在心力衰竭中使用多种omics将中间表型与疾病联系起来.
Anni Moore1, Rasika Venkatesh1, Michael G Levin2
1Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA.
medRxiv : the preprint server for health sciences
|August 16, 2024
概括
这项研究将多omics数据与心脏MRI集成,以揭示心力衰竭 (HF) 的遗传联系. 它揭示了心脏结构变化和HF发育之间的共同遗传因素,提供了新的机制性见解.
科学领域:
- 遗传学 遗传学 是一个
- 心血管医学 心血管医学
- 生物信息学是一种生物信息学.
背景情况:
- 心力衰竭 (HF) 影响全球人口的1-3%,造成严重的健康负担.
- 心脏磁共振成像 (MRI) 测量左心室 (LV) 结构和功能,以追踪HF进展.
- 全基因组关联研究 (GWAS) 识别HF风险变体,但缺乏组织特异性和机理性细节.
研究的目的:
- 将全转录组和全蛋白质组关联研究 (TWAS和PWAS) 与MRI衍生的心脏测量和HF数据相结合.
- 确定与HF前体和全因HF相关的基因调节的基因表达和蛋白质丰度变化.
- 通过使用多omics方法来探索HF发展的共同遗传和分子途径.
主要方法:
- 结合TWAS和PWAS与MRI数据 (LV喷射分数,终端透缩体积,终端静缩体积) 和所有原因的HF数据.
- 利用基因组丰富分析和蛋白质-蛋白质相互作用网络来识别涉及的途径.
- 从MRI测量和HF中研究的基因和蛋白质关联之间的重叠.
主要成果:
- 在LV射出分数和末缩体积测量之间确定了显著的基因和蛋白质重叠.
- 发现使用MRI数据的TWAS/PWAS的许多重叠与所有原因的高频率共享.
- 涉及与已识别的基因和蛋白质相关的几个假定的高频相关途径.
结论:
- 多omics方法提高对HF的遗传贡献的理解.
- 这项研究为心脏结构/功能变化和HF之间的关系提供了新的见解.
- 这些发现突出了HF研究和干预的潜在分子目标.
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