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A highly adaptive microbiome-based association test for survival traits.

Hyunwook Koh1, Alexandra E Livanos2, Martin J Blaser3,4

  • 1Department of Population Health, New York University School of Medicine, 650 First Avenue, Room 547, New York, NY, 10016, USA.

BMC Genomics
|March 22, 2018
PubMed
Summary

We introduce optimal microbiome-based survival analysis (OMiSA), a novel adaptive test for identifying microbial taxa linked to survival outcomes. OMiSA effectively detects associations regardless of microbial abundance or phylogenetic relatedness.

Keywords:
Community-level association testHigh-dimensional compositional data analysisMicrobial group analysisMicrobiome-based association testMicrobiome-based survival analysisPhylogenetic tree

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Area of Science:

  • Microbiome research
  • Genomics
  • Statistical genetics

Background:

  • Growing interest in the human microbiome's role in health and disease.
  • Advances in next-generation sequencing enable microbial community analysis.
  • Prospective studies increasingly examine survival outcomes, but current statistical methods for microbiome associations are limited.

Purpose of the Study:

  • To develop an adaptive microbiome-based association test for survival outcomes.
  • To address limitations of existing methods in identifying microbial associations with time-to-event data.
  • To create a powerful and robust statistical tool for microbiome survival analysis.

Main Methods:

  • Proposed optimal microbiome-based survival analysis (OMiSA).
  • OMiSA integrates microbiome-based survival analysis using linear and non-linear bases of operational taxonomic units (OTUs) (MiSALN) and microbiome regression-based kernel association test for survival traits (MiRKAT-S).
  • Employs a semi-parametric variance-component score test and re-sampling method, free from distributional assumptions.

Main Results:

  • OMiSA powerfully identifies microbial taxa associated with survival, irrespective of their abundance (rare or abundant) or phylogenetic relatedness.
  • The method demonstrates robust performance in controlling type I error rates.
  • Simulations confirm OMiSA's effectiveness, and real data applications are presented.

Conclusions:

  • OMiSA offers an attractive solution for microbiome survival analysis where association patterns are unpredictable.
  • It is the first adaptive microbiome-based association test specifically designed for survival outcomes.
  • The method provides a robust and powerful approach to discover microbial associations with survival data.