ArticleGenome research2023
Leveraging family data to design Mendelian randomization that is provably robust to population stratification.
Article in Genome research, 2023. 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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10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Instability of Global Burden of Disease Estimates of Deaths and Disability-Adjusted Life-Years From Major Risk Factors: A Meta-Epidemiological Analysis.JAMA health forum · 2026Pooled it
- Exploring and experimental validation of SLC7A11 and ferroptosis related prognostic genes in non-small cell lung cancer using bulk RNA sequencing and single-cell RNA sequencing.Biology direct · 2026Article
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
- Confounding fuels misinterpretation in human genetics.Proceedings. Biological sciences · 2025Article
- Confounding Fuels Misinterpretation in Human Genetics.bioRxiv : the preprint server for biology · 2025Article
- Advances in Mendelian Randomization Studies of Obesity Over the Past Decade: Uncovering Key Genetic Mechanisms.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Review
- Enhancing the robustness of Mendelian randomization studies: lessons from a two-sample analysis of viral infections and colorectal cancer.Infectious agents and cancer · 2024Article
- Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study.Journal of orthopaedic surgery and research · 2024Article
- Identifying the causal relationship between immune factors and osteonecrosis: a two-sample Mendelian randomization study.Scientific reports · 2024Article
- Metabolic pathways, genomic alterations, and post-translational modifications in pulmonary hypertension and cancer as therapeutic targets and biomarkers.Frontiers in pharmacology · 2024Article
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Abstract
Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.
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