A Bayesian joint model for compositional mediation effect selection in microbiome data.

Area of Science:

• Microbiome research
• Statistical modeling
• Causal inference

Background:

• High-throughput sequencing generates high-dimensional, compositional microbiome data, posing analytical challenges.
• Understanding microbiome's role in mediating treatment-phenotype relationships is crucial.
• Existing compositional mediation methods lack the ability to simultaneously identify and quantify direct, relative indirect, and overall indirect effects.

Purpose of the Study:

• To propose a Bayesian joint model for compositional data analysis in high-dimensional mediation.
• To enable identification, estimation, and uncertainty quantification of various causal estimands.
• To address limitations of existing methods in microbiome mediation analysis.

Main Methods:

• Developed a Bayesian joint model tailored for high-dimensional compositional microbiome data.
• Incorporated causal inference principles for mediation analysis.
• Conducted simulation studies to evaluate performance against existing methods.

Main Results:

• The proposed Bayesian model successfully identifies and quantifies direct, relative indirect, and overall indirect effects in compositional mediation analysis.
• Simulation studies demonstrated superior mediation effect selection performance compared to existing approaches.
• The method was applied to a real-world dataset on antibiotic treatment effects in mice.

Conclusions:

• The novel Bayesian joint model offers a robust framework for causal mediation analysis in microbiome research.
• This approach enhances the ability to dissect complex treatment-host-microbiome interactions.
• The findings have implications for understanding how microbiome mediates treatment effects on health and disease.