Deep learning enables genetic analysis of the human thoracic aorta
- 26 November 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Genetics
- Vol. 54 (1), 40-51
- https://doi.org/10.1038/s41588-021-00962-4
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.Keywords
Funding Information
- Fondation Leducq (14CVD01)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (1RO1HL092577, R01HL128914, K24HL105780, T32HL007208, T32HL007208, 5K01HL140187, R01HL128914, 2R01HL092577, 1R01HL141434, R01HL134893, R01HL140224, 1R01HL139731)
- American Heart Association (Strategically Focused Research Networks, 18SFRN34110082, 18SFRN34110082, 18SFRN34110082)
- John S LaDue Memorial Fellowship
- Career Award for Medical Scientists from the Burroughs Wellcome Fund
- The Fredman Fellowship for Aortic DiseaseThe Toomey Fund for Aortic Dissection Research
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