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MiRKAT specifications


Unique identifier OMICS_25081
Alternative name Microbiome Regression-Based Kernel Association Test
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Version 0.02
Stability Stable
Maintained Yes


No version available



  • person_outline Jun Chen
  • person_outline Michael Wu
  • person_outline Michael Wu

Publication for Microbiome Regression-Based Kernel Association Test

MiRKAT citations


A highly adaptive microbiome based association test for survival traits

BMC Genomics
PMCID: 5859547
PMID: 29558893
DOI: 10.1186/s12864-018-4599-8

[…] ications; hence, in practice, Optimal MiRKAT is attractive. OMiAT [] is a further adaptive test which approximates to an optimal test adaptively throughout the sum of powered score tests (SPU) [] and MiRKAT tests. By including SPU tests in the search space, OMiAT robustly discovers rare, mid-abundant, and abundant associated lineages along with the functionality of Optimal MiRKAT.There has also be […]


Experimental design and quantitative analysis of microbial community multiomics

Genome Biol
PMCID: 5708111
PMID: 29187204
DOI: 10.1186/s13059-017-1359-z

[…] nivariate multi-level or hierarchical models. These methods account for multiple responses per observation but consider each target variable (feature) separately. Other distance-based methods such as MiRKAT [] are essentially multivariable methods as they usually consider the whole community profiles (or a mathematical function of the community distance matrix) as explanatory variables along with […]


Exploring Relationships between Host Genome and Microbiome: New Insights from Genome Wide Association Studies

Front Microbiol
PMCID: 5061000
PMID: 27785127
DOI: 10.3389/fmicb.2016.01611
call_split See protocol

[…] 8) (). This statistical package has the capability to correct for skewness and kurtosis in the score statistics that are a result of small sample sizes. Another recently published software package is MiRKAT (), which is a kernel regression based test to find associations between genome-wide SNPs and β-diversity computed using a generalized UniFrac (). MiRKAT is limited by its computationally ineff […]


The Microbiome of Aseptically Collected Human Breast Tissue in Benign and Malignant Disease

Sci Rep
PMCID: 4971513
PMID: 27485780
DOI: 10.1038/srep30751
call_split See protocol

[…] technical covariates such as sequencing batch if necessary. A Wald test was used to determine significance. To assess the association between with β-diversity measures, we used the recently proposed MiRKAT, which is a kernel-based association test based on ecological distance matrices. MiRKAT also allows easy adjustment of covariates such as sequencing batch. To further address the potential conc […]


The Microbiota of Breast Tissue and Its Association with Breast Cancer

Appl Environ Microbiol
PMCID: 4968547
PMID: 27342554
DOI: 10.1128/AEM.01235-16

[…] he CLR-transformed data set indicated two clusters, and the PCoA plot in shows clear separation between the healthy and cancer groups. Differences between the groups were further confirmed using the microbiome regression-based kernel association test (Mirkat) ().ALDEx2, which allows for the direct comparison of bacterial taxa between groups showed significantly higher compositional abundances of […]


PERMANOVA S: association test for microbial community composition that accommodates confounders and multiple distances

PMCID: 5013911
PMID: 27197815
DOI: 10.1093/bioinformatics/btw311

[…] in this paper is very general and can be applied to any unified tests minimizing P-values. For instance, cast the problem in the kernel machine framework and developed a regression-based kernel test MiRKAT. Their framework allows for multiple distances. However, the P-value calculations of their unified test depend on the asymptotic approximation, which is not accurate when the sample size is mod […]


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MiRKAT institution(s)
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Division of Gastroenterology and Hepatology, Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Human Genetics, Emory University, Atlanta, GA, USA; Department of Statistics, North Carolina State University, Cary, Raleigh, NC, USA; Division of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA; Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
MiRKAT funding source(s)
Supported in part by NIH grants K01DK092330, R01HG007508, R01HG006139, and R01GM097505; Center for Gastrointestinal Biology and Disease pilot feasibility grant P30DK03498; the Hope Foundation; and the Gerstner Family Career Development Award in Individualized Medicine.

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