MR.RGM

MR.RGM implements Bayesian multivariate bidirectional Mendelian randomization networks to enable the exploration of causal relationships among multiple variables in complex biological systems.

Key Features:

• Holistic causal network construction: Constructs causal networks with possibly cyclic or reciprocal causation among multiple variables rather than analyzing only a single exposure-outcome pair at a time.
• Bayesian reciprocal graphical model: Implements the Bayesian reciprocal graphical model for network-based bidirectional Mendelian randomization analysis.
• Uncertainty quantification: Provides proper uncertainty quantifications for inferred causal relationships within the network.

Scientific Applications:

• Genetic network analysis: Unveils intricate interactions in genetic networks through causal relationship inference among multiple variables.
• Disease risk investigation: Advances understanding of disease risks by modeling complex causal interconnections between exposures and outcomes.
• Phenotypic complexity exploration: Enables comprehensive analysis of phenotypic complexities through holistic causal network modeling.

Methodology:

MR.RGM applies bidirectional Mendelian randomization using a network-based strategy with a Bayesian reciprocal graphical model, using genetic variants as instrumental variables to infer causal relationships.