Human Microbiome Mixture Analysis using Weighted Quantile Sum Regression

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BACKGROUND AND AIM: Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, and do not consider the correlated relationships between taxa in the microbiome. The aim of this study was to test the use of random subset weighted quantile sum regression (WQSRS) on microbiome data to identify mixture effects. METHODS: We applied WQSRS, 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), 2 strong, 8 medium and 10 weak, which were chosen based on previously identified links to health outcomes. All other OTUs were assigned no association. WQSRS was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRS method was also compared to other standard methods for microbiome analysis. RESULTS:WQSRS predicted the correct directionality of association between the microbiome and the simulated variable, with a sensitivity and specificity of 75% and 60%, respectively, in identifying the 20 associated OTUs. Regression analysis using alpha diversity as the outcome identified an association with the simulated binary variable in the correct direction, however permutational analysis of variance (PERMANOVA) analysis using Bray-Curtis distances did not identify an association with the simulated binary variable. Similarity percentage analysis identified the associated OTUs with a sensitivity of 40% and specificity of 78%, and performed the same when tested with a random variable. CONCLUSIONS:The application of WQSRS to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important taxa in the mixture, and allowing for covariate adjustment. KEYWORDS: Microbiome, mixtures analysis, modeling