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Updated: Jun 29, 2025

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A mediation analysis framework based on variance component to remove genetic confounding effect.

Zihan Dong1,2, Hongyu Zhao3, Andrew T DeWan4,5

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Journal of Human Genetics
|March 26, 2024
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Summary
This summary is machine-generated.

This study introduces REML-mediation, a novel method to correct for genetic confounding in mediation analysis. It accurately estimates cross-trait causal effects by addressing shared genetic influences between phenotypes.

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Area of Science:

  • Genetics
  • Epidemiology
  • Statistical Genetics

Background:

  • Pleiotropy, where a single nucleotide polymorphism (SNP) affects multiple traits, offers insights into shared genetic signals.
  • Mediation analysis decomposes SNP effects into direct and indirect pathways via a mediator.
  • Genetic correlation between phenotypes can confound mediation analysis, yielding inaccurate effect estimates.

Purpose of the Study:

  • To develop a statistical framework to address confounding from genetic correlation in genetic mediation analysis.
  • To introduce REML-mediation, a restricted maximum likelihood-based method for robust genetic mediation analysis.
  • To validate the REML-mediation framework using simulations and real-world data.

Main Methods:

  • Proposed a restricted maximum likelihood (REML)-based mediation analysis framework, termed REML-mediation.
  • Developed REML-mediation to be applicable to both individual-level and summary statistics data.
  • Validated the method through simulations and application to UK Biobank data for mediator-outcome trait pairs and pleiotropic SNPs.

Main Results:

  • Simulations confirmed that REML-mediation provides unbiased estimates of cross-trait causal effects under specific assumptions.
  • The method demonstrated slightly inflated standard errors compared to traditional linear regression but maintained accuracy.
  • Application to UK Biobank data revealed significant genetic confounding effects, with REML-mediation achieving correction magnitudes of 7% to 39%.

Conclusions:

  • REML-mediation effectively identifies and corrects for genetic confounding in cross-trait analyses.
  • The findings underscore the prevalence of genetic confounding in epidemiological studies.
  • Accounting for genetic confounding is crucial for accurate interpretation of genetic mediation analyses.