MR.RGM: An R Package for Fitting Bayesian Multivariate Bidirectional Mendelian Randomization Networks
arxiv(2024)
摘要
Motivation: Mendelian randomization (MR) infers causal relationships between
exposures and outcomes using genetic variants as instrumental variables.
Typically, MR considers only a pair of exposure and outcome at a time, limiting
its capability of capturing the entire causal network. We overcome this
limitation by developing 'MR.RGM' (Mendelian randomization via reciprocal
graphical model), a fast R-package that implements the Bayesian reciprocal
graphical model and enables practitioners to construct holistic causal networks
with possibly cyclic/reciprocal causation and proper uncertainty
quantifications, offering a comprehensive understanding of complex biological
systems and their interconnections. Results: We developed 'MR.RGM', an
open-source R package that applies bidirectional MR using a network-based
strategy, enabling the exploration of causal relationships among multiple
variables in complex biological systems. 'MR.RGM' holds the promise of
unveiling intricate interactions and advancing our understanding of genetic
networks, disease risks, and phenotypic complexities.
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