ArticleFrontiers in genetics2019
Learning Causal Biological Networks With the Principle of Mendelian Randomization.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inflammatory bowel disease and risk of coronary heart disease : A Mendelian randomization study.Wiener klinische Wochenschrift · 2022Pooled it
- NLCD: A method to discover nonlinear causal relations among genes.PLoS computational biology · 2026Article
- Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states.PLoS computational biology · 2026Article
- Bayesian network imputation methods applied to multi-omics data identify putative causal relationships in a type 2 diabetes dataset containing incomplete data: An IMI DIRECT Study.PLoS genetics · 2025Article
- Causal network inference of cis- and trans-gene regulation of expression quantitative trait loci across human tissues.Genetics · 2025Article
- Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.American journal of human genetics · 2025Article
- Clock Proteins Have the Potential to Improve Term Delivery Date Prediction: A Proof-of-Concept Study.Life (Basel, Switzerland) · 2025Article
- Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.BMC medical research methodology · 2024Article
- The Application of Mendelian Randomization in Cardiovascular Disease Risk Prediction: Current Status and Future Prospects.Reviews in cardiovascular medicine · 2024Review
- MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.BMC bioinformatics · 2024Article
- Symptom contributors to quality of life in schizophrenia: Exploratory factor and network analyses.Schizophrenia research · 2024Article
- MRSL: a causal network pruning algorithm based on GWAS summary data.Briefings in bioinformatics · 2024Article
- Effect of genetic liability to migraine and its subtypes on breast cancer: a mendelian randomization study.BMC cancer · 2023Article
- Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2023Article
- No causal effect of genetically determined circulating homocysteine levels on psoriasis in the European population: evidence from a Mendelian randomization study.Frontiers in immunology · 2023Article
- Causal effects for genetic variants of osteoprotegerin on the risk of acute myocardial infarction and coronary heart disease: A two-sample Mendelian randomization study.Frontiers in cardiovascular medicine · 2023Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Connectivity in eQTL networks dictates reproducibility and genomic properties.Cell reports methods · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- Causal associations between blood pressure and the risk of myocardial infarction: A bidirectional Mendelian randomization study.Frontiers in cardiovascular medicine · 2022Article
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2 authors.
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Abstract
Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in the PC algorithm, a classical algorithm for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating individual-level genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms several popular general-purpose network inference methods and PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci. Our method is implemented in the R package MRPC, available on CRAN (https://cran.r-project.org/web/packages/MRPC/index.html).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.