Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
Genes (Basel). 2022 May 25;13(6):940. doi: 10.3390/genes13060940.
Recently, we have seen a growing volume of evidence linking the microbiome and human diseases or clinical outcomes, as well as evidence linking the microbiome and environmental exposures. Now comes the time to assess whether the microbiome mediates the effects of exposures on the outcomes, which will enable researchers to develop interventions to modulate outcomes by modifying microbiome compositions. Use of distance matrices is a popular approach to analyzing complex microbiome data that are high-dimensional, sparse, and compositional. However, the existing distance-based methods for mediation analysis of microbiome data, MedTest and MODIMA, only work well in limited scenarios. PERMANOVA is currently the most commonly used distance-based method for testing microbiome associations. Using the idea of inverse regression, here we extend PERMANOVA to test microbiome-mediation effects by including both the exposure and the outcome as covariates and basing the test on the product of their F statistics. This extension of PERMANOVA, which we call PERMANOVA-med, naturally inherits all the flexible features of PERMANOVA, e.g., allowing adjustment of confounders, accommodating continuous, binary, and multivariate exposure and outcome variables including survival outcomes, and providing an omnibus test that combines the results from analyzing multiple distance matrices. Our extensive simulations indicated that PERMANOVA-med always controlled the type I error and had compelling power over MedTest and MODIMA. Frequently, MedTest had diminished power and MODIMA had inflated type I error. Using real data on melanoma immunotherapy response, we demonstrated the wide applicability of PERMANOVA-med through 16 different mediation analyses, only 6 of which could be performed by MedTest and 4 by MODIMA.
最近，越来越多的证据表明微生物组与人类疾病或临床结果有关，也有证据表明微生物组与环境暴露有关。现在是评估微生物组是否介导暴露对结果的影响的时候了，这将使研究人员能够通过改变微生物组组成来开发调节结果的干预措施。距离矩阵的使用是分析高维、稀疏和组成型复杂微生物组数据的一种流行方法。然而，现有的用于微生物组数据中介分析的基于距离的方法 MedTest 和 MODIMA 仅在有限的场景下效果良好。PERMANOVA 目前是最常用的基于距离的测试微生物组关联的方法。利用逆回归的思想，我们将 PERMANOVA 扩展到通过将暴露和结果都作为协变量来测试微生物组中介效应，并基于它们的 F 统计量的乘积进行测试。这种对 PERMANOVA 的扩展，我们称之为 PERMANOVA-med，自然继承了 PERMANOVA 的所有灵活特性，例如允许调整混杂因素，适应连续、二分类和多变量的暴露和结果变量，包括生存结果，并提供一个综合多个距离矩阵分析结果的整体测试。我们广泛的模拟表明，PERMANOVA-med 始终控制着第一类错误，并且比 MedTest 和 MODIMA 更有说服力。经常情况下，MedTest 的功效降低，而 MODIMA 的第一类错误增加。通过黑色素瘤免疫治疗反应的真实数据，我们通过 16 种不同的中介分析证明了 PERMANOVA-med 的广泛适用性，其中只有 6 种可以通过 MedTest 进行，4 种可以通过 MODIMA 进行。
