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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.

Jack Bowden1, George Davey Smith2, Stephen Burgess3

  • 1MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK, MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK and Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK, MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK and Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK jack.bowden@mrc-bsu.cam.ac.uk.

International Journal of Epidemiology
|June 8, 2015
PubMed
Summary
This summary is machine-generated.

Mendelian randomization (MR) studies use genetic variants to estimate causal effects. An adapted Egger regression, MR-Egger, detects pleiotropy bias and provides robust causal estimates, enhancing the reliability of MR investigations.

Keywords:
MR-Egger testMendelian randomizationinvalid instrumentsmeta-analysispleiotropysmall study bias

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Biostatistics

Background:

  • Mendelian randomization (MR) analyses are increasingly common, utilizing genome-wide association studies for precise causal effect estimates.
  • Pleiotropy, where genetic variants influence outcomes through multiple pathways, can invalidate instrumental variables in MR studies.

Purpose of the Study:

  • To adapt Egger regression for detecting pleiotropy bias in multi-instrument MR.
  • To provide a robust causal effect estimate even when instrumental variables are potentially invalid.

Main Methods:

  • Mendelian randomization with multiple instruments is treated as a meta-analysis.
  • Egger regression, typically used for small study bias, is adapted (MR-Egger) to detect pleiotropy.
  • Funnel plots are used to visualize asymmetry and assess potential bias.

Main Results:

  • The adapted Egger regression (MR-Egger) effectively detects violations of instrumental variable assumptions.
  • The slope coefficient from MR-Egger provides a consistent causal effect estimate.
  • Re-analysis of height-lung function and blood pressure-coronary artery disease studies demonstrates the approach's conservative nature.

Conclusions:

  • MR-Egger regression serves as a valuable sensitivity analysis for MR studies.
  • This method enhances the robustness of causal inference in the presence of pleiotropy.
  • MR-Egger provides a reliable effect estimate, mitigating bias from invalid instrumental variables.