Integration of time-resolved single-cell multi-omic profiling of cardiac organoid development with base-resolution deep learning models systematically decodes cell-state-specific transcription factor motif syntax governing human cardiogenesis and enables prediction and experimental validation of noncoding variants that disrupt developmental regulatory networks to cause congenital heart disease, including identification of ANGPTL2 as a novel CHD gene through an intronic variant that alters endothelial differentiation.

Defining temporal gene regulatory programs driving human organogenesis is essential for understanding congenital defects. We combined a time-resolved, single-cell multi-omic atlas of human induced pluripotent stem cell-derived cardiac organoids with deep learning models of chromatin accessibility, enabling systematic discovery of cis-regulatory syntax underlying heart development and disease. This framework identified cell-state-specific motif syntax, linked motif instances to candidate target genes, and resolved programs governing lineage divergence. Integrating cell-state-resolved molecular profiles with computationally predicted variant effects from congenital heart disease (CHD) cases enabled the prioritization of noncoding variants predicted to disrupt developmental transitions, supporting the paradigm that disease etiology derives from perturbations to regulatory networks governing cardiogenesis. Experimental validation demonstrated that an intronic ANGPTL2 variant altered differentiation outcomes, implicating ANGPTL2 in CHD. This study bridges developmental regulation with disease genetics, establishing a framework for discovering the genetic and molecular basis of congenital disorders.