A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies

Area of Science:

• Microbiome research
• Statistical genetics
• Computational biology

Background:

• Human microbiota is crucial in disease development.
• Alpha diversity metrics assess microbial community composition but studies yield inconsistent results.
• Existing meta-analysis tools fail to account for technical variability like sequencing protocols.

Purpose of the Study:

• To develop a robust statistical framework for the integrative analysis of microbiome datasets.
• To assess the association between alpha diversity and clinical conditions while accounting for study-specific characteristics.
• To provide flexible hypothesis testing approaches for microbiome research.

Main Methods:

• A mixed-effect kernel machine regression model was proposed.
• The model incorporates study-specific characteristics, including sequencing protocols, using a kernel similarity matrix.
• Three hypothesis testing methods were developed within the framework.

Main Results:

• The model's performance was evaluated through extensive simulations.
• The framework was applied to HIV reanalysis consortium data to investigate gut dysbiosis in HIV infection.
• The proposed method allows for flexible modeling of microbiome effects.

Conclusions:

• The developed statistical framework enables robust integrative analysis of microbiome data with varying sequencing protocols.
• This approach improves the assessment of associations between microbial diversity and clinical conditions.
• The framework offers valuable tools for microbiome research, particularly in understanding disease-related dysbiosis.