Work

Spatial mixed models for assessing environmental exposure effects on the microbiome

Year: 2026

Type: article

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.

Source: Genome Medicine

DOI: 10.1186/s13073-026-01770-3

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

Institutions: New York University, Kyungpook National University, Hebrew University of Jerusalem, NYU Langone Health, NYU Langone’s Laura and Isaac Perlmutter Cancer Center

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,290

Funders: National Institutes of Health, National Institute on Aging, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences, U.S. National Library of Medicine

Awards: G1698645379, G4324908329, G5317738447, G7115067815, G7852944891, G8124029843, G9000103134, G9000260196, G9000353121, G9000386677, Maintenance and Enhancement of the Asian American enriched Cohort, Novel Computational Methods for Microbiome Data Analysis in Longitudinal Study, G931171319

OpenAlex ID: W7165019748

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