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Mediation effect selection in high-dimensional and compositional microbiome data.

Haixiang Zhang1, Jun Chen2, Yang Feng3

  • 1Center for Applied Mathematics, Tianjin University, Tianjin, China.

Statistics in Medicine
|November 18, 2020
PubMed
Summary
This summary is machine-generated.

The human microbiome influences health by mediating environmental exposures. This study introduces a novel statistical method to identify significant gut microbiome mediators, revealing Coprobacillus and Adlercreutzia as key players in antibiotic effects on body weight.

Keywords:
closed testingcompositional microbiome datahigh-dimensional dataisometric log-ratio transformationmediation analysis

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Area of Science:

  • Microbiome research
  • Statistical genetics
  • Environmental health

Background:

  • The gut microbiome is crucial for mediating environmental exposures and health outcomes.
  • High-dimensional microbiome data with unit-sum constraints challenge standard Euclidean statistical methods.
  • Understanding microbiome mediation is vital for public health and personalized medicine.

Purpose of the Study:

  • To develop a statistically rigorous method for analyzing microbiome mediation effects.
  • To identify specific gut bacteria that mediate the relationship between antibiotic exposure and body weight.
  • To address the unique challenges posed by compositional microbiome data.

Main Methods:

  • Utilized isometric log-ratio (ILR) transformations to convert relative microbiome abundances into Euclidean space.
  • Employed a closed testing-based selection procedure for robust identification of significant mediators.
  • Applied the method to analyze murine gut microbiome data from subtherapeutic antibiotic treatment studies.

Main Results:

  • The proposed ILR-based mediation analysis method effectively handles compositional microbiome data.
  • Successfully identified specific microbial taxa mediating the effects of antibiotic treatment on host body weight.
  • Coprobacucu and Adlercreutzia were identified as significant mediators in the murine model.

Conclusions:

  • The developed statistical framework provides a reliable approach for microbiome mediation analysis.
  • This method enhances our ability to understand host-microbiome-environment interactions.
  • Findings highlight the role of specific gut bacteria in mediating antibiotic-induced physiological changes.