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MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery.

William A Nickols1,2, Thomas Kuntz1,2, Jiaxian Shen1,3,4

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|January 15, 2026
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Summary

MaAsLin 3 accurately identifies microbiome associations by analyzing both feature abundance and prevalence, even in complex datasets. This advanced tool improves microbial community analysis for health and environmental studies.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microbial community analysis links microbial features to phenotypes.
  • Sparsity and compositionality hinder accurate association identification.
  • Existing methods struggle with complex microbiome data designs.

Purpose of the Study:

  • Introduce MaAsLin 3 (microbiome multivariable associations with linear models).
  • Enable simultaneous identification of abundance and prevalence relationships.
  • Address compositionality and complex study designs in microbiome research.

Main Methods:

  • MaAsLin 3 employs multivariable linear models.
  • Accounts for compositionality via experimental or computational methods.
  • Expands testable hypotheses and covariate types.

Main Results:

  • MaAsLin 3 outperformed state-of-the-art differential abundance methods on synthetic and real datasets.
  • Identified 77% of associations in the Inflammatory Bowel Disease Multi-omics Database based on feature prevalence.
  • Demonstrated superior accuracy and specificity in complex microbiome datasets.

Conclusions:

  • MaAsLin 3 enhances the accuracy and specificity of microbiome association studies.
  • It is particularly effective for complex datasets with sparsity and compositionality.
  • Facilitates more precise identification of microbial feature-phenotype relationships.