Estimation of total mediation effect for high-dimensional omics mediators

Tianzhong Yang, Jingbo Niu, Han Chen, Peng Wei

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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 (R2) 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 R2-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 (R2) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

Original languageEnglish (US)
Article number414
JournalBMC bioinformatics
Volume22
Issue number1
DOIs
StatePublished - Dec 2021

Bibliographical note

Funding Information:
The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the FHS and does not necessarily reflect the opinions or views of the FHS, Boston University or NHLBI. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC resources. The authors thank Dr. David MacKinnon for discussions in the early stage of this work, Dr. Mark Lachowicz for helpful discussion on the V measure, and Dr. Lee Ann Chastain and Ms. Jessica Swann for editorial assistance.

Funding Information:
This research was supported by the National Institutes of Health (NIH) grants R01CA169122 and R21HL126032; PW was supported by NIH grant R01HL116720; HC was supported by NIH grant R00HL130593. The NIH was not involved in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Funding Information:
The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the FHS and does not necessarily reflect the opinions or views of the FHS, Boston University or NHLBI. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC resources. The authors thank Dr. David MacKinnon for discussions in the early stage of this work, Dr. Mark Lachowicz for helpful discussion on the measure, and Dr. Lee Ann Chastain and Ms. Jessica Swann for editorial assistance.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Aging
  • High-dimensional mediators
  • Iterative sure independence screening
  • Mediation analysis
  • R-based effect

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