论文标题
孟德尔随机化和因果网络用于系统分析
Mendelian randomization and causal networks for systematic analysis of omics
论文作者
论文摘要
通过因果关系经常讨论通过仪器变量分析实施的孟德尔随机化，而实际数据应用程序的数量正在增加。但是，很少有讨论可以解决现代生物医学问题，例如大规模的因果关系中的整合。尽管在大型魔术师时代，我们面临数百千万个组成部分，对基础结构知识很少，但该领域的重点是小规模，主要是已知的结构。大量OMIC数据的可用性加剧了对识别OMIC组件之间互连的技术的需求，并揭示了控制关系的原理。这项研究扩展了仪器变量技术，以识别大规模的因果网络并评估假设。大规模的因果网络是复杂的，需要进一步的分析才能发现组件在OMICS内部和与疾病终点相关的组件之间的相关机制。这项研究将回顾这些因果网络的这些实用程序，以了解机械理解。
Mendelian randomization implemented through instrumental variable analysis is frequently discussed in causality and recently the number of applications on real data is increasing. However, there are very few discussions to address modern biomedical questions such as the integration of large scale omics in causality. While in the age of large omics, we face several hundred or thousands of components with little knowledge about the underlying structures, the focus of the field is on small scales and mostly with known structures. The availability of large omic data accentuates the need for techniques to identify interconnectivity among the omic components and reveal the principles that govern the relationships. This study extends instrumental variable techniques to identify causal networks in large scales and assess the assumptions. Large-scale causal networks are complex and further analyses are required to uncover mechanisms by which the components are related within and between omics and linked to disease endpoints. This study will review these utilities of causal networks for mechanistic understanding.
