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Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression
Journal article   Open access   Peer reviewed

Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression

Shoshannah Eggers, Moira Bixby, Stefano Renzetti, Paul Curtin and Chris Gennings
International journal of environmental research and public health, Vol.20(1), p.94
12/21/2022
DOI: 10.3390/ijerph20010094
PMCID: PMC9819204
PMID: 36612415
url
https://doi.org/10.3390/ijerph20010094View
Published (Version of record) Open Access

Abstract

Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQS ), a mixture method successfully applied to 'omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQS was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQS method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQS predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQS performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQS analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQS to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings.
 Body Mass Index Feces Gastrointestinal Microbiome Humans Microbiota Obesity

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