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Related Experiment Videos

Bayesian Bidirectional Mendelian Randomization Under Correlated and Uncorrelated Pleiotropy Using GWAS Summary

¹School of Public Health, LSU Health Sciences Center New Orleans, New Orleans, Louisiana, USA.

Statistics in Medicine

|July 11, 2026

Summary

Bayesian bidirectional Mendelian randomization (MR) improves causal inference. The new BayBiMR framework models pleiotropy and bidirectional effects, enhancing accuracy for complex trait analysis.

Keywords:

Bayesian bidirectional causality GWAS summary statistics Mendelian randomization blocked Gibbs sampler correlated pleiotropy spike‐and‐slab prior

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Area of Science:

Biostatistics
Statistical Genetics
Epidemiology

Background:

Mendelian randomization (MR) infers causality using genetic variants.
Standard MR methods struggle with pleiotropy and bidirectional causality.
Existing approaches often assume independent pleiotropic effects, which may not hold.

Purpose of the Study:

Introduce BayBiMR, a Bayesian framework for bidirectional MR.
Address limitations of unidirectional MR, including pleiotropy and feedback loops.
Provide a robust tool for estimating causal effects in complex biological systems.

Main Methods:

Developed a Bayesian bidirectional MR framework (BayBiMR).
Incorporated hierarchical spike-and-slab priors for pleiotropy and Inverse-Wishart prior for SNP effect covariance.
Utilized a small-feedback reduced-form approximation for efficient posterior computation via Gibbs sampling.
Introduced BayBiMR(DP) for improved frequentist calibration in finite samples.

Main Results:

BayBiMR and BayBiMR(DP) demonstrated superior type I error control compared to existing methods.
The proposed methods showed particular strength in settings with correlated and mixed pleiotropy.
Simulations confirmed improved performance in complex causal structures.
Applications to real-world GWAS data highlighted practical utility.

Conclusions:

BayBiMR offers a robust Bayesian approach for bidirectional causal inference.
The framework effectively handles correlated and uncorrelated pleiotropy.
BayBiMR serves as a valuable exploratory and sensitivity analysis tool for genetic epidemiology.