MedElevator
Войти в исследования Genetics research · 2026
Genomic Structural Equation Modeling Combined With Post-GWAS Analysis Identifies Two Risk Gene Loci and Functionally Sensitive Genes Associated With Cardiac Conduction Block.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Tongyu, Miao Xinge, Xia Yunlong
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.