心脏动态流量体积的遗传结构
Bruna Gomes1,2,3, Aditya Singh1, Jack W O'Sullivan1
1Departments of Medicine, Genetics, Computer Science and Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nature genetics
|December 11, 2023
概括
DeepFlow是一种新型的人工智能系统,分析心脏磁共振成像,揭示了与心脏血液流动的遗传联系. 这有助于我们更好地了解大动脉的功能和相关的遗传因素.
科学领域:
- 心血管成像和遗传学
- 人工智能在医学中的应用
背景情况:
- 心脏血液流动对健康至关重要,但由于成像方面的挑战,其遗传基础尚不清楚.
- 目前的方法很难在规模上分析动态心脏流量.
研究的目的:
- 开发和验证DeepFlow,这是一个用于从MRI中提取心脏流量和体积的深度学习系统.
- 通过大规模的英国生物库数据,研究心脏动态流量体积的遗传结构.
主要方法:
- 开发了DeepFlow,一个深度学习系统,用于处理相对比心脏磁共振成像.
- 将混合线性模型应用于37653名英国生物库参与者的数据.
- 利用孟德尔的随机化来评估因果关系.
主要成果:
- 确定了心脏动态流量体积的全基因组显著关联,包括大动脉前进速度和吐率.
- 证明了大动脉根大小和大动脉回之间存在因果关系.
- 在ELN,PRDM6和ADAMTS7附近突出显示的基因,涉及连接组织和血压通路.
结论:
- DeepFlow使大规模的心脏流表型化成为可能,推动了对心血管动态的遗传研究.
- 与结合组织,血压和大动脉根大小相关的遗传因素显著影响大动脉功能.
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