ArticleAmerican journal of human genetics2024
A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.
Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- New-Onset Type 2 Diabetes Mellitus and Cancer Risk: A Matched Cohort Study in China Kadoorie Biobank.International journal of cancer · 2026Article
- Effects of Schizophrenia, Bipolar Disorder, and Depression on Cardiopulmonary and Abdominal Organ Structure.Biological psychiatry global open science · 2026Article
- Bayesian Mendelian randomization methods for index trait bias correction in subsequent trait genome-wide association studies.HGG advances · 2026Article
- Bayesian Bidirectional Mendelian Randomization Under Correlated and Uncorrelated Pleiotropy Using GWAS Summary Statistics.Statistics in medicine · 2026Article
- Comparison of methods for assessing effects of risk factors on disease progression in Mendelian randomization under index event bias.American journal of human genetics · 2026Article
- Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies.Molecular psychiatry · 2026Review
- From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome.Briefings in bioinformatics · 2026Article
- Natural menopause, menarche and breast cancer risk in BRCA1 and BRCA2 pathogenic variant carriers: a Mendelian randomization analysis.British journal of cancer · 2026Article
- Testing bidirectional associations of major depressive disorder with medical conditions: two-sample Mendelian randomization study.Npj mental health research · 2026Article
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- Winner's Curse Free Robust Mendelian Randomization with Summary Data.Journal of the American Statistical Association · 2026Article
- MTG2 as a Causal and Druggable Target in Uveal Melanoma via Mitochondrial DNA Dynamics: Evidence From Functional Validation and DMSA Identification.Investigative ophthalmology & visual science · 2026Article
- Serum urate, cardiovascular mediators, and atrial fibrillation: genetic evidence for URAT1-targeted therapy.Clinical rheumatology · 2026Article
- Disentangling the Causal Effects of Education and Participation Bias on Alzheimer Disease Using Mendelian Randomization.Neurology. Genetics · 2025Article
- An application of the MR-Horse method to reduce selection bias in genome-wide association studies of disease progression.European journal of human genetics : EJHG · 2025Article
- Genome-Wide Aggregated Trans-Effects Analysis Implicates Deficient Type III Interferon Signaling as a Key Cause of Inflammatory Bowel Disease.Inflammatory bowel diseases · 2025Article
- Evidence of bidirectional relationship between type 2 diabetes and depression; a Mendelian randomization study.Molecular psychiatry · 2025Article
- Genome-Wide Aggregated Trans Effects Analysis for Circulating Proteins Indicates a Key Role of Immune Checkpoints in Type 1 Diabetes.Diabetes · 2025Article
- Clinical implications of bone marrow adiposity identified by phenome-wide association and Mendelian randomization in the UK Biobank.Nature communications · 2025Article
- Integrative Mendelian randomization for detecting exposure-by-group interactions using group-specific and combined summary statistics.PLoS genetics · 2025Article
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Abstract
Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.
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