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Deep learning enables genetic analysis of the human thoracic aorta

ABSTRACT: Deep learning enables genetic analysis of the human thoracic aorta

PROVIDER: PRJNA697975 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR13582722_1.fastq.gz	Fastqsanger.gz
	SRR13582722_2.fastq.gz	Fastqsanger.gz
	SRR13582723_1.fastq.gz	Fastqsanger.gz
	SRR13582723_2.fastq.gz	Fastqsanger.gz
	SRR13582724_1.fastq.gz	Fastqsanger.gz

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Deep learning enables genetic analysis of the human thoracic aorta

Project description: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, revealing 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 (HR = 1.43 per SD; CI 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.

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