ArticleAmerican journal of human genetics2024
A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics.
Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A flexible and unified framework for single- and multi-outcome Mendelian randomization using summary statistics.American journal of human genetics · 2026Article
- Human genetics suggests differing causal pathways from HMGCR inhibition to coronary artery disease and type 2 diabetes.International journal of epidemiology · 2026Article
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Specific plasma lipid species in children are causally associated with kawasaki disease: a mendelian randomization analysis.Italian journal of pediatrics · 2025Article
- A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias.Briefings in bioinformatics · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multi-omics investigation of metabolic dysregulation in depression: integrating metabolomics, weighted gene co-expression network analysis, and mendelian randomization.Frontiers in psychiatry · 2025Article
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3 authors.
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Abstract
Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.
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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.