Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials. The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.

中文翻译：

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用于识别系统流行病学中不同的特定路径的新颖的路径特定效果统计数据。

生物途径在诸如癌症的复杂疾病的发生，发展和恢复中起重要作用，所述癌症是通常由多个基因突变或途径失调引起的多因素复杂疾病。我们提出了一种路径特定的效果统计量（PSE），以检测两种情况下的差异特定路径（例如，病例与对照组，暴露与非暴露组）。在观察性研究中，可以通过调整特定路径中节点的父节点并在每种条件下将它们相乘来分别计算每个有向边的平均因果效应来获得特定于路径的效果。进行了理论证明和一系列模拟，以验证特定于路径的效果统计量。还进行了应用程序以评估其实际性能。一系列仿真研究表明，采用置换测试的PSE I型错误率在标称值为0.05时更稳定，并且与其他方法相比可以准确地检测出微分特定路径。具体而言，该能力随着两种路径条件下特定路径效应及其效应差异的增大而显示出增加的趋势。此外，PSE的功能对于特定路径上节点的父节点或子节点的变化具有鲁棒性。应用到多形性胶质母细胞瘤（GBM）的真实数据中，我们成功地在mTOR通路中鉴定出14条阳性特异性通路，这有助于GBM患者的生存时间。在补充材料中可以找到所有用于自动搜索链接两个连续变量和调整集以及PSE统计信息的特定路径的代码。拟议的PSE统计信息可以准确地检测出导致复杂疾病的差异性特定途径，因此有可能提供新的见解和方法来解锁疾病机制的黑匣子。