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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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multiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Han Sun1,2,3, Yue Wang1,2, Zhen Xiao1,2,3

  • 1Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.

Briefings in Bioinformatics
|January 26, 2023
PubMed
Summary
This summary is machine-generated.

We developed multiMiAT, a novel microbiome association test, to accurately link the human microbiome with complex diseases. This method improves our understanding of microbiome

Keywords:
Kernel machine regressionMicrobiome-based association testMultinomial logit modelOrdinal/Nominal multicategory phenotypes

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Area of Science:

  • Microbiome research
  • Human health and disease
  • Statistical genetics

Background:

  • The human microbiome significantly influences host metabolism and immunity.
  • Microbiome dysbiosis is linked to disease occurrence and progression.
  • Existing microbiome association tests struggle with multicategory phenotypes like disease severity or tumor subtypes.

Purpose of the Study:

  • To develop an optimal microbiome-based association test for multicategory phenotypes.
  • To address limitations of current methods in analyzing complex disease statuses.
  • To enhance the detection of microbiome-host phenotype associations.

Main Methods:

  • Proposed multiMiAT, an optimal microbiome association test for multicategory phenotypes.
  • Utilized a multinomial logit model framework.
  • Introduced multiMiRKAT (microbiome regression-based kernel association test) for multicategory phenotypes.
  • Integrated multiMiRKAT, score test, and MiRKAT-MC for a data-driven optimal test.

Main Results:

  • Massive simulation experiments validated the method's success.
  • multiMiAT demonstrated excellent performance across diverse association patterns.
  • The method successfully analyzed real microbiome data.

Conclusions:

  • multiMiAT offers a robust approach for microbiome association testing with multicategory phenotypes.
  • The study successfully detected associations between the gut microbiome and colorectal cancer clinical statuses.
  • The method was also applied to diverse statuses of Clostridium difficile infections.