ArticlePLoS genetics2023
Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Causal inference for multiple risk factors and diseases from genomics data.Nature communications · 2026Article
- Recalibrating Mendelian randomization under winner's curse, sample structure and polygenicity.medRxiv : the preprint server for health sciences · 2026Article
- MR2G: A novel framework for causal network inference using GWAS summary data.PLoS genetics · 2026Article
- Lp(a) Has Specific Effects on Coronary Artery Disease Independent of LDL-C: A Mendelian Randomization Study.JACC. Advances · 2026Article
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- Efficient Mendelian randomization analysis with self-adaptive determination of sample structure and multiple pleiotropic effects.American journal of human genetics · 2025Article
- The Circulating Lipidome In Severe Obesity.medRxiv : the preprint server for health sciences · 2025Article
- Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.American journal of human genetics · 2025Article
- Mendelian Randomization Analysis of the Relationship Between Immune-Related Diseases and Alzheimer's Disease.Journal of multidisciplinary healthcare · 2025Article
- On network deconvolution for undirected graphs.Biometrics · 2024Article
- Estimation of a genetic Gaussian network using GWAS summary data.Biometrics · 2024Article
- A robust cis-Mendelian randomization method with application to drug target discovery.Nature communications · 2024Article
- Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.Bioinformatics (Oxford, England) · 2024Article
- Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.PLoS genetics · 2024Article
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3 authors.
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Abstract
Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.
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