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

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.