ArticleBiometrics2017
A fast small-sample kernel independence test for microbiome community-level association analysis.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Plasma proteins associated with chronic graft-versus-host disease organ involvement.JCI insight · 2026Article
- TCVS: tree-guided compositional variable selection analysis of microbiome data.Bioinformatics (Oxford, England) · 2025Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
- Multivariate differential association analysis.Stat (International Statistical Institute) · 2024Article
- JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.bioRxiv : the preprint server for biology · 2023Article
- GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.The annals of applied statistics · 2023Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
- A maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.Bioinformatics (Oxford, England) · 2023Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- Accommodating multiple potential normalizations in microbiome associations studies.BMC bioinformatics · 2023Article
- A fast kernel independence test for cluster-correlated data.Scientific reports · 2022Article
- High-sensitivity pattern discovery in large, paired multiomic datasets.Bioinformatics (Oxford, England) · 2022Article
- MiRKAT-MC: A Distance-Based Microbiome Kernel Association Test With Multi-Categorical Outcomes.Frontiers in genetics · 2022Article
- A Kernel-based Test of Independence for Cluster-correlated Data.Advances in neural information processing systems · 2021Article
- Identifying prognostic pairwise relationships among bacterial species in microbiome studies.PLoS computational biology · 2021Article
- Compositional knockoff filter for high-dimensional regression analysis of microbiome data.Biometrics · 2021Article
- MiRKAT: kernel machine regression-based global association tests for the microbiome.Bioinformatics (Oxford, England) · 2021Article
- SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies.Genome biology · 2021Article
- Variant-set association test for generalized linear mixed model.Genetic epidemiology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
To fully understand the role of microbiome in human health and diseases, researchers are increasingly interested in assessing the relationship between microbiome composition and host genomic data. The dimensionality of the data as well as complex relationships between microbiota and host genomics pose considerable challenges for analysis. In this article, we apply a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition. The KRV statistic can capture nonlinear correlations and complex relationships among the individual data types and between gene expression and microbiome composition through measuring general dependency. Testing proceeds via a similar route as existing tests of the generalized RV coefficients and allows for rapid p-value calculation. Strategies to allow adjustment for confounding effects, which is crucial for avoiding misleading results, and to alleviate the problem of selecting the most favorable kernel are considered. Simulation studies show that KRV is useful in testing statistical independence with finite samples given the kernels are appropriately chosen, and can powerfully identify existing associations between microbiome composition and host genomic data while protecting type I error. We apply the KRV to a microbiome study examining the relationship between host transcriptome and microbiome composition within the context of inflammatory bowel disease and are able to derive new biological insights and provide formal inference on prior qualitative observations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.