ArticleBiostatistics (Oxford, England)2020
A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.Briefings in bioinformatics · 2026Article
- Accelerometer-Measured Moderate-to-Vigorous Physical Activity and Cancer Risk: Dose-Response from Observational and Nonlinear Mendelian Randomization in UK Biobank.Healthcare (Basel, Switzerland) · 2026Article
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- Proteomic ratio reveals subtype-specific genetic mechanisms and therapeutic targets in osteoarthritis.Clinical proteomics · 2025Article
- Integrative Mendelian randomization for detecting exposure-by-group interactions using group-specific and combined summary statistics.PLoS genetics · 2025Article
- Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.Human genetics · 2024Article
- An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.Science China. Life sciences · 2024Review
- Bayesian Mendelian randomization with an interval causal null hypothesis: ternary decision rules and loss function calibration.BMC medical research methodology · 2024Article
- A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.American journal of human genetics · 2024Article
- Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology.Nature communications · 2022Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Causal inference on neuroimaging data with Mendelian randomisation.NeuroImage · 2022Review
- Genetic Evidence for a Causal Role of Serum Phosphate in Coronary Artery Calcification: The Rotterdam Study.Journal of the American Heart Association · 2022Article
- Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies.BMC medical research methodology · 2022Article
- An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting.Biostatistics (Oxford, England) · 2022Article
- Mendelian randomization.Nature reviews. Methods primers · 2022Article
- Statistical methods for Mendelian randomization in genome-wide association studies: A review.Computational and structural biotechnology journal · 2022Review
- Bayesian variable selection with a pleiotropic loss function in Mendelian randomization.Statistics in medicine · 2021Article
- Transcriptome-wide association studies: a view from Mendelian randomization.Quantitative biology (Beijing, China) · 2021Article
- Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.PLoS genetics · 2021Article
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4 authors.
Funding
Abstract
We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.
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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.