Robust Phylogenetic Tree-based Microbiome Association Test using Repeatedly Measured Data for Composition Bias

Abstract

MotivationThe effects of microbiota on the host phenotypes can substantially differ depending on his/her age. Longitudinally measured microbiome data allows us to detect the age modification effect and are useful for the detection of microorganisms related to the progression of disease which change identification over time. Moreover, longitudinal analysis enables the estimation of within-subject covariate effect, is robust against the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, they suffer from compositional bias, and few statistical methods can estimate their effect on host diseases with repeatedly measured 16S rRNA gene data. In this article, we proposed mTMAT which can be applied to longitudinal microbiome data and is robust against compositional bias. ResultsmTMAT normalized the microbial abundance and utilized the ratio of the pooled abundances for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. With extensive simulation studies, we showed that mTMAT is statistically more powerful and is robust against compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insight into bacterial pathology. AvailabilityThe 16S rRNA amplicon sequencing metagenomics datasets for Korea Association REsource cohort is available from the NCBI Sequence Read Archive database under project accession number PRJNA716550. mTMAT was implemented in the R package. Detailed information is available at https://healthstat.snu.ac.kr/software/mtmat. Contactwon1@snu.ac.kr Supplementary informationSupplementary data are available at Bioinformatics online.