同表达范围的关联研究将基因调节的相互作用与复杂的特征联系起来
Mykhaylo M Malakhov1, Wei Pan2
1Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Nature communications
|December 11, 2025
概括
引入共同表达全局关联研究 (COWAS),一种新方法,用于找到与复杂特征相关的基因和蛋白质对. 牛牛 (COWAS) 揭示了相互作用的蛋白质如何影响疾病,即使他们单独行动时也不会.
科学领域:
- 遗传学和系统生物学 系统生物学
- 计算生物学 计算生物学
- 疾病风险预测 疾病风险预测
背景情况:
- 转录组和蛋白质组范围的关联研究 (TWAS/PWAS) 确定与疾病风险相关的基因/蛋白质.
- 现有的方法忽略了基因或蛋白质之间的共同表达和相互作用效应.
- 了解分子网络对于复杂的特征遗传学至关重要.
研究的目的:
- 介绍共同表达全局关联研究 (COWAS) 方法.
- 识别具有与复杂特征相关的遗传调节共同表达的基因/蛋白质对.
- 研究蛋白质与蛋白质相互作用在疾病病因学中的作用.
主要方法:
- 牛 (COWAS) 训练模型来预测基因变异的表达和共同表达.
- 测试假定的共同表达和特征之间的关联,考虑直接影响.
- 将该方法应用于英国生物银行的血蛋白质组数据.
主要成果:
- 确定了数十种相互作用的蛋白质对,与胆固醇水平,阿尔茨海默氏症和帕金森病相关.
- 证明蛋白质共同表达可以影响复杂的特征,独立于单个蛋白质效应.
- 展示了COWAS在分子网络中解开直接和相互作用效应的能力.
结论:
- 牛提供了一种新的方法来发现与复杂特征相关的基因调节的共同表达模式.
- 由COWAS揭示的蛋白质与蛋白质相互作用为疾病机制提供了新的见解.
- 该方法增强了对调解疾病遗传影响的分子网络的理解.
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