Deciphering epistatic genetic regulation of cardiac hypertrophy.

ABSTRACT: Although genetic variant effects often interact non-additively, strategies to uncover epistasis remain in their infancy. Here, we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, using deep learning-derived left ventricular mass estimates from 29,661 UK Biobank cardiac MRIs. We report epistatic variants near CCDC141, IGF1R, TTN, and TNKS, identifying loci deemed insignificant in genome-wide association studies. Functional genomic and integrative enrichment analyses reveal that genes mapped from these loci share biological process gene ontologies and myogenic regulatory factors. Transcriptomic network analyses using 313 human hearts demonstrate strong co-expression correlations among these ge

SUBMITTER: Wang Q 

PROVIDER: S-EPMC10659487 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature