NDLI: A Bayesian Negative Binomial Hierarchical Model for Identifying Diet

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Alma Revers Xiang Zhang Aeilko H. Zwinderman
Abstract	The human gut microbiota composition plays an important role in human health. Long-term diet intervention may shape human gut microbiome. Therefore, many studies focus on discovering links between long-term diets and gut microbiota composition. This study aimed to incorporate the phylogenetic relationships between the operational taxonomic units (OTUs) into the diet-microbe association analysis, using a Bayesian hierarchical negative binomial (NB) model. We regularized the dispersion parameter of the negative binomial distribution by assuming a mean-dispersion association. A simulation study showed that, if over-dispersion is present in the microbiome data, our approach performed better in terms of mean squared error (MSE) of the slope-estimates compared to the standard NB regression model or a Bayesian hierarchical NB model without including the phylogenetic relationships. Data of the Healthy Life in an Urban Setting (HELIUS) study showed that for some phylogenetic families the (posterior) variances of the slope-estimates were decreasing when including the phylogenetic relationships into the analyses. In contrast, when OTUs of the same family were not similarly affected by the food item, some bias was introduced, leading to larger (posterior) variances of the slope-estimates. Overall, the Bayesian hierarchical NB model, with a dependency between the mean and dispersion parameters, proved to be a robust method for analyzing diet-microbe associations.
e-ISSN	1664302X
DOI	10.3389/fmicb.2021.711861
Journal	Frontiers in Microbiology
Volume Number	12
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2021-10-01
Publisher Place	Switzerland
Access Restriction	Open
Subject Keyword	Microbiology Associations Bayesian Diet Hierarchical Model Microbiome Data
Content Type	Text
Resource Type	Article

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