Abstract
The Microbiome Regression-based Kernel Association Test (MiRKAT) is widely used in testing for the association between microbiome compositions and an outcome of interest. The MiRKAT statistic is derived as a variance-component score test in a kernel machine regression-based generalized linear mixed model. In this brief report, we show that the MiRKAT statistic is proportional to the R 2 (coefficient of determination) statistic in a similarity matrix regression, which characterizes the fraction of variability in outcome similarity, explained by microbiome similarity (up to a constant).
Highlights
Recent research has highlighted the vital role of the human microbiome in many diseases and health conditions, including obesity [1], diabetes [2], cancer [3], inflammatory disorders [4], and bacterial vaginosis [5]
We study the interpretation of Microbiome Regression-based Kernel Association Test (MiRKAT) results by investigating the MiRKAT statistic and show that the MiRKAT statistic corresponds to the ratio of explained variation and total variation
We simulated the microbiome data Z from an estimated Dirichlet-multinomial distribution, following the same strategy used in MiRKAT [10], and considered a sample size of n = 200 in this simulation
Summary
Recent research has highlighted the vital role of the human microbiome in many diseases and health conditions, including (but not limited to) obesity [1], diabetes [2], cancer [3], inflammatory disorders [4], and bacterial vaginosis [5]. One of the primary limitations to leveraging this large body of big microbiome and metagenomics data is the computational and statistical challenges: high-dimensionality, count and compositional data structure, sparsity (zero-inflation), over-dispersion, phylogenetic relatedness, among others. To combat these challenges, specialized computational tools and quantitative approaches, to aid in understanding the role of the microbiota in maintaining homeostasis in their animal host, as well as in the initiation and propagation of disease, are desired. Many novel statistical methods and computational tools have been proposed for efficiently testing for associations between outcomes and microbial community composition, using either alpha-diversity [11] or beta-diversity [10,12,13,14,15,16,17,18,19]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
