Dataset Information

Using SAS Macros for Multiple Mediation Analysis in R.

ABSTRACT: Mediation analysis refers to the process of making inferences on effects of third variables that intervene in the relationship between an exposure and response variable. The relationships among variables can be modelled by generalized linear models (GLM). However, GLM are not sufficient to describe relationships among variables when there are nonlinear relationships and potential interaction effects. A general mediation analysis method was developed using not only GLMs, but also multiple additive regression trees and smoothing splines by Yu and Li (2017). The method is implemented in the R package, mma. In this paper, we developed SAS macros so that functions in the mma package can be called and the mediation analysis performed in the SAS environment.

SUBMITTER: Fisher P

PROVIDER: S-EPMC8336624 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using SAS Macros for Multiple Mediation Analysis in R.

Fisher Paige P Cao Wentao W Yu Qingzhao Q

Journal of open research software 20201007 1

Mediation analysis refers to the process of making inferences on effects of third variables that intervene in the relationship between an exposure and response variable. The relationships among variables can be modelled by generalized linear models (GLM). However, GLM are not sufficient to describe relationships among variables when there are nonlinear relationships and potential interaction effects. A general mediation analysis method was developed using not only GLMs, but also multiple additiv ...[more]

PMID: 34354832

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Project description:BackgroundMapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators.ResultsWe proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators.ConclusionsTrans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.

Dataset Information

Using SAS Macros for Multiple Mediation Analysis in R.

Publications

Using SAS Macros for Multiple Mediation Analysis in R.

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