ArticleBiometrics2020
Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Body Mass Index and Risk of Female Reproductive System Tumors Subtypes: A Meta-Analysis Using Mendelian Randomization.Technology in cancer research & treatmentPooled it
- Causal Interplay Between Platelet Indices and Rheumatoid Arthritis: Genetic Evidence From Bidirectional Mendelian Randomization.International journal of genomics · 2026Article
- A hybrid high activity aware framework integrating graph attention network and transformer for half maximal inhibitory concentration prediction.Frontiers in immunology · 2026Article
- A causal association between osteoporosis and postural instability: a Mendelian randomization study.Scientific reports · 2025Article
- Article
- Exploring the Causal Relationship Between Inflammatory Cytokines and MRI-Derived Brain Iron: A Mendelian Randomization Study.Brain and behavior · 2024Article
- Personal history of irradiation and risk of breast cancer: A Mendelian randomisation study.Journal of global health · 2024Article
- Depression and risk of arthritis: A Mendelian randomization study.Brain and behavior · 2024Article
- Article
- Power calculation for the general two-sample Mendelian randomization analysis.Genetic epidemiology · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Mendelian randomization (MR) is a type of instrumental variable (IV) analysis that uses genetic variants as IVs for a risk factor to study its causal effect on an outcome. Extensive investigations on the performance of IV analysis procedures, such as the one based on the two-stage least squares (2SLS) procedure, have been conducted under the one-sample scenario, where measures on IVs, the risk factor, and the outcome are assumed to be available for each study participant. Recent MR analysis usually is performed with data from two independent or partially overlapping genetic association studies (two-sample setting), with one providing information on the association between the IVs and the outcome, and the other on the association between the IVs and the risk factor. We investigate the performance of 2SLS in the two-sample-based MR when the IVs are weakly associated with the risk factor. We derive closed form formulas for the bias and mean squared error of the 2SLS estimate and verify them with numeric simulations under realistic circumstances. Using these analytic formulas, we can study the pros and cons of conducting MR analysis under one-sample and two-sample settings and assess the impact of having overlapping samples. We also propose and validate a bias-corrected estimator for the causal effect.
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