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A Bayesian approach to Mendelian randomisation with dependent instruments.

Chin Yang Shapland1, John R Thompson1, Nuala A Sheehan1

  • 1Department of Health Sciences and Genetics, University of Leicester, Leicester, UK.

Statistics in Medicine
|November 29, 2018
PubMed
Summary
This summary is machine-generated.

Bayesian model averaging (BMA) improves Mendelian randomisation (MR) by considering all genetic variants, offering more precise causal inference than selecting single variants. This method enhances the reliability of genetic association studies for risk factors and outcomes.

Keywords:
Bayesian model averagingMendelian randomisationdependent SNPsmany weak instruments

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Mendelian randomisation (MR) uses genetic variants as instrumental variables to infer causality between risk factors and outcomes.
  • Weak genetic associations can reduce MR precision and bias estimates.
  • Selecting single significant variants may not capture all regional genetic effects or guarantee causality.

Purpose of the Study:

  • To develop and evaluate a novel MR approach using Bayesian model averaging (BMA) for improved causal inference.
  • To assess the performance of BMA-MR against traditional methods and penalized regression-based MR.
  • To investigate the robustness of BMA-MR to assumption violations and sensitivity to invalid instruments.

Main Methods:

  • Bayesian model averaging (BMA) was applied to average MR estimates across all possible combinations of genetic variants within a region.
  • Simulations were conducted to compare BMA-MR with classical MR and penalized regression MR.
  • The method's robustness was tested against violations of MR assumptions and the inclusion of invalid instruments.
  • The approach was illustrated using an MR analysis of body mass index on blood pressure, utilizing FTO gene SNPs.

Main Results:

  • BMA-MR demonstrated superior performance compared to classical estimation with multiple dependent variants.
  • BMA-MR significantly outperformed MR methods relying on variants selected by penalized regression.
  • Simulations confirmed the method's robustness to violations in model assumptions.
  • Sensitivity analyses highlighted the impact of including invalid instruments on MR estimates.

Conclusions:

  • Bayesian model averaging provides a more precise and robust approach to Mendelian randomisation, especially when dealing with multiple genetic variants.
  • This method enhances causal inference by accounting for all potential genetic instruments, improving upon single-variant selection or penalized regression approaches.
  • BMA-MR offers a valuable tool for epidemiological research, enabling more reliable identification of causal relationships between risk factors and health outcomes.