A small‐sample multivariate kernel machine test for microbiome association studies. Issue 3 (26th December 2016)

• Record Type: Journal Article
• Authors: Zhan, Xiang; Tong, Xingwei; Zhao, Ni; Maity, Arnab; Wu, Michael C.; Chen, Jun
• Is Part Of: Genetic epidemiology. Volume 41:Issue 3(2017)
• Page Start: 210
• Page End: 220
• Publication Date: 2016-12-26
• Subjects: Bray‐Curtis -- kernel association test -- multivariate outcomes -- small sample -- UniFrac; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22030
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: High‐throughput sequencing technologies have enabled large‐scale studies of the role of the human microbiome in health conditions and diseases. Microbial community level association test, as a critical step to establish the connection between overall microbiome composition and an outcome of interest, has now been routinely performed in many studies. However, current microbiome association tests all focus on a single outcome. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery. As these outcomes may share common mechanisms, jointly analyzing these outcomes can amplify the association signal and improve statistical power to detect potential associations. We propose the multivariate microbiome regression‐based kernel association test (MMiRKAT) for testing association between multiple continuous outcomes and overall microbiome composition, where the kernel used in MMiRKAT is based on Bray‐Curtis or UniFrac distance. MMiRKAT directly regresses all outcomes on the microbiome profiles via a semiparametric kernel machine regression framework, which allows for covariate adjustment and evaluates the association via a variance‐component score test. Because most of the current microbiome studies have small sample sizes, a novel small‐sample correction procedure is implemented in MMiRKAT to correct for the conservativeness of the association test when the sample size is small or moderate. The proposed method is assessed via simulation studies and an application to a real data set examining the association between host gene expression and mucosal microbiome composition. We demonstrate that MMiRKAT is more powerful than large sample based multivariate kernel association test, while controlling the type I error. A free implementation of MMiRKAT in R language is available athttp://research.fhcrc.org/wu/en.html .