Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

AbstractMotivationRecent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.ResultsWe propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.

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Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

Bioinformatics ◽

10.1093/bioinformatics/btz565 ◽

2019 ◽

Author(s):

Chan Wang ◽

Jiyuan Hu ◽

Martin J Blaser ◽

Huilin Li

Keyword(s):

Regression Model ◽

Association Studies ◽

Statistical Significance ◽

Mediation Effect ◽

Supplementary Information ◽

High Dimensional ◽

Model Framework ◽

Mediation Effects ◽

Causal Mediation ◽

Microbiome Data

Abstract Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. Availability and implementation https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary information Supplementary data are available at Bioinformatics online.

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Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

10.1101/2020.09.20.20198226 ◽

2020 ◽

Author(s):

Zhonghua Liu ◽

Jincheng Shen ◽

Richard Barfield ◽

Joel Schwartz ◽

Andrea Baccarelli ◽

...

Keyword(s):

Error Rate ◽

Type I Error ◽

Statistical Significance ◽

Null Distribution ◽

Mediation Effect ◽

Type I ◽

Mediation Effects ◽

Type I Error Rate ◽

Causal Mediation ◽

Genome Wide

In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel's test and the joint significant test are underpowered and thus can miss important scientific discoveries. In this paper, we show that the null distribution of Sobel's test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results clearly explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful Divide-Aggregate Composite-null Test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron's empirical null framework for assessing statistical significance. We show that the proposed DACT method has improved power, and can well control type I error rate. Our extensive simulation studies showed that the DACT method properly controls the type I error rate and outperforms Sobel's test and the joint significance test for detecting mediation effects. We applied the DACT method to the Normative Aging Study and identified additional DNA methylation CpG sites that might mediate the effect of smoking on lung function. We then performed a comprehensive sensitivity analysis to demonstrate that our mediation data analysis results were robust to unmeasured confounding. We also developed a computationally-efficient R package DACT for public use, available at https://github.com/zhonghualiu/DACT.

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Causal mediation analysis with multiple causally non-ordered mediators

Statistical Methods in Medical Research ◽

10.1177/0962280215615899 ◽

2015 ◽

Vol 27 (1) ◽

pp. 3-19 ◽

Cited By ~ 15

Author(s):

Masataka Taguri ◽

John Featherstone ◽

Jing Cheng

Keyword(s):

Indirect Effect ◽

Interactive Effect ◽

Mediation Effect ◽

Mediation Effects ◽

Mediation Analyses ◽

Intermediate Variable ◽

Causal Mediation ◽

Health Studies ◽

Using Data ◽

The Individual

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

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Compositional Mediation Analysis for Microbiome Studies

10.1101/149419 ◽

2017 ◽

Cited By ~ 3

Author(s):

Michael B. Sohn ◽

Hongzhe Li

Keyword(s):

Mediation Analysis ◽

Compositional Data ◽

Fat Intake ◽

Simulation Studies ◽

Mediation Model ◽

Mediation Effects ◽

Microbiome Composition ◽

Causal Mediation Analysis ◽

Simplex Space ◽

Microbiome Data

AbstractMotivated by recent advances in causal mediation analysis and problems in the analysis of microbiome data, we consider the setting where the effect of a treatment on an outcome is transmitted through perturbing the microbial communities or compositional mediators. Compositional and high-dimensional nature of such mediators makes the standard mediation analysis not directly applicable to our setting. We propose a sparse compositional mediation model that can be used to estimate the causal direct and indirect (or mediation) effects utilizing the algebra for compositional data in the simplex space. We also propose tests of total and component-wise mediation effects using bootstrap. We conduct extensive simulation studies to assess the performance of the proposed method and apply the method to a real metagenomic dataset to investigate the effect of fat intake on body mass index mediated through the gut microbiome composition.

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Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators

Biometrics ◽

10.1111/biom.12421 ◽

2015 ◽

Vol 72 (2) ◽

pp. 402-413 ◽

Cited By ~ 37

Author(s):

Yen-Tsung Huang ◽

Wen-Chi Pan

Keyword(s):

Hypothesis Test ◽

Mediation Effect ◽

High Dimensional ◽

Mediation Model ◽

Causal Mediation

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Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model

Statistics in Biosciences ◽

10.1007/s12561-020-09294-z ◽

2020 ◽

Author(s):

Jing Ma

Keyword(s):

Graphical Model ◽

Association Studies ◽

Joint Analysis ◽

Gaussian Graphical Model ◽

Model Framework ◽

Data Set ◽

Conditional Dependence ◽

Metabolomic Data ◽

Network Estimation ◽

Microbiome Data

Abstract Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.

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Estimation of Mediation Effect for High-dimensional Omics Mediators with Application to the Framingham Heart Study

10.1101/774877 ◽

2019 ◽

Author(s):

Tianzhong Yang ◽

Jingbo Niu ◽

Han Chen ◽

Peng Wei

Keyword(s):

Gene Expression ◽

Framingham Heart Study ◽

Mixed Model ◽

Expression Profiles ◽

Estimation Procedure ◽

Mediation Effect ◽

High Dimensional ◽

Model Framework ◽

Intermediate Phenotypes ◽

Heart Study

SUMMARYEnvironmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (Rsq) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our Rsq measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We evaluate the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we find that more than half of the aging-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study.

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Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model

10.1101/2020.09.07.286880 ◽

2020 ◽

Author(s):

Jing Ma

Keyword(s):

Graphical Model ◽

Association Studies ◽

Joint Analysis ◽

Gaussian Graphical Model ◽

Model Framework ◽

Data Set ◽

Conditional Dependence ◽

Metabolomic Data ◽

Network Estimation ◽

Microbiome Data

AbstractJoint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.

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Estimation of total mediation effect for high-dimensional omics mediators

BMC Bioinformatics ◽

10.1186/s12859-021-04322-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianzhong Yang ◽

Jingbo Niu ◽

Han Chen ◽

Peng Wei

Keyword(s):

Gene Expression ◽

Mixed Model ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Estimation Procedure ◽

Mediation Effect ◽

High Dimensional ◽

Model Framework ◽

Intermediate Phenotypes ◽

Age Related

Abstract Background Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R$$^2$$ 2 ) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. Results Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R$$^2$$ 2 -based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package “RsqMed” is available on CRAN. Conclusion R-squared (R$$^2$$ 2 ) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

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