Robust phylogenetic tree-based microbiome association test using repeatedly measured data for composition bias.

ABSTRACT:

Background

The effects of microbiota on the host phenotypes can differ substantially depending on their age. Longitudinally measured microbiome data allow for the detection of the age modification effect and are useful for the detection of microorganisms related to the progression of disease whose identification change over time. Moreover, longitudinal analysis facilitates the estimation of the within-subject covariate effect, is robust to the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, this method of analysis is limited by compositional bias, and few statistical methods can estimate the effect of microbiota on host diseases with repeatedly measured 16S rRNA gene data. Herein, we propose mTMAT, which i

SUBMITTER: Kim K 

PROVIDER: S-EPMC11887327 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature