ArticleQuantitative biology (Beijing, China)2017
On the use of kernel machines for Mendelian randomization.
Article in Quantitative biology (Beijing, China), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MACFIV: a novel framework for nonlinear causal inference in the body mass index-hypertension relationship with many weak and pleiotropic genetic instruments.Briefings in bioinformatics · 2026Article
- Causal Effects of Artificially Sweetened Foods on Chronic Pain Mediated by Gut Microbiota: A Mendelian Randomization Study.Food science & nutrition · 2025Article
- Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data.PLoS genetics · 2022Article
- A general approach to sensitivity analysis for Mendelian randomization.Statistics in biosciences · 2021Article
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2 authors.
Funding
Abstract
backgroundProperly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure's causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage.
resultsIn this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting.
conclusionsOur results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.
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