ArticlePLoS genetics2023
Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A burden of proof study on alcohol consumption and ischemic heart disease.Nature communications · 2024Pooled it
- Vitamin D and human health: evidence from Mendelian randomization studies.European journal of epidemiology · 2024Pooled it
- Deciphering the interplay between serum urate and bone mineral density: insights from observational and genetic perspectives.Journal of the International Society of Sports Nutrition · 2026Article
- Context-Stratified Mendelian Randomization: Exploiting Regional Exposure Variation to Explore Causal Effect Heterogeneity and Nonlinearity.Statistics in medicine · 2026Article
- Adiposity and infection-related mortality: Mendelian randomization study of 126 000 Mexican adults.International journal of epidemiology · 2026Article
- DNA methylation informs adult cardiovascular risk and prevention for low birthweight individuals.Journal of global health · 2026Article
- Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization.American journal of human genetics · 2026Review
- Widespread genetic effect heterogeneity impacts bias and power in nonlinear Mendelian randomization.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating the causal role of LEPR signaling in 16 cancers: A drug-target Mendelian randomization study.Medicine · 2026Article
- Estimating the causal effect of cardiometabolic conditions on socioeconomic and healthcare outcomes: a scoping review of Mendelian randomization studies.Health economics review · 2026Article
- Alcohol use and risk of dementia in diverse populations: evidence from cohort, case-control and Mendelian randomisation approaches.BMJ evidence-based medicine · 2026Article
- Sex-Stratified Genetic Analyses Mapping the Influences of Sedentary Behaviors and Physical Activity on Female Reproductive Health.Research (Washington, D.C.) · 2026Article
- Stratification-based instrumental variable analysis framework for nonlinear effect analysis.Biostatistics (Oxford, England) · 2025Article
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- Article
- No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.The Journal of clinical endocrinology and metabolism · 2025Article
- Causal relationship between body mass index and insulin resistance: Linear and nonlinear Mendelian randomization study in a Japanese population.Journal of diabetes investigation · 2025Article
- A Mendelian randomization study of the effect of body mass index on 52 causes of death among 125 000 Mexican adults with admixed ancestry.International journal of epidemiology · 2025Article
- A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.American journal of human genetics · 2025Article
- Investigating the non-linear association between sleep duration and type 2 diabetes: conventional and Mendelian randomization analyses from the UK Biobank.Journal of diabetes investigation · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Non-linear Mendelian randomization is an extension to standard Mendelian randomization to explore the shape of the causal relationship between an exposure and outcome using an instrumental variable. A stratification approach to non-linear Mendelian randomization divides the population into strata and calculates separate instrumental variable estimates in each stratum. However, the standard implementation of stratification, referred to as the residual method, relies on strong parametric assumptions of linearity and homogeneity between the instrument and the exposure to form the strata. If these stratification assumptions are violated, the instrumental variable assumptions may be violated in the strata even if they are satisfied in the population, resulting in misleading estimates. We propose a new stratification method, referred to as the doubly-ranked method, that does not require strict parametric assumptions to create strata with different average levels of the exposure such that the instrumental variable assumptions are satisfied within the strata. Our simulation study indicates that the doubly-ranked method can obtain unbiased stratum-specific estimates and appropriate coverage rates even when the effect of the instrument on the exposure is non-linear or heterogeneous. Moreover, it can also provide unbiased estimates when the exposure is coarsened (that is, rounded, binned into categories, or truncated), a scenario that is common in applied practice and leads to substantial bias in the residual method. We applied the proposed doubly-ranked method to investigate the effect of alcohol intake on systolic blood pressure, and found evidence of a positive effect of alcohol intake, particularly at higher levels of alcohol consumption.
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