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Updated: Sep 16, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

CAT: a conditional association test for microbiome data using a permutation approach.

Yushu Shi¹, Liangliang Zhang², Kim-Anh Do³

¹Department of Population Health Sciences, Weill Cornell Medicine, 575 Lexington Avenue, New York, NY 10065, United States.

Briefings in Bioinformatics

|July 11, 2025

Summary

This summary is machine-generated.

This study introduces a new conditional association test (CAT) for microbiome analysis. CAT quantifies a feature's unique contribution to predicting outcomes, accounting for intercorrelations and phylogenetic relationships.

Keywords:

beta diversity metrics coefficient of determination conditional association test microbiome data permutation

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Area of Science:

Microbiome research
Statistical genetics
Bioinformatics

Background:

Microbiome analysis often aims to link taxonomic features to specific outcomes.
Intercorrelated microbiome features and phylogenetic relationships complicate individual feature association testing.
Existing methods may not fully capture the unique contribution of individual features.

Purpose of the Study:

To propose a novel conditional association test (CAT) for microbiome analysis.
To develop a method that accounts for feature intercorrelations and phylogenetic relatedness.
To provide a direct quantification of feature importance in predicting outcomes.

Main Methods:

CAT employs a permutation approach to assess feature importance.
It quantifies the weakening of the association with an outcome upon feature permutation, measured by the change in R-squared.
Leverages global tests like PERMANOVA and MiRKAT-based methods for various outcome types (continuous, binary, categorical, count, survival, correlated).

Main Results:

CAT provides a direct quantification of feature importance, distinct from marginal association tests.
Simulation studies confirm CAT's ability to isolate the added value of a feature.
Applications to melanoma patient microbiome data demonstrate its utility in immunotherapy response and survival outcome studies.

Conclusions:

CAT offers a robust method for assessing individual microbiome feature importance.
It can help disentangle complex relationships within microbiome data.
The findings support the potential of CAT for designing targeted microbiome interventions to improve clinical outcomes.