小柯机器人

深度学习使人类胸腔主动脉的遗传分析成为可能
2021-11-30 15:43

美国Broad研究所Patrick T. Ellinor小组通过深度学习实现人类胸腔主动脉的遗传分析。2021年11月26日,《自然—遗传学》杂志在线发表了这项成果。

研究人员训练了一个深度学习模型来评估来自英国生物库的460万张心脏磁共振图像中升主动脉和降主动脉的尺寸。然后,研究人员对39,688名个体进行了全基因组关联研究,确定了82个与升主动脉直径相关的位点和47个与降主动脉直径相关的位点,其中有14个位点重叠。
 
全转录组分析、罕见变异负担测试和人类主动脉单核RNA测序对包括SVIL在内的基因进行了优先排序,后者与降主动脉直径密切相关。在385,621名英国生物库参与者中,升主动脉直径的多基因评分与胸主动脉瘤相关(风险率=1.43/s.d.,置信区间1.32-1.54,P=3.3×10-20)。这些结果说明了用深度学习快速定义定量性状的潜力,这种方法可以广泛地应用于生物医学图像。
 
据悉,主动脉的扩大或动脉瘤容易导致夹层,这是导致猝死的一个重要原因。
 
附:英文原文

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

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

Issue&Volume: 2021-11-26

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 × 1020). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.

DOI: 10.1038/s41588-021-00962-4

Source: https://www.nature.com/articles/s41588-021-00962-4

Nature Genetics:《自然—遗传学》,创刊于1992年。隶属于施普林格·自然出版集团,最新IF:25.455
官方网址:https://www.nature.com/ng/
投稿链接:https://mts-ng.nature.com/cgi-bin/main.plex


本期文章:《自然—遗传学》:Online/在线发表

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