ArticleeLife2023
Sparse dimensionality reduction approaches in Mendelian randomisation with highly correlated exposures.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.Genetic epidemiology · 2026Article
- The Omnicausal Model Reveals the Highly Polyfactorial Nature of Complex Diseases.Genetic epidemiology · 2026Article
- Integration of latent factor analysis into multivariable Mendelian randomization.European journal of epidemiology · 2026Article
- Causal impact of air pollution on head and neck cancer: a Mendelian randomization study.Discover oncology · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Lipid Transporter ABCA1 in Diabetic Polyneuropathy: Potential Mediation by Circulating Metabolites.Journal of pain research · 2025Article
- A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics.American journal of human genetics · 2024Article
- An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset.PLoS genetics · 2023Article
- Using genetic association data to guide drug discovery and development: Review of methods and applications.American journal of human genetics · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of MVMR estimates thus depends heavily on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures with the aim of providing more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted
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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.