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

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 75 - 18
• Main Authors: Kim, Kangjin, Won, Sungho
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.03.2025; BioMed Central Ltd; BMC
• Subjects: Abundance; Algorithms; Association analysis; Bias; Bioinformatics; Biomedical and Life Sciences; Computational biology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Disease; Estimation; Genetic aspects; Health aspects; Humans; Life Sciences; Longitudinal studies; Microarrays; Microbiome data; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Microbiota - genetics; Microorganisms; MTMAT; Phenotypes; Phylogenetics; Phylogeny; RNA, Ribosomal, 16S - genetics; Robustness; rRNA 16S; Sparsity; Statistical methods; Taxonomy; Tree-based microbiome association test; Trees (Graph theory); South Korea; Microbiota; Tree-based microbiome association test; Microbiome data; MTMAT
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-06002-2

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 is applicable to longitudinal microbiome data and is robust to compositional bias. Results mTMAT normalized the microbial abundance and utilized the ratio of the pooled abundance for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. The robustness of mTMAT against compositional bias is underscored by its utilization of abundance ratios. Conclusions With extensive simulation studies, we showed that mTMAT is statistically relatively powerful and is robust to compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insights into bacterial pathology.