ArticleScientific reports2026
Genomic structural equation modeling reveals shared genetic structure of cardiac function and structure-function association studies of CLCNKA mutations.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cardiac dysfunction is a prevalent feature of multiple cardiovascular diseases, driven by a complex genetic architecture coordinating structural and functional traits. However, systematic dissection of multidimensional cardiac function phenotypes remains scarce, highlighting the need for integrative models to uncover shared genetic mechanisms. We combined genome-wide association study (GWAS) summary statistics for six cardiac phenotypes—left ventricular ejection fraction (LVEF), left ventricular stroke volume (LVSV), longitudinal and radial myocardial strain (LS, RS), right ventricular ejection fraction (RVEF), and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Multivariate Linkage Disequilibrium Score regression estimated their genetic covariance, and a Genomic Structural Equation Model (Genomic-SEM) extracted latent genetic factors. Transcriptome-wide association studies (TWAS), fine-mapping, and functional enrichment identified key susceptibility loci and genes. We further integrated AlphaFold3-based structural prediction, molecular dynamics simulations, and AI-driven thermodynamic stability assessment to evaluate the functional consequences of pathogenic variants. Genomic-SEM identified a robust latent factor explaining genetic covariation across phenotypes. GWAS and fine-mapping pinpointed six potential causal loci, while TWAS identified 29 significant genes enriched in cardiac contraction, calcium signaling, and mitochondrial pathways. CLCNKA emerged as a critical gene; molecular dynamics revealed its mutations disrupt protein conformation, increase flexibility, and reduce thermal stability, suggesting pathogenic potential. This study provides the first comprehensive genetic architecture of latent cardiac function phenotypes. Integrating Genomic-SEM with AI-assisted structural analysis identified novel loci and elucidated mutation-driven functional disruptions, offering insights into genetic regulation of cardiac function and guiding precision medicine strategies.
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