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Pirruccello, James P; Chaffin, Mark D; Chou, Elizabeth L; Fleming, Stephen J; Lin, Honghuang; Nekoui, Mahan; Khurshid, Shaan; Friedman, Samuel F; Bick, Alexander G; Arduini, Alessandro; Weng, Lu-Chen; Choi, Seung Hoan; Akkad, Amer-Denis; Batra, Puneet; Tucker, Nathan R; Hall, Amelia W; Roselli, Carolina; Benjamin, Emelia J; Vellarikkal, Shamsudheen K; Gupta, Rajat M; Stegmann, Christian M; Juric, Dejan; Stone, James R; Vasan, Ramachandran S; Ho, Jennifer E; Hoffmann, Udo; Lubitz, Steven A; Philippakis, Anthony A; Lindsay, Mark E; Ellinor, Patrick T
Nature genetics, 01/2022, Volume: 54, Issue: 1Journal Article
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 ). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.
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