ArticleStatistical methods in medical research2020
Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies.Molecular psychiatry · 2026Review
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- Transfusion-related immunomodulation effect of erythrocyte characteristics on immune cells traits based on a multivariable Mendelian randomization study.International journal of surgery (London, England) · 2026Article
- Assessing the correlates of wasting among children under five and their mothers in the Bay and Hiran regions of Somalia.Journal of global health · 2025Article
- 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
- Causal Association Between Genetically Predicted Circulating Metabolites and Pressure Ulcers: A Two-Sample Mendelian Randomization Study.Journal of inflammation research · 2025Article
- GWAS advancements to investigate disease associations and biological mechanisms.Clinical and translational discovery · 2024Article
- simmrd: An open-source tool to perform simulations in Mendelian randomization.Genetic epidemiology · 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
- Associations between Sjogren syndrome and psychiatric disorders in European populations: a 2-sample bidirectional Mendelian randomization study.Frontiers in psychiatry · 2024Article
- Can metabolomic signature foster precision medicine approaches in nonalcoholic fatty liver disease arena?Hepatobiliary surgery and nutrition · 2022Article
- Causal inference on neuroimaging data with Mendelian randomisation.NeuroImage · 2022Review
- Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update.BMC medical informatics and decision making · 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
- Polygenic Mendelian Randomization.Cold Spring Harbor perspectives in medicine · 2021Review
- Genetic Associations Between IL-6 and the Development of Autoimmune Arthritis Are Gender-Specific.Frontiers in immunology · 2021Article
- Plasma proteomic biomarker signature of age predicts health and life span.eLife · 2020 · on this mapArticle
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or health-related outcome from observational data. Instrumental variables must satisfy strong, often untestable assumptions, which means that finding good genetic instruments among a large list of potential candidates is challenging. This difficulty is compounded by the fact that many genetic variants influence more than one phenotype through different causal pathways, a phenomenon called horizontal pleiotropy. This leads to errors not only in estimating the magnitude of the causal effect but also in inferring the direction of the putative causal link. In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation. The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, we use Bayesian model averaging to determine how much more likely the inferred direction is relative to the reverse direction.
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