Interactions between species introduce spurious associations in microbiome studies

Area of Science:

• Microbiome research
• Statistical genetics
• Host-microbe interactions

Background:

• Microbiota influence host phenotypes, including disease susceptibility.
• Microbiome-wide association studies (MWAS) compare microbial abundances between sample groups to link microbes to phenotypes.
• Observed microbial abundance differences can arise from direct associations or indirect effects of microbial interactions, complicating interpretation.

Purpose of the Study:

• To develop a method to distinguish direct microbial associations from indirect effects in MWAS.
• To apply this method to a large dataset of pediatric inflammatory bowel disease (IBD) to identify robust microbial associations.

Main Methods:

• Utilized techniques from statistical physics to model and remove indirect associations caused by microbial interactions.
• Applied the developed method to a comprehensive dataset of pediatric IBD cases and controls.

Main Results:

• The novel method effectively corrected p-value inflation common in standard MWAS.
• Identified a significantly smaller subset of microbial associations directly linked to pediatric IBD.
• Directly associated microbes demonstrated higher accuracy in distinguishing IBD cases from controls.

Conclusions:

• Microbial interactions can generate numerous spurious associations in MWAS, obscuring true disease links.
• The statistical physics-based method enhances the reliability of MWAS by isolating direct microbial associations.
• Direct associations in pediatric IBD implicate immunomodulation, butyrate production, and the brain-gut axis as critical factors.