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Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized

Han Sun1,2, Xiaoyun Huang2,3, Ban Huo2,4

  • 1School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.

Briefings in Bioinformatics
|May 13, 2022
PubMed
Summary

We developed aGEEMiHC, a new statistical method to find sparse microbial association signals in longitudinal microbiome data. This method improves the detection of gut microbiome links to diseases like Crohn's disease.

Keywords:
generalized estimating equationshigher criticismlongitudinal microbiome datamicrobiome-based association testsparse microbial association signals

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Area of Science:

  • Microbiome research
  • Statistical genetics
  • Computational biology

Background:

  • Understanding host-microbiome interactions is crucial for human health.
  • Longitudinal microbiome data analysis presents unique statistical challenges.
  • Existing methods struggle to detect subtle association signals in longitudinal microbiome data.

Purpose of the Study:

  • To develop a novel statistical method for detecting sparse microbial association signals in longitudinal microbiome data.
  • To improve the analysis of microbiome-phenotype associations over time.
  • To identify potential microbial biomarkers for host phenotypes.

Main Methods:

  • Developed aGEEMiHC (adaptive microbiome higher criticism analysis based on generalized estimating equations).
  • Utilized a generalized estimating equations framework to account for correlations in longitudinal data.
  • Integrated multiple microbiome higher criticism analyses with varying correlation structures for robustness.

Main Results:

  • aGEEMiHC controls type I error rates effectively.
  • Demonstrated superior statistical power compared to existing methods in simulations.
  • Identified a significant association between the gut microbiome and Crohn's disease in real-world data.
  • Successfully ranked significant factors associated with host phenotypes.

Conclusions:

  • aGEEMiHC is a robust and powerful tool for analyzing longitudinal microbiome data.
  • The method enhances the detection of sparse microbial association signals.
  • aGEEMiHC can identify potential microbial biomarkers and advance our understanding of microbiome-related diseases.