Zhang, Haixiang

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high‐dimensional microbiome data have an unit‐sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log‐ratio transformations of the relative abundances as the mediator variables. To select significant mediators, we consider a closed testing‐based selection procedure with desirable confidence. Simulations are provided to verify the effectiveness of our method. As an illustrative example, we apply the proposed method to study the mediation effects of murine gut microbiome between subtherapeutic antibiotic treatment and body weight gain, and identify Coprobacillus and Adlercreutzia as two significant mediators.