An adaptive association test for microbiome data

Wu, Chong; Chen, Jun; Kim, Junghi; Pan, Wei

doi:10.1186/s13073-016-0302-3

Cited by 75 publications

(87 citation statements)

References 40 publications

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“…In this paper, we propose CSKAT to test the association between microbiome compositions and an outcome of interest, where microbiome samples and outcome measurements within the same cluster are related to each other. Similar to other methods (Koh et al, 2017;Wu, Chen, Kim, & Pan, 2016), our CSKAT framework can also be used as a taxon association mapping tool by shifting the analysis unit to a lower taxonomic rank (e.g., genus or family). Through extensive numerical studies, we have seen that CSKAT can protect the correct Type I error and achieve higher power than these existing methods, especially when the sample size is small or moderate.…”

Section: Discussionmentioning

confidence: 99%

“…To detect biologically meaningful results, we can form the OTUset according to a particular taxonomic rank (e.g., kingdom, phylum, class, order, family, genus, or species). Similar to other methods (Koh et al, 2017;Wu, Chen, Kim, & Pan, 2016), our CSKAT framework can also be used as a taxon association mapping tool by shifting the analysis unit to a lower taxonomic rank (e.g., genus or family). For example, by targeting OTUs within each genus at a time, our method can identify associated genera.…”

Section: Discussionmentioning

confidence: 99%

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A small‐sample kernel association test for correlated data with application to microbiome association studies

Zhan

Xue

Zheng

et al. 2018

Genetic Epidemiology

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Recent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A small‐sample kernel association test for correlated data with application to microbiome association studies

Zhan

Xue

Zheng

et al. 2018

Genetic Epidemiology

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“…where p MAT(γ) is the p-value of the MAT(γ) test and the default setting of Γ is Γ = {1, 10, 30, 50}. It is worth noting that using the minimum p-value of multiple tests as test statistics to maintain robust performance has been widely used in gene-based tests [16,17], pathway-based tests [18], and microbiome association analysis [19].…”

Section: New Method: Amatmentioning

confidence: 99%

Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

2019

Preprint

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Since many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated traits, one can improve the statistical power of GWAS by testing the association of multiple traits simultaneously. While appealing, most existing methods focus on analyzing a relatively small number of traits and may yield inflated Type I error rates when analyzing a large number of traits. We introduce a new method called aMAT for multi-trait analysis of GWAS summary statistics of an arbitrary number (e.g. hundreds) of traits. We first conduct extensive simulations and demonstrate that aMAT yields well-controlled Type I error rates and achieves robust statistical power when analyzing a large number of traits. Next, we apply aMAT to summary statistics for a group of 58 volume-related imaging phenotypes in UK Biobank. aMAT yields a genomic inflation factor of 1.04 and identifies 28 lead SNPs spanning in 24 distinct risk loci, 13 of which are missed by any individual univariate GWAS. In comparison, the competing methods either yield a suspicious genomic inflation factor or identify much fewer risk loci. Finally, four additional groups of traits have been analyzed and provided similar conclusions.

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“…The simulated data from the MB-GAN framework can facilitate microbiome research. For example, researchers can assign different effect sizes for the simulated taxa and develop differential abundance analysis methods used in MWAS where a group of taxa can be jointly tested [30] . As MB-GAN can preserve taxa-taxa relationships, the simulated data can also facilitate microbiome network studies or microbial community studies.…”

Section: Discussionmentioning

confidence: 99%

MB-GAN: Microbiome Simulation via Generative Adversarial Network

Rong

Jiang

et al. 2019

Preprint

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Simulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models. To address this challenge, we designed a novel simulation framework for microbiome data using a generative adversarial network (GAN), called MB-GAN, by utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given dataset and compute simulated datasets that are indistinguishable from it. When MB-GAN was applied to a case-control microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.

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An adaptive association test for microbiome data

Cited by 75 publications

References 40 publications

A small‐sample kernel association test for correlated data with application to microbiome association studies

A small‐sample kernel association test for correlated data with application to microbiome association studies

Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

MB-GAN: Microbiome Simulation via Generative Adversarial Network

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