Deep learning enables genetic analysis of the human thoracic aorta
James P. Pirruccello,
Mark D. Chaffin,
Stephen J. Fleming,
Elizabeth L Chou,
Samuel N Friedman,
Alexander G. Bick,
Seung Hoan Choi,
Nathan R Tucker,
Amelia W. Hall,
Emelia J. Benjamin,
Shamsudheen K Vellarikkal,
Rajat M Gupta,
Christian M Stegman,
Jennifer E. Ho,
Steven A. Lubitz,
Mark E. Lindsay,
Patrick T. Ellinor
Posted 14 May 2020
bioRxiv DOI: 10.1101/2020.05.12.091934
Posted 14 May 2020
The aorta is the largest blood vessel in the body, and enlargement or aneurysm of the aorta can predispose to dissection, an important cause of sudden death. While rare syndromes have been identified that predispose to aortic aneurysm, the common genetic basis for the size of the aorta remains largely unknown. By leveraging a deep learning architecture that was originally developed to recognize natural images, we trained a model to evaluate the dimensions of the ascending and descending thoracic aorta in cardiac magnetic resonance imaging. After manual annotation of just 116 samples, we applied this model to 3,840,140 images from the UK Biobank. We then conducted a genome-wide association study in 33,420 individuals, revealing 68 loci associated with ascending and 35 with descending thoracic aortic diameter, of which 10 loci overlapped. Integration of common variation with transcriptome-wide analyses, rare-variant burden tests, and single nucleus RNA sequencing prioritized SVIL, a gene highly expressed in vascular smooth muscle, that was significantly associated with the diameter of the ascending and descending aorta. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in the remaining 391,251 UK Biobank participants who did not undergo imaging (HR = 1.44 per standard deviation; P = 3.7 · 10-12). Defining the genetic basis of the diameter of the aorta may enable the identification of asymptomatic individuals at risk for aneurysm or dissection and facilitate the prioritization of potential therapeutic targets for the prevention or treatment of aortic aneurysm. Finally, our results illustrate the potential for rapidly defining novel quantitative traits derived from a deep learning model, an approach that can be more broadly applied to biomedical imaging data. ### Competing Interest Statement Drs. Pirruccello and Bick have served as consultants for Maze Therapeutics. Drs. Akkad and Stegmann are employees of Bayer US LLC (a subsidiary of Bayer AG), and may own stock in Bayer AG. Dr. Philippakis is employed as a Venture Partner at GV; he is also supported by a grant from Bayer AG to the Broad Institute focused on machine learning for clinical trial design. Dr. Ho is supported by a grant from Bayer AG focused on machine-learning and cardiovascular disease. Dr. Batra is supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. Dr. Ellinor is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. Dr. Ellinor has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. Dr. Lubitz receives sponsored research support from Bristol Myers Squibb / Pfizer, Bayer AG, Boehringer Ingelheim, and Fitbit, and has consulted for Bristol Myers Squibb / Pfizer and Bayer AG, and participates in a research collaboration with IBM. The Broad Institute has filed for a patent on an invention from Drs. Ellinor, Lindsay, and Pirruccello related to a genetic risk predictor for aortic disease.
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