Deep learning enables genetic analysis of the human thoracic aorta

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, revealing 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests, and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (HR = 1.43 per SD; CI 1.32-1.54; P = 3.3·10-20). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images. Overall design: We incorporated an analysis of single-nucleus RNA sequencing (snRNA-seq) using paired samples from the ascending and descending aorta from 3 individuals to identify potentially relevant cell types for the genes at aortic GWAS loci. We sequenced the transcriptomes of 54,092 nuclei and identified 12 primary cell clusters. Through comparison of unique transcriptional profiles in each cluster to canonical cell markers, we identified populations comprising vascular smooth muscle cells, fibroblasts, three distinct types of endothelial cells, as well as macrophages and lymphocytes. Please note that processed file is an h5ad file containing output from all samples