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, identifying 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 (hazard ratio = 1.43 per s.d., confidence interval 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.

主动脉扩大或动脉瘤容易发生夹层，这是猝死的一个重要原因。我们训练了一个深度学习模型来评估英国生物银行 460 万张心脏磁共振图像中胸主动脉升主动脉和降主动脉的尺寸。然后，我们对 39,688 名个体进行了全基因组关联研究，确定了 82 个与升主动脉直径相关的基因座和 47 个与降主动脉直径相关的基因座，其中 14 个基因座重叠。全转录组分析、罕见变异负荷测试和人主动脉单核 RNA 测序优先考虑了包括SVIL在内的基因，该基因与主动脉直径下降密切相关。在 385,621 名英国生物银行参与者中，升主动脉直径的多基因评分与胸主动脉瘤相关（风险比 = 1.43 per sd，置信区间 1.32–1.54，P = 3.3 × 10 ^-20）。我们的结果说明了通过深度学习快速定义数量特征的潜力，这种方法可以广泛应用于生物医学图像。