ArticleAmerican journal of human genetics2024
Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.
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 43 papers.
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Who cites it
43 citing papers in PubMed.
- Association Between Sleep Disorders and Breast Cancer: An Analysis Based on NHANES and Mendelian Randomization.World journal of oncology · 2026Article
- Inferring multi-organ genetic connections using imaging and clinical data through Mendelian randomization.Nature biomedical engineering · 2026Article
- Early‑life head circumference is causally associated with adult executive function through cortical surface area expansion.Developmental cognitive neuroscience · 2026Article
- Article
- Genetic evidence linking circadian rhythm genes and hypertension: A two-sample Mendelian randomization study.The Journal of international medical research · 2026Article
- Recalibrating Mendelian randomization under winner's curse, sample structure and polygenicity.medRxiv : the preprint server for health sciences · 2026Article
- Assessing the impact of metabolomic markers on gastric cancer risk: a two-sample Mendelian randomization study.Korean journal of family medicine · 2026Article
- The genetic architecture of cortical similarity networks.Nature communications · 2026Article
- Reassessing Instrument Strength in Two-Sample Mendelian Randomization Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Sex-specific roles of insulin-like growth factor-1, lean mass and fat mass in type 2 diabetes prevention: Mendelian randomization studies in Western and East Asian populations.Cardiovascular diabetology. Endocrinology reports · 2026Article
- Testing bidirectional associations of major depressive disorder with medical conditions: two-sample Mendelian randomization study.Npj mental health research · 2026Article
- MRBEE-TL: improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning.Genome biology · 2026Article
- The Mental Health-Acute Coronary Syndrome Continuum: Bidirectional Pathophysiological Links and Clinical Implications.Medical sciences (Basel, Switzerland) · 2026Review
- Single-cell transcriptomic profiling combined with Mendelian randomization illuminates molecular drivers of bladder cancer.Molecular genetics and genomics : MGG · 2026Article
- Elucidating biopsychosocial mechanisms in migraine: an integrative analytics approach combining genetics, neuroimaging, and machine learning.The journal of headache and pain · 2026Article
- A Risk Factor Atlas for Allergy-Related Airway Diseases: Evidence From Multi-Biobank Genetic Study.Allergy, asthma & immunology research · 2026Article
- Integrating genomic and exposomic data identifies endocrine disruptors potentially associated with chronic obstructive pulmonary disease.Human genomics · 2026Article
- Prenatal Alcohol Exposure and Mental Health Outcomes: A Two-Sample Mendelian Randomization Study of DNA Methylation Signatures.medRxiv : the preprint server for health sciences · 2026Article
- Circulating sex steroids do not influence risk of thyroid malignancy: insights from a bidirectional Mendelian randomization.Gland surgery · 2026Article
- Bidirectional Mendelian Randomization analysis of iron status and uremia: no evidence of a causal relationship.Renal failure · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.
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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.