ArticleNature communications2023
Reciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Winner's Curse Free Robust Mendelian Randomization with Summary Data.Journal of the American Statistical Association · 2026Article
- Multi-omics and Mendelian randomization identifyTranslational lung cancer research · 2025Article
- Circulating linoleic acid and its interplay with gut microbiota during pregnancy for gestational diabetes mellitus.BMC medicine · 2025Article
- Identification of Potential Diagnostic Biomarkers and Drug Targets for Endometriosis from a Genetic Perspective: A Mendelian Randomization Study.Gynecologic and obstetric investigation · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Article
- Identification of biomarkers and potential drug targets in osteoarthritis based on bioinformatics analysis and mendelian randomization.Frontiers in pharmacology · 2024Article
- Two-sample mendelian randomization analysis investigates ambient fine particulate matter's impact on cardiovascular disease development.Scientific reports · 2023Article
- Association of Barrett's esophagus with obstructive sleep apnea syndrome: a bidirectional analysis of Mendelian randomization.Frontiers in psychiatry · 2023Article
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Authors and funding
9 authors.
Funding
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Abstract
Mendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.
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