A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association...

Area of Science:

• Microbiome research
• Computational biology
• Statistical genetics

Background:

• The human microbiome's role in health and disease is increasingly recognized, driven by high-throughput technologies.
• Identifying specific microbial contributions to disease mechanisms requires advanced analytical methods.

Purpose of the Study:

• Introduce the optimal microbiome-based association test (OMiAT), a novel data-driven method for microbiome association testing.
• Present the microbiome comprehensive association mapping (MiCAM) framework for fine-mapping microbial taxa across different taxonomic ranks.

Main Methods:

• OMiAT integrates sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT) for optimal testing.
• MiCAM applies OMiAT to analyze associations at various taxonomic levels (phylum to genus) and the entire microbial community.

Main Results:

• Simulations show OMiAT is robust, powerful, and controls type I error rates effectively compared to existing methods.
• Real data analyses demonstrate MiCAM's efficiency in assessing upper-level taxa by incorporating OMiAT.

Conclusions:

• OMiAT and MiCAM are valuable tools for analyzing complex microbiome data, addressing challenges in microbiome research.
• MiCAM provides hierarchical association maps, guiding further investigation into microbial roles in human health and disease.