genes analyzed, seven (OSGIN2, STX4, GPX3, NCKIPSD, B3GALNT1, ARL6IP4, and ITFG1) were positively associated with childhood asthma, while 13 (IRF1, LIN9, PARD3B, MPHOSPH9, TNIP1, HADH, PIK3C3, TPSAB1, VAMP5, H3F3A, BLVRA, TDRD9, and PPP2R3C) were inversely associated. Importantly, all 20 genes passed the HEIDI test for heterogeneity (P__HEIDI > 0.05), and their effect directions remained consistent across MR and SMR analyses, supporting the robustness of our findings. (Fig. 3; Supplementary Table S3).

Fig. 3.

Forest plot of the SMR analysis between gene and childhood asthma. Top_SNP_chr: Chromosome of top SNP; Top_SNP: most significant SNP; b_SMR: effect size from the SMR analysis; P_HEIDI: The P value from the HEIDI test; IVW, inverse variance weighted; CI, confidence interval; OR, odds ratio

deCODE and UKB-PPP validation elevates STX4’s role

Further, we validated the 20 candidate genes identified through MR analysis by evaluating their cis-eQTL associations with childhood asthma in the deCODE and UKB-PPP datasets. By integrating pQTL data, we pinpointed three genes—STX4, HADH, and TPSAB1—with established pQTL associations (Fig. 4A). Subsequent MR analysis, leveraging data from these datasets, explored their potential causal relationships with childhood asthma. In deCODE, STX4 showed a positive association with childhood asthma risk (IVW: OR = 1.504, 95% CI 1.056–2.140, P = 0.024). By contrast, HADH in UKB-PPP exhibited an inverse relationship (OR = 0.781, 95% CI 0.654–0.933, P = 0.007; Fig. 4B), while TPSAB1 in deCODE also indicated a protective effect (OR = 0.964, 95% CI 0.944–0.984, P < 0.001; Fig. 4B). Sensitivity analyses confirmed no heterogeneity or pleiotropy for STX4 and HADH (Supplementary Table S4). However, TPSAB1 displayed variability across datasets, possibly due to cohort-specific factors. Supplementary Fig. 1A and 1B presents the leave-one-out sensitivity analysis for STX4’s cis-eQTL and pQTL with childhood asthma, showing stable associations with no individual SNP disproportionately influencing the results, further supporting the robustness of the observed relationship.

Fig. 4.

Validation analysis of pQTL and childhood asthma. A: Venn diagram of three datasets. B: MR validation analysis of common pQTL in the deCODE and UKB-PPP databases for childhood asthma. CI, confidence interval; OR, odds ratio

Colocalization elevates STX4’s genetic role in childhood asthma

We assessed whether cis-eQTL signals for STX4, HADH, and TPSAB1 align with childhood asthma GWAS loci using colocalization analysis. STX4 demonstrated robust colocalization (PPH4 = 0.913), suggesting a shared causal variant with childhood asthma risk (Fig. 5A). By comparison, TPSAB1 showed moderate colocalization (PPH4 = 0.725), indicating a potential but less definitive association, whereas HADH exhibited weak evidence (PPH4 = 0.106), pointing to distinct variants (Fig. 5A; Supplementary Table S5). Regional association plots further supported these findings (Fig. 5B). These validations underscore STX4’s role, paving the way for refined genetic confirmation.

Fig. 5.

Colocalization analysis of STX4, HADH, and TPSAB1. A: Colocalization analysis heatmap. B: Regional plot showing colocalization evidence between STX4 and childhood asthma. PPH, posterior probability of colocalization

Epigenetic regulation of STX4 in childhood asthma

We explored STX4’s epigenetic regulation in childhood asthma by assessing DNA methylation at CpG sites near its gene locus. From an initial set of 13 CpG sites, only cg06233904, cg07404961, and cg08893833 passed rigorous IV selection (Supplementary Table S6). MR analysis showed that elevated methylation at cg06233904 (β_all = − 0.456, 95% CI − 0.642 to − 0.270, P < 0.001) and cg08893833 (βall = − 0.367, 95% CI − 0.517 to − 0.217, P < 0.001) correlated with reduced childhood asthma risk (Fig. 6A, B; Supplementary Tables S7–S9). Further analysis revealed that methylation suppressed STX4 expression, with cg06233904 showing β₁ = − 1.963 (95% CI − 2.046 to − 1.879, P < 0.001) and cg08893833 β₁ = − 1.580 (95% CI − 1.648 to − 1.513, P < 0.001) (Fig. 6A, B; Supplementary Tables S7–S9). Mediation analysis confirmed that STX4 suppression mediated over 90% of this effect, with proportions of 0.908 at cg06233904 (95% CI 0.546–1.270, P < 0.001) and 0.909 at cg08893833 (95% CI 0.616–1.202, P < 0.001) (Fig. 6A, B; Supplementary Tables S7–S9). Limited SNPs precluded reverse MR and sensitivity analyses, restricting further validation. These results suggest a CpG-STX4-childhood asthma regulatory axis, with methylation potentially modulating childhood asthma susceptibility.

Fig. 6.

Epigenetic regulation of STX4 in childhood asthma. A: Forest plot of the analysis results of methylation sites, childhood asthma, and STX4. B: Two-step MR indicating significant causal relationships between two methylation sites and childhood asthma risk mediated by STX4. CI, confidence interval

Immune cell mediation in the STX4-childhood asthma axis

The downstream immunological mechanisms through which STX4 contributes to childhood asthma risk were examined using stepwise MR mediation analysis that incorporated STX4 expression, immune cell traits, and childhood asthma GWAS data.

Among the 731 immune cell subtypes analyzed, 69 showed significant differences and were subsequently tested in IVW analysis, which identified significant causal associations for childhood asthma (P < 0.05, Supplementary Table S10). We then assessed the effect of STX4 on these 69 immune cell subtypes, identifying 31 significant associations using the IVW method (P < 0.05) (Supplementary Table S11).

Mediation analysis evaluated specific immune cell roles in the STX4-childhood asthma pathway (Fig. 7A, B; Supplementary Tables S10–S12). CD3 on CD39+ CD8br cells showed a modest mediation effect (β_1*2 = 0.019, P = 0.033), with elevated STX4 expression reducing their proportion (β₁ = − 0.141, 95% CI − 0.242 to − 0.041, P = 0.006), and this reduction heightened childhood asthma risk (β₂ = − 0.136, 95% CI − 0.213 to − 0.059, P < 0.001; mediation proportion: 0.091, 95% CI 0.007–0.175, P = 0.033).

Fig. 7.

Immune cell mediation in the STX4-childhood asthma axis. A: Forest plot of the analysis results between immune cells and childhood asthma, and STX4 and immune cells. B: two-step MR indicating significant causal relationships between STX4 and childhood asthma risk mediated by immune cells. CI, confidence interval

Similarly, CD3 on effector memory (EM) CD8br cells exhibited mediation (β_1*2 = 0.030, P = 0.019). STX4 upregulation lowered their levels (β₁ = − 0.194, 95% CI − 0.297 to − 0.091, P < 0.001), amplifying asthma risk (β₂ = − 0.153, 95% CI − 0.247 to − 0.058, P = 0.002; mediation proportion: 0.141, 95% CI 0.023–0.258, P = 0.019; Fig. 7A, B; Supplementary Tables S10–S12).

Most strikingly, CD3 on terminally differentiated (TD) CD4+ cells demonstrated the strongest mediation effect (β_1*2 = 0.052, P = 0.008). Increased STX4 expression diminished their proportions (β₁ = − 0.246, 95% CI − 0.349 to − 0.144, P < 0.001), markedly elevating childhood asthma risk (β₂ = − 0.210, 95% CI − 0.349 to − 0.071, P = 0.003; mediation proportion: 0.245, 95% CI 0.063–0.428, P = 0.008; Fig. 7A, B; Supplementary Tables S10–S12).

Sensitivity analyses confirmed no pleiotropy or heterogeneity across these cell types (Supplementary Tables S13, S14), though limited SNP precluded reverse MR validation. The leave-one-out sensitivity analysis of STX4 and immune cell mediation in childhood asthma, showing consistent results and supporting the robustness of STX4’s immune regulation (Supplementary Fig. 2A–F). These findings elucidate STX4’s immune regulation in childhood asthma, prompting broader genetic correlation analysis.

STX4’s genetic correlation with childhood asthma via LDSC

We evaluated STX4’s genetic correlation with childhood asthma using LDSC. Initial analysis revealed a robust correlation (rg_unadjusted = 0.5499, P = 0.0012). Given negligible sample overlap between STX4 and childhood asthma datasets, we adjusted for genetic covariance, yielding an intercept near zero. Adjusted LDSC results substantiated a significant positive correlation (rg_corrected = 0.4609, P = 0.0069). These findings underscore STX4’s role in childhood asthma susceptibility, reinforcing its status as a risk-associated gene (Table 1).

Table 1.

Genome-wide genetic correlations between STX4 and childhood asthma via LDSC analysis

LDSC		Unadjusted			Corrected
Trait1	Trait2	rg	se	p	rg	se	p
STX4	Childhood asthma	0.550	0.171	0.001	0.461	0.143	0.007

rg: genetic correlation; se: standard error

STX4 and childhood asthma risk factors

We evaluated STX4’s associations with four known childhood asthma risk factors-mood swings, eczema/dermatitis, hay fever/allergic rhinitis, and BMI-using MR analysis (Fig. 8; Supplementary Table S15). IVW results revealed a positive link between STX4 and mood swings (OR = 1.012, 95% CI 1.004–1.020, P = 0.002) and between STX4 and BMI (OR = 1.032, 95% CI 1.009–1.056, P = 0.006). In contrast, no association emerged with eczema/dermatitis or hay fever/allergic rhinitis. Sensitivity analyses confirmed the STX4-mood swings relationship’s consistency, showing no evidence of pleiotropy or heterogeneity (P > 0.05; Supplementary Table S16). Leave-one-out sensitivity analysis for STX4 cis-eQTL and childhood asthma risk factors further confirmed stable associations, with no single SNP exerting undue influence on the results (Supplementary Fig. 3A and 3B). However, the STX4-BMI link exhibited variability, as indicated by Cochran’s Q (P = 0.040) and Rucker’s Q tests (P = 0.035), suggesting potential influence from unmeasured factors (Supplementary Table S16).

Fig. 8.

Analysis results of STX4 with risk factor for childhood asthma. BMI, body mass index; IVW, inverse variance weighted; CI, confidence interval; OR, odds ratio

STX4’s role across asthma subtypes

To address asthma’s heterogeneity, we applied MR analysis to assess STX4’s role in allergic, eosinophilic, obesity-related, and non-allergic asthma subtypes (Fig. 9; Supplementary Table S17). STX4 showed strong associations with allergic asthma (IVW OR = 1.119, 95% CI 1.052–1.191, P < 0.001) and eosinophilic asthma (IVW OR = 1.202, 95% CI 1.087–1.328, P < 0.001). By contrast, its link to obesity-related asthma was modest (IVW OR = 1.053, 95% CI 1.000–1.109, P = 0.048), implying a limited contribution to pathogenesis in obese individuals. No association emerged with non-allergic asthma (IVW OR = 1.047, P = 0.212). Sensitivity analyses revealed no heterogeneity or pleiotropy across subtypes (P > 0.05; Supplementary Table S18), confirming the reliability of these results. Leave-one-out sensitivity analysis of STX4 and asthma subtypes confirmed consistent findings, with no SNP disproportionately affecting the results (Supplementary Fig. 4A and 4B). Thus, STX4 primarily influences childhood asthma through eosinophilic inflammation and Th2-mediated pathways, with minimal impact on other subtypes.

Fig. 9.

Forest plot of the MR analysis between STX4 and asthma subtypes. IVW, inverse variance weighted; CI, confidence interval; OR, odds ratio

PheWAS assessment of STX4 specificity

We conducted a PheWAS to assess the broader phenotypic effects of STX4, using data from the AstraZeneca PheWAS Portal and PheWeb. At a genome-wide significance level (P < 5 × 10⁻⁸), STX4 showed no associations with other phenotypes (Fig. 10; Supplementary Table S19). This result underscores the specificity of our findings and suggests that STX4-targeted therapy for childhood asthma poses minimal risk of significant adverse effects or unintended pleiotropic outcomes.

Fig. 10.

PheWAS association of binary traits with STX4

Discussion

Childhood asthma is a complex immune-mediated disease driven by intricate genetic, epigenetic, and environmental interactions [2]. While GWAS have identified numerous susceptibility loci, the mechanisms linking these variants to gene expression and downstream biological pathways remain poorly understood [19, 20]. This study addresses this gap by integrating multi-omics data—spanning GWAS, eQTL, mQTL, and pQTL—with MR, colocalization, and immune cell mediation analyses. Through this approach, we systematically identified STX4 as a likely key contributor to childhood asthma. Unlike prior studies that mainly reported genetic associations, our work extends these findings by integrating multi-omic data to reveal potential functional mechanisms linking STX4 to childhood asthma pathogenesis.

Through a rigorous multi-step selection process, we established STX4 as a robust candidate gene, revealing a significant positive association between its elevated expression and increased childhood asthma risk. Colocalization analysis demonstrated strong overlap between STX4 eQTL signals and childhood asthma GWAS loci, far exceeding the 0.8 threshold for robust evidence and suggesting a shared causal variant. Additionally, LDSC confirmed a notable genetic correlation, underscoring STX4’s contribution to asthma susceptibility. Furthermore, STX4 was also associated with key asthma-related risk factors, including mood swings and BMI, suggesting its broader role in asthma pathophysiology. These findings provide a foundation for exploring STX4’s mechanistic pathways in asthma.

STX4, a SNARE complex protein essential for membrane fusion and vesicle trafficking, has been previously linked to insulin regulation and cytotoxic granule secretion [48–50]. However, its involvement in childhood asthma had not been systematically explored until now. Our study provides the systematic assessment of STX4’s immunological impact in asthma, revealing that STX4 influences asthma risk partially through CD4⁺ and CD8⁺ T cell subsets. Specifically, STX4 suppression mediated over 90% of the inverse association between methylation at cg06233904 and cg08893833 and childhood asthma risk, indicating a novel epigenetic-immune axis in asthma pathogenesis. This finding builds upon previous literature that primarily associated STX4 with insulin secretion and glucose homeostasis, and newly implicates STX4 in the immune dysregulation underlying childhood asthma [48–50].

Epigenetic modifications, particularly DNA methylation, serve as a mechanistic link between environmental exposures and asthma susceptibility by regulating STX4 expression. Our findings indicate that increased methylation at specific CpG sites downregulates STX4 expression, subsequently reducing asthma risk. This aligns with prior research, indicating that DNA methylation regulates gene expression and immune function in chronic inflammatory diseases [8, 51, 52]. Notably, our mediation analysis revealed that STX4 methylation influences immune responses via T cell modulation—a finding not previously reported in asthma literature. Although no direct evidence currently links environmental exposures to STX4 methylation, we hypothesize that factors such as allergens or pollutants may dynamically influence this regulatory mechanism. Future studies are needed to test this hypothesis and evaluate the potential for epigenetic therapeutic strategies targeting STX4.

The role of STX4 in asthma appears to be subtype-specific. Additional MR analyses across asthma subtypes revealed that STX4 is significantly associated with allergic (11,867 cases and 248,261 controls) and eosinophilic asthma (3104 cases and 401,589 controls), supporting its involvement in Th2-driven pathogenesis [53, 54]. However, its role in obesity-related asthma (13,159 cases and 401,589 controls) was weaker, and no significant association was observed for non-allergic asthma (8,505 cases and 238,922 controls) [55]. These findings reinforce the idea that STX4 primarily contributes to eosinophilic inflammation and Th2 immune responses, rather than non-Th2-mediated asthma phenotypes [56]. Future research should further explore how STX4-driven pathways differ between asthma subtypes and whether subtype-specific therapeutic strategies targeting STX4 may be beneficial.

To better understand STX4’s immune role, we investigated its effect on immune cell activity. STX4 facilitates vesicle fusion and immune synapse formation, enabling cytokine secretion and T cell activation [48, 57–59]. Our immune cell mediation analysis suggests that STX4 may contribute to childhood asthma risk through CD3 on TD CD4+ and CD3 on EM CD8br T cells, highlighting its role in modulating the immune microenvironment via T cell activation and regulation. These findings align with previous research, indicating that STX4 facilitates Rab11a⁺ endosomal vesicle fusion with the plasma membrane, thereby stabilizing immune synapse formation [49]. Furthermore, STX4 collaborates with the Munc18-3/SNAP23/VAMP7/VAMP8 complex to regulate cytotoxic granule exocytosis, impacting the immune activity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells [49]. These synapses not only amplify CTL and NK cell cytotoxicity but likely exacerbate chronic inflammation in asthma by promoting Th2 and Th17 cell activation [60, 61]. In CD4⁺ T cells, STX4 likely drives childhood asthma pathogenesis by modulating Th2 and Th17 differentiation, intensifying airway inflammation—a mechanism aligned with Th2 cells’ established role in asthma immunopathology [62, 63].

Beyond cytokine secretion, STX4 may also influence childhood asthma through KCa3.1 potassium channels, expressed in T cells, dendritic cells, and airway epithelial cells, and critical for immune cell activation [64–67]. Evidence suggests that STX4 interacts with the VAMP3/SNAP23 complex to regulate KCa3.1 trafficking, directing the channel to the basolateral membrane (BLM) and modulating ion flux, thereby influencing T cell function. In particular, KCa3.1-mediated potassium flux is essential for maintaining calcium ion homeostasis, which directly affects T cell receptor (TCR) signaling and determines the activation and differentiation of T cells [68]. Given KCa3.1’s high expression in Th2 cells and its link to asthma, we propose that STX4 enhances Th2 activation via KCa3.1, potentially synergizing with EM CD8br T cell activity to shape the asthmatic immune microenvironment [69]. Moreover, STX4 may affect immune tolerance through regulatory T cells (Tregs), which are essential for maintaining immune homeostasis and curbing excessive inflammation. Prior studies suggest KCa3.1 dysfunction destabilizes Tregs, fostering Th2-dominant inflammation [69]. Our findings suggest that STX4’s role in TD CD4⁺ T cells extend to modulating the Treg-Th2 equilibrium. Future research should validate whether STX4-mediated KCa3.1 regulation directly influences Treg stability and explore its broader impact on asthma’s immune network.

Our PheWAS analysis revealed that STX4 is not significantly associated with other complex diseases, suggesting that its effects may be highly specific to asthma-related immune processes. This makes STX4 a particularly attractive therapeutic target. In contrast, currently approved asthma biologics, such as IL-4R and IL-5 antagonists, have been linked to unintended immune-related adverse effects in certain patients [70, 71]. STX4's potentially more targeted role may provide a safer and more precise therapeutic strategy for childhood asthma, though further preclinical and clinical validation is required.

Despite the comprehensive multi-omics integration approach adopted in this study to systematically investigate the role of STX4 in childhood asthma, several limitations should be acknowledged. This study primarily relied on publicly available GWAS and QTL datasets, and while MR mitigates confounding biases, the findings require validation in larger and more diverse populations to enhance generalizability. One limitation of our study lies in the use of exposure and outcome datasets derived from European-ancestry individuals with different genetic backgrounds. Specifically, the FinnGen cohort comprises Finnish individuals, who have distinct LD patterns compared to broader European populations due to historical bottlenecks. Although both eQTLGen and FinnGen are of European ancestry, this population mismatch may introduce subtle biases in two-sample MR estimation. We therefore acknowledge this as a limitation and emphasize the need for future replication in more genetically homogeneous or ethnically diverse datasets to enhance the robustness and generalizability of our findings. Additionally, as the analyses were predominantly based on statistical genetics approaches, the lack of experimental validation remains a limitation. Future research should incorporate single-cell RNA sequencing, in vitro functional assays, and animal models to elucidate the precise immunological mechanisms underlying STX4-mediated asthma pathogenesis. While pQTL analysis initially identified three candidate genes (STX4, HADH, and TPSAB1), only STX4 demonstrated strong colocalization evidence with childhood asthma risk loci. The other two genes exhibited weaker colocalization signals, and we did not perform further functional validation. This remains a limitation, as these genes might still contribute to asthma susceptibility through alternative mechanisms. Moreover, the mQTL dataset used in this study was relatively limited, restricting the ability to assess whether childhood asthma reciprocally regulates STX4 methylation. Expanding the analysis with larger-scale epigenetic datasets could provide further insights into potential bidirectional regulatory mechanisms. Lastly, although PheWAS analysis showed no substantial pleiotropy or unexpected systemic effects, further preclinical and clinical research is essential to thoroughly assess the safety and therapeutic viability of targeting STX4 in childhood asthma.

Conclusions

This study systematically investigated the genetic regulatory role and immunological mechanisms of STX4 in childhood asthma by integrating multi-level genomic data. Additionally, PheWAS analysis was conducted to evaluate the safety of targeting STX4, suggesting that it may be a potential target for precision interventions in childhood asthma, which requires further experimental and clinical validation. These results contribute to our understanding of the genetic and immune mechanisms underlying childhood asthma, while emphasizing STX4 as an important susceptibility gene and a potential therapeutic target, warranting further experimental validation to assess its clinical relevance.

Data Availability Statement

No datasets were generated or analyzed during the current study.

Abbreviations

GWAS

Genome-Wide Association Study

eQTL

Expression quantitative trait loci

pQTL

Protein quantitative trait loci

mQTL

Methylation quantitative trait loci

MR

Mendelian randomization

LDSC

Linkage disequilibrium score regression

FDR

False discovery rate

PheWAS

Phenome-Wide Association Study

STX4

Syntaxin-4

scRNA-seq

Single-cell RNA sequencing

HEIDI

Heterogeneity in dependent instruments

Author contributions

All authors have read and approved the submission of manuscript. YZ, JH and HW did conceptualization; YZ gave the methodology and resources and done investigation, visualization and writing—original draft preparation; YZ and ZY developed the software; XD, YZ and LZ validated the study; YR performed formal analysis;; YY did data curation; HW done writing—review and editing; HW supervised the study; HW and JH contributed to project administration; LZ was involved in funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Wujiang Science Education and Health Project (WWK202122, WWK202503), the Suzhou Science and Technology Development Guidance Project (SKYD2022066), the Suzhou Science and Technology Program (Youth Guidance Project No. 49) and the Youth Research Project of Children’s Hospital of Wujiang District (2020QN03).

Declarations

Ethical approval

All data used in this study were obtained from publicly available GWAS datasets. No new data collection was conducted, and ethical approval was not required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.