# Genomic Structural Equation Modeling Combined With Post‐GWAS Analysis Identifies Two Risk Gene Loci and Functionally Sensitive Genes Associated With Cardiac Conduction Block

## TL;DR

This study identifies new genetic loci and genes linked to cardiac conduction disorders, offering potential biomarkers for better diagnosis and treatment.

## Contribution

The study introduces a novel genomic SEM approach combined with post-GWAS methods to discover new risk loci and functionally sensitive genes for cardiac conduction disorders.

## Key findings

- The SNP rs112720315 is a novel genetic locus significantly associated with cardiac conduction disorders.
- Five genes (CCDC141, SCN10A, SH3PXD2A, FKBP7, and ESR2) are identified as potential biomarkers for a wide spectrum of CCDs.
- The APOL1 gene is specifically linked to CCD risk in individuals of African ancestry.

## Abstract

Cardiac conduction disorders (CCDs) represent a broad spectrum of severe cardiovascular conditions associated with syncope and sudden cardiac death. Therefore, identification of reliable biomarkers is necessary to significantly improve the diagnostic accuracy and therapeutic outcomes of CCDs. This study analyzed GWAS summary datasets using a genomic structural equation model (Genomic‐SEM), fine mapping, linkage disequilibrium score regression (LDSC), and two‐sample Mendelian randomization (TSMR) analyses to identify genetic loci and genes associated with CCDs.

GWAS summary datasets of European subjects were obtained from the GWAS Catalog and FinnGen databases. The GenomicSEM R package was used to construct a structural equation model to identify common latent factors influencing CCD progression. The Functional Mapping and Annotation of Genome‐Wide Association Studies (FUMA) platform was used to annotate the lead SNPs and candidate genes. Fine‐mapping tools, such as SuSiE and FINEMAP, and Phenome‐Wide Association Study (PheWAS) analysis were used to identify causal SNPs associated with CCDs. Transcriptome‐Wide Association Study (TWAS) and Functional Summary Statistics (FOCUS) analyses were performed to identify CCD susceptibility genes. LDSC and TSMR were performed to determine causal relationships between the candidate risk genes and specific CCDs.

Newly explored CCD‐associated leading SNPs (rs71208329 and rs112720315) were generated from genomic SEM and FUMA analyses. Fine‐mapping and PheWAS analysis confirmed that rs112720315 was linked to nonischemic cardiomyopathy. TWAS, FUMA, and FOCUS analyses showed that five genes (CCDC141, SCN10A, SH3PXD2A, FKBP7, and ESR2) were associated with CCDs. The APOL1 gene is associated with the risk of CCDs in African ancestry. TSMR and LDSC analyses further demonstrated that these genes were significantly associated with CCDs and were potential prediction biomarkers for CCDs.

The novel genetic locus rs112720315 is significantly associated with the occurrence of CCDs. Biomarkers such as CCDC141, SCN10A, ESR2, FKBP7, and SH3PXD2A can predict a wide spectrum of CCDs. The APOL1 gene is a specific marker for CCDs in African ancestry.