  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.
