ArticleHuman molecular genetics2014
Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.
Article in Human molecular genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma.PloS one · 2015Pooled it
- Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes.Schizophrenia bulletin · 2014Pooled it
- Quantifying pathway perturbations based on gene network rewiring in sepsis progression toward death or survival.Frontiers in bioinformatics · 2026Article
- Exploring drought-responsive crucial genes iniScience · 2022Article
- SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.BMC bioinformatics · 2022Article
- Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.PLoS genetics · 2022Article
- M2PP: a novel computational model for predicting drug-targeted pathogenic proteins.BMC bioinformatics · 2022Article
- Comparative study of disease progression for heart failure with different etiologies via time-ordered network analysis.American journal of translational research · 2022Article
- Statistical Identification of Important Nodes in Biological Systems.Journal of systems science and complexity · 2021Article
- Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome.Frontiers in public health · 2021Article
- Statistical Identification of Important Nodes in Biological Systems.Journal of systems science and complexity · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- Article
- Expression correlation attenuates within and between key signaling pathways in chronic kidney disease.BMC medical genomics · 2020Article
- Sensitivity of comorbidity network analysis.JAMIA open · 2020Article
- Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies.PLoS genetics · 2020Article
- Article
- Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.Genome medicine · 2019Article
- A statistical framework for cross-tissue transcriptome-wide association analysis.Nature genetics · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Although Genome Wide Association Studies (GWAS) have identified many susceptibility loci for common diseases, they only explain a small portion of heritability. It is challenging to identify the remaining disease loci because their association signals are likely weak and difficult to identify among millions of candidates. One potentially useful direction to increase statistical power is to incorporate functional genomics information, especially gene expression networks, to prioritize GWAS signals. Most current methods utilizing network information to prioritize disease genes are based on the 'guilt by association' principle, in which networks are treated as static, and disease-associated genes are assumed to locate closer with each other than random pairs in the network. In contrast, we propose a novel 'guilt by rewiring' principle. Studying the dynamics of gene networks between controls and patients, this principle assumes that disease genes more likely undergo rewiring in patients, whereas most of the network remains unaffected in disease condition. To demonstrate this principle, we consider the changes of co-expression networks in Crohn's disease patients and controls, and how network dynamics reveals information on disease associations. Our results demonstrate that network rewiring is abundant in the immune system, and disease-associated genes are more likely to be rewired in patients. To integrate this network rewiring feature and GWAS signals, we propose to use the Markov random field framework to integrate network information to prioritize genes. Applications in Crohn's disease and Parkinson's disease show that this framework leads to more replicable results, and implicates potentially disease-associated 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.