Deep Learning Enables Genome-Wide Association Studies of Microvascular Features

Microvascular dysfunction underlies a wide range of cardiovascular, neurodegenerative and metabolic diseases, yet its genetic architecture remains largely unexplored owing to the lack of scalable in vivo phenotyping. Optical coherence tomography angiography (OCTA) enables direct visualization of human capillary networks and provides a unique window into microvascular biology. Here we perform, to our knowledge, the first genome-wide association study of capillary-scale microvasculature directly measured in vivo, using OCTA-derived foveal avascular zone (FAZ) area as a quantitative trait. We analysed two population-based European cohorts with automated deep learning–based image quality control and FAZ segmentation, followed by cohort-specific genome-wide association analyses and fixed-effects meta-analysis. Genomic inflation was minimal across analyses. Meta-analysis identified genome-wide significant loci on chromosomes 17 and 9 associated with FAZ area. These findings establish OCTA-derived FAZ metrics as genetically tractable microvascular phenotypes and provide a foundation for large-scale genetic studies of human microvasculature and its role in systemic disease.