ArticlePloS one2013
The structure of a gene co-expression network reveals biological functions underlying eQTLs.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Overexpression of CHAF1A is associated with poor prognosis, tumor immunosuppressive microenvironment and treatment resistance.Frontiers in genetics · 2023Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways.PLoS computational biology · 2021Article
- Tissue specific expression of sialic acid metabolic pathway: role in GNE myopathy.Journal of muscle research and cell motility · 2021Article
- Low Expression of the Polycomb Protein RING1 Predicts Poor Prognosis in Human Breast Cancer.Frontiers in oncology · 2020Article
- TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.Journal of applied statistics · 2020Article
- Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis.Genes · 2019Review
- Network-based feature selection reveals substructures of gene modules responding to salt stress in rice.Plant direct · 2019Article
- Transcriptional Dysregulation Study Reveals a Core Network Involving the Progression of Alzheimer's Disease.Frontiers in aging neuroscience · 2019Article
- Prognostic Significance of Potential Immune Checkpoint Member HHLA2 in Human Tumors: A Comprehensive Analysis.Frontiers in immunology · 2019Article
- A new approach of gene co-expression network inference reveals significant biological processes involved in porcine muscle development in late gestation.Scientific reports · 2018Article
- Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process.Molecular & cellular proteomics : MCP · 2018Article
- Gene Co-expression Network Reveals Potential New Genes Related to Sugarcane Bagasse Degradation inFrontiers in bioengineering and biotechnology · 2018Article
- Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs.Scientific reports · 2017Article
- Deciphering mechanisms underlying the genetic variation of general production and liver quality traits in the overfed mule duck by pQTL analyses.Genetics, selection, evolution : GSE · 2017Article
- Network analysis and juvenile idiopathic arthritis (JIA): a new horizon for the understanding of disease pathogenesis and therapeutic target identification.Pediatric rheumatology online journal · 2016Review
- Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction.Frontiers in physiology · 2016Article
- Article
- System model network for adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance.PLoS computational biology · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.
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