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A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

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Testing microbiome association using integrated quantile regression models.

Tianying Wang^1,2, Wodan Ling³, Anna M Plantinga⁴

¹Center for Statistical Science, Tsinghua University, Beijing 100084, China.

Bioinformatics (Oxford, England)

|September 23, 2021

Summary

This summary is machine-generated.

This study introduces MiRKAT-IQ, a new tool for microbiome association analysis that examines the entire outcome distribution, not just the mean. This method is robust to irregular data and identifies associations beyond the average effect.

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Area of Science:

Microbiome research
Statistical genetics
Bioinformatics

Background:

Current microbiome association analyses primarily focus on the conditional mean of health outcomes.
Existing methods are limited when associations occur outside the mean or with irregular outcome distributions (e.g., zero-inflated).

Purpose of the Study:

To address the limitations of mean-centric microbiome association analyses.
To develop a novel method for investigating microbiome composition associations across the entire distribution of health or disease-related outcomes.

Main Methods:

Introduce MiRKAT-IQ (Microbiome Regression-based Kernel Association Test using Integrated Quantile regression).
Utilize kernel machine regression for individual quantiles and integrate across quantiles to assess the whole outcome distribution.
The method is robust to the location of association signals and heterogeneous outcome distributions.

Main Results:

MiRKAT-IQ effectively examines microbiome associations with conditional outcome quantiles.
Extensive simulations demonstrate the validity and robustness of the MiRKAT-IQ test.
The tool shows potential utility in analyzing real-world microbiome data.

Conclusions:

MiRKAT-IQ provides a powerful new approach for microbiome association studies, moving beyond mean-level analysis.
This method enhances the ability to detect microbiome-outcome associations in complex biological data.
The MiRKAT package with R codes is available on CRAN for broader implementation.