MiRKAT: kernel machine regression-based global association tests for the microbiome

Nehemiah Wilson, Ni Zhao, Xiang Zhan, Hyunwook Koh, Weijia Fu, Jun Chen, Hongzhe Li, Michael C. Wu, Anna M. Plantinga

Research output: Contribution to journalArticlepeer-review


Summary: Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels.

Original languageEnglish (US)
Pages (from-to)1595-1597
Number of pages3
Issue number11
StatePublished - Jun 1 2021

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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