Genomic Structural Equation Modeling Elucidates the Shared Genetic Architecture of Allergic Disorders

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Methods Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. Results A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. Conclusion Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions. Genomic SEM Allergic disorders FAM114A1 rs145982144 rs78017269 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Allergy exemplifies the dual nature of the immune system—it serves as both a protector and a potential threat. It is a systemic disorder caused by immune system dysregulation and encompasses conditions such as allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis. Given the high prevalence of allergic diseases, the World Health Organization (WHO) has classified them among the top three diseases requiring priority prevention and treatment in the 21st century. Recent estimates from the Global Burden of Disease Study reveal substantial global allergic disease burdens, with approximately 260 million prevalent cases of asthma and 204 million cases of atopic dermatitis worldwide[ 1 , 2 ]. Allergic rhinitis, meanwhile, is estimated to afflict approximately 20% of the global population[ 3 ]. Alarmingly, the prevalence of these conditions continues to escalate, reflecting a persistent upward trajectory in allergic disease morbidity that underscores urgent public health priorities[ 3 ]. Allergic disorders arise from complex gene-environment interactions and are closely associated with type 2 inflammation and epithelial barrier dysfunction. While these conditions are often viewed as distinct diseases, comorbidities and multimorbidities are common. For instance, cross-sectional studies indicate that individuals with other allergic disorders are more susceptible to developing asthma[ 4 , 5 ]. A birth cohort study of 2,311 Canadian children revealed that infants diagnosed with atopic dermatitis at one year of age had an 11-fold and 7-fold increased risk of developing allergic asthma and allergic rhinitis, respectively, by the age of three, compared to those without atopic dermatitis[ 6 ]. This temporal pattern of disease progression, where different allergic disorders emerge sequentially over time, is known as the "atopic march". The mechanisms underlying allergic disease comorbidities and multimorbidities are highly complex, with current research suggesting that genetic factors, gut microbiota, IgE-mediated and non-IgE-mediated pathways all contribute[ 7 , 8 ]. With the advent of the Human Genome Project (HGP), genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have underscored the role of genetic heritability in allergic disorders[ 9 ]. GWAS have identified key susceptibility loci, such as rs11740584 and rs2299007 in KIF3A for food allergy, rs8111930 near MRPL4 for allergic rhinitis, and rs2243250 in IL4 and rs20541 in IL13 for allergic asthma[ 10 ]. For instance, a recent study leveraging GWAS data and innovative analytical approaches identified multiple shared loci between eosinophilic disorders and allergic diseases (e.g., C11orf30 and SMAD3 )[ 11 ]. Using genomic structural equation modeling (Genomic-SEM), Gong et al. demonstrated substantial genetic correlation, represented by a common latent factor, among asthma, eczema, and allergic rhinitis [ 12 ]. These findings highlight the crucial role of genetic factors in allergic disease comorbidities and multimorbidities. To date, numerous susceptibility single nucleotide polymorphisms (SNPs) and genes linked to specific allergic disorders have been identified. However, our understanding of the precise genetic and biological mechanisms underlying allergic disorder comorbidities remains limited, and existing GWAS findings are still underexplored in the context of combined allergic conditions. To address these challenges, this study aims to integrate multiple genetic analysis methodologies and highly correlated association analysis tools to uncover potential molecular mechanisms and expand the understanding of disease interconnections. Specifically, we focus on genomic loci and chromosomal regions associated with allergic disorders to identify potential therapeutic targets. This research not only enhances our comprehension of allergic disorders but also provides a theoretical and practical foundation for prevention and intervention strategies aimed at reducing allergic disease burden and associated complications. In this study, we constructed a Genomic-SEM utilizing publicly available GWAS summary statistics for specific allergic disorders. This method integrates genome-wide association data with structural equation modeling to elucidate shared genetic architectures and causal relationships between traits, while rigorously accounting for sample overlap and pleiotropic effects. The analytical framework has been validated in prior studies investigating comorbidities such as inflammatory bowel disease and asthma, as well as associations between body mass index and childhood asthma, among others[ 12 – 14 ]. By leveraging GWAS statistics, we estimated the associations of SNPs with latent phenotypes, effectively conducting a GWAS on an unmeasured allergic disorder construct. Furthermore, we employed integrative approaches from systems biology, defining unexplained genetic variation within allergic disorders as potential novel genetic markers and conducting extensive GWAS-related analyses. Although this approach does not fully capture the intricate interactions between allergic disorder pathways and multifactorial influences—given that allergic disorders are driven by a combination of genetic, environmental, and stochastic factors—our methodology minimizes confounding effects from known biomarkers, enabling analysis of previously challenging datasets. Additionally, we performed thousands of causal inference analyses based on GWAS summary data to help clinicians and biologists predict potential plasma biomarkers and clinical disease causal relationships, ultimately informing prevention strategies and potential interventions for patients. Methods A flowchart overview is presented in Fig. 1 . Genomic-SEM GWAS Data Source The GWAS summary statistics used for Genomic-SEM analyses were derived from eight independent GWAS related to allergic disorders. These datasets were obtained from previously published GWAS studies, as well as the FinnGen R12 and UK Biobank (UKB) databases. The allergic phenotypes included in the analysis were allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis. All contributing GWAS studies received ethical approval from their respective institutional review boards, and all participants provided informed consent. The summary statistics underwent rigorous quality control procedures to ensure data integrity. A detailed list of the included GWAS datasets is available in Table S1 . Quality Control for Genomic-SEM We implemented a rigorous quality control pipeline based on recommended filtering criteria for all autosomal SNPs from the eight input GWAS datasets. To ensure consistency across studies, SNPs were filtered against the 1000 Genomes Phase 3 European (EUR) reference panel, excluding those with a minor allele frequency (MAF) < 0.01, effect size estimates of zero, reference panel mismatches, or allele inconsistencies. Given that the GWAS datasets originated from different genomic repositories and study populations, we accounted for potential sample overlap to prevent inflation in test statistics and improve result robustness. Statistical methods were applied to estimate and adjust for any residual overlap, minimizing bias in effect size estimates and ensuring the reliability of downstream analyses. Genomic-SEM Construction We applied Genomic-SEM using GenomicSEM R package (v0.0.5) to investigate the shared genetic architecture underlying allergic traits. Genomic-SEM is a recently developed multivariate method that enables the exploration of latent structures across multiple phenotypes by estimating a multivariable genetic model[ 15 ]. A key advantage of Genomic-SEM is that it is robust to sample overlap and differences in sample sizes, minimizing biases associated with these factors. Additionally, it facilitates the identification of genetic variants that influence only a subset of traits rather than all complex traits under investigation, thereby distinguishing trait-specific effects from broader cross-trait genetic susceptibility. The Genomic-SEM analysis was conducted in two stages. In the first stage, we estimated the empirical genetic covariance matrix and the corresponding sampling covariance matrix. To achieve this, we compiled GWAS summary statistics for allergic disorders and applied a multivariate extension of Linkage Disequilibrium (LD) Score Regression (LDSC). LDSC is a robust statistical framework that quantifies polygenic architecture using GWAS summary statistics. By modeling the relationship between variant association statistics and their LD profiles, LDSC disentangles true polygenic signals from confounding biases such as cryptic relatedness and population stratification. In our study, we used a multivariate LDSC to generate an empirical genetic covariance matrix across eight allergic traits. This matrix served as the input for the common factor model in SEM, while SNP-based heritability estimates (h 2 ) for each trait are reported in Table S2 . In the second stage, we specified an SEM model to minimize the discrepancy between the hypothesized covariance structure and the empirical covariance matrix estimated in the first stage. Our primary objective was to identify the shared genetic factors underlying eight allergic disorders. To this end, we tested the fit of a single-factor model, evaluating model adequacy using Standardized Root Mean Square Residual (SRMR), model chi-square (χ²), Akaike Information Criterion (AIC), and Comparative Fit Index (CFI) (see Table S3 and Table S4 ). By implementing an appropriate common factor SEM specification, we incorporated individual autosomal SNP associations into the genetic and sample covariance matrix, allowing for a genome-wide analysis of shared covariance across the eight allergic disorder GWAS datasets. Furthermore, to ensure that the SNPs identified by the novel genomic SEM exhibit consistent effect directions, we conducted a heterogeneity test for each SNP and excluded those with a Cochran's Q statistic p-value less than 0.05. Multilevel Evaluation of Genomic Structural Equation Model In addition to the aforementioned methods for testing genomic-SEM fitting, we implemented another LDSC approach to evaluate the stability of the genomic-SEM fit. Specifically, we assessed the model using parameters such as the mean chi-square, lambda GC, maximum chi-square, h², intercept, and ratio (calculated as (LDSC intercept – 1) / (mean χ² – 1)). The detailed control of LDSC parameters included: retaining SNPs with missing values, retaining SNPs with an INFO score < 0.9, retaining SNPs with a MAF < 0.01, and excluding SNPs with p-values outside the valid range or with ambiguous strand orientation. Identification of Significant and Novel Genomic Loci We used FUMA (Functional Mapping and Annotation; https://fuma.ctglab.nl/ ) to identify genomic loci and to detect both independent significant and lead SNPs associated with the Genomic-SEM of allergic disorders[ 16 ]. These SNPs exhibited low LD with other variants (r² < 0.6 for independent SNPs and r² < 0.1 for lead SNPs) and surpassed the genome-wide significance threshold (p < 5 × 10 − 8 ). We then compared the identified lead SNPs and loci with those reported in single-trait GWAS to assess potential pleiotropy, cross-referencing significant published associations (p < 5 × 10 − 8 ) in the GWAS Catalog. Next, we performed a risk locus analysis of this novel model (p < 5 × 10 − 8 ) using FUMA. Subsequently, we conducted post-GWAS analysis with Multi-marker Analysis of GenoMic Annotation (MAGMA) to investigate gene-level and gene-set associations between genetic variants and phenotypes, applying a False Discovery Rate Correction (FDR)-corrected p-value threshold of < 0.05 for statistical significance. In addition, we applied the "GWAS-by-Subtraction" method, which involves comparing the lead loci identified through Genomic-SEM with those pinpointed using the genome-wide significance threshold (p < 5 × 10⁻⁸) in single-input GWAS. This approach facilitates the discovery of novel, high-utility loci by "subtracting" previously identified findings, thereby enhancing the efficiency and precision of genetic discoveries. FINEMAP To identify the most likely causal variants associated with our GWAS, we employed - fine-mapping methods of Bayesian Fine-Mapping Method (FINEMAP), that was implemented in the R package echolocatoR (v2.0.3). We set a posterior probability threshold of 0.95 to define a credible set of candidate causal variants. Specifically, we analyzed a 250-kb window around each lead SNP, computing each SNP’s posterior probability of causality within these regions. Any variant with a posterior probability exceeding 0.95 was considered a putative causal variant. FINEMAP is a sophisticated Bayesian fine-mapping tool designed to pinpoint the most probable causal variants driving observed associations. FINEMAP leverages a Bayesian statistical approach to compute the posterior probability of causality for each SNP within a defined genomic region, adjusting for LD. Transcriptome-Wide Association Study Because proximal SNP-level fine-mapping can be overly simplistic, we performed a cross-tissue sCCA-TWAS (sparse canonical correlation analysis-based Transcriptome-Wide Association Study) after identifying putative causal variants. This approach aims to pinpoint genes most relevant to the Genomic-SEM of allergic disorders by leveraging 37,920 precomputed eQTL features from the GTEx (v8) dataset[ 17 ]. We then selected genes with FDR-corrected TWAS p-values < 0.05 for further analysis using fine-mapping of gene sets (FOCUS), which evaluates the likelihood of a causal gene–phenotype relationship based on the FOCUS posterior inclusion probability. FOCUS applies a Bayesian framework to probabilistically attribute causality to genes associated with GWAS signals. By integrating multi-omics data, it addresses pleiotropy and LD confounding, calculating posterior inclusion probabilities (PIPs) to quantify the likelihood of a gene’s causal role. A PIP threshold of > 0.8 is commonly adopted to identify genes with strong evidence of causality. Gene Set and Pathway Enrichment Analysis We performed pathway enrichment analyses using the Molecular Signatures Database (MsigDB)[ 18 ] with Gene Set Enrichment Analysis (GSEA) to investigate potential relationships between allergic disorders and related pathways for the genes identified by MAGMA through FUMA gene-to-function. Additionally, we conducted further gene enrichment analyses using MendelVar ( https://mendelvar.mrcieu.ac.uk/submit/ ). Cell Type Annotation Analysis and Regional Contribution Analysis To identify etiologically relevant cell types associated with genomic-SEM–based allergic disorders, we used the CELLECT pipeline, which integrates cell-type expression specificity from single-cell RNA sequencing (scRNA-seq) data[ 19 ]. We employed the Tabula Muris dataset, comprising transcriptomic data from Mus musculus across multiple organs and tissues[ 20 ]. Preprocessing and normalization were performed in CELLEX to compute expression specificity likelihood scores for each region[ 21 ]. We then carried out cell-type–specific analyses with Stratified LD Score Regression (S-LDSC), classified the cell types, and applied an FDR-adjusted p-value threshold of < 0.05 to determine significance. To evaluate each region’s contribution to phenotypic variance, we also employed S-LDSC to estimate partitioned heritability across genomic annotations—such as genes, enhancers, and repressors. Specifically, S-LDSC uses a weighted LD matrix, allele frequency data, and GWAS summary statistics to apportion heritability among distinct genomic segments[ 22 ]. Genetic Correlations with External Traits We estimated the genetic correlation (rg) between the newly constructed genomic-SEM GWAS dataset and each of the 115 GWAS datasets from the FinnR12 database for common diseases in respiratory, digestive, circulatory, and other categories, as listed in Table S5 . Identification of Plasma Protein Biomarkers Next, we also leveraged plasma protein GWAS data from the deCODE (Iceland)[ 23 ] and UKB-PPP databases[ 24 ] as exposures, with allergic disorders as the outcome, to identify putative plasma biomarkers. We performed Mendelian randomization (MR) analyses using the MendelianRandomization R package (v0.7.0), with random-effect inverse-variance weighting (IVW) as the primary method for assessing associations. A Bonferroni correction was applied to account for multiple comparisons, with a significance threshold set at p < 0.05. The deCODE cohort acted as the discovery dataset, and the UKB-PPP cohort served as the validation dataset. Instrumental variables were selected based on the following criteria: p < 5 × 10 − 8 for association with plasma protein levels, not in LD with any other selected SNP (r² 10. Polygenic Risk Scores Evaluation We constructed PRS from genome-wide summary-level data to assess the genetic contributions of distinct chromosomal regions to disease susceptibility. Specifically, we applied PRS with Continuous Shrinkage (PRS-CS) algorithm, which estimates posterior SNP effect sizes by incorporating an external LD reference panel. This Bayesian regression framework integrates summary-level GWAS data with LD information to produce shrinkage-based effect estimates and, ultimately, the PRS. Results Structural Equation Model Fitting Based on LD-Score regression analysis, the Z-scores of genetic heritability contributions for the 8 GWAS that constitute the allergic disorders genomic-SEM are almost greater than 3, indicating strong statistical significance and robust heritable components ( Table S2 ). The genetic covariance values between each pair of traits are presented in Table S6 and Fig. 2 . The factor model of the genetic covariance matrix and the empirical covariance matrix in the Genomic-SEM fits well (CFI = 0.964, SRMR = 0.115 (Supplementary Table S7 )). Estimates of the latent variable loadings and residual covariances are provided in Table S8 . These results provide evidence for the presence of shared genetic factors within the Genomic-SEM. After filtering SNPs with significant heterogeneity (Q p-value < 0.05), 511,970 SNPs were removed. The final genomic-SEM analysis generated an indirect measurement-based GWAS comprising 6,918,772 SNPs to investigate the genetic architecture of allergic disorder multimorbidity. Assessment of Genomic Structural Equation Model Stability via LDSC for Genomic-SEM Using the LDSC method, we applied parameter controls and removed a total of 6,296,401 SNPs. The average chi-square value for the retained SNPs for genomic-SEM of allergic disorders was Mean chi-square = 1.514, the Lambda GC was 1.323, the Max chi-square was 121.56, the was h² = 0.0019 (SE = 0.0001), and the intercept was 1.0915 (SE = 0.0133) in the regression model, indicating a relatively good fit, with minor signs of population stratification or residual confounding. Additionally, the attenuation ratio of 0.1817 a moderate influence of confounding factors, with a stronger contribution from genetic effects. Risk Genetic Loci In the novel genomic-SEM, a total of 6,918,772 SNPs were analyzed, with 2,038 variants having a p-value < 5e-8 ( Figure S1 ). Employing the GWAS-by-Subtraction methodology, we identified 31 novel significant variants that were not previously detected in the input trait GWAS statistics or documented in the GWAS Catalog databases (Fig. 3 , Table S9 ). Functional annotation via the FUMA tool revealed that the majority of these loci were situated in intergenic (46.6%) and intronic (36.4%) regions, with a smaller fraction of 49 SNPs (1.0%) located in exonic regions. Further analysis identified 135 independent SNPs (r² < 0.6) and 83 lead SNPs (r² < 0.1) ( Table S10 ). Among the lead SNPs, three novel loci—chr1: rs114695117 (near RAVER2), chr6: rs145982144 (near UBDP1), and chr17: rs140397920 (near MED24)—were identified, representing genomic regions previously unreported in association with allergic disorders. These lead SNPs contributed to a total of 66 distinct risk loci ( Table S11 ). Gene-based analysis using MAGMA highlighted 62 potential allergic disorder-related genes, with the most significant enrichment observed on chromosome 2 and chromosome 6. Functional annotation analysis revealed that 59 out of 62 candidate genes (95.2%) encompassed genomic regions harboring at least one variant with high predicted pathogenicity (CADD Scaled C-Score ≥ 10), with 9 genes (14.5%) containing extremely deleterious variants (CADD ≥ 20) ( Table S12 , Figure S2 ). FINEMAP Fine-mapping analyses revealed strong associations at two genomic loci exceeding a posterior probability (PP) threshold of 0.95 and achieving genome-wide significance (GWAS P < 5 × 10⁻⁸). These loci include chr6: rs145982144 (near UBDP1 ) and chr12: rs78017269 (near SOX5 ), with fine-mapping t-statistics of -5.61 and − 5.59, and corresponding GWAS P-values of 2.03 × 10⁻⁸ and 2.33 × 10⁻⁸, respectively (Fig. 4 ). Gene-Level Identification of Susceptibility We performed sCCA-based TWAS, identifying 1350 genes that surpassed the FDR significance threshold. Among these, 35 genes had a TWAS P-value 6 (Fig. 5 B). Subsequently, we conducted fine-mapping analysis using the FOCUS method on the genomic structural equation data, identifying 127 genes with a posterior inclusion probability (PIP) greater than 0.8, which suggests they may represent potential pathogenic signals. To further confirm the "high-confidence" gene-level associations, we performed an intersection test to include 31 unique genes (Table 1 ). The TWAS identified FAM114A1 as the most strongly associated locus, demonstrating the highest positive association statistic (Z = 11.13) and most stringent significance threshold (FDR-P = 1×10 − 24 ). This was followed by RFTN2 (Z = 6.93, FDR-P = 7×10 − 9 ) and PLCL1 (Z = 6.76, FDR-P = 1.6×10 − 8 ), collectively indicating that elevated predicted expression of these genes is robustly correlated with allergic disorder susceptibility. Conversely, RP5-1115A15.1 exhibited the strongest inverse association (Z = − 6.81, FDR-P = 1×10 − 8 ), with RERE (Z = − 6.62, FDR-P = 3.9×10 − 8 ) ranking second, suggesting that reduced expression of these loci may confer increased disease risk. Table 1 Genetic Associations with Allergic Disorders in sCCA and FOCUS Analysis Gene CHR Srart position End position Heritability Squared TWAS Z TWAS FDR P FOCUS pip RP5-1115A15.1 1 8424644 8424645 0.1611 -6.81478 1.2E-08 0.923 RERE 1 8817642 8817643 0.3321 -6.61544 3.9E-08 0.997 RFTN2 2 197676044 197676045 0.1435 6.93279 7.2E-09 0.969 PLCL1 2 197804701 197804702 0.1789 6.757755 1.6E-08 0.837 IL1R2 2 101991843 101991844 0.4395 4.325578 1.8E-03 0.95 FARP2 2 241356242 241356243 0.3243 3.11431 5.0E-02 0.932 ATP1B3 3 141876123 141876124 0.0905 -4.92 2.0E-04 0.999 FAM114A1 4 38867676 38867677 0.3223 11.1337 1.0E-24 1 KLF3 4 38680586 38680587 0.0735 -6.40443 1.3E-07 0.999 FAM114A1 4 38867676 38867677 0.5296 5.56709 1.1E-05 1 FAM114A1 4 38867676 38867677 0.3542 5.0457 1.2E-04 0.997 FAM114A1 4 38867676 38867677 0.3542 5.0457 1.2E-04 0.971 RFC1 4 39366374 39366375 0.1071 4.52361 9.2E-04 0.997 RFC1 4 39366374 39366375 0.1071 4.52361 9.2E-04 1 SPATA5 4 122923073 122923074 0.2112 3.83259 8.5E-03 0.98 U91328.19 6 25992661 25992662 0.7309 5.824 3.0E-06 0.964 U91328.19 6 25992661 25992662 0.475 -5.64151 7.9E-06 0.998 ABT1 6 26596951 26596952 0.2157 -3.6894 1.3E-02 0.902 PVT1 8 127794532 127794533 0.2175 5.58441 1.0E-05 0.883 RANBP6 9 6015624 6015625 0.1052 4.31736 1.8E-03 0.956 KIAA2026 9 6007824 6007825 0.1628 4.26985 2.1E-03 0.984 TGFBR1 9 99104037 99104038 0.1129 -3.76475 1.0E-02 0.985 IL15RA 10 5978186 5978187 0.3351 6.06753 8.8E-07 1 SUFU 10 102503986 102503987 0.109 5.95708 1.5E-06 0.916 RBM17 10 6088986 6088987 0.2453 5.18371 6.9E-05 0.994 CUEDC2 10 102432660 102432661 0.0362 -4.91882 2.0E-04 0.874 RP11-563J2.2 10 6277686 6277687 0.2516 -3.89475 7.0E-03 0.993 PRKCQ-AS1 10 6580418 6580419 0.1954 3.78693 9.8E-03 0.999 SIK2 11 111602390 111602391 0.1017 6.103014 7.2E-07 0.998 C11orf1 11 111878934 111878935 0.1407 -4.76819 3.7E-04 0.933 ELF1 13 41061272 41061273 0.0806 -5.17324 7.1E-05 0.983 CHP1 15 41230838 41230839 0.2169 5.69736 6.0E-06 0.95 TOM1L2 17 17972421 17972422 0.0527 -4.43104 1.2E-03 0.936 KRT15 17 41522528 41522529 0.3557 -4.138814 3.2E-03 0.834 CCDC116 22 21632715 21632716 0.4066 -4.66051 5.7E-04 0.945 LINC01637 22 20957091 20957092 0.2046 -3.639734 1.5E-02 0.823 Pathway and Cell Type Enrichment Analysis GSEA of MAGMA-derived risk loci highlighted REACTOME_ADAPTIVE_IMMUNE_ SYSTEM as the most significantly enriched pathway within the Curated Gene Sets collection. Delving into Canonical Pathways, we observed robust associations within three prominent immune-related modules including REACTOME_ADAPTIVE_IMMUNE_SYSTEM, REACTOME_ DISEASES_OF_IMMUNE_SYSTEM and REACTOME_COSTIMULATION_BY_THE_CD28_ FAMILY. Notably, these enriched pathways encompassed genes critically involved in Th2 polarization, antigen presentation, and T-cell receptor signaling ( Table S13 ). This mechanistic convergence strongly links these pathways to established allergic phenotypes such as asthma and atopic dermatitis, as well as immune-mediated comorbidities like type 1 diabetes and systemic lupus erythematosus. Furthermore, cataloged associations for these pathways extend beyond allergic disorders, encompassing conditions like asthma, type 1 diabetes, and systemic lupus erythematosus, and also extending to neuropsychiatric conditions such as autism spectrum disorder and schizophrenia ( Figure S3 ). Additionally, through MendelVar enrichment mapping, we observed significant gene overlap with diseases such as Mayer-Rokitansky-Kuster-Hauser syndrome , osteochondrodysplasia , and immune system diseases like primary immunodeficiency . Diseases related to glucose metabolism and liver conditions, including liver cirrhosis , were also enriched ( Figure S4 ). The most significant cell types enriched in the allergic disorder GWAS were primarily immune-related, with a strong representation of T cells, natural killer (NK) cells, and B cells. These cell types, including Fat T cells, Marrow B cells, Limb Muscle T cell, Lung T cells, and Marrow regulatory T cell, Trachea blood cell, Spleen T cell, play crucial roles in immune regulation and allergic responses. Additionally, myeloid cells and leukocytes from various tissues, such as the Lung myeloid cells and Kidney leukocytes, were also significantly enriched ( Table S14 ). Heritability Enrichment Across Genomic Functional and Regulatory Regions Based on the genetic enrichment analysis of genomic functional regions using S-LDSC ( Table S15 ), we observed a significant enrichment pattern of heritability in epigenetic regulatory elements and core functional regions of genes. Specifically, evolutionarily enhancer regions, conserved elements, and DNase I hypersensitive sites (DGF) showed the strongest positive genetic contribution, suggesting that these regulatory elements play a key role in influencing complex traits through cis-regulatory networks. These findings underscore the critical role of cis-regulatory networks, orchestrated through these genomic elements, in influencing the complex genetic architecture of allergic disorders, highlighting that variations within these regulatory regions are likely key drivers of disease susceptibility and multimorbidity. Genetic Correlations with External Traits and Plasma Biomarkers Identification Comprehensive genetic correlation analyses using LDSC revealed significant pleiotropy between allergic disorders and 12 out of 115 common diseases. Notably, acute upper respiratory infections exhibited the strongest positive genetic correlation (rg = 0.143 ± 0.046, P = 0.002), followed by bipolar affective disorders (rg = 0.136 ± 0.05, P = 0.007) and Crohn's disease (rg = 0.148 ± 0.075, P = 0.048). Conversely, inverse associations were observed for disorders of lipoprotein metabolism (rg = -0.101 ± 0.043, P = 0.02) and heart failure (rg = -0.088 ± 0.043, P = 0.04), suggesting potential protective mechanisms (Fig. 6 ). Two-sample MR analysis revealed seven plasma proteins with genetically predicted levels significantly associated with allergic disorders after Bonferroni correction in the deCODE discovery cohort: MANF , GLB1 , HEXIM1 , KYNU , ICAM4 , KRT5 , and LAYN . Of these, four proteins ( MANF , GLB1 , ICAM4 , and LAYN ) were consistently replicated in the UKB-PPP cohort using inverse-variance weighted models, demonstrating bidirectional concordance (Fig. 7 ). Construction of Polygenic Risk Scores from Summary Data Our analysis demonstrates that the variants in our PRS are strongly associated with disease onset risk, and that the genetic contribution to disease susceptibility varies significantly across chromosomal regions ( Table S16 ). Specifically, when considering all SNPs, chromosomes 14 and 20 exhibit the highest positive genetic contribution. However, among the top 1000 most contributing SNPs, chromosomes 6 (86 SNPs) and 5 (85 SNPs) are most prominent, while within the top 10,000, chromosomes 2 (821 SNPs) and 1 (805 SNPs) are most frequent. This shift in prominent chromosomes suggests that different sets of genes and regulatory elements, located in these regions, influence disease susceptibility at different levels of genetic contribution. Discussion Allergic disorders, encompassing conditions such as allergic asthma, atopic dermatitis, allergic rhinitis, and others, represent a significant global health burden, characterized by not only highly prevalent but also exhibit complex comorbidities and multimorbidities, often progressing through a well-documented sequence known as the "atopic march". The genetic basis of allergic multimorbidity remains underexplored, as traditional single-trait GWAS approaches face inherent limitations in dissecting the intricate genetic and phenotypic interplay underlying co-occurring allergic disorders. Previously, genomic SEM has demonstrated utility in identifying shared genetic architectures, such as the comorbidity between inflammatory bowel disease and asthma, as well as associations between allergic disorders and inflammatory diseases, body mass index, and gastroesophageal reflux disease[ 12 – 14 ]. We applied this novel approach to integrate genome-wide association data by first constructing a genetic covariance matrix via multivariable LDSC, followed by specifying a latent factor model representing eight allergic traits within the SEM framework. The application of Genomic-SEM to metabolic syndrome research recently successfully identified 159 novel SNPs not previously cataloged in the GWAS Catalog, along with 82 additional SNPs exhibiting significant pleiotropic effects independent of the constituent metabolic syndrome traits[ 25 ]. Similarly, Andrew et al. used this method to analyze gene expression profiles across 13 major psychiatric disorders. This analysis successfully identified genes associated with both shared and disorder-specific genetic risks, leading to the proposal of repurposing existing drugs as potential therapeutic agents[ 26 ]. Through the novel established model and a suite of integrative post-GWAS analyses, we identified 31 novel, pleiotropic variants and prioritized 31 key genes significantly associated with allergic disorder multimorbidity. Among the lead SNPs, three—chr1:rs114695117 (near RAVER2 ), chr6:rs145982144 (near UBDP1 ), and chr17:rs140397920 (near MED24 )—represent previously unreported associations with allergic disorders. The variant rs114695117 is located near RAVER2 , which encodes a ribonucleoprotein involved in the regulation of alternative splicing. Previous studies have implicated RAVER2 in splicing events within the thymic epithelium [ 27 ] and identified it as a susceptibility gene for liver injury in rheumatoid arthritis patients treated with immunomodulatory drugs[ 28 ]. Consequently, RAVER2 may contribute to allergic inflammation by modulating the splicing patterns of immune-related genes. The variant rs145982144 lies near the pseudogene UBDP1 , a component of the ubiquitin system. While the precise role of UBDP1 in immune regulation remains to be elucidated, ubiquitination is known to play a critical role in protein degradation, DNA repair, and immune signaling, and its dysregulation has been implicated in autoimmunity via modulation of NF-κB and STAT signaling pathways[ 29 ]. Therefore, rs145982144 might influence allergic disease susceptibility by altering UBDP1 function, potentially affecting the activity of these pathways. Finally, rs140397920 is located near MED24 , a subunit of the Mediator complex, which bridges transcription factors and RNA polymerase II, thereby regulating the expression of specific genes. Within the immune system, the Mediator complex is involved in the transcriptional regulation of T cell programs[ 30 , 31 ]. Notably, MED24 has previously been identified within a region containing asthma risk alleles, supporting its potential role in allergic disease susceptibility[ 32 ]. Furthermore, fine-mapping analyses identified two putative causal SNPs associated with allergic disorders—rs145982144 (near UBDP1 ) and rs78017269 (near SOX5 ). Although these SNPs represent novel discoveries in allergic disease GWAS, their positional genes have prior indirect evidence supporting immunological relevance. Specifically, SOX5 regulates T-cell receptor signaling to maintain immune tolerance—a mechanism critically implicated in suppressing type 2 inflammation[ 33 ]. This aligns with existing GWAS evidence linking SOX5 variants to asthma susceptibility, further supporting its pathological significance in allergic pathogenesis[ 34 , 35 ]. Our sCCA and subsequent FOCUS fine-mapping identified 31 genes with potential causal roles in allergic disorders. While some of these genes, such as IL1R2 , IL15RA , TGFBR1 , and PVT1 , have established associations with allergic traits[ 36 , 37 ], others represent novel candidates with plausible links to allergic pathways. Notably, FAM114A1 (Z = 11.13, FDR-P = 1 × 10–24), RFTN2 (Z = 6.93, FDR-P = 7 × 10 − 9), PLCL1 (Z = 6.76, FDR-P = 1.6 × 10 − 8), RP5-1115A15.1 (Z = -6.81, FDR-P = 1.2 × 10 − 8), and RERE (Z = -6.62, FDR-P = 3.9 × 10 − 8) emerged as high-confidence loci, underscoring their potential critical roles in allergic pathogenesis. FAM114A1 , a relatively uncharacterized gene, has been implicated in immune responses and apoptosis[ 38 ]. Furthermore, its role in myocardial fibrosis via regulation of angiotensin II has been demonstrated[ 39 ], and angiotensin II itself has been linked to increased asthma risk and airway inflammation[ 40 , 41 ]. This suggests a potential mechanism whereby FAM114A1 might influence allergic processes, possibly modulated by Angiotensin-(1–7). However, the precise function of FAM114A1 remains largely unexplored and warrants further investigation. RFTN2 is involved in, or upstream of, dsRNA transport and is predicted to correlate with type I interferon expression, a pathway implicated in ulcerative colitis susceptibility[ 42 ]. Recent reports also highlight RFTN2 's function in neuroglia[ 43 , 44 ], adding further complexity to its potential roles. PLCL1 , located on chromosome 2, encodes a phospholipase C-like enzyme involved in signal transduction. It generates second messengers, such as IP3 and DAG, activating calcium signaling and protein kinase C, thereby influencing B and T cell function. Because allergic diseases involve IgE production by B cells and mast cell degranulation, increased PLCL1 expression could potentially enhance these immune cell responses, elevating allergic risk. While direct studies linking PLCL1 to allergy are limited, research on rheumatoid arthritis demonstrates its involvement in regulating key inflammatory cytokines, including IL-6 and IL-1β[ 45 ], suggesting a possible parallel mechanism in allergic diseases. Intriguingly, several other identified genes, while not directly implicated in allergic diseases in previous reports, have demonstrated roles in the pathogenesis of glioma, including SUFU [ 46 ], RBM17 [ 47 ], FAM114A1 [ 48 ], CHP1 [ 49 ], CUEDC2 [ 50 ], RFC1 [ 51 ], RANBP6 [ 52 ], PRKCQ-AS1 [ 53 ], and FARP2 [ 54 ]. Given the emerging concept of glioma as a marker AllergoOncology disease, these findings may shed light on the complex genetic underpinnings of AllergoOncology. The enrichment of genes associated with Th2 polarization, T-cell receptor signaling, and other immune regulatory processes in this analysis aligns seamlessly with previous research. Han et al. identified genetic variants in these immune pathways as significant contributors to asthma susceptibility, demonstrating that asthma-associated variants are enriched in open chromatin regions of immune cells, particularly CD4⁺ and CD8⁺ T lymphocytes. Their findings also revealed an overrepresentation of T-cell receptor signaling pathways among asthma risk loci [ 55 ]. Recent studies have further highlighted the role of key genes, such as CD52 —encoding a membrane glycoprotein expressed on various leukocytes—in linking T-cell-related pathways to asthma phenotypes [ 55 – 57 ]. Furthermore, the PRS analysis underscores the variability and intricate genetic architecture underlying allergic disorders. This observation highlights the distinct roles of specific chromosomes in the pathogenesis of these conditions. Our novel SEM analysis explored the shared genetic architecture and its correlation with external traits, identifying strong comorbidities, primarily neuropsychiatric disorders. These included bipolar disorder, manic episode, migraine, and attention deficit hyperactivity disorder (ADHD). This finding aligns with prior clinical observations and post-GWAS research on allergic traits like asthma, atopic dermatitis, allergic rhinitis, and mental disorders [ 58 – 62 ]. It suggests that the complex comorbidity of allergic traits is driven by a shared genetic and environmental etiology, with our research indicating that the link between allergic disorders and neuropsychiatric conditions may stem from this shared genetic architecture. This further underscores the importance of investigating causal loci. Our MR findings highlight four plasma proteins – MANF, GLB1, ICAM4, and LAYN – exhibiting robust associations indicative of a potential causal role in allergic disorders. This finding addresses limitations of prior retrospective studies[ 63 – 65 ], providing genetic evidence of causal effects. These proteins, collectively implicated in regulating immune homeostasis and inflammation, offer novel mechanistic targets in allergy. The replicated associations and suggestive causal evidence underscore their translational value as potential therapeutic interventions or diagnostic biomarkers, urgently necessitating functional studies to elucidate their precise regulatory networks in allergic inflammation. Our study has several limitations. First, the shared genetic architecture of allergic disorders was investigated primarily in individuals of European ancestry. Since genomic-SEM relies on LDSC—a method sensitive to ancestry-specific LD patterns—our analysis was confined to a genetically homogeneous population. Consequently, validation of this model across diverse ethnic populations is imperative to establish its generalizability in future studies. Second, the susceptibility loci identified in this study require experimental validation, particularly functional studies at the protein level, to elucidate their translational relevance and mechanistic roles in allergic pathophysiology. Third, while Cochran’s Q-test was applied to assess heterogeneity (Q SNP ), this approach prioritizes the detection of SNPs with pleiotropic effects across multiple traits rather than those exhibiting heterogeneous effects on specific allergic phenotypes. This methodological focus may have obscured SNPs with trait-specific associations critical to individual allergic disorders. Finally, the scarcity of GWAS data for allergic disorders in East Asian populations precluded validation of our European ancestry-derived genetic model in other ancestries. Addressing this gap through expanded sample sizes in underrepresented populations remains a critical priority to enhance the robustness and global applicability of our findings. Targeting pleiotropic loci identified in future research holds significant promise for drug development, as these loci could enable simultaneous therapeutic intervention across multiple allergic diseases. However, given the intricate relationship between allergy and immunity, it is equally crucial to actively investigate the role of these genes in immune regulation. In this study, we identified a genetic correlation between allergic disorders and other traits, notably psychiatric disorders. However, integrating biological experiments such as functional validation of these loci through gene expression analysis or CRISPR-based gene editing can substantiate the biological significance of these genetic findings and elucidate their underlying mechanisms[ 66 , 67 ]. Moreover, given the recent successes of clustering and non-clustering machine learning algorithms, as well as artificial intelligence, developing additional computational methods represents a viable and promising strategy to further elucidate the mechanisms underlying allergic disorders [ 68 – 71 ]. Conclusion Leveraging genomic SEM, our novel GWAS elucidated the shared genetic architecture of allergic traits. Employing a suite of post-GWAS methodologies, we robustly identified 2038 genome-wide significant SNP loci, including 31 previously unreported loci. Furthermore, integrating sCCA with FOCUS, we precisely pinpointed 31 candidate causal genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Moreover, we identified four putative causal plasma protein biomarkers: MANF, GLB1, ICAM4, and LAYN. Declarations Author contributions X.L. 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Supplementary Files Additionalfile1.docx Additionalfile2.docx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Accept 07 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 28 Mar, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6162322","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435994383,"identity":"e2e6a256-16de-41fe-91e2-cbc0f62fad7f","order_by":0,"name":"Jingsheng Ruan","email":"","orcid":"","institution":"Jinshan Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jingsheng","middleName":"","lastName":"Ruan","suffix":""},{"id":435994384,"identity":"5da6cc65-197d-494a-acc7-303695a2fd27","order_by":1,"name":"Xinglin Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZiB+YMDAwMbe2PhAokJCTp4oLQkgLTyHDxtYnLEwNmwgxqYEECGRliZR2VaRyHCAgGqD47yHXyQU3EnsY8gxkLg5TyKBsYH54aMbeLRINvOlWSQYPEtsYzhjYDhzm0QeOwObsXEOHi38zDxmBgkGhxPbGHsMkiW3SRQzNvCwSePTwgbXwsxjcPjvHInEhgMEtABtMX4A1sLGltgg2UCEFslmHjNgIB82buNhPswgcUzC2LCZgF8Mzp8x/vDhz2HZ+fMftv+QqKmTk2dvfvgYnxaQdySAhGMDnM+MXzlYyQcgYU9Y3SgYBaNgFIxYAACUx0oGFkQRUAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9675-9043","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xinglin","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2025-03-05 11:57:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6162322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6162322/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-025-06465-8","type":"published","date":"2025-04-15T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79659511,"identity":"ef4b24e6-f119-4fe3-8e42-f0f116e5c4f1","added_by":"auto","created_at":"2025-04-01 09:24:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528234,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustration\u003c/p\u003e","description":"","filename":"Binder11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/83c229e1fb9774b40f5a2d81.jpg"},{"id":79659532,"identity":"8c5345ef-e605-4085-8853-4f31c2a888b0","added_by":"auto","created_at":"2025-04-01 09:24:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322842,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic Correlation Matrix of Allergic Disorders. The color intensity and circle size represent correlation strength, ranging from -1 to +1.\u003c/p\u003e","description":"","filename":"Binder12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/999c5641138acba3b2129537.jpg"},{"id":79659517,"identity":"00988536-0b35-4c72-9955-9898a4534bea","added_by":"auto","created_at":"2025-04-01 09:24:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":236603,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan Plot of Novel Genomic-SEM Results for Allergic Disorders. The x-axis denotes chromosomal positions, while the y-axis represents the negative logarithm of the P-value (−log10(P)). The dashed line indicates the genome-wide significance threshold at P = 5 × 10⁻⁸. Labeled points correspond to novel SNPs identified through the novel GWAS approach.\u003c/p\u003e","description":"","filename":"Binder13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/93c040a11a8abce1cf6a18c7.jpg"},{"id":79659583,"identity":"5baa9458-ed56-476c-9b72-c7f1928aeb24","added_by":"auto","created_at":"2025-04-01 09:24:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":263338,"visible":true,"origin":"","legend":"\u003cp\u003eFine-mapping Results of Genomic Loci with Strong Associations (PP \u0026gt; 0.95) Identified by FINEMAP\u003c/p\u003e","description":"","filename":"Binder14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/a143d6473c065af13ea2af23.jpg"},{"id":79659516,"identity":"e17a40ba-5101-4435-88fb-1d08e9386233","added_by":"auto","created_at":"2025-04-01 09:24:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":455253,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan Plot of Results from sCCA TWAS Analysis for Allergic Disorders. Manhattan plot of the TWAS P-values for allergic disorders. The x-axis represents chromosomes, and the y-axis shows the negative logarithm of the P-value (−log10(p)). The horizontal red line corresponds to a threshold of TWAS P = 1e-12 (A). Manhattan plot of the TWAS Z-scores for allergic disorders. The x-axis represents chromosomes, and the y-axis displays the Z-scores. The horizontal blue lines mark the absolute Z-score value of 7, which represents the threshold for significance (B).\u003c/p\u003e","description":"","filename":"Binder15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/c61130fa02502f8b88369840.jpg"},{"id":79660322,"identity":"0983149d-cf07-42dd-85ca-d05f20333937","added_by":"auto","created_at":"2025-04-01 09:32:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":702223,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic Associations Between Allergic Disorders and External Traits\u003c/p\u003e","description":"","filename":"Binder16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/d74bd3fe67fbf7ef59c60a9b.jpg"},{"id":79659587,"identity":"1c86c680-83d6-42a6-83fa-e81fae3206cf","added_by":"auto","created_at":"2025-04-01 09:24:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":129445,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot of Plasma Protein Exposures and Associations with Allergic Disorders in Mendelian Randomization Analysis. Volcano plots of Mendelian randomization results for plasma protein exposures and allergic disorders in the deCODE discovery cohort (A) and UKB-PPP cohort (B).\u003c/p\u003e","description":"","filename":"Binder17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/e722d0118cc9edd78b8001b7.jpg"},{"id":81050996,"identity":"087f85b7-ad94-44a4-ac9f-a4ac31a1dc6f","added_by":"auto","created_at":"2025-04-21 16:09:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3933445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/d5d1d0b0-655b-4016-b2f0-80b207d12db3.pdf"},{"id":79659586,"identity":"1cfe3d82-a1bc-41df-8b89-f77a606c626c","added_by":"auto","created_at":"2025-04-01 09:24:08","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":667696,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/c6a548fdb412170763a963ac.docx"},{"id":79659585,"identity":"a31e6ca9-8d42-42ae-a696-02c84bd63f38","added_by":"auto","created_at":"2025-04-01 09:24:08","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":211543,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6162322/v1/c11cff90fadb51bc9687785e.docx"}],"financialInterests":"","formattedTitle":"Genomic Structural Equation Modeling Elucidates the Shared Genetic Architecture of Allergic Disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllergy exemplifies the dual nature of the immune system\u0026mdash;it serves as both a protector and a potential threat. It is a systemic disorder caused by immune system dysregulation and encompasses conditions such as allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis. Given the high prevalence of allergic diseases, the World Health Organization (WHO) has classified them among the top three diseases requiring priority prevention and treatment in the 21st century. Recent estimates from the Global Burden of Disease Study reveal substantial global allergic disease burdens, with approximately 260\u0026nbsp;million prevalent cases of asthma and 204\u0026nbsp;million cases of atopic dermatitis worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Allergic rhinitis, meanwhile, is estimated to afflict approximately 20% of the global population[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Alarmingly, the prevalence of these conditions continues to escalate, reflecting a persistent upward trajectory in allergic disease morbidity that underscores urgent public health priorities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAllergic disorders arise from complex gene-environment interactions and are closely associated with type 2 inflammation and epithelial barrier dysfunction. While these conditions are often viewed as distinct diseases, comorbidities and multimorbidities are common. For instance, cross-sectional studies indicate that individuals with other allergic disorders are more susceptible to developing asthma[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A birth cohort study of 2,311 Canadian children revealed that infants diagnosed with atopic dermatitis at one year of age had an 11-fold and 7-fold increased risk of developing allergic asthma and allergic rhinitis, respectively, by the age of three, compared to those without atopic dermatitis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This temporal pattern of disease progression, where different allergic disorders emerge sequentially over time, is known as the \"atopic march\".\u003c/p\u003e \u003cp\u003eThe mechanisms underlying allergic disease comorbidities and multimorbidities are highly complex, with current research suggesting that genetic factors, gut microbiota, IgE-mediated and non-IgE-mediated pathways all contribute[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With the advent of the Human Genome Project (HGP), genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have underscored the role of genetic heritability in allergic disorders[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. GWAS have identified key susceptibility loci, such as rs11740584 and rs2299007 in \u003cem\u003eKIF3A\u003c/em\u003e for food allergy, rs8111930 near \u003cem\u003eMRPL4\u003c/em\u003e for allergic rhinitis, and rs2243250 in \u003cem\u003eIL4\u003c/em\u003e and rs20541 in \u003cem\u003eIL13\u003c/em\u003e for allergic asthma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, a recent study leveraging GWAS data and innovative analytical approaches identified multiple shared loci between eosinophilic disorders and allergic diseases (e.g., \u003cem\u003eC11orf30\u003c/em\u003e and \u003cem\u003eSMAD3\u003c/em\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Using genomic structural equation modeling (Genomic-SEM), Gong et al. demonstrated substantial genetic correlation, represented by a common latent factor, among asthma, eczema, and allergic rhinitis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings highlight the crucial role of genetic factors in allergic disease comorbidities and multimorbidities.\u003c/p\u003e \u003cp\u003eTo date, numerous susceptibility single nucleotide polymorphisms (SNPs) and genes linked to specific allergic disorders have been identified. However, our understanding of the precise genetic and biological mechanisms underlying allergic disorder comorbidities remains limited, and existing GWAS findings are still underexplored in the context of combined allergic conditions.\u003c/p\u003e \u003cp\u003eTo address these challenges, this study aims to integrate multiple genetic analysis methodologies and highly correlated association analysis tools to uncover potential molecular mechanisms and expand the understanding of disease interconnections. Specifically, we focus on genomic loci and chromosomal regions associated with allergic disorders to identify potential therapeutic targets. This research not only enhances our comprehension of allergic disorders but also provides a theoretical and practical foundation for prevention and intervention strategies aimed at reducing allergic disease burden and associated complications.\u003c/p\u003e \u003cp\u003eIn this study, we constructed a Genomic-SEM utilizing publicly available GWAS summary statistics for specific allergic disorders. This method integrates genome-wide association data with structural equation modeling to elucidate shared genetic architectures and causal relationships between traits, while rigorously accounting for sample overlap and pleiotropic effects. The analytical framework has been validated in prior studies investigating comorbidities such as inflammatory bowel disease and asthma, as well as associations between body mass index and childhood asthma, among others[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By leveraging GWAS statistics, we estimated the associations of SNPs with latent phenotypes, effectively conducting a GWAS on an unmeasured allergic disorder construct. Furthermore, we employed integrative approaches from systems biology, defining unexplained genetic variation within allergic disorders as potential novel genetic markers and conducting extensive GWAS-related analyses. Although this approach does not fully capture the intricate interactions between allergic disorder pathways and multifactorial influences\u0026mdash;given that allergic disorders are driven by a combination of genetic, environmental, and stochastic factors\u0026mdash;our methodology minimizes confounding effects from known biomarkers, enabling analysis of previously challenging datasets. Additionally, we performed thousands of causal inference analyses based on GWAS summary data to help clinicians and biologists predict potential plasma biomarkers and clinical disease causal relationships, ultimately informing prevention strategies and potential interventions for patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA flowchart overview is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenomic-SEM GWAS Data Source\u003c/h2\u003e \u003cp\u003eThe GWAS summary statistics used for Genomic-SEM analyses were derived from eight independent GWAS related to allergic disorders. These datasets were obtained from previously published GWAS studies, as well as the FinnGen R12 and UK Biobank (UKB) databases. The allergic phenotypes included in the analysis were allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis. All contributing GWAS studies received ethical approval from their respective institutional review boards, and all participants provided informed consent. The summary statistics underwent rigorous quality control procedures to ensure data integrity. A detailed list of the included GWAS datasets is available in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality Control for Genomic-SEM\u003c/h3\u003e\n\u003cp\u003eWe implemented a rigorous quality control pipeline based on recommended filtering criteria for all autosomal SNPs from the eight input GWAS datasets. To ensure consistency across studies, SNPs were filtered against the 1000 Genomes Phase 3 European (EUR) reference panel, excluding those with a minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.01, effect size estimates of zero, reference panel mismatches, or allele inconsistencies. Given that the GWAS datasets originated from different genomic repositories and study populations, we accounted for potential sample overlap to prevent inflation in test statistics and improve result robustness. Statistical methods were applied to estimate and adjust for any residual overlap, minimizing bias in effect size estimates and ensuring the reliability of downstream analyses.\u003c/p\u003e\n\u003ch3\u003eGenomic-SEM Construction\u003c/h3\u003e\n\u003cp\u003eWe applied Genomic-SEM using GenomicSEM R package (v0.0.5) to investigate the shared genetic architecture underlying allergic traits. Genomic-SEM is a recently developed multivariate method that enables the exploration of latent structures across multiple phenotypes by estimating a multivariable genetic model[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A key advantage of Genomic-SEM is that it is robust to sample overlap and differences in sample sizes, minimizing biases associated with these factors. Additionally, it facilitates the identification of genetic variants that influence only a subset of traits rather than all complex traits under investigation, thereby distinguishing trait-specific effects from broader cross-trait genetic susceptibility.\u003c/p\u003e \u003cp\u003eThe Genomic-SEM analysis was conducted in two stages. In the first stage, we estimated the empirical genetic covariance matrix and the corresponding sampling covariance matrix. To achieve this, we compiled GWAS summary statistics for allergic disorders and applied a multivariate extension of Linkage Disequilibrium (LD) Score Regression (LDSC). LDSC is a robust statistical framework that quantifies polygenic architecture using GWAS summary statistics. By modeling the relationship between variant association statistics and their LD profiles, LDSC disentangles true polygenic signals from confounding biases such as cryptic relatedness and population stratification. In our study, we used a multivariate LDSC to generate an empirical genetic covariance matrix across eight allergic traits. This matrix served as the input for the common factor model in SEM, while SNP-based heritability estimates (h\u003csup\u003e2\u003c/sup\u003e) for each trait are reported in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn the second stage, we specified an SEM model to minimize the discrepancy between the hypothesized covariance structure and the empirical covariance matrix estimated in the first stage. Our primary objective was to identify the shared genetic factors underlying eight allergic disorders. To this end, we tested the fit of a single-factor model, evaluating model adequacy using Standardized Root Mean Square Residual (SRMR), model chi-square (χ\u0026sup2;), Akaike Information Criterion (AIC), and Comparative Fit Index (CFI) (see \u003cb\u003eTable S3\u003c/b\u003e and \u003cb\u003eTable S4\u003c/b\u003e). By implementing an appropriate common factor SEM specification, we incorporated individual autosomal SNP associations into the genetic and sample covariance matrix, allowing for a genome-wide analysis of shared covariance across the eight allergic disorder GWAS datasets. Furthermore, to ensure that the SNPs identified by the novel genomic SEM exhibit consistent effect directions, we conducted a heterogeneity test for each SNP and excluded those with a Cochran's Q statistic p-value less than 0.05.\u003c/p\u003e\n\u003ch3\u003eMultilevel Evaluation of Genomic Structural Equation Model\u003c/h3\u003e\n\u003cp\u003eIn addition to the aforementioned methods for testing genomic-SEM fitting, we implemented another LDSC approach to evaluate the stability of the genomic-SEM fit. Specifically, we assessed the model using parameters such as the mean chi-square, lambda GC, maximum chi-square, h\u0026sup2;, intercept, and ratio (calculated as (LDSC intercept \u0026ndash; 1) / (mean χ\u0026sup2; \u0026ndash; 1)). The detailed control of LDSC parameters included: retaining SNPs with missing values, retaining SNPs with an INFO score\u0026thinsp;\u0026lt;\u0026thinsp;0.9, retaining SNPs with a MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and excluding SNPs with p-values outside the valid range or with ambiguous strand orientation.\u003c/p\u003e\n\u003ch3\u003eIdentification of Significant and Novel Genomic Loci\u003c/h3\u003e\n\u003cp\u003eWe used FUMA (Functional Mapping and Annotation; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fuma.ctglab.nl/\u003c/span\u003e\u003cspan address=\"https://fuma.ctglab.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify genomic loci and to detect both independent significant and lead SNPs associated with the Genomic-SEM of allergic disorders[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These SNPs exhibited low LD with other variants (r\u0026sup2; \u0026lt; 0.6 for independent SNPs and r\u0026sup2; \u0026lt; 0.1 for lead SNPs) and surpassed the genome-wide significance threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). We then compared the identified lead SNPs and loci with those reported in single-trait GWAS to assess potential pleiotropy, cross-referencing significant published associations (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) in the GWAS Catalog. Next, we performed a risk locus analysis of this novel model (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) using FUMA. Subsequently, we conducted post-GWAS analysis with Multi-marker Analysis of GenoMic Annotation (MAGMA) to investigate gene-level and gene-set associations between genetic variants and phenotypes, applying a False Discovery Rate Correction (FDR)-corrected p-value threshold of \u0026lt;\u0026thinsp;0.05 for statistical significance. In addition, we applied the \"GWAS-by-Subtraction\" method, which involves comparing the lead loci identified through Genomic-SEM with those pinpointed using the genome-wide significance threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸) in single-input GWAS. This approach facilitates the discovery of novel, high-utility loci by \"subtracting\" previously identified findings, thereby enhancing the efficiency and precision of genetic discoveries.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFINEMAP\u003c/h2\u003e \u003cp\u003eTo identify the most likely causal variants associated with our GWAS, we employed - fine-mapping methods of Bayesian Fine-Mapping Method (FINEMAP), that was implemented in the R package echolocatoR (v2.0.3). We set a posterior probability threshold of 0.95 to define a credible set of candidate causal variants. Specifically, we analyzed a 250-kb window around each lead SNP, computing each SNP\u0026rsquo;s posterior probability of causality within these regions. Any variant with a posterior probability exceeding 0.95 was considered a putative causal variant. FINEMAP is a sophisticated Bayesian fine-mapping tool designed to pinpoint the most probable causal variants driving observed associations. FINEMAP leverages a Bayesian statistical approach to compute the posterior probability of causality for each SNP within a defined genomic region, adjusting for LD.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTranscriptome-Wide Association Study\u003c/h3\u003e\n\u003cp\u003eBecause proximal SNP-level fine-mapping can be overly simplistic, we performed a cross-tissue sCCA-TWAS (sparse canonical correlation analysis-based Transcriptome-Wide Association Study) after identifying putative causal variants. This approach aims to pinpoint genes most relevant to the Genomic-SEM of allergic disorders by leveraging 37,920 precomputed eQTL features from the GTEx (v8) dataset[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We then selected genes with FDR-corrected TWAS p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for further analysis using fine-mapping of gene sets (FOCUS), which evaluates the likelihood of a causal gene\u0026ndash;phenotype relationship based on the FOCUS posterior inclusion probability. FOCUS applies a Bayesian framework to probabilistically attribute causality to genes associated with GWAS signals. By integrating multi-omics data, it addresses pleiotropy and LD confounding, calculating posterior inclusion probabilities (PIPs) to quantify the likelihood of a gene\u0026rsquo;s causal role. A PIP threshold of \u0026gt;\u0026thinsp;0.8 is commonly adopted to identify genes with strong evidence of causality.\u003c/p\u003e\n\u003ch3\u003eGene Set and Pathway Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eWe performed pathway enrichment analyses using the Molecular Signatures Database (MsigDB)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] with Gene Set Enrichment Analysis (GSEA) to investigate potential relationships between allergic disorders and related pathways for the genes identified by MAGMA through FUMA gene-to-function. Additionally, we conducted further gene enrichment analyses using MendelVar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mendelvar.mrcieu.ac.uk/submit/\u003c/span\u003e\u003cspan address=\"https://mendelvar.mrcieu.ac.uk/submit/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Type Annotation Analysis and Regional Contribution Analysis\u003c/h2\u003e \u003cp\u003eTo identify etiologically relevant cell types associated with genomic-SEM\u0026ndash;based allergic disorders, we used the CELLECT pipeline, which integrates cell-type expression specificity from single-cell RNA sequencing (scRNA-seq) data[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We employed the Tabula Muris dataset, comprising transcriptomic data from Mus musculus across multiple organs and tissues[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Preprocessing and normalization were performed in CELLEX to compute expression specificity likelihood scores for each region[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We then carried out cell-type\u0026ndash;specific analyses with Stratified LD Score Regression (S-LDSC), classified the cell types, and applied an FDR-adjusted p-value threshold of \u0026lt;\u0026thinsp;0.05 to determine significance.\u003c/p\u003e \u003cp\u003eTo evaluate each region\u0026rsquo;s contribution to phenotypic variance, we also employed S-LDSC to estimate partitioned heritability across genomic annotations\u0026mdash;such as genes, enhancers, and repressors. Specifically, S-LDSC uses a weighted LD matrix, allele frequency data, and GWAS summary statistics to apportion heritability among distinct genomic segments[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Correlations with External Traits\u003c/h2\u003e \u003cp\u003eWe estimated the genetic correlation (rg) between the newly constructed genomic-SEM GWAS dataset and each of the 115 GWAS datasets from the FinnR12 database for common diseases in respiratory, digestive, circulatory, and other categories, as listed in \u003cb\u003eTable S5\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Plasma Protein Biomarkers\u003c/h2\u003e \u003cp\u003eNext, we also leveraged plasma protein GWAS data from the deCODE (Iceland)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and UKB-PPP databases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] as exposures, with allergic disorders as the outcome, to identify putative plasma biomarkers. We performed Mendelian randomization (MR) analyses using the MendelianRandomization R package (v0.7.0), with random-effect inverse-variance weighting (IVW) as the primary method for assessing associations. A Bonferroni correction was applied to account for multiple comparisons, with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The deCODE cohort acted as the discovery dataset, and the UKB-PPP cohort served as the validation dataset. Instrumental variables were selected based on the following criteria: p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e for association with plasma protein levels, not in LD with any other selected SNP (r\u0026sup2; \u0026lt; 0.001), and F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePolygenic Risk Scores Evaluation\u003c/h2\u003e \u003cp\u003eWe constructed PRS from genome-wide summary-level data to assess the genetic contributions of distinct chromosomal regions to disease susceptibility. Specifically, we applied PRS with Continuous Shrinkage (PRS-CS) algorithm, which estimates posterior SNP effect sizes by incorporating an external LD reference panel. This Bayesian regression framework integrates summary-level GWAS data with LD information to produce shrinkage-based effect estimates and, ultimately, the PRS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStructural Equation Model Fitting\u003c/h2\u003e \u003cp\u003eBased on LD-Score regression analysis, the Z-scores of genetic heritability contributions for the 8 GWAS that constitute the allergic disorders genomic-SEM are almost greater than 3, indicating strong statistical significance and robust heritable components (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). The genetic covariance values between each pair of traits are presented in \u003cb\u003eTable S6\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The factor model of the genetic covariance matrix and the empirical covariance matrix in the Genomic-SEM fits well (CFI\u0026thinsp;=\u0026thinsp;0.964, SRMR\u0026thinsp;=\u0026thinsp;0.115 (Supplementary \u003cb\u003eTable S7\u003c/b\u003e)). Estimates of the latent variable loadings and residual covariances are provided in \u003cb\u003eTable S8\u003c/b\u003e. These results provide evidence for the presence of shared genetic factors within the Genomic-SEM. After filtering SNPs with significant heterogeneity (Q p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 511,970 SNPs were removed. The final genomic-SEM analysis generated an indirect measurement-based GWAS comprising 6,918,772 SNPs to investigate the genetic architecture of allergic disorder multimorbidity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Genomic Structural Equation Model Stability via LDSC for Genomic-SEM\u003c/h2\u003e \u003cp\u003eUsing the LDSC method, we applied parameter controls and removed a total of 6,296,401 SNPs. The average chi-square value for the retained SNPs for genomic-SEM of allergic disorders was Mean chi-square\u0026thinsp;=\u0026thinsp;1.514, the Lambda GC was 1.323, the Max chi-square was 121.56, the was h\u0026sup2; = 0.0019 (SE\u0026thinsp;=\u0026thinsp;0.0001), and the intercept was 1.0915 (SE\u0026thinsp;=\u0026thinsp;0.0133) in the regression model, indicating a relatively good fit, with minor signs of population stratification or residual confounding. Additionally, the attenuation ratio of 0.1817 a moderate influence of confounding factors, with a stronger contribution from genetic effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRisk Genetic Loci\u003c/h2\u003e \u003cp\u003eIn the novel genomic-SEM, a total of 6,918,772 SNPs were analyzed, with 2,038 variants having a p-value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Employing the GWAS-by-Subtraction methodology, we identified 31 novel significant variants that were not previously detected in the input trait GWAS statistics or documented in the GWAS Catalog databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable S9\u003c/b\u003e). Functional annotation via the FUMA tool revealed that the majority of these loci were situated in intergenic (46.6%) and intronic (36.4%) regions, with a smaller fraction of 49 SNPs (1.0%) located in exonic regions. Further analysis identified 135 independent SNPs (r\u0026sup2; \u0026lt; 0.6) and 83 lead SNPs (r\u0026sup2; \u0026lt; 0.1) (\u003cb\u003eTable S10\u003c/b\u003e). Among the lead SNPs, three novel loci\u0026mdash;chr1: rs114695117 (near RAVER2), chr6: rs145982144 (near UBDP1), and chr17: rs140397920 (near MED24)\u0026mdash;were identified, representing genomic regions previously unreported in association with allergic disorders. These lead SNPs contributed to a total of 66 distinct risk loci (\u003cb\u003eTable S11\u003c/b\u003e). Gene-based analysis using MAGMA highlighted 62 potential allergic disorder-related genes, with the most significant enrichment observed on chromosome 2 and chromosome 6. Functional annotation analysis revealed that 59 out of 62 candidate genes (95.2%) encompassed genomic regions harboring at least one variant with high predicted pathogenicity (CADD Scaled C-Score\u0026thinsp;\u0026ge;\u0026thinsp;10), with 9 genes (14.5%) containing extremely deleterious variants (CADD\u0026thinsp;\u0026ge;\u0026thinsp;20) (\u003cb\u003eTable S12\u003c/b\u003e, \u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFINEMAP\u003c/h2\u003e \u003cp\u003eFine-mapping analyses revealed strong associations at two genomic loci exceeding a posterior probability (PP) threshold of 0.95 and achieving genome-wide significance (GWAS P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸). These loci include chr6: rs145982144 (near \u003cem\u003eUBDP1\u003c/em\u003e) and chr12: rs78017269 (near \u003cem\u003eSOX5\u003c/em\u003e), with fine-mapping t-statistics of -5.61 and \u0026minus;\u0026thinsp;5.59, and corresponding GWAS P-values of 2.03 \u0026times; 10⁻⁸ and 2.33 \u0026times; 10⁻⁸, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGene-Level Identification of Susceptibility\u003c/h2\u003e \u003cp\u003eWe performed sCCA-based TWAS, identifying 1350 genes that surpassed the FDR significance threshold. Among these, 35 genes had a TWAS P-value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), while 60 genes exhibited a TWAS Z-value\u0026thinsp;\u0026gt;\u0026thinsp;6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Subsequently, we conducted fine-mapping analysis using the FOCUS method on the genomic structural equation data, identifying 127 genes with a posterior inclusion probability (PIP) greater than 0.8, which suggests they may represent potential pathogenic signals. To further confirm the \"high-confidence\" gene-level associations, we performed an intersection test to include 31 unique genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TWAS identified FAM114A1 as the most strongly associated locus, demonstrating the highest positive association statistic (Z\u0026thinsp;=\u0026thinsp;11.13) and most stringent significance threshold (FDR-P\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;24\u003c/sup\u003e). This was followed by RFTN2 (Z\u0026thinsp;=\u0026thinsp;6.93, FDR-P\u0026thinsp;=\u0026thinsp;7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e) and PLCL1 (Z\u0026thinsp;=\u0026thinsp;6.76, FDR-P\u0026thinsp;=\u0026thinsp;1.6\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), collectively indicating that elevated predicted expression of these genes is robustly correlated with allergic disorder susceptibility. Conversely, RP5-1115A15.1 exhibited the strongest inverse association (Z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;6.81, FDR-P\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), with RERE (Z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;6.62, FDR-P\u0026thinsp;=\u0026thinsp;3.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) ranking second, suggesting that reduced expression of these loci may confer increased disease risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic Associations with Allergic Disorders in sCCA and FOCUS Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" 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colname=\"c8\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101991843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101991844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.325578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141876123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141876124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM114A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38867676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38867677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.1337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38680586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38680587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.40443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM114A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38867676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38867677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.56709\u003c/p\u003e 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\u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM114A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38867676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38867677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c4\"\u003e \u003cp\u003e122923074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.83259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU91328.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25992661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25992662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU91328.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25992661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25992662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.64151\u003c/p\u003e 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\u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127794532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127794533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.58441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRANBP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6015624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6015625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.31736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIAA2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6007824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6007825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.26985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99104037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99104038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.76475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL15RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5978186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5978187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.06753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.8E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUFU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102503986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102503987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.95708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBM17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6088986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6088987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.18371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUEDC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102432660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102432661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.91882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP11-563J2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6277686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6277687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.89475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.0E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRKCQ-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6580418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6580419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.78693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.8E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111602390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111602391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.103014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.2E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC11orf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111878934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111878935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.76819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41061272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41061273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.17324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41230838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41230839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.69736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOM1L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17972421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17972422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.43104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41522528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41522529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.138814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCDC116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21632715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21632716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.66051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20957091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20957092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.639734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePathway and Cell Type Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGSEA of MAGMA-derived risk loci highlighted REACTOME_ADAPTIVE_IMMUNE_ SYSTEM as the most significantly enriched pathway within the Curated Gene Sets collection. Delving into Canonical Pathways, we observed robust associations within three prominent immune-related modules including REACTOME_ADAPTIVE_IMMUNE_SYSTEM, REACTOME_ DISEASES_OF_IMMUNE_SYSTEM and REACTOME_COSTIMULATION_BY_THE_CD28_ FAMILY. Notably, these enriched pathways encompassed genes critically involved in Th2 polarization, antigen presentation, and T-cell receptor signaling (\u003cb\u003eTable S13\u003c/b\u003e). This mechanistic convergence strongly links these pathways to established allergic phenotypes such as asthma and atopic dermatitis, as well as immune-mediated comorbidities like type 1 diabetes and systemic lupus erythematosus. Furthermore, cataloged associations for these pathways extend beyond allergic disorders, encompassing conditions like asthma, type 1 diabetes, and systemic lupus erythematosus, and also extending to neuropsychiatric conditions such as autism spectrum disorder and schizophrenia (\u003cb\u003eFigure S3\u003c/b\u003e). Additionally, through MendelVar enrichment mapping, we observed significant gene overlap with diseases such as \u003cem\u003eMayer-Rokitansky-Kuster-Hauser syndrome\u003c/em\u003e, \u003cem\u003eosteochondrodysplasia\u003c/em\u003e, and \u003cem\u003eimmune system diseases\u003c/em\u003e like \u003cem\u003eprimary immunodeficiency\u003c/em\u003e. Diseases related to glucose metabolism and liver conditions, including \u003cem\u003eliver cirrhosis\u003c/em\u003e, were also enriched (\u003cb\u003eFigure S4\u003c/b\u003e). The most significant cell types enriched in the allergic disorder GWAS were primarily immune-related, with a strong representation of T cells, natural killer (NK) cells, and B cells. These cell types, including Fat T cells, Marrow B cells, Limb Muscle T cell, Lung T cells, and Marrow regulatory T cell, Trachea blood cell, Spleen T cell, play crucial roles in immune regulation and allergic responses. Additionally, myeloid cells and leukocytes from various tissues, such as the Lung myeloid cells and Kidney leukocytes, were also significantly enriched (\u003cb\u003eTable S14\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHeritability Enrichment Across Genomic Functional and Regulatory Regions\u003c/h2\u003e \u003cp\u003eBased on the genetic enrichment analysis of genomic functional regions using S-LDSC (\u003cb\u003eTable S15\u003c/b\u003e), we observed a significant enrichment pattern of heritability in epigenetic regulatory elements and core functional regions of genes. Specifically, evolutionarily enhancer regions, conserved elements, and DNase I hypersensitive sites (DGF) showed the strongest positive genetic contribution, suggesting that these regulatory elements play a key role in influencing complex traits through cis-regulatory networks. These findings underscore the critical role of cis-regulatory networks, orchestrated through these genomic elements, in influencing the complex genetic architecture of allergic disorders, highlighting that variations within these regulatory regions are likely key drivers of disease susceptibility and multimorbidity.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGenetic Correlations with External Traits and Plasma Biomarkers Identification\u003c/h2\u003e \u003cp\u003eComprehensive genetic correlation analyses using LDSC revealed significant pleiotropy between allergic disorders and 12 out of 115 common diseases. Notably, acute upper respiratory infections exhibited the strongest positive genetic correlation (rg\u0026thinsp;=\u0026thinsp;0.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046, P\u0026thinsp;=\u0026thinsp;0.002), followed by bipolar affective disorders (rg\u0026thinsp;=\u0026thinsp;0.136\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, P\u0026thinsp;=\u0026thinsp;0.007) and Crohn's disease (rg\u0026thinsp;=\u0026thinsp;0.148\u0026thinsp;\u0026plusmn;\u0026thinsp;0.075, P\u0026thinsp;=\u0026thinsp;0.048). Conversely, inverse associations were observed for disorders of lipoprotein metabolism (rg = -0.101\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043, P\u0026thinsp;=\u0026thinsp;0.02) and heart failure (rg = -0.088\u0026thinsp;\u0026plusmn;\u0026thinsp;0.043, P\u0026thinsp;=\u0026thinsp;0.04), suggesting potential protective mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Two-sample MR analysis revealed seven plasma proteins with genetically predicted levels significantly associated with allergic disorders after Bonferroni correction in the deCODE discovery cohort: \u003cem\u003eMANF\u003c/em\u003e, \u003cem\u003eGLB1\u003c/em\u003e, \u003cem\u003eHEXIM1\u003c/em\u003e, \u003cem\u003eKYNU\u003c/em\u003e, \u003cem\u003eICAM4\u003c/em\u003e, \u003cem\u003eKRT5\u003c/em\u003e, and \u003cem\u003eLAYN\u003c/em\u003e. Of these, four proteins (\u003cem\u003eMANF\u003c/em\u003e, \u003cem\u003eGLB1\u003c/em\u003e, \u003cem\u003eICAM4\u003c/em\u003e, and \u003cem\u003eLAYN\u003c/em\u003e) were consistently replicated in the UKB-PPP cohort using inverse-variance weighted models, demonstrating bidirectional concordance (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Polygenic Risk Scores from Summary Data\u003c/h2\u003e \u003cp\u003eOur analysis demonstrates that the variants in our PRS are strongly associated with disease onset risk, and that the genetic contribution to disease susceptibility varies significantly across chromosomal regions (\u003cb\u003eTable S16\u003c/b\u003e). Specifically, when considering all SNPs, chromosomes 14 and 20 exhibit the highest positive genetic contribution. However, among the top 1000 most contributing SNPs, chromosomes 6 (86 SNPs) and 5 (85 SNPs) are most prominent, while within the top 10,000, chromosomes 2 (821 SNPs) and 1 (805 SNPs) are most frequent. This shift in prominent chromosomes suggests that different sets of genes and regulatory elements, located in these regions, influence disease susceptibility at different levels of genetic contribution.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAllergic disorders, encompassing conditions such as allergic asthma, atopic dermatitis, allergic rhinitis, and others, represent a significant global health burden, characterized by not only highly prevalent but also exhibit complex comorbidities and multimorbidities, often progressing through a well-documented sequence known as the \"atopic march\". The genetic basis of allergic multimorbidity remains underexplored, as traditional single-trait GWAS approaches face inherent limitations in dissecting the intricate genetic and phenotypic interplay underlying co-occurring allergic disorders. Previously, genomic SEM has demonstrated utility in identifying shared genetic architectures, such as the comorbidity between inflammatory bowel disease and asthma, as well as associations between allergic disorders and inflammatory diseases, body mass index, and gastroesophageal reflux disease[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We applied this novel approach to integrate genome-wide association data by first constructing a genetic covariance matrix via multivariable LDSC, followed by specifying a latent factor model representing eight allergic traits within the SEM framework. The application of Genomic-SEM to metabolic syndrome research recently successfully identified 159 novel SNPs not previously cataloged in the GWAS Catalog, along with 82 additional SNPs exhibiting significant pleiotropic effects independent of the constituent metabolic syndrome traits[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, Andrew et al. used this method to analyze gene expression profiles across 13 major psychiatric disorders. This analysis successfully identified genes associated with both shared and disorder-specific genetic risks, leading to the proposal of repurposing existing drugs as potential therapeutic agents[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough the novel established model and a suite of integrative post-GWAS analyses, we identified 31 novel, pleiotropic variants and prioritized 31 key genes significantly associated with allergic disorder multimorbidity. Among the lead SNPs, three\u0026mdash;chr1:rs114695117 (near \u003cem\u003eRAVER2\u003c/em\u003e), chr6:rs145982144 (near \u003cem\u003eUBDP1\u003c/em\u003e), and chr17:rs140397920 (near \u003cem\u003eMED24\u003c/em\u003e)\u0026mdash;represent previously unreported associations with allergic disorders. The variant rs114695117 is located near \u003cem\u003eRAVER2\u003c/em\u003e, which encodes a ribonucleoprotein involved in the regulation of alternative splicing. Previous studies have implicated \u003cem\u003eRAVER2\u003c/em\u003e in splicing events within the thymic epithelium [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and identified it as a susceptibility gene for liver injury in rheumatoid arthritis patients treated with immunomodulatory drugs[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, \u003cem\u003eRAVER2\u003c/em\u003e may contribute to allergic inflammation by modulating the splicing patterns of immune-related genes. The variant rs145982144 lies near the pseudogene \u003cem\u003eUBDP1\u003c/em\u003e, a component of the ubiquitin system. While the precise role of \u003cem\u003eUBDP1\u003c/em\u003e in immune regulation remains to be elucidated, ubiquitination is known to play a critical role in protein degradation, DNA repair, and immune signaling, and its dysregulation has been implicated in autoimmunity via modulation of NF-κB and STAT signaling pathways[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, rs145982144 might influence allergic disease susceptibility by altering \u003cem\u003eUBDP1\u003c/em\u003e function, potentially affecting the activity of these pathways. Finally, rs140397920 is located near \u003cem\u003eMED24\u003c/em\u003e, a subunit of the Mediator complex, which bridges transcription factors and RNA polymerase II, thereby regulating the expression of specific genes. Within the immune system, the Mediator complex is involved in the transcriptional regulation of T cell programs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, \u003cem\u003eMED24\u003c/em\u003e has previously been identified within a region containing asthma risk alleles, supporting its potential role in allergic disease susceptibility[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, fine-mapping analyses identified two putative causal SNPs associated with allergic disorders\u0026mdash;rs145982144 (near \u003cem\u003eUBDP1\u003c/em\u003e) and rs78017269 (near \u003cem\u003eSOX5\u003c/em\u003e). Although these SNPs represent novel discoveries in allergic disease GWAS, their positional genes have prior indirect evidence supporting immunological relevance. Specifically, \u003cem\u003eSOX5\u003c/em\u003e regulates T-cell receptor signaling to maintain immune tolerance\u0026mdash;a mechanism critically implicated in suppressing type 2 inflammation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This aligns with existing GWAS evidence linking \u003cem\u003eSOX5\u003c/em\u003e variants to asthma susceptibility, further supporting its pathological significance in allergic pathogenesis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur sCCA and subsequent FOCUS fine-mapping identified 31 genes with potential causal roles in allergic disorders. While some of these genes, such as \u003cem\u003eIL1R2\u003c/em\u003e, \u003cem\u003eIL15RA\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e, and \u003cem\u003ePVT1\u003c/em\u003e, have established associations with allergic traits[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], others represent novel candidates with plausible links to allergic pathways. Notably, \u003cem\u003eFAM114A1\u003c/em\u003e (Z\u0026thinsp;=\u0026thinsp;11.13, FDR-P\u0026thinsp;=\u0026thinsp;1 \u0026times; 10\u0026ndash;24), \u003cem\u003eRFTN2\u003c/em\u003e (Z\u0026thinsp;=\u0026thinsp;6.93, FDR-P\u0026thinsp;=\u0026thinsp;7 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;9), \u003cem\u003ePLCL1\u003c/em\u003e (Z\u0026thinsp;=\u0026thinsp;6.76, FDR-P\u0026thinsp;=\u0026thinsp;1.6 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8), \u003cem\u003eRP5-1115A15.1\u003c/em\u003e (Z = -6.81, FDR-P\u0026thinsp;=\u0026thinsp;1.2 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8), and \u003cem\u003eRERE\u003c/em\u003e (Z = -6.62, FDR-P\u0026thinsp;=\u0026thinsp;3.9 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8) emerged as high-confidence loci, underscoring their potential critical roles in allergic pathogenesis. \u003cem\u003eFAM114A1\u003c/em\u003e, a relatively uncharacterized gene, has been implicated in immune responses and apoptosis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, its role in myocardial fibrosis via regulation of angiotensin II has been demonstrated[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and angiotensin II itself has been linked to increased asthma risk and airway inflammation[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This suggests a potential mechanism whereby \u003cem\u003eFAM114A1\u003c/em\u003e might influence allergic processes, possibly modulated by Angiotensin-(1\u0026ndash;7). However, the precise function of \u003cem\u003eFAM114A1\u003c/em\u003e remains largely unexplored and warrants further investigation. \u003cem\u003eRFTN2\u003c/em\u003e is involved in, or upstream of, dsRNA transport and is predicted to correlate with type I interferon expression, a pathway implicated in ulcerative colitis susceptibility[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Recent reports also highlight \u003cem\u003eRFTN2\u003c/em\u003e's function in neuroglia[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], adding further complexity to its potential roles. \u003cem\u003ePLCL1\u003c/em\u003e, located on chromosome 2, encodes a phospholipase C-like enzyme involved in signal transduction. It generates second messengers, such as IP3 and DAG, activating calcium signaling and protein kinase C, thereby influencing B and T cell function. Because allergic diseases involve IgE production by B cells and mast cell degranulation, increased \u003cem\u003ePLCL1\u003c/em\u003e expression could potentially enhance these immune cell responses, elevating allergic risk. While direct studies linking \u003cem\u003ePLCL1\u003c/em\u003e to allergy are limited, research on rheumatoid arthritis demonstrates its involvement in regulating key inflammatory cytokines, including IL-6 and IL-1β[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], suggesting a possible parallel mechanism in allergic diseases. Intriguingly, several other identified genes, while not directly implicated in allergic diseases in previous reports, have demonstrated roles in the pathogenesis of glioma, including \u003cem\u003eSUFU\u003c/em\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], \u003cem\u003eRBM17\u003c/em\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], \u003cem\u003eFAM114A1\u003c/em\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], \u003cem\u003eCHP1\u003c/em\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], \u003cem\u003eCUEDC2\u003c/em\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], \u003cem\u003eRFC1\u003c/em\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], \u003cem\u003eRANBP6\u003c/em\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], \u003cem\u003ePRKCQ-AS1\u003c/em\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and \u003cem\u003eFARP2\u003c/em\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Given the emerging concept of glioma as a marker AllergoOncology disease, these findings may shed light on the complex genetic underpinnings of AllergoOncology. The enrichment of genes associated with Th2 polarization, T-cell receptor signaling, and other immune regulatory processes in this analysis aligns seamlessly with previous research. Han et al. identified genetic variants in these immune pathways as significant contributors to asthma susceptibility, demonstrating that asthma-associated variants are enriched in open chromatin regions of immune cells, particularly CD4⁺ and CD8⁺ T lymphocytes. Their findings also revealed an overrepresentation of T-cell receptor signaling pathways among asthma risk loci [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Recent studies have further highlighted the role of key genes, such as \u003cem\u003eCD52\u003c/em\u003e\u0026mdash;encoding a membrane glycoprotein expressed on various leukocytes\u0026mdash;in linking T-cell-related pathways to asthma phenotypes [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Furthermore, the PRS analysis underscores the variability and intricate genetic architecture underlying allergic disorders. This observation highlights the distinct roles of specific chromosomes in the pathogenesis of these conditions.\u003c/p\u003e \u003cp\u003eOur novel SEM analysis explored the shared genetic architecture and its correlation with external traits, identifying strong comorbidities, primarily neuropsychiatric disorders. These included bipolar disorder, manic episode, migraine, and attention deficit hyperactivity disorder (ADHD). This finding aligns with prior clinical observations and post-GWAS research on allergic traits like asthma, atopic dermatitis, allergic rhinitis, and mental disorders [\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. It suggests that the complex comorbidity of allergic traits is driven by a shared genetic and environmental etiology, with our research indicating that the link between allergic disorders and neuropsychiatric conditions may stem from this shared genetic architecture. This further underscores the importance of investigating causal loci.\u003c/p\u003e \u003cp\u003eOur MR findings highlight four plasma proteins \u0026ndash; MANF, GLB1, ICAM4, and LAYN \u0026ndash; exhibiting robust associations indicative of a potential causal role in allergic disorders. This finding addresses limitations of prior retrospective studies[\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], providing genetic evidence of causal effects. These proteins, collectively implicated in regulating immune homeostasis and inflammation, offer novel mechanistic targets in allergy. The replicated associations and suggestive causal evidence underscore their translational value as potential therapeutic interventions or diagnostic biomarkers, urgently necessitating functional studies to elucidate their precise regulatory networks in allergic inflammation.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, the shared genetic architecture of allergic disorders was investigated primarily in individuals of European ancestry. Since genomic-SEM relies on LDSC\u0026mdash;a method sensitive to ancestry-specific LD patterns\u0026mdash;our analysis was confined to a genetically homogeneous population. Consequently, validation of this model across diverse ethnic populations is imperative to establish its generalizability in future studies. Second, the susceptibility loci identified in this study require experimental validation, particularly functional studies at the protein level, to elucidate their translational relevance and mechanistic roles in allergic pathophysiology. Third, while Cochran\u0026rsquo;s Q-test was applied to assess heterogeneity (Q\u003csub\u003eSNP\u003c/sub\u003e), this approach prioritizes the detection of SNPs with pleiotropic effects across multiple traits rather than those exhibiting heterogeneous effects on specific allergic phenotypes. This methodological focus may have obscured SNPs with trait-specific associations critical to individual allergic disorders. Finally, the scarcity of GWAS data for allergic disorders in East Asian populations precluded validation of our European ancestry-derived genetic model in other ancestries. Addressing this gap through expanded sample sizes in underrepresented populations remains a critical priority to enhance the robustness and global applicability of our findings. Targeting pleiotropic loci identified in future research holds significant promise for drug development, as these loci could enable simultaneous therapeutic intervention across multiple allergic diseases. However, given the intricate relationship between allergy and immunity, it is equally crucial to actively investigate the role of these genes in immune regulation. In this study, we identified a genetic correlation between allergic disorders and other traits, notably psychiatric disorders. However, integrating biological experiments such as functional validation of these loci through gene expression analysis or CRISPR-based gene editing can substantiate the biological significance of these genetic findings and elucidate their underlying mechanisms[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Moreover, given the recent successes of clustering and non-clustering machine learning algorithms, as well as artificial intelligence, developing additional computational methods represents a viable and promising strategy to further elucidate the mechanisms underlying allergic disorders [\u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLeveraging genomic SEM, our novel GWAS elucidated the shared genetic architecture of allergic traits. Employing a suite of post-GWAS methodologies, we robustly identified 2038 genome-wide significant SNP loci, including 31 previously unreported loci. Furthermore, integrating sCCA with FOCUS, we precisely pinpointed 31 candidate causal genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Moreover, we identified four putative causal plasma protein biomarkers: MANF, GLB1, ICAM4, and LAYN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L. Y. conceived and conducted the analysis, R.J.S. completed the visualization, and they jointly wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData generated during this study are available in the Supplementary Files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available data and therefore did not require ethical approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShin YH, Hwang J, Kwon R, Lee SW, Kim MS, GBD 2019 Allergic Disorders Collaborators, et al. 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Sci Rep. 2025;15:3607. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Genomic SEM, Allergic disorders, FAM114A1, rs145982144, rs78017269","lastPublishedDoi":"10.21203/rs.3.rs-6162322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6162322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe intricate shared genetic architecture underlying allergic disorders\u0026mdash;including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis\u0026mdash;remains incompletely characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNotably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.\u003c/p\u003e","manuscriptTitle":"Genomic Structural Equation Modeling Elucidates the Shared Genetic Architecture of Allergic Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 09:23:19","doi":"10.21203/rs.3.rs-6162322/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-07T06:41:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-03-30T19:50:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T19:49:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T10:37:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-03-28T00:58:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e3a0c90-35b9-4022-975e-3122ceb0c40f","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:04:39+00:00","versionOfRecord":{"articleIdentity":"rs-6162322","link":"https://doi.org/10.1186/s12967-025-06465-8","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-04-15 15:57:56","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2025-04-01 09:23:19","video":"","vorDoi":"10.1186/s12967-025-06465-8","vorDoiUrl":"https://doi.org/10.1186/s12967-025-06465-8","workflowStages":[]},"version":"v1","identity":"rs-6162322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6162322","identity":"rs-6162322","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}