Spatial mixed models for assessing environmental exposure effects on the microbiome.

Authors

Sooran Kim, Chan Wang, Soyoung Kwak, Fares Darawshy, Alexander Bain, Leopoldo N Segal, Jiyoung Ahn, Huilin Li

Published in

Genome medicine. Volume 18. Issue 1. Sep 23, 2026. Epub Sep 23, 2026.

Abstract

Environmental exposures such as air pollution may influence human health partly through microbiome, but statistical modeling is challenged by spatial dependence across regions and ecological correlations among taxa. We propose SpaMixed, a novel spatial mixed modeling framework for microbiome count data that incorporates both sources of dependence using conditional autoregressive priors. In simulations, SpaMixed achieved high feature selection power while maintaining low false positive rates and reduced estimation error relative to existing methods. Applied to two real studies with fine particulate matter (PM_2.5) exposures, SpaMixed identified biologically plausible PM_2.5-associated taxa, supporting its utility for environmental microbiome research.

PMID:
42907900
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.

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