ArticleScientific reports2019
Inferring Gene Regulatory Networks from a Population of Yeast Segregants.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Quantitative omnigenic model discovers interpretable genome-wide associations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Implementation of Bayesian networks and Bayesian inference using a CuNanoscale advances · 2024Article
- SIGNET: transcriptome-wide causal inference for gene regulatory networks.Scientific reports · 2023Article
- SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory Networks.Research square · 2023Article
- Analysis of the mechanism of propagated sensation along meridians based on gene expression profiles.Journal of traditional and complementary medicine · 2023Article
- Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat.Genes · 2021Article
- Variation in Pleiotropic Hub Gene Expression Is Associated with Interspecific Differences in Head Shape and Eye Size in Drosophila.Molecular biology and evolution · 2021Article
- Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes.Journal of the Royal Society, Interface · 2021Article
- A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks inComputational and structural biotechnology journal · 2021Article
- Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression.Evolutionary bioinformatics online · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately controlled by transcription factors driven by trans-acting expression quantitative trait loci. Altogether, the integration of documented transcription factor regulatory associations with subnetworks defined by a system of structural equations using quantitative trait loci data is an effective means to delineate the transcriptional control of biological pathways.
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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.