Deep learning enables genetic analysis of the human thoracic aorta

James P. Pirruccello, Mark D. Chaffin, Elizabeth L. Chou, Stephen J. Fleming, Honghuang Lin, Mahan Nekoui, Shaan Khurshid, Samuel F. Friedman, Alexander G. Bick, Alessandro Arduini, Lu Chen Weng, Seung Hoan Choi, Amer Denis Akkad, Puneet Batra, Nathan R. Tucker, Amelia W. Hall, Carolina Roselli, Emelia J. Benjamin, Shamsudheen K. Vellarikkal, Rajat M. GuptaChristian M. Stegmann, Dejan Juric, James R. Stone, Ramachandran S. Vasan, Jennifer E. Ho, Udo Hoffmann, Steven A. Lubitz, Anthony A. Philippakis, Mark E. Lindsay, Patrick T. Ellinor

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)40-51
Number of pages12
JournalNature Genetics
Issue number1
StatePublished - Jan 2022
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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