ArticleGenetic epidemiology2017
Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization.
Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers, 8 of them syntheses that pooled it.
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Who cites it
166 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults.Journal of the American Heart Association · 2024Pooled it
- Causal relationships of metabolites with allergic diseases: a trans-ethnic Mendelian randomization study.Respiratory research · 2024Pooled it
- Genetic insights into resting heart rate and its role in cardiovascular disease.Nature communications · 2023Pooled it
- Sex Hormone-Binding Globulin and Risk of Coronary Heart Disease in Men and Women.Clinical chemistry · 2023Pooled it
- Alcohol consumption in relation to cardiovascular diseases and mortality: a systematic review of Mendelian randomization studies.European journal of epidemiology · 2022Pooled it
- Genome-wide meta-analysis of iron status biomarkers and the effect of iron on all-cause mortality in HUNT.Communications biology · 2022Pooled it
- High Blood Pressure and Intraocular Pressure: A Mendelian Randomization Study.Investigative ophthalmology & visual science · 2022Pooled it
- Systematic review and meta-analyses: What has the application of Mendelian randomization told us about the causal effect of adiposity on health outcomes?Wellcome open research · 2022Pooled it
- Leukocytes and age-related macular degeneration.European journal of epidemiology · 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
- Accelerometer-Measured Moderate-to-Vigorous Physical Activity and Cancer Risk: Dose-Response from Observational and Nonlinear Mendelian Randomization in UK Biobank.Healthcare (Basel, Switzerland) · 2026Article
- Comment on "The effects of GLUT9 and URAT1 inhibitors on cardiovascular diseases: a drug-targeted Mendelian randomization study".International urology and nephrology · 2026Article
- Article
- Widespread genetic effect heterogeneity impacts bias and power in nonlinear Mendelian randomization.medRxiv : the preprint server for health sciences · 2026Article
- Estimating the causal effect of cardiometabolic conditions on socioeconomic and healthcare outcomes: a scoping review of Mendelian randomization studies.Health economics review · 2026Article
- 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
- Transfer learning-based two-sample Mendelian randomization method for heterogeneous population.Briefings in bioinformatics · 2026Article
- A two-stage GAN-based instrumental variable method for causal analysis of omics data.Briefings in bioinformatics · 2026Article
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
106 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure-outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure-outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to estimate the true exposure-outcome relationship well, particularly when the relationship was a fractional polynomial (for the fractional polynomial method) or was piecewise linear (for the piecewise linear method). The methods were used to investigate the shape of relationship of body mass index with systolic blood pressure and diastolic blood pressure.
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