Bayesian variable selection with a pleiotropic loss function in Mendelian randomization.

Area of Science:

• Epidemiology
• Genetic Epidemiology
• Statistical Genetics

Background:

• Mendelian randomization (MR) uses genetic variants as instrumental variables to infer causal relationships between risk factors and outcomes.
• A key challenge in MR is selecting valid genetic instruments that are associated with the exposure but not the outcome through other pathways (pleiotropy).

Purpose of the Study:

• To develop and evaluate a novel variable selection algorithm for two-sample summary-data Mendelian randomization.
• To address pleiotropy by incorporating a loss function within a Bayesian statistics framework.
• To assess the causal effect of blood pressure on coronary heart disease (CHD) risk.

Main Methods:

• A new variable selection algorithm for two-sample summary-data Mendelian randomization was developed.
• The algorithm utilizes a Bayesian framework extension with a loss function to penalize pleiotropic variants.
• Model averaging is employed for robust inference, and the method is validated through simulations and a real-data application.

Main Results:

• The novel algorithm successfully identifies suitable genetic variants for Mendelian randomization analyses.
• Simulations demonstrate robust inference and performance comparable to existing pleiotropy-robust MR methods.
• In a real-data application, both systolic and diastolic blood pressure were found to significantly increase the risk of coronary heart disease.

Conclusions:

• The developed algorithm provides a robust approach for variable selection in two-sample summary-data Mendelian randomization.
• The findings confirm a significant causal relationship between elevated blood pressure and increased risk of coronary heart disease.