ArticleHGG advances2022
Polynomial Mendelian randomization reveals non-linear causal effects for obesity-related traits.
Article in HGG advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Context-Stratified Mendelian Randomization: Exploiting Regional Exposure Variation to Explore Causal Effect Heterogeneity and Nonlinearity.Statistics in medicine · 2026Article
- NLCD: A method to discover nonlinear causal relations among genes.PLoS computational biology · 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
- 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
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
- Stratification-based instrumental variable analysis framework for nonlinear effect analysis.Biostatistics (Oxford, England) · 2025Article
- A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.American journal of human genetics · 2025Article
- Assessing the causal effects of type 2 diabetes and obesity-related traits on COVID-19 severity.Human genomics · 2025Article
- Causal inference in health and disease: a review of the principles and applications of Mendelian randomization.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Body mass index and all-cause mortality in HUNT and UK biobank studies: revised non-linear Mendelian randomisation analyses.BMJ open · 2024Article
- Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.European journal of epidemiology · 2024Article
- DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.Biostatistics (Oxford, England) · 2024Article
- Unravelling the complex causal effects of substance use behaviours on common diseases.Communications medicine · 2024Article
- Causal relationship between psychological factors and hepatocellular carcinoma as revealed by Mendelian randomization.Journal of cancer research and clinical oncology · 2024Article
- Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants.Nature communications · 2023Article
- Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method.PLoS genetics · 2023Article
- Using genetic association data to guide drug discovery and development: Review of methods and applications.American journal of human genetics · 2023Review
- Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies.Human molecular genetics · 2022Article
- Guidelines for performing Mendelian randomization investigations: update for summer 2023.Wellcome open research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Causal inference is a critical step in improving our understanding of biological processes, and Mendelian randomization (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here, we propose PolyMR, an MR-based method that provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes, and found most of these (84%) to have causal effects that deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g., a 1 kg/m
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.