Interactions between species introduce spurious associations in microbiome studies

TL;DR

This study reveals that microbial interactions can cause false associations in microbiome studies and introduces a method to distinguish direct microbial links to disease, improving the accuracy of microbiome-disease connections.

Contribution

We developed a statistical physics-based method to remove indirect associations in microbiome data, enhancing the identification of microbes directly linked to disease.

Findings

Most associations are indirect and spurious.

The method reduces false positives in microbiome-disease links.

Identified key microbes related to inflammatory bowel disease.

Abstract

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.