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| Netzwerkbasierte Analyse von Kopienzahlvariationen× | Netzwerkbasierte Genomweite Assoziationsstudien× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2011–2015 | 2011–2013 (early tools); mature framework by 2015 |
| Urheber≠ | Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2) | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups |
| Typ≠ | Computational network analysis pipeline | Network-augmented association analysis |
| Wegweisende Quelle≠ | Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385. DOI ↗ | Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594. link ↗ |
| Aliasnamen | network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies. | Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone. |
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