A Phylogeny-based Test of Mediation Effect in Microbiome

TL;DR

This paper introduces PhyloMed, a phylogeny-based mediation analysis method that models microbiome effects through local mediation models on a phylogenetic tree, improving power and specificity in detecting mediation effects.

Contribution

The paper presents a novel phylogeny-based mediation analysis method (PhyloMed) that models microbiome mediation effects across the phylogenetic tree, enhancing detection power over existing methods.

Findings

PhyloMed effectively detects mediation effects in simulated data.

It shows increased power compared to standard methods.

Application to real data demonstrates practical advantages.

Abstract

Recent studies suggest that the microbiome can be an important mediator in the effect of a treatment on an outcome. Microbiome data generated from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator invalidates standard mediation analyses. We propose a phylogeny-based mediation analysis method (PhyloMed) for the microbiome mediator. PhyloMed models the microbiome mediation effect through a cascade of independent local mediation models on the internal nodes of the phylogenetic tree. Each local model captures the mediation effect of a subcomposition at a given taxonomic resolution. The method improves the power of the mediation test by enriching weak and sparse signals across mediating taxa that tend to cluster on the tree. In each local model, we further empower PhyloMed by using a mixture distribution to obtain the subcomposition mediation test p-value, which takes into account the composite nature of the null hypothesis. PhyloMed enables us to test the overall mediation effect of the entire microbial community and pinpoint internal nodes with significant subcomposition mediation effects. Our extensive simulations demonstrate the validity of PhyloMed and its substantial power gain over existing methods. An application to a real study further showcases the advantages of our method.