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Variance component tests of multivariate mediation effects under composite null hypotheses.

Yen-Tsung Huang1

  • 1Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.

Biometrics
|April 23, 2019
PubMed
Summary

This study introduces a new statistical test to accurately assess mediation effects with multiple mediators. The novel approach corrects for composite null hypotheses, improving Type 1 error rates in complex genetic analyses.

Keywords:
composite null hypothesisintersection-union testjoint significance testmediation analysesnormal product distribution

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Area of Science:

  • Biostatistics
  • Genomics
  • Cancer Research

Background:

  • Mediation analysis is crucial for understanding indirect effects of exposures on outcomes.
  • Existing methods for multiple mediators struggle with composite null hypotheses, potentially leading to inaccurate Type 1 error rates.
  • Accurate statistical testing is essential for identifying biological pathways in complex diseases like lung cancer.

Purpose of the Study:

  • To develop and validate a novel statistical test for mediation effects involving multiple mediators.
  • To address the limitations of existing methods concerning composite null hypotheses in mediation analysis.
  • To investigate the role of DNA methylation in mediating the effect of smoking on gene expression in lung cancer.

Main Methods:

  • Proposed a novel test examining exposure-mediator and mediator-outcome associations using separate variance component tests.
  • Constructed score tests for variance components assuming a zero-mean working distribution.
  • Transformed test statistics to normally distributed values and conducted hypothesis tests accounting for the composite null by adjusting variances.

Main Results:

  • The proposed test demonstrates improved accuracy in controlling Type 1 error rates compared to existing methods, particularly in scenarios with numerous mediators.
  • Simulation studies confirmed the advantages of the novel test.
  • Application to The Cancer Genome Atlas lung cancer data revealed significant mediation effects of DNA methylation in smoking-related gene expression changes across 15,114 genes.

Conclusions:

  • The developed statistical test offers a robust solution for mediation analysis with multiple mediators, effectively handling composite null hypotheses.
  • This method enhances the reliability of identifying indirect effects in complex biological systems.
  • The findings provide insights into the molecular mechanisms underlying smoking-induced lung cancer, highlighting the role of DNA methylation.