Extension of PERMANOVA to Testing the Mediation Effect of the Microbiome

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 wil...

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Bibliographic Details
Published inGenes Vol. 13; no. 6; p. 940
Main Authors Yue, Ye, Hu, Yi-Juan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 25.05.2022
MDPI
Subjects
Clinical outcomes
community-level test
Design
distance matrix
Environmental Exposure
global test
Humans
Immunotherapy
inverse regression
Medical microbiology
Medical research
Medicine, Experimental
Melanoma
Methods
microbial community
Microbiomes
Microbiota
Microbiota (Symbiotic organisms)
Resampling (Statistics)
Research Design
Statistical analysis
Statistical hypothesis testing
Variables
United States
global test
community-level test
microbial community
inverse regression
distance matrix
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Summary: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.
ISSN:2073-4425
2073-4425
DOI:10.3390/genes13060940