PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple...

Area of Science:

• Microbiome research
• Computational biology
• Statistical genetics

Background:

• High-throughput sequencing enables microbiome analysis, crucial for understanding host-microbe interactions.
• Current distance-based methods for microbiome analysis often use single metrics, limiting power and flexibility in handling confounders.
• Dysbiosis, an imbalance in microbial communities, is linked to various human health conditions.

Purpose of the Study:

• To develop a novel distance-based method for robust microbiome association analysis.
• To improve the power and flexibility of microbiome association tests, particularly in the presence of confounding variables.
• To introduce presence-weighted UniFrac as a complementary metric for assessing species richness variation.

Main Methods:

• Development of PERMANOVA-S, a new distance-based statistical test for microbiome association studies.
• Incorporation of presence-weighted UniFrac to enhance species richness detection.
• Ensembling multiple distance metrics within PERMANOVA-S to improve generalizability.
• Flexible adjustment for confounding variables within the PERMANOVA-S framework.

Main Results:

• PERMANOVA-S demonstrates improved power compared to traditional methods, especially when multiple distances are combined.
• The choice of distance metric significantly impacts the power of microbiome association tests.
• PERMANOVA-S effectively handles confounding variables, reducing false-positive findings.
• Presence-weighted UniFrac provides a valuable complement to existing UniFrac distances for species richness analysis.

Conclusions:

• PERMANOVA-S offers a powerful and flexible approach for microbiome association studies.
• Ensembling distances and adjusting for confounders are critical for reliable microbiome analysis.
• The developed methods and software (miProfile) can advance the understanding of microbiome-host interactions.