Yazdani Azam, Yazdani Akram, Mendez-Giraldez Raul, Samiei Ahmad, Kosorok Michael R, Schaid Daniel J
Center of Perioperative Genetics and Genomics, Brigham Women's Hospital, Harvard Medical School, Boston, MA, United States.
Health Science Center at Houston, McGovern Medical School, Division of Clinical and Translational Sciences, University of Texas, Houston, TX, United States.
Front Genet. 2022 Sep 15;13:990486. doi: 10.3389/fgene.2022.990486. eCollection 2022.
The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.
近年来，涉及基因组学、蛋白质组学和代谢组学等多个生物粒度水平信息的研究数量逐年增加，一个生物医学问题是如何系统地整合这些数据，以发现可能阐明健康和疾病过程的新生物机制。因果框架，如孟德尔随机化（MR），为开始整合数据以进行新的生物学发现提供了基础。尽管MR在各种生物医学研究中的应用越来越多，但用于系统分析组学数据的方法却很少。复杂疾病中涉及的大量且多样的分子成分通过复杂网络相互作用，而针对单个成分的经典MR方法并未考虑潜在关系。相比之下，基于MR原理建立的因果网络模型在理解组学数据方面对经典MR框架有显著改进。整合这些大多不同的统计学分支是最近的发展，我们在此回顾当前的进展。为了为因果网络模型奠定基础，我们回顾经典MR框架中的一些最新进展。然后我们解释如何从经典MR框架过渡到因果网络。我们讨论因果网络的识别并评估潜在假设。我们还介绍一些用于因果网络敏感性分析和稳定性评估的检验。然后我们回顾进行实际数据分析和识别因果网络的实际细节，并强调因果网络的一些效用。具有经过验证的新发现的效用揭示了因果网络作为一种系统方法的全部潜力，这对于整合大规模组学数据将变得必不可少。
