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Related Experiment Video

Updated: Jul 6, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

MR.RGM: an R package for fitting Bayesian multivariate bidirectional Mendelian randomization networks.

Bitan Sarkar1, Yang Ni1,2

  • 1De partment of Statistics, Texas A&M University, College Station, TX 77843, United States.

Bioinformatics (Oxford, England)
|March 25, 2025
PubMed
Summary

Mendelian randomization via reciprocal graphical model (MR.RGM) constructs complex causal networks. This R package enables holistic analysis of biological systems, moving beyond pairwise relationships for deeper insights.

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Last Updated: Jul 6, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Mendelian randomization (MR) typically analyzes pairwise causal relationships between exposures and outcomes using genetic variants.
  • Existing MR methods are limited in capturing complex, interconnected causal networks within biological systems.

Purpose of the Study:

  • To develop a novel R package, MR.RGM (Mendelian randomization via reciprocal graphical model), for constructing holistic causal networks.
  • To enable the analysis of potentially cyclic or reciprocal causal relationships among multiple variables.

Main Methods:

  • MR.RGM implements a Bayesian reciprocal graphical model approach.
  • It utilizes a network-based strategy for bidirectional Mendelian randomization.

Main Results:

  • The MR.RGM R package facilitates the construction of comprehensive causal networks.
  • It allows for the exploration of intricate interactions within complex biological systems.

Conclusions:

  • MR.RGM enhances the understanding of genetic networks, disease risks, and phenotypic complexities.
  • The package provides proper uncertainty quantification for a more complete biological systems analysis.