Integration of Genomic structural equation and post-GWAS analysis reveal the risk gene loci and sensitive genes for cardiac conduction block risk.

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Tongyu Wang, Xinge Miao, Yunlong Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6928678/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiac conduction disorders (CCDs) is a wide spectrum of severe cardiovascular events related to syncope and sudden cardiac death. Identifying biomarkers of CCDs benefits the diagnosis and therapy for the disease. Methods: GWAS summary datasets were retrieved from GWAS catalogue and Finngen database. GenomicSEM R package was utilized to construct the structural equation model to identify the common latent factor affecting the progression of CCDs. FUMA platform was employed to delineate lead SNPs and genes. SuSIE and FINEMAP are fine-mapping tools used to identify confidential SNPs. TWAS and FOCUS methods were used to identify sensitivity genes. LDSC and Two-sample Mendelian randomization were used to identify the causal relationship between genes and each type of conduction disorders. Results: Two novel lead SNPs (rs71208329 and rs112720315) are identified in cardiac conduction disorders after construction of Genomic structural equation and FUMA analysis. Through Fine-mapping analysis, we confirm the validity of rs112720315 in causing diseases and subsequent PheWAS analysis revealed association between rs112720315 and non-ischemic cardiomyopathy. TWAS, FUMA and FOCUS analysis revealed gene markers( CCDC141, SCN10A, SH3PXD2A, FKBP7 and ESR2 ) are related to conduction disorders. Then Mendelian randomization and LDSC revealed the connection between the identified genetic markers and CCDs. Conclusion： rs112720315 is the novel genetic loci associated with conduction disorders. Circulating gene markers( CCDC141, SCN10A, SH3PXD2A, FKBP7 and ESR2 ) are potential biomarkers for conduction disorders. Cardiac conduction disorders Geomic structural equation biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cardiac conduction disorders (CCDs) cover a wide array of clinical conditions, which are defined by heart conduction abnormalities[ 1 ]. They include both acquired and inherited types and can present either in isolation or along with structural heart defects. When there are cardiac conduction disorders (CCDs), the heart's depolarization can be disturbed and bradycardia may even be caused. Common manifestations of CCDs are sick sinus syndrome (SSS), atrioventricular block (AVB), left bundle branch block (LBBB) and right bundle branch block (RBBB)[ 2 ]. Damage of cardiac conduction system caused by ischemia and aging lead to disruption of normal heart rhythm and adult mammalian heart lacks regenerative capacity and is unable to replace lost cells after injury[ 3 , 4 ]. Thus CCD is somehow irreversible process[ 4 ]. In severe cases, patients with combined cardiac conduction disorders may experience severe symptoms such as syncope and may even suffer from sudden cardiac death. Multiple population-based epidemiological studies have shown that patients with prolonged PR interval and right bundle branch block have an increased risk of cardiovascular disease (CVD) and death[ 5 , 6 ]. Molecular mechanisms play a key role in the pathogenesis of CCD (cardiac conduction system disorders). Animal studies have confirmed that abnormalities in calcium ion-regulation proteins can lead to dyregulated Ca 2+ concentration in endoplasm and delayed atrioventricular node conduction[ 7 , 8 ]. In human patients, mutations in the connexin 45 can cause progressive atrioventricular block[ 9 ]. Purkinje cells (PCs) damage are associated with intra-ventricular conduction disorders and lead to heart failure. Loss-of-function mutations in the alpha subunit of sodium channel Nav1.5 ( SCN5A ) are associated with His-Purkinje conduction disease and hyperexcitability[ 10 ]. Cx43 polymorphism is associated with left bundle branch block (LBBB), and its existence is related to the genetic susceptibility of LBBB[ 11 ]. Also epigenetic markers played role in the pathogenesis of sinoatrial node dysfunction, miR-486-3p has a significantly lower expression level in normal SAN and has been proven to control the expression of HCN4[ 12 ]. Human HF hearts have shown increased SAN expression of miR-486-3p , which offers a possible explanation for SND development in HF patients[ 13 ]. Current research has not yet been able to determine the actual circulating biomarkers that can be used to predict and intervene in the risk of conduction disorders. We adopted the genomical-structural equation model (Genomic-SEM) method, combined with the previously published genome-wide association study (GWAS) dataset, to identify the potential factors among different types of conduction disorders. With the help of this method, we were able to determine novel single nucleotide polymorphisms (SNPs) that affect the risk of cardiac conduction system disorders (CCD). Also multiple methods were adopted to explore the association between the CCDs and genetic markers. Based on these newly discovered potential factors, we can also further search for circulating genetic markers that can predict the risk of wide spectrum of CCD, thereby providing new options for biomarkers and therapeutic targets in clinical work. 2. Methods 2.1 Source and quality of involved GWAS datasets GWAS summary data of conduction disorders from European ancestry was selected. Detailed information including the construction of databases and quality control parameters are shown in the following Table 1 . Table 1 Information of GWAS summary datasets used for structural genomic equation. Accession No. Ancestry Cases Controls Definition Source λGC NSNP GCST90475955 European 2012 447369 Second degree AV block GWAS catalogue 1.0288 1173070 GCST90477911 European 6274 443107 third-degree atrioventricular block GWAS catalogue 1.0606 1173072 GCST90475980 European 6431 439404 Sinoatrial node dysfunction (Bradycardia) GWAS catalogue 1.0767 1173071 GCST90475954 European 6072 435543 First degree atrioventricular block GWAS catalogue 1.0784 1173067 I9_LBBB European 2766 375343 Left bundle-branch block Finngen 1.059 1159470 I9_RBBB European 1347 375343 Right bundle-branch block Finngen 1.0291 1159466 2.2 Construction of Genomic Structural Equation The genome-wide structural equation modeling (Genomic SEM) analysis use GenomicSEM R package (v.0.0.5) to investigate the broad genetic susceptibility underlying sick sinus syndrome, IAVB, IIAVB, IIIAVB, LBBB and RBBB. Genomic SEM is a newly developed multivariate method capable of examining multiple potential multivariate models to explore the latent structure of traits of interest. Genomic SEM exhibits robustness against biases induced by sample overlap or sample size imbalance. This method additionally facilitates the identification of variants exerting effects on only a subset of complex traits, thereby excluding those representing broad cross-trait susceptibility. The Genomic SEM analysis is implemented through a two-stage procedure. In the first stage, the empirical genetic covariance matrix and its corresponding sampling covariance matrix are estimated. Utilizing a multivariate extension of cross-trait LD score regression, we generated an empirical genetic covariance matrix among n traits, which serves as input for the SEM common factor model. In the second stage, a structural equation modeling (SEM) framework was employed to minimize the discrepancy between the hypothesized covariance matrix and the empirically calculated covariance matrix obtained in the first stage. Our primary research objective focused on identifying the genetic architecture underlying 6 traits; therefore, a single-factor model was tested. Model fit was evaluated using multiple indices: the standardized root mean square residual (SRMR), model χ² statistic, Akaike information criterion (AIC), and comparative fit index (CFI) shown in table S4 . 2.3 Defining Genomic Loci and Identifying Novel Variants The FUMA (Functional Mapping and Annotation of Genetic Associations) platform was employed to delineate genomic loci and identify lead single nucleotide polymorphisms (SNPs) associated with conduction disorders[ 14 ]. Lead SNPs were defined as those exhibiting genome-wide significance (P < 5*10 − 8 ) and low linkage disequilibrium (LD r 2 < 0.1). Summary statistics of potential risk SNPs in Genomic structural equations were input to evaluate their association strength. Subsequently, the lead SNPs and loci were compared with those from the original univariate GWAS. Novel loci were defined as SNP with p-value in Structural equation is more significant than each single uni-variant GWAS. 2.4 Fine mapping identify the causal SNP in structural Genomic equation To identify the most probable causal variants associated with your GWAS, we employed SuSIE (Sum of Single Effects) and FINEMAP, both implemented in the R package echolocatoR(v.2.0.3). A probability threshold of 0.95 was set to define credible sets of potential causal variants. SuSIE and FINEMAP are fine-mapping tools designed to pinpoint the most likely causal variants associated with a given phenotype[ 15 ]. In this analysis, a 250kb window was applied to encompass regions linked to each lead SNP, followed by computation of the posterior inclusion probability (PIP) for each SNP within these regions. A probability threshold of 0.95 was established, wherein variants exceeding this posterior probability threshold were considered potential causal variants. 2.5 Transcriptome-Wide Association Study (TWAS) Following the identification of potential causal variants, we conducted a transcriptome-wide association study (TWAS) to prioritize genes associated with conduction disorders based on the relationship between gene expression and phenotypes. The FUSION method was employed for TWAS analysis, utilizing precomputed expression quantitative trait loci (eQTL) weights from GTEx v8 (weight panel download website : http://gusevlab.org/projects/fusion/#gtex-cross-tissue-scca-expression ), encompassing 37,920 gene-tissue pairs[ 16 ]. These weights were applied to assess expression-phenotype associations across different genes and tissues. Further conduct the FOCUS method. FOCUS method assesses whether there is a causal relationship between genes and phenotypes based on the FOCUS posterior inclusion probability. 2.6 LDSC analysis Global genetic association analysis using LD score regression (LDSC)[ 17 ] to estimate genetic correlations by quantifying the average shared genetic effect between quantitative traits. Following the instructions provided in the LDSC manual, the first step involves converting the GWAS data of both traits into LDSC format, followed by the calculation of “rg” by R package “ldscr”. 2.7 Two sample Mendelian Randomization analysis In order to identify the role of involved genes and each type of conduction disorders, we performed two sample Mendelian randomization analysis to further identify the potential of gene markers in biomarkers for wide spectrum of cardiac conduction disorders. Firstly, we retrieved eQTL data from eQTLgen database including blood samples of 31,684 individuals ( https://www.eqtlgen.org/cis-eqtls.html ) and 16989 genes eQTL loci. In order to fulfill the core assumptions for MR analysis[ 18 ]: i. instrumental variables have a strong correlation; ii. SNPs are not associated with any confounders; iii. instrumental variables have no effect on outcome. Exposure datasets were filtered by p value < 5e-8 and removing LD SNPs with parameter clump length 10000KB and r2 threshold equals to 0.6. Then as for outcome datasets those instrumental variables with p-value < 5e-8 was removed to avoid the direct of SNPs effect on outcome. In order to identify the effect of confounders, test of horizontal pleiotropy and causal direction test (steiger test) were performed. Major Mendelian randomization analysis was performed using the “TwoSampleMR” R package. The random-effects inverse variance weighting (IVW) and Wald ratio analytical methods were used to evaluate the causal relationships between exposure and outcome datasets. 3. Results 3.1 Construction of Statistical Indicators for Structural Equation Modeling (SEM) LD-score regression analysis revealed that the heritability contributions (h²) of the four input GWAS univariate traits were as follows: SSS :0.006 (0.0012), IAVB : 0.0059 (0.0012), IIAVB: 0.0019 (0.0011), IIIAVB: 0.0061 (0.001), LBBB: 0.006 (0.0014), and RBBB: 0.0027 (0.0014). The genetic covariance values between pairwise traits are provided in Fig. 1 A. Positive genetic correlation was found among different degrees of AVB and intraventricular conduction disorders(LBBB and RBBB). Also the SSS showed positive correlation with AVB, but negatively correlated with bundle bunch blocks. Correlation between bundle bunch blocks and AVBs is negative. Then Genomic SEM was constructed based on the configuration in Fig. 1 B. Prior to model construction, structural equation modeling (SEM) was performed. The common factor model demonstrated good fit between the genetic covariance matrix of the four input GWAS traits and the empirical covariance matrix (comparative fit index [CFI] = 1; standardized root mean square residual [SRMR] = 0.0984). Latent factor calculated by Genome structural equation and univariate SEM parameters association can be seen in Table S1 . These results provide evidence for the existence of shared genetic factors across the GWAS traits. 3.2 Quality Control and Statistical Genetic Analysis Report In order to investigate the quality of latent factor GWAS summary datasets we adopted methodological parameter controls and removed a total of 6401566 SNPs. There are 1117809 effective SNPs remained. Key metrics of the remaining SNPs include: Mean Chi²= 1.1023, Genomic Control Lambda (λGC) = 1.1491, Max chi 2 = 33.324, Contribution ratio of genetic component to environment = 0.1066 (0.0623) and h 2 = 0.0699 (0.0083). 3.3 Structural equation modeling based conduction disorder GWAS evaluation using FUMA software Through FUMA software, a total of 4 risk loci (rs71208329, rs13031826, rs2634071 and rs112720315) were identified in the structural equation modeling-based evaluation of the genome (p-value < 5e-8, R 2 < 0.1). Of them, we found two novel SNPs (rs71208329 and rs112720315) in cardiac conduction disorders. Additionally, 4 potential (your GWAS)-associated genes( CCDC141, SCN10A, SH3PXD2A and ESR2 ) were detected under genome-wide significant control (FDR < 0.05; Fig. 2 ). 3.3 Fine mapping method identify the causal SNP with latent factor Fine mapping analysis identified strong associations at multiple genomic loci, including: chromosome 2 (rs71208329, a variant in AC023469.1); chromosome 6 (rs112720315, the TCP10L2 locus). Regional plots showed clear peaks at these loci, and other credible set variants also demonstrated evidence of association (Fig. 3 ). 3.4 PheWAS analysis Based on above identified significant SNPs, we retrieved the related traits related with the SNPs from Finngen database. With setting p-value threshold of 1*10 − 4 , we found that the Non ischemic cardiomyopathy is only significantly associated with rs112720315 with odds ratio > 1, indicating the latent association between cardiac conduction disorders and cardiomyopathy(Fig. 4 ). 3.5 Transcriptome-Wide Association Analysis Subsequently, we performed transcriptome-wide association analysis (TWAS) using FUSION to identify gene-level associations related to the genetic characteristics of cardiac conduction disorders. There are 37917 genes in total identified by TWAS analysis (Table S2 ). Only FKBP7 gene pass the significance test with p-value 0.9) that may represent potential causal signals associated with conduction disorders. To further validate these "high-confidence" gene-level associations, an intersection test was performed shown in Table S3 . 3.6 LDSC and Two sample Mendelian randomization revealed association between identified genes and conduction disorders. In order to further identify the potential of above genes in predicting the occurrence of each type of conduction disorders. We retrieved eQTL data from eQTLlgen cohort for identified genes. LDSC analysis revealed genetic correlation of Genes and conduction disorders shown in Fig. 6 A. We found that CCDC141 is positively correlated with RBBB, while negatively correlated with SSS, IIIAVB, IIAVB and IAVB. Also the correlation between CCDC141 and latent factor identified from Genome SE. On the contrary ESR2 gene is significantly positively correlated with SSS, IIIAVB, IIAVB and IAVB, while negatively related to the RBBB. FKBP7 is significantly positively correlated with F1, SSS, IIIAVB, IIAVB, LBBB and IAVB, while negatively related to the RBBB. SH3PXD2A is negatively correlated with F1, IIIAVB, IIAVB and IAVB. In terms of TSMR analysis in Fig. 6 B, we found CCDC141 have positive causal relationship with RBBB and LBBB. CCDC141 have negative causal relationship with IIIAVB and F1. ESR2 gene is significantly positively correlated with SSS, IIIAVB, IIAVB and IAVB, while negatively related to the RBBB and LBBB. FKBP7 is significantly positively correlated with F1, IIAVB, and IAVB, while negatively related to the RBBB. SH3PXD2A is negatively correlated with RBBB and positively correlated with IIAVB and F1. The results for steiger test and pleiotropy test indicate the stability of major TSMR(Table S4 and Table S5 ). In summary, we found genetic markers with high confidential in predicting wide spectrum of conduction disorders. CCDC141 exhibit consensus (in LDSC and TSMR) in predicting SSS, RBBB, IIAVB and latent factor in causing conduction disorders. While ESR2 gene exhibit high confidential in predicting the risk of SSS, IIIAVB, IIAVB ,IAVB and RBBB. FKBP7 exhibit high confidential in predicting risk of IAVB, RBBB and IIAVB. 4. Discussion Conduction disorders is an severe cardiovascular event related to syncope and even sudden cardiac death[ 19 ]. Investigating deep into the genetic basis of different kinds of cardiac conduction disorders benefit the management of diseases. Through the joint analysis of these complex traits, we identified multiple new genetic markers. This research provides a new theoretical basis for understanding how genetic loci shape cardiac conduction disorders and offers an important reference for the implementation of future precision medicine and public health interventions. Our research, through the analysis of the genomic structural equation model, revealed the genetic covariance of different types. The results show that genetic factors are shared among sick sinus syndrome, bundle bunch blocks and AVB in different degree which was rarely reported before. We found close positive association among AVBs and bundle bunch clocks. Sick sinus syndrome exhibit negative correlation with bundle bunch blocks, but exhibited positive correlation with AVBs. AVBs and bundle bunches exhibit negative genetic correlations, indicating different genetic backgrounds between the two types of diseases. GenomicSEM helped us found latent factor affecting the occurrence of CCDs. Another important finding of this study is that we identified some new risk factors that have not been widely reported in CCDs or other similar studies before. Via subsequent multi-method fine-mapping analysis on identified factor we found the rs112720315 is a novel lead loci for conduction disorders and subsequent PheWAS analysis indicated the association with non-ischemic cardiomyopathy(NICM). NICM is closely related to conduction disorder like LBBB[ 20 ] and genetic connection between the bundle bunch block especially LBBB and NICM/heart failure haven’t been assessed. Our study gives insight into the underlying mechanisms. Besides based on TWAS, FOCUS and FUMA analysis, we found causal gene markers are related to the risk of conduction disorders and subsequent LDSC analysis and MR analysis revealed the potential for markers in predicting wide spectrum of conduction disorders. Identified marker FKBP7 was found to increase the risk of atrial fibrillation and can be a therapeutic target for atrial fibrillation and the above study was based on GWAS analysis[ 21 – 23 ]. In our study, we also found positive correlation between different degree of atrioventricular block is correlated to circulating FKBP7 gene via TSMR method. Thus the causal effect of circulating FKBP7 genetic marker was established. Previous studies also reported the association between first degree of atrioventricular block and atrial fibrillation and common underlying mechanism can explain the coexist of the two diseases[ 24 ]. Hence, the FKBP7 may get involved above mechanism. CCDC141 gene was previously reported in GWAS analysis to get involved in the sinoatrial node dysfunction, distal conduction disorders and pacemaker implantation, thus our study confirm the previous findings and give evidence of that genetic as circulating biomarker[ 25 ]. ESR2 gene showed contrary effect to CCDC141 gene proved by our research. Previous study indicated the promoting role of ESR2 gene in causing atrial fibrillation[ 26 ] and combined to our results the effect of AF causing may derive from the promotion of atrioventricular block[ 27 ]. SH3PXD2A was proved to be associated with occurrence of atrial fibrillation[ 28 ], however, SH3PXD2A didn’t showed consensus results in predicting conduction disorders from LDSC and TSMR analysis. Overall our study revealed the genetic correlations among different types of CCDs. We also detected novel locus for latent common influence factor in cardiac conduction disorders. rs112720315 which was seldom reported in human diseases located chromosome 6 showed significant causal relationships with NICM, which is a disease closely linked to CCDs, indicating the value of this loci in conduction disorders therapy. Besides, we identify the significant genetic markers in latent influencing factor and their effect in predicting conduction disorders. Limitation Although this study have many advantages in deciphering the latent mechanisms in various types of conduction disorders. However, the effect of SCN10A was not detected because there is no corresponding eQTL data for that gene. Also GWAS datasets were from European ancestry, thus the explanation of genetic markers in predicting CCDs in other ancestry is less valid. Second, associated genetic locus was analyzed from fine mapping and transcriptomic analysis, how to link these markers to specific biological mechanisms remains a burning question. Furthermore, although our research has revealed the significant role of genetic factors in CCDs, the role of environmental factors cannot be ignored either. However, Genomic SEM can develop a new latent factor for CCDs, which can partly rescue the drawbacks above. Conclusion This study constructed the Genomic structural equation with cardiac conduction disorder GWAS summary data to identify the latent influencing factor for cardiac conduction disorders. Via the result from structural equation and integration of Finemapping analysis, we identify novel lead gene locus associated with latent factor and non-ischemic cardiomyopathy. Then TWAS, FUMA and FOCUS analysis identify the markers( CCDC141, FKBP7, SH3PXD2A and ESR2 ) significantly related to latent factor. Subsequent LDSC and Mendelian randomization analysis confirm the potential of those markers in predicting wide spectrum of conduction disorders. Abbreviations CCD：Cardiac conduction disorders; GWAS: Genome wide association study; TSMR: two-sample Mendelian randomization; LDSC:linkage disequilibrium score regression; SRMR: standardized root mean square residual; TWAS: Transcriptome-Wide Association Study; FUMA:Functional Mapping and Annotation of Genetic Associations; SSS: sick sinus syndrome; AVB: atrioventricular block; LBBB:left bundle branch block; RBBB:right bundle branch block; NICM:non-ischemic cardiomyopathy; Declarations Ethics approval and consent to participate :Not applicable Consent for publication: Not applicable Availability of data and materials: All data generated or analysed during this study are included in this published article. Competing interests: Authors declared no conflict of interest. Funding: This study was supported by Liao Ning Revitalization Talent program(NO: XLYC2002096) and National Key Research and Development Program of China( NO:2022YFC2405002). Authors' contributions: YL.X performed the design of the whole study. TY.W and XG.M performed the data collection and analysis. TY.W and XG.M performed the visualization of results. YL.X performed the language editing. TY.W and XG.M majorly write the original draft. All authors read and approved the final manuscript. Acknowledgements: Thanks for all authors for contributing to this work. References Cristina B, Luca C, Marco Z, Luca DL, Maria LB, Beatrice dC, Assunta DD, Elisabetta T, Francesco V, Michele M, et al. Cardiac Conduction Disorders Due to Acquired or Genetic Causes in Young Adults: A Review of the Current Literature. J Am Heart Assoc. 2025;14(9):e040274. Bingxun L, Hongxuan X, Lin W. Genetic insights into cardiac conduction disorders from genome-wide association studies. Hum Genomics. 2025;19(1):20. 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Shih-Yin C, Yu-Chia C, Ting-Yuan L, Kuan-Cheng C, Shih-Sheng C, Ning W, Donald LW, Rylee Kay D, Chia-Jung C, Jai-Sing Y, et al. Novel Genes Associated With Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Score and PheWAS-Derived Risk Factors. Can J Cardiol. 2024;40(11):2117–27. Supplementary Files TableS1.txt Table S1: Latent factor calculated by Genome structural equation and univariate SEM parameters association. TableS2.txt Table S2: Result for TWAS analysis. TableS3.txt Table S3: Result for FOCUS analysis. TableS4.csv Table S4: Pleiotropy test results for Mendelian randomization analysis to detect the horizontal pleiotropy. TableS5.csv Table S5: Result of steiger test to identify the direction of results. 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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-6928678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484186440,"identity":"8871cecd-18d5-410a-bc6a-862d2ff8496d","order_by":0,"name":"Tongyu Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tongyu","middleName":"","lastName":"Wang","suffix":""},{"id":484186441,"identity":"cd2a0cc2-9cf1-460c-93f0-7deb86717658","order_by":1,"name":"Xinge Miao","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinge","middleName":"","lastName":"Miao","suffix":""},{"id":484186442,"identity":"bde2200c-a76f-4110-b157-6943c9ecf563","order_by":2,"name":"Yunlong Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDCC42BSAkQwHgAScmzs7QfwazmM0MIAUmrMx3MmgRgtDHAtifMkHAzw6uA7zJ0mwdhmIW9wI/fAgY87atPbJBgSGH5UbMOpRfIw7zYJhjMShhtu5CUcnHnmeG6bdOMBxp4zt3FqMQBrqZBg3HAjB8huO5bbJnMggZmxjZAWAwl7mJZ0NokEAyK0VEgkQrXUJBDUAvTLZgugX5JnnnljcHBm2wHDNmAgH8TnF77jvRtvMLbV2fYdzzF88LGtTl6+vf3ggx8VuLWAAPMfIKFwAMyGRNMBvOphQL4BTNURpXgUjIJRMApGFgAARPRc1dmQguIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7985-3273","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2025-06-19 07:47:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6928678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6928678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86784327,"identity":"37e5aa12-7a17-400b-b4e7-f2cd8dd41946","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eCorrelation plot of LD score regression analysis among different types of conduction disorders. B.construction of the structural equation model.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/6e531a1339f4a8301984b602.jpeg"},{"id":86784318,"identity":"0f31cc1f-807e-4b80-beab-e343798db318","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":337219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plot from FUMA analysis: \u003c/strong\u003eA. Lead SNPs identified by FUMA analysis were annotated. SNPs with P-value\u0026lt;5*10-8 and R2\u0026lt;0.1. B. Genes (\u003cem\u003eCCDC141, SCN10A, SH3PXD2A and ESR2\u003c/em\u003e) identified from FUMA analysis with significant adjust P value were identified.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/dfff562bfb5a7f622ecef8e4.jpeg"},{"id":86784316,"identity":"3a89ec34-f581-4b7f-980b-796bd0270559","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":359720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocuszoom plot for fine mapping analysis: \u003c/strong\u003ePlots depict the results from two methods of fine-mapping analysis. The lowest panel indicated the final results for fine mapping analysis. \u003cstrong\u003eA.\u003c/strong\u003efine mapping plot for rs112720315. This SNP exhibited Fine maping PP\u0026gt;0.95 in SUSiE and FINEMAP methods, indicating the causal role of this SNP in the pathogenesis of cardiac conduction disorders. \u003cstrong\u003eB.\u003c/strong\u003e fine mapping plot for rs71208329. This SNP exhibited Fine maping PP\u0026gt;0.95 in SUSiE and FINEMAP methods, indicating the causal role of this SNP in the pathogenesis of cardiac conduction disorders.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/b874e15d36b4ababc482690f.jpeg"},{"id":86784338,"identity":"65fc9cb3-0642-4306-a6e5-d615cdd720db","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePheWAS analysis based on Finngen databases: \u003c/strong\u003eTraits association between Fine mapping identified SNPs and related traits. Only non-ischemic cardiomyopathy showed correlation with rs112720315 and Odds ratio\u0026gt;1.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/78307341b6b56207adc22a27.png"},{"id":86786526,"identity":"71c8bf64-8dbb-4ddb-8373-bc5a7820fdc7","added_by":"auto","created_at":"2025-07-15 14:11:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":272298,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot exhibit the FUSION TWAS results. Only \u003cem\u003eFKBP7\u003c/em\u003e gene exhibited significance in adjust P value.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/cfd3dc37388f1b4f7353e79b.png"},{"id":86784976,"identity":"573afbc0-2a2e-4930-a0d0-0214b819f1a8","added_by":"auto","created_at":"2025-07-15 14:03:27","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":171417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eLDSC results between genes and conduction disorders. Heatmap with red colors are traits with positive correlations.\u003cstrong\u003e B. \u003c/strong\u003eResults from two sample Mendelian randomization between genes and conduction disorders.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/3a1e1835224f23388dd6dfa0.jpeg"},{"id":87841540,"identity":"f6d56137-2b51-4b50-ad45-fa96a2f12620","added_by":"auto","created_at":"2025-07-29 14:15:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2392746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/183cb651-32ab-4278-8532-ce0111218d45.pdf"},{"id":86784315,"identity":"4d0e2dab-e5af-435e-a89c-7a5dba0069fc","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1: \u003c/strong\u003eLatent factor calculated by Genome structural equation and univariate SEM parameters association.\u003c/p\u003e","description":"","filename":"TableS1.txt","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/204dacb48b64a43ee1710b20.txt"},{"id":86784330,"identity":"0edcb395-6127-4286-a716-f9f44f2f323f","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10300820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2: \u003c/strong\u003eResult for TWAS analysis.\u003c/p\u003e","description":"","filename":"TableS2.txt","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/1a659a4775d13900f7596ce0.txt"},{"id":86784975,"identity":"83c06eac-e433-420c-b00b-3eb6f260a89f","added_by":"auto","created_at":"2025-07-15 14:03:27","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3: \u003c/strong\u003eResult for FOCUS analysis.\u003c/p\u003e","description":"","filename":"TableS3.txt","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/a483ca7caaee5bacb704550c.txt"},{"id":86786525,"identity":"aace4ca6-d896-45cb-87c3-c37187e4dee5","added_by":"auto","created_at":"2025-07-15 14:11:27","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4: \u003c/strong\u003ePleiotropy test results for Mendelian randomization analysis to detect the horizontal pleiotropy.\u003c/p\u003e","description":"","filename":"TableS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/f51f0f1b47fdb57e6aac10e5.csv"},{"id":86784321,"identity":"8d54148b-d346-4f90-9d63-fcbacf1d2139","added_by":"auto","created_at":"2025-07-15 13:55:27","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S5:\u003c/strong\u003e Result of steiger test to identify the direction of results.\u003c/p\u003e","description":"","filename":"TableS5.csv","url":"https://assets-eu.researchsquare.com/files/rs-6928678/v1/a065f3cb1bc3167e71a46425.csv"}],"financialInterests":"","formattedTitle":"Integration of Genomic structural equation and post-GWAS analysis reveal the risk gene loci and sensitive genes for cardiac conduction block risk.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiac conduction disorders (CCDs) cover a wide array of clinical conditions, which are defined by heart conduction abnormalities[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. They include both acquired and inherited types and can present either in isolation or along with structural heart defects. When there are cardiac conduction disorders (CCDs), the heart's depolarization can be disturbed and bradycardia may even be caused. Common manifestations of CCDs are sick sinus syndrome (SSS), atrioventricular block (AVB), left bundle branch block (LBBB) and right bundle branch block (RBBB)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDamage of cardiac conduction system caused by ischemia and aging lead to disruption of normal heart rhythm and adult mammalian heart lacks regenerative capacity and is unable to replace lost cells after injury[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus CCD is somehow irreversible process[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In severe cases, patients with combined cardiac conduction disorders may experience severe symptoms such as syncope and may even suffer from sudden cardiac death. Multiple population-based epidemiological studies have shown that patients with prolonged PR interval and right bundle branch block have an increased risk of cardiovascular disease (CVD) and death[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMolecular mechanisms play a key role in the pathogenesis of CCD (cardiac conduction system disorders). Animal studies have confirmed that abnormalities in calcium ion-regulation proteins can lead to dyregulated Ca\u003csup\u003e2+\u003c/sup\u003e concentration in endoplasm and delayed atrioventricular node conduction[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In human patients, mutations in the connexin 45 can cause progressive atrioventricular block[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Purkinje cells (PCs) damage are associated with intra-ventricular conduction disorders and lead to heart failure. Loss-of-function mutations in the alpha subunit of sodium channel Nav1.5 (\u003cem\u003eSCN5A\u003c/em\u003e) are associated with His-Purkinje conduction disease and hyperexcitability[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eCx43\u003c/em\u003e polymorphism is associated with left bundle branch block (LBBB), and its existence is related to the genetic susceptibility of LBBB[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Also epigenetic markers played role in the pathogenesis of sinoatrial node dysfunction, \u003cem\u003emiR-486-3p\u003c/em\u003e has a significantly lower expression level in normal SAN and has been proven to control the expression of HCN4[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Human HF hearts have shown increased SAN expression of \u003cem\u003emiR-486-3p\u003c/em\u003e, which offers a possible explanation for SND development in HF patients[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent research has not yet been able to determine the actual circulating biomarkers that can be used to predict and intervene in the risk of conduction disorders. We adopted the genomical-structural equation model (Genomic-SEM) method, combined with the previously published genome-wide association study (GWAS) dataset, to identify the potential factors among different types of conduction disorders. With the help of this method, we were able to determine novel single nucleotide polymorphisms (SNPs) that affect the risk of cardiac conduction system disorders (CCD). Also multiple methods were adopted to explore the association between the CCDs and genetic markers. Based on these newly discovered potential factors, we can also further search for circulating genetic markers that can predict the risk of wide spectrum of CCD, thereby providing new options for biomarkers and therapeutic targets in clinical work.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Source and quality of involved GWAS datasets\u003c/h2\u003e\u003cp\u003eGWAS summary data of conduction disorders from European ancestry was selected. Detailed information including the construction of databases and quality control parameters are shown in the following Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eInformation of GWAS summary datasets used for structural genomic equation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccession No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAncestry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eλGC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNSNP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCST90475955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e447369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSecond degree AV block\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGWAS catalogue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1173070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCST90477911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e443107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ethird-degree atrioventricular block\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGWAS catalogue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1173072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCST90475980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e439404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSinoatrial node dysfunction (Bradycardia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGWAS catalogue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1173071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCST90475954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e435543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFirst degree atrioventricular block\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGWAS catalogue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1173067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI9_LBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e375343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeft bundle-branch block\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFinngen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1159470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI9_RBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e375343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRight bundle-branch block\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFinngen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1159466\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=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Construction of Genomic Structural Equation\u003c/h2\u003e\u003cp\u003eThe genome-wide structural equation modeling (Genomic SEM) analysis use GenomicSEM R package (v.0.0.5) to investigate the broad genetic susceptibility underlying sick sinus syndrome, IAVB, IIAVB, IIIAVB, LBBB and RBBB. Genomic SEM is a newly developed multivariate method capable of examining multiple potential multivariate models to explore the latent structure of traits of interest. Genomic SEM exhibits robustness against biases induced by sample overlap or sample size imbalance. This method additionally facilitates the identification of variants exerting effects on only a subset of complex traits, thereby excluding those representing broad cross-trait susceptibility. The Genomic SEM analysis is implemented through a two-stage procedure. In the first stage, the empirical genetic covariance matrix and its corresponding sampling covariance matrix are estimated. Utilizing a multivariate extension of cross-trait LD score regression, we generated an empirical genetic covariance matrix among n traits, which serves as input for the SEM common factor model. In the second stage, a structural equation modeling (SEM) framework was employed to minimize the discrepancy between the hypothesized covariance matrix and the empirically calculated covariance matrix obtained in the first stage. Our primary research objective focused on identifying the genetic architecture underlying 6 traits; therefore, a single-factor model was tested. Model fit was evaluated using multiple indices: the standardized root mean square residual (SRMR), model χ\u0026sup2; statistic, Akaike information criterion (AIC), and comparative fit index (CFI) shown in table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Defining Genomic Loci and Identifying Novel Variants\u003c/h2\u003e\u003cp\u003eThe FUMA (Functional Mapping and Annotation of Genetic Associations) platform was employed to delineate genomic loci and identify lead single nucleotide polymorphisms (SNPs) associated with conduction disorders[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Lead SNPs were defined as those exhibiting genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and low linkage disequilibrium (LD r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Summary statistics of potential risk SNPs in Genomic structural equations were input to evaluate their association strength. Subsequently, the lead SNPs and loci were compared with those from the original univariate GWAS. Novel loci were defined as SNP with \u003cem\u003ep-value\u003c/em\u003e in Structural equation is more significant than each single uni-variant GWAS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Fine mapping identify the causal SNP in structural Genomic equation\u003c/h2\u003e\u003cp\u003eTo identify the most probable causal variants associated with your GWAS, we employed SuSIE (Sum of Single Effects) and FINEMAP, both implemented in the R package echolocatoR(v.2.0.3). A probability threshold of 0.95 was set to define credible sets of potential causal variants. SuSIE and FINEMAP are fine-mapping tools designed to pinpoint the most likely causal variants associated with a given phenotype[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this analysis, a 250kb window was applied to encompass regions linked to each lead SNP, followed by computation of the posterior inclusion probability (PIP) for each SNP within these regions. A probability threshold of 0.95 was established, wherein variants exceeding this posterior probability threshold were considered potential causal variants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Transcriptome-Wide Association Study (TWAS)\u003c/h2\u003e\u003cp\u003eFollowing the identification of potential causal variants, we conducted a transcriptome-wide association study (TWAS) to prioritize genes associated with conduction disorders based on the relationship between gene expression and phenotypes. The FUSION method was employed for TWAS analysis, utilizing precomputed expression quantitative trait loci (eQTL) weights from GTEx v8 (weight panel download website : \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gusevlab.org/projects/fusion/#gtex-cross-tissue-scca-expression\u003c/span\u003e\u003cspan address=\"http://gusevlab.org/projects/fusion/#gtex-cross-tissue-scca-expression\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), encompassing 37,920 gene-tissue pairs[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These weights were applied to assess expression-phenotype associations across different genes and tissues. Further conduct the FOCUS method. FOCUS method assesses whether there is a causal relationship between genes and phenotypes based on the FOCUS posterior inclusion probability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 LDSC analysis\u003c/h2\u003e\u003cp\u003eGlobal genetic association analysis using LD score regression (LDSC)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to estimate genetic correlations by quantifying the average shared genetic effect between quantitative traits. Following the instructions provided in the LDSC manual, the first step involves converting the GWAS data of both traits into LDSC format, followed by the calculation of \u0026ldquo;rg\u0026rdquo; by R package \u0026ldquo;ldscr\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Two sample Mendelian Randomization analysis\u003c/h2\u003e\u003cp\u003eIn order to identify the role of involved genes and each type of conduction disorders, we performed two sample Mendelian randomization analysis to further identify the potential of gene markers in biomarkers for wide spectrum of cardiac conduction disorders. Firstly, we retrieved eQTL data from eQTLgen database including blood samples of 31,684 individuals (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/cis-eqtls.html\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/cis-eqtls.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and 16989 genes eQTL loci. In order to fulfill the core assumptions for MR analysis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]: i. instrumental variables have a strong correlation; ii. SNPs are not associated with any confounders; iii. instrumental variables have no effect on outcome. Exposure datasets were filtered by p value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 and removing LD SNPs with parameter clump length 10000KB and r2 threshold equals to 0.6. Then as for outcome datasets those instrumental variables with p-value\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 was removed to avoid the direct of SNPs effect on outcome. In order to identify the effect of confounders, test of horizontal pleiotropy and causal direction test (steiger test) were performed. Major Mendelian randomization analysis was performed using the \u0026ldquo;TwoSampleMR\u0026rdquo; R package. The random-effects inverse variance weighting (IVW) and Wald ratio analytical methods were used to evaluate the causal relationships between exposure and outcome datasets.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Construction of Statistical Indicators for Structural Equation Modeling (SEM)\u003c/h2\u003e\u003cp\u003eLD-score regression analysis revealed that the heritability contributions (h\u0026sup2;) of the four input GWAS univariate traits were as follows: SSS :0.006 (0.0012), IAVB : 0.0059 (0.0012), IIAVB: 0.0019 (0.0011), IIIAVB: 0.0061 (0.001), LBBB: 0.006 (0.0014), and RBBB: 0.0027 (0.0014). The genetic covariance values between pairwise traits are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Positive genetic correlation was found among different degrees of AVB and intraventricular conduction disorders(LBBB and RBBB). Also the SSS showed positive correlation with AVB, but negatively correlated with bundle bunch blocks. Correlation between bundle bunch blocks and AVBs is negative. Then Genomic SEM was constructed based on the configuration in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrior to model construction, structural equation modeling (SEM) was performed. The common factor model demonstrated good fit between the genetic covariance matrix of the four input GWAS traits and the empirical covariance matrix (comparative fit index [CFI]\u0026thinsp;=\u0026thinsp;1; standardized root mean square residual [SRMR]\u0026thinsp;=\u0026thinsp;0.0984). Latent factor calculated by Genome structural equation and univariate SEM parameters association can be seen in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. These results provide evidence for the existence of shared genetic factors across the GWAS traits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Quality Control and Statistical Genetic Analysis Report\u003c/h2\u003e\u003cp\u003eIn order to investigate the quality of latent factor GWAS summary datasets we adopted methodological parameter controls and removed a total of 6401566 SNPs. There are 1117809 effective SNPs remained. Key metrics of the remaining SNPs include: Mean Chi\u0026sup2;= 1.1023, Genomic Control Lambda (λGC)\u0026thinsp;=\u0026thinsp;1.1491, Max chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;33.324, Contribution ratio of genetic component to environment\u0026thinsp;=\u0026thinsp;0.1066 (0.0623) and \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0699 (0.0083).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Structural equation modeling based conduction disorder GWAS evaluation using FUMA software\u003c/h2\u003e\u003cp\u003eThrough FUMA software, a total of 4 risk loci (rs71208329, rs13031826, rs2634071 and rs112720315) were identified in the structural equation modeling-based evaluation of the genome\u003cem\u003e(p-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5e-8, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Of them, we found two novel SNPs (rs71208329 and rs112720315) in cardiac conduction disorders. Additionally, 4 potential (your GWAS)-associated genes(\u003cem\u003eCCDC141, SCN10A, SH3PXD2A and ESR2\u003c/em\u003e) were detected under genome-wide significant control (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Fine mapping method identify the causal SNP with latent factor\u003c/h2\u003e\u003cp\u003eFine mapping analysis identified strong associations at multiple genomic loci, including: chromosome 2 (rs71208329, a variant in AC023469.1); chromosome 6 (rs112720315, the TCP10L2 locus). Regional plots showed clear peaks at these loci, and other credible set variants also demonstrated evidence of association (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 PheWAS analysis\u003c/h2\u003e\u003cp\u003eBased on above identified significant SNPs, we retrieved the related traits related with the SNPs from Finngen database. With setting p-value threshold of 1*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, we found that the Non ischemic cardiomyopathy is only significantly associated with rs112720315 with odds ratio\u0026thinsp;\u0026gt;\u0026thinsp;1, indicating the latent association between cardiac conduction disorders and cardiomyopathy(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Transcriptome-Wide Association Analysis\u003c/h2\u003e\u003cp\u003eSubsequently, we performed transcriptome-wide association analysis (TWAS) using FUSION to identify gene-level associations related to the genetic characteristics of cardiac conduction disorders. There are 37917 genes in total identified by TWAS analysis (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Only FKBP7 gene pass the significance test with \u003cem\u003ep-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05/37917\u003c/em\u003e shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Subsequently, we conducted further FOCUS fine-mapping analysis on the genomic structural equation modeling data, revealing same gene (PIP\u0026thinsp;\u0026gt;\u0026thinsp;0.9) that may represent potential causal signals associated with conduction disorders. To further validate these \"high-confidence\" gene-level associations, an intersection test was performed shown in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 LDSC and Two sample Mendelian randomization revealed association between identified genes and conduction disorders.\u003c/h2\u003e\u003cp\u003eIn order to further identify the potential of above genes in predicting the occurrence of each type of conduction disorders. We retrieved eQTL data from eQTLlgen cohort for identified genes. LDSC analysis revealed genetic correlation of Genes and conduction disorders shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. We found that \u003cem\u003eCCDC141\u003c/em\u003e is positively correlated with RBBB, while negatively correlated with SSS, IIIAVB, IIAVB and IAVB. Also the correlation between \u003cem\u003eCCDC141\u003c/em\u003e and latent factor identified from Genome SE. On the contrary \u003cem\u003eESR2\u003c/em\u003e gene is significantly positively correlated with SSS, IIIAVB, IIAVB and IAVB, while negatively related to the RBBB. \u003cem\u003eFKBP7\u003c/em\u003e is significantly positively correlated with F1, SSS, IIIAVB, IIAVB, LBBB and IAVB, while negatively related to the RBBB. \u003cem\u003eSH3PXD2A\u003c/em\u003e is negatively correlated with F1, IIIAVB, IIAVB and IAVB.\u003c/p\u003e\u003cp\u003eIn terms of TSMR analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, we found \u003cem\u003eCCDC141\u003c/em\u003e have positive causal relationship with RBBB and LBBB. \u003cem\u003eCCDC141\u003c/em\u003e have negative causal relationship with IIIAVB and F1. \u003cem\u003eESR2\u003c/em\u003e gene is significantly positively correlated with SSS, IIIAVB, IIAVB and IAVB, while negatively related to the RBBB and LBBB. \u003cem\u003eFKBP7\u003c/em\u003e is significantly positively correlated with F1, IIAVB, and IAVB, while negatively related to the RBBB. \u003cem\u003eSH3PXD2A\u003c/em\u003e is negatively correlated with RBBB and positively correlated with IIAVB and F1. The results for steiger test and pleiotropy test indicate the stability of major TSMR(Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e and Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summary, we found genetic markers with high confidential in predicting wide spectrum of conduction disorders. \u003cem\u003eCCDC141\u003c/em\u003e exhibit consensus (in LDSC and TSMR) in predicting SSS, RBBB, IIAVB and latent factor in causing conduction disorders. While \u003cem\u003eESR2\u003c/em\u003e gene exhibit high confidential in predicting the risk of SSS, IIIAVB, IIAVB ,IAVB and RBBB. FKBP7 exhibit high confidential in predicting risk of IAVB, RBBB and IIAVB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eConduction disorders is an severe cardiovascular event related to syncope and even sudden cardiac death[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Investigating deep into the genetic basis of different kinds of cardiac conduction disorders benefit the management of diseases. Through the joint analysis of these complex traits, we identified multiple new genetic markers. This research provides a new theoretical basis for understanding how genetic loci shape cardiac conduction disorders and offers an important reference for the implementation of future precision medicine and public health interventions.\u003c/p\u003e\u003cp\u003eOur research, through the analysis of the genomic structural equation model, revealed the genetic covariance of different types. The results show that genetic factors are shared among sick sinus syndrome, bundle bunch blocks and AVB in different degree which was rarely reported before. We found close positive association among AVBs and bundle bunch clocks. Sick sinus syndrome exhibit negative correlation with bundle bunch blocks, but exhibited positive correlation with AVBs. AVBs and bundle bunches exhibit negative genetic correlations, indicating different genetic backgrounds between the two types of diseases. GenomicSEM helped us found latent factor affecting the occurrence of CCDs. Another important finding of this study is that we identified some new risk factors that have not been widely reported in CCDs or other similar studies before. Via subsequent multi-method fine-mapping analysis on identified factor we found the rs112720315 is a novel lead loci for conduction disorders and subsequent PheWAS analysis indicated the association with non-ischemic cardiomyopathy(NICM). NICM is closely related to conduction disorder like LBBB[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and genetic connection between the bundle bunch block especially LBBB and NICM/heart failure haven\u0026rsquo;t been assessed. Our study gives insight into the underlying mechanisms. Besides based on TWAS, FOCUS and FUMA analysis, we found causal gene markers are related to the risk of conduction disorders and subsequent LDSC analysis and MR analysis revealed the potential for markers in predicting wide spectrum of conduction disorders.\u003c/p\u003e\u003cp\u003eIdentified marker \u003cem\u003eFKBP7\u003c/em\u003e was found to increase the risk of atrial fibrillation and can be a therapeutic target for atrial fibrillation and the above study was based on GWAS analysis[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, we also found positive correlation between different degree of atrioventricular block is correlated to circulating \u003cem\u003eFKBP7\u003c/em\u003e gene via TSMR method. Thus the causal effect of circulating \u003cem\u003eFKBP7\u003c/em\u003e genetic marker was established. Previous studies also reported the association between first degree of atrioventricular block and atrial fibrillation and common underlying mechanism can explain the coexist of the two diseases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hence, the \u003cem\u003eFKBP7\u003c/em\u003e may get involved above mechanism. \u003cem\u003eCCDC141\u003c/em\u003e gene was previously reported in GWAS analysis to get involved in the sinoatrial node dysfunction, distal conduction disorders and pacemaker implantation, thus our study confirm the previous findings and give evidence of that genetic as circulating biomarker[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cem\u003eESR2\u003c/em\u003e gene showed contrary effect to \u003cem\u003eCCDC141\u003c/em\u003e gene proved by our research. Previous study indicated the promoting role of \u003cem\u003eESR2\u003c/em\u003e gene in causing atrial fibrillation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and combined to our results the effect of AF causing may derive from the promotion of atrioventricular block[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eSH3PXD2A\u003c/em\u003e was proved to be associated with occurrence of atrial fibrillation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], however, \u003cem\u003eSH3PXD2A\u003c/em\u003e didn\u0026rsquo;t showed consensus results in predicting conduction disorders from LDSC and TSMR analysis.\u003c/p\u003e\u003cp\u003eOverall our study revealed the genetic correlations among different types of CCDs. We also detected novel locus for latent common influence factor in cardiac conduction disorders. rs112720315 which was seldom reported in human diseases located chromosome 6 showed significant causal relationships with NICM, which is a disease closely linked to CCDs, indicating the value of this loci in conduction disorders therapy. Besides, we identify the significant genetic markers in latent influencing factor and their effect in predicting conduction disorders.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003cp\u003eAlthough this study have many advantages in deciphering the latent mechanisms in various types of conduction disorders. However, the effect of \u003cem\u003eSCN10A\u003c/em\u003e was not detected because there is no corresponding eQTL data for that gene. Also GWAS datasets were from European ancestry, thus the explanation of genetic markers in predicting CCDs in other ancestry is less valid. Second, associated genetic locus was analyzed from fine mapping and transcriptomic analysis, how to link these markers to specific biological mechanisms remains a burning question. Furthermore, although our research has revealed the significant role of genetic factors in CCDs, the role of environmental factors cannot be ignored either. However, Genomic SEM can develop a new latent factor for CCDs, which can partly rescue the drawbacks above.\u003c/p\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study constructed the Genomic structural equation with cardiac conduction disorder GWAS summary data to identify the latent influencing factor for cardiac conduction disorders. Via the result from structural equation and integration of Finemapping analysis, we identify novel lead gene locus associated with latent factor and non-ischemic cardiomyopathy. Then TWAS, FUMA and FOCUS analysis identify the markers(\u003cem\u003eCCDC141, FKBP7, SH3PXD2A and ESR2\u003c/em\u003e) significantly related to latent factor. Subsequent LDSC and Mendelian randomization analysis confirm the potential of those markers in predicting wide spectrum of conduction disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCCD：Cardiac conduction disorders; GWAS: Genome wide association study; TSMR: two-sample Mendelian randomization; LDSC:linkage disequilibrium score regression; SRMR: standardized root mean square residual; TWAS: Transcriptome-Wide Association Study; FUMA:Functional Mapping and Annotation of Genetic Associations; SSS: sick sinus syndrome; AVB: atrioventricular block; LBBB:left bundle branch block; RBBB:right bundle branch block; NICM:non-ischemic cardiomyopathy;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:Not applicable\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003eNot applicable\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All data generated or analysed during this study are included in this published article.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAuthors declared no conflict of interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by Liao Ning Revitalization Talent program(NO: XLYC2002096) and National Key Research and Development Program of China( NO:2022YFC2405002).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eYL.X performed the design of the whole study. TY.W and XG.M performed the data collection and analysis. TY.W and XG.M performed the visualization of results. YL.X performed the language editing. TY.W and XG.M majorly write the original draft. All authors read and approved the final manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003eThanks for all authors for contributing to this work.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCristina B, Luca C, Marco Z, Luca DL, Maria LB, Beatrice dC, Assunta DD, Elisabetta T, Francesco V, Michele M, et al. Cardiac Conduction Disorders Due to Acquired or Genetic Causes in Young Adults: A Review of the Current Literature. J Am Heart Assoc. 2025;14(9):e040274.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBingxun L, Hongxuan X, Lin W. Genetic insights into cardiac conduction disorders from genome-wide association studies. Hum Genomics. 2025;19(1):20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStefan vD, Christopher PN, Zahra R-E, Julia R, Michele O, Qingning W, Nay A, Veryan C, Svetlana S, Elias A, et al. Leucocyte telomere length and conduction system ageing. Heart. 2024;111(7):314\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJudy RS, Hector M-N, Xin S, Carla dV, Sarah S, Michael W, Claudio Cortes R, Leto Luana R, Lucas Arantes B, Julia C, et al. Cardiac conduction system regeneration prevents arrhythmias after myocardial infarction. Nat Cardiovasc Res. 2025;4(2):163\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetri H, Ismo A, Kjell N, Markku E, Heini H, Tuomo N, Antti J, Veikko S, Antti R, Markku SN, et al. Prognostic implications of intraventricular conduction delays in a general population: the Health 2000 Survey. 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FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32(10):1493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander G, Arthur K, Huwenbo S, Gaurav B, Wonil C, Brenda WJHP, Rick J, Eco JC, dG, Dorret IB, Fred AW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrendan B-S, Hilary KF, Verneri A, Alexander G, Felix RD, Po-Ru L, Laramie D, John RBP, Nick P, Elise BR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKevin N, Braxton DM. A Guide to Understanding Mendelian Randomization Studies. Arthritis Care Res (Hoboken). 2024;76(11):1451\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFred MK, Mark HS, Coletta B, James RE, Kenneth AE, Michael RG, Nora FG, Robert MH, Jos\u0026eacute; AJ, Robert JK, et al. 2018 ACC/AHA/HRS Guideline on the Evaluation and Management of Patients With Bradycardia and Cardiac Conduction Delay: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Circulation. 2018;140(8):e382\u0026ndash;482.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiuseppe DS, Annamaria DB, Massimo Z, Eleonora B, Patrizia C, Eleonora M, Mario EC, Guido P, Gianfranco S, Marco M. Prevalence, clinical and instrumental features of left bundle branch block-induced cardiomyopathy: the CLIMB registry. 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Exp Gerontol. 2025;207(0):112797.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZain SA, Abdullah B, Purav V, Andres F, M-A, Gary T, George B, Cengiz B, Wael A, Sharen L, Shyla G, et al. PR prolongation as a predictor of atrial fibrillation onset: A state-of-the-art review. Curr Probl Cardiol. 2024;49(4):102469.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShih-Yin C, Yu-Chia C, Ting-Yuan L, Kuan-Cheng C, Shih-Sheng C, Ning W, Donald LW, Rylee Kay D, Chia-Jung C, Jai-Sing Y, et al. Novel Genes Associated With Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Score and PheWAS-Derived Risk Factors. Can J Cardiol. 2024;40(11):2117\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiac conduction disorders, Geomic structural equation, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6928678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6928678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCardiac conduction disorders (CCDs) is a wide spectrum of severe cardiovascular events related to syncope and sudden cardiac death. Identifying biomarkers of CCDs benefits the diagnosis and therapy for the disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eGWAS summary datasets were retrieved from GWAS catalogue and Finngen database. GenomicSEM R package was utilized to construct the structural equation model to identify the common latent factor affecting the progression of CCDs. FUMA platform was employed to delineate lead SNPs and genes. SuSIE and FINEMAP are fine-mapping tools used to identify confidential SNPs. TWAS and FOCUS methods were used to identify sensitivity genes. LDSC and Two-sample Mendelian randomization were used to identify the causal relationship between genes and each type of conduction disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTwo novel lead SNPs (rs71208329 and rs112720315) are identified in cardiac conduction disorders after construction of Genomic structural equation and FUMA analysis. Through Fine-mapping analysis, we confirm the validity of rs112720315 in causing diseases and subsequent PheWAS analysis revealed association between rs112720315 and non-ischemic cardiomyopathy. TWAS, FUMA and FOCUS analysis revealed gene markers(\u003cem\u003eCCDC141, SCN10A, SH3PXD2A, FKBP7 and ESR2\u003c/em\u003e) are related to conduction disorders. Then Mendelian randomization and LDSC revealed the connection between the identified genetic markers and CCDs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003ers112720315 is the novel genetic loci associated with conduction disorders. Circulating gene markers(\u003cem\u003eCCDC141, SCN10A, SH3PXD2A, FKBP7 and ESR2\u003c/em\u003e) are potential biomarkers for conduction disorders.\u003c/p\u003e","manuscriptTitle":"Integration of Genomic structural equation and post-GWAS analysis reveal the risk gene loci and sensitive genes for cardiac conduction block risk.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:55:22","doi":"10.21203/rs.3.rs-6928678/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"769216e9-fef3-4e49-9bf9-6767829387ae","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-29T14:07:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 13:55:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6928678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6928678","identity":"rs-6928678","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}