ArticleBiometrics2024
Estimation of a genetic Gaussian network using GWAS summary data.
Article in Biometrics, 2024. 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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Who cites it
5 citing papers in PubMed.
- Recalibrating Mendelian randomization under winner's curse, sample structure and polygenicity.medRxiv : the preprint server for health sciences · 2026Article
- Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning.bioRxiv : the preprint server for biology · 2025Article
- A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias.Briefings in bioinformatics · 2025Article
- HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs.Bioinformatics advances · 2025Article
- On network deconvolution for undirected graphs.Biometrics · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators.
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