학술논문
Deep learning enables genetic analysis of the human thoracic aorta
Document Type
Article
Author
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.
Source
Nature Genetics; 20210101, Issue: Preprints p1-12, 12p
Subject
Language
ISSN
10614036; 15461718
Abstract
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.