| Title: | Deep learning enables genetic analysis of the human thoracic aorta |
| Journal: | Nature Genetics |
| Published: | 26 Nov 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34837083/ |
| DOI: | https://doi.org/10.1038/s41588-021-00962-4 |
| URL: | https://research.rug.nl/en/publications/ce4c99a0-5d2f-4c16-9ed7-0d90413b8117 |
| Citations: | 109 (71 in last 2 years) as of 8 Aug 2024 |
Publication 10743
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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.</p>
15 Keywords
- Adult
- Aged
- Aorta, Thoracic
- Aortic Aneurysm
- Biological Variation, Population
- Deep Learning
- Female
- Genome-Wide Association Study
- Humans
- Image Processing, Computer-Assisted
- Magnetic Resonance Imaging
- Male
- Middle Aged
- Quantitative Trait Loci
- Transcriptome
30 Authors
- 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. Gupta
- Christian 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
1 Application
| Application ID | Title |
|---|---|
| 7089 | Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank |
Enabling scientific discoveries that improve human health