ArticleStatistics in medicine2021
Bayesian variable selection with a pleiotropic loss function in Mendelian randomization.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Relationship between collider bias and interactions on the log-additive scale.Statistical methods in medical research · 2025Article
- Causal relationship between hypothyroidism and ulcerative colitis: a bidirectional Mendelian randomization study.BMC gastroenterology · 2024Article
- Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.Human genetics · 2024Article
- Using instruments for selection to adjust for selection bias in Mendelian randomization.Statistics in medicine · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Associations between Sjogren syndrome and psychiatric disorders in European populations: a 2-sample bidirectional Mendelian randomization study.Frontiers in psychiatry · 2024Article
- Using genetic association data to guide drug discovery and development: Review of methods and applications.American journal of human genetics · 2023Review
- Statistical methods for cis-Mendelian randomization with two-sample summary-level data.Genetic epidemiology · 2023Review
- Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology.Nature communications · 2022Article
- A Mendelian randomization study investigating the causal role of inflammation on Parkinson's disease.Brain : a journal of neurology · 2022Article
- A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.American journal of epidemiology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.
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Registered trials
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.