ArticleGenetic epidemiology2022
Penalized mediation models for multivariate data.
Article in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 7 citations in OpenAlex.
- The interplay of sleep characteristics with health factors and gut microbiome.Nature communications · 2026Article
- HILAMA: High-dimensional multi-omics mediation analysis with latent confounding.BMC medical research methodology · 2025Article
- Single-cell Analysis of Intracellular Transport and Expression of Cell Surface Proteins.bioRxiv : the preprint server for biology · 2025Article
- Epigenome-wide association study on the plasma metabolome suggests self-regulation of the glycine and serine pathway through DNA methylation.Clinical epigenetics · 2024Article
- Comparison of regmed and BayesNetty for exploring causal models with many variables.Genetic epidemiology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Statistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high-dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single-nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.
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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.