Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions.

Area of Science:

• Genetics
• Epidemiology
• Biostatistics

Background:

• Mendelian randomization (MR) strengthens causal inference using genome-wide association studies.
• Horizontal pleiotropy in genetic instruments can bias causal estimates.
• Gene-environment interactions offer potential for detecting and correcting pleiotropic bias.

Purpose of the Study:

• Introduce MR using Gene-by-Environment interactions (MRGxE) as a framework to identify and correct pleiotropic bias.
• Assess the validity of individual genetic instruments within MR analyses.
• Improve the accuracy of causal effect estimation in genetic epidemiology.

Main Methods:

• MRGxE framework utilizes instrument-covariate interactions to detect pleiotropic effects.
• Linear regression framework implemented for MR analyses.
• Assesses instrument validity by examining variation in instrument-exposure association across interaction subgroups.

Main Results:

• MRGxE findings for adiposity (BMI) on systolic blood pressure (SBP) align with two-sample summary MR approaches.
• Simulations demonstrate the utility of MRGxE even when its core assumptions are violated.
• The study successfully applied MRGxE to real-world data from UK Biobank and GIANT consortium.

Conclusions:

• Instrument-covariate interactions in MR analyses can identify and correct horizontal pleiotropic bias.
• MRGxE provides a robust method for enhancing causal inference in the presence of pleiotropy.
• The approach is reliable under specific conditions regarding the constancy of pleiotropy magnitude across subgroups.