ArticleJournal of medical genetics2006
Predicting disease genes using protein-protein interactions.
Article in Journal of medical genetics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 245 papers, 3 of them syntheses that pooled it.
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Who cites it
245 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Common variants in the MKL1 gene confer risk of schizophrenia.Schizophrenia bulletin · 2015Pooled it
- Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes.Schizophrenia bulletin · 2014Pooled it
- Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.American journal of human genetics · 2013Pooled it
- TOMOC: a topology-driven multi-objective evolutionary framework for robust overlapping protein complex detection in noisy PPI networks.BMC bioinformatics · 2026Article
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- Identifying genetic and cellular connections and distinctions among 15 autoimmune diseases using an in-silico approach.Communications medicine · 2026Article
- In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network.Schizophrenia (Heidelberg, Germany) · 2026Article
- Multimodal graph, surface, and language-based model for protein protein interaction prediction.Scientific reports · 2026Article
- Prediction of protein-protein interactions using point transformer and spherical Convex Hull graphs.Computational and structural biotechnology journal · 2026Article
- Network physiology in space: vision and perspectives on exploring physiological networks during spaceflight for the benefit of life on earth.Frontiers in network physiology · 2026Article
- Protein-Protein Interactions in Papillary and Nonpapillary Urothelial Carcinoma Architectures: Comparative Study.JMIR bioinformatics and biotechnology · 2025Article
- A network model for patient-derived drug response in breast cancer integrating multi-omics datasets.bioRxiv : the preprint server for biology · 2025Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.Scientific reports · 2025Article
- The Atlas of Protein-Protein Interactions in Cancer (APPIC)-a webtool to visualize and analyze cancer subtypes.NAR cancer · 2025Article
- Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network andInternational journal of molecular sciences · 2024Article
- Mendelian randomization reveals plasminogen as a common therapeutic target for myocardial infarction and atrial fibrillation.Journal of cardiovascular and thoracic research · 2024Article
- Adopting Integrated Bioinformatics and Systems Biology Approaches to Pinpoint the COVID-19 Patients' Risk Factors That Uplift the Onset of Posttraumatic Stress Disorder.Bioinformatics and biology insights · 2024Article
- Strategic pathway analysis for dual management of epilepsy and comorbid depression: a systems biology perspective.In silico pharmacology · 2024Article
185 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes.
objectiveTo investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases.
methods72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes.
resultsAlmost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci.
conclusionsExploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.
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