Testing microbiome association using integrated quantile regression models

Abstract

Motivation Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes, and within this vein, vast computational tools and methods have been devised for standard binary or continuous outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular (e.g., zero-inflated or mixtures) such that conditional outcome mean is less meaningful. We address this gap by investigating association analysis between microbiome compositions and conditional outcome quantiles. Results We introduce a new association analysis tool named MiRKAT-IQ within the Microbiome Regression-based Kernel Association Test framework using Integrated Quantile regression models to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features (e.g., microbiome community compositions). Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles over a process, and thus our new MiRKAT-IQ test is robust to both the location of association signals (e.g.,mean, variance, median) and the heterogeneous distribution of the outcome. Extensive numerical simulation studies have been conducted to show the validity of the new MiRKAT-IQ test. We demonstrate the potential usefulness of MiRKAT-IQ with applications to actual biological data collected from a previous microbiome study. Availability R codes to implement the proposed methodology is provided in the MiRKAT package, which is available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

Compositional variable selection in quantile regression for microbiome data with false discovery rate control

“…Thus, we applied AMZm and CVSQm to analyze the IBD data in order to identify potential microbial taxa that are associated with host gene expressions after adjusting for effects of clinical variables. Among the 19,908 genes, a previous study [30] identified eight of them (HSPA9, CCDC86, TXNRD1, SEC24D, DLAT, HSPA1A, YWHAG, TUBB4B), whose conditional distribution was significantly associated with the whole microbiome community (but no specific microbial taxa had been identified in the previous study). Thus, these eight genes were selected as outcomes of interest in our analyses.…”

MK-BMC: a Multi-Kernel framework with Boosted distance metrics for Microbiome data for Classification

“…Over the past decades, studies have established associations between microbiome and health outcomes ( Morgan et al 2012 , Wu et al 2016b ). Different statistical methods have been developed for microbiome data, and many of them used distance-based methods ( Zhao et al 2015 , Wu et al 2016a , Koh et al 2017 , Ma et al 2020 , Wang et al 2022 ). The performance of distance-based methods is known to be greatly affected by distance metrics used ( Chen et al 2012 ).…”