Authors: Bitan Sarkar, Yang Ni

Published on: March 06, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2403.03944

Summary

• What is new: Developed ‘MR.RGM’, a novel R-package that allows for constructing holistic causal networks in Mendelian randomization studies, overcoming the limitation of only considering pairwise exposure-outcome relationships.
• Why this is important: Traditional Mendelian randomization methods are limited as they consider only a pair of exposure and outcome at a time, failing to capture entire causal networks.
• What the research proposes: ‘MR.RGM’ uses a Bayesian reciprocal graphical model to construct causal networks with cyclic/reciprocal causation, enabling a comprehensive study of complex biological systems.
• Results: The ‘MR.RGM’ open-source R package enables exploring causal relationships among multiple variables in complex biological systems, advancing understanding of genetic networks and disease risks.

Technical Details

Technological frameworks used: R-package

Models used: Bayesian reciprocal graphical models

Data used: Multiple genetic variants as instrumental variables

Potential Impact

Biotech and pharmaceutical companies, healthcare analytics

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