Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies

Chen, Jun; Chen, Wenan; Zhao, Ni; Wu, Michael C.; Schaid, Daniel J.

doi:10.1002/gepi.21934

Cited by 46 publications

(91 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The asymptotic kernel association tests derived for large‐sample genetic data may be conservative for microbiome data, leading to loss of power to detect associations. Such a small‐sample conservativeness problem has been well observed in univariate outcome kernel association tests (Chen, Chen, Zhao, Wu, & Schaid, ; Lee et al., ), which is also expected for multivariate outcomes situations. We thus implement a novel small‐sample adjustment in our MMiRKAT to correct for the potential small‐sample conservativeness issue.…”

Section: Introductionmentioning

confidence: 73%

“…The P ‐values calculated in such a way works sufficiently well for large sample size (Wu et al., ; Wu & Pankow, ). However, when the sample size is small or modest (e.g., less than 1,000), current kernel‐based association tests developed for large sample size can be very conservative, leading to potential power loss in detecting meaningful associations, especially for binary outcomes (Lee et al., ) and microbiome association studies (Chen et al., ). Given small‐sample conservatism, a feasible approach is the permutation test.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A small-sample multivariate kernel machine test for microbiome association studies

et al. 2016

Self Cite

View full text Add to dashboard Cite

High-throughput sequencing technologies have enabled large-scale studies of the role of the human microbiome in health conditions and diseases. Microbial community level association test, as a critical step to establish the connection between overall microbiome composition and an outcome of interest, has now been routinely performed in many studies. However, current microbiome association tests all focus on a single outcome. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery. As these outcomes may share common mechanisms, jointly analyzing these outcomes can amplify the association signal and improve statistical power to detect potential associations. We propose the multivariate microbiome regression-based kernel association test (MMiRKAT) for testing association between multiple continuous outcomes and overall microbiome composition, where the kernel used in MMiRKAT is based on Bray-Curtis or UniFrac distance. MMiRKAT directly regresses all outcomes on the microbiome profiles via a semiparametric kernel machine regression framework, which allows for covariate adjustment and evaluates the association via a variance-component score test. Because most of the current microbiome studies have small sample sizes, a novel small-sample correction procedure is implemented in MMiRKAT to correct for the conservativeness of the association test when the sample size is small or moderate. The proposed method is assessed via simulation studies and an application to a real data set examining the association between host gene expression and mucosal microbiome composition. We demonstrate that MMiRKAT is more powerful than large sample based multivariate kernel association test, while controlling the type I error. A free implementation of MMiRKAT in R language is available at http://research.fhcrc.org/wu/en.html.

show abstract

Section: Introductionmentioning

confidence: 73%

Section: Methodsmentioning

confidence: 99%

A small-sample multivariate kernel machine test for microbiome association studies

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…This conservativeness is caused by not accounting for the variability in the estimated

σ_{ε}^{2}

. To overcome this, J. Chen, Chen, Zhao, Wu, and Schaid () proposed an exact P ‐value that directly incorporates the estimate uncertainty of

{\hat{σ}}_{ε}^{2}

by recognizing that

Q

is a ratio of two quadratic statistics. Specifically, the exact P ‐value was calculated based on the adjusted score statistic

Q_{a} = \frac{(y - \hat{y} false) false' K (y - \hat{y})}{(y - \hat{y} false) false' (y - \hat{y})} .

Using the same trick above, where

false(y - \overset{y}{true} false) = \overset{ε}{true} = σ_{ε} P_{0}^{1 / 2} ε^{⁎}

, and substituting into

Q_{a}

, the P ‐value can be determined by

P (Q_{a} > Q_{a, normalo normalb normals}) = P true(\frac{ε^{⁎}' P_{0}^{1 / 2} K P_{0}^{1 / 2} ε^{⁎}}{ε^{⁎}' P_{0}^{1 / 2} P_{0}^{1 / 2} ε^{⁎}} > Q_{a, normalo normalb normals} true) = P true(ε^{⁎}' P_{0}^{1 / 2} K P_{0}^{1 / 2} ε^{⁎} > Q_{a, normalo normalb normals} [ε^{⁎}' P_{0} ε^{⁎}] true) = P true(ε^{⁎}' [P

…”

Section: Hypothesis Testing and P‐valuesmentioning

confidence: 99%

“…Small‐sample moment corrections for variance and kurtosis for binomial for case–control data have been developed to improve kernel methods (Lee, Emond, et al, ). Although such correction improves the accuracy at small

α

levels, anticonservativeness has been observed at traditional

α

levels (e.g., ɑ = 0.05, 0.1; J. Chen et al, ).…”

Section: Hypothesis Testing and P‐valuesmentioning

confidence: 99%

A review of kernel methods for genetic association studies

Larson

Chen

Schaid

2019

Genetic Epidemiology

Self Cite

View full text Add to dashboard Cite

Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

show abstract

“…Chen, Chen, Zhao, Wu, & Schaid, 2016;Zhan et al, 2017;Zhao et al, 2015). Chen, Chen, Zhao, Wu, & Schaid, 2016;Zhan et al, 2017;Zhao et al, 2015).…”

mentioning

confidence: 99%

Exact variance component tests for longitudinal microbiome studies

Zhai

Knox

Twigg

et al. 2019

Genetic Epidemiology

View full text Add to dashboard Cite

In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lung human immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and (b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study of HIV‐infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open‐source, high‐performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.

show abstract

Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies

Cited by 46 publications

References 22 publications

A small-sample multivariate kernel machine test for microbiome association studies

A small-sample multivariate kernel machine test for microbiome association studies

A review of kernel methods for genetic association studies

Exact variance component tests for longitudinal microbiome studies

Contact Info

Product

Resources

About