Population genomics of four-dimensional cardiac motion reveals non-myocyte regulatory programmes
Journal:
medRxiv
Published Date:
Jul 31, 2026
Abstract
Genome-wide studies of cardiac structure and function have relied on global imaging metrics that fail to capture the full complexity of myocardial behaviour, leaving the genetic and environmental architecture of motion largely unexplored. Here we show, using unsupervised deep learning applied to four-dimensional cardiac imaging data in 78,113 adult participants, that latent representations of left-ventricular motion reveal 39 genetic loci, two-thirds undetected by conventional indices, exposing a diversity of cell-type specificity validated across single-cell transcriptional atlases of the human heart. Conventional traits resolve to cardiomyocyte and conduction-system regulatory elements, while motion-specific variants converge on non-myocyte programmes encompassing autonomic neuronal, endocardial, fibroblast, and endothelial compartments. All of Us, FinnGen and GPMap data provided locus-level corroboration. Motion-specific traits further implicate environmental exposures including smoking and air pollution linked to motion profiles, and unsupervised clustering stratifies participants by genetic burden and clinical risk, supporting disease-relevant endophenotypes invisible to standard clinical assessment.
