Methoden vergleichen
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| Netzwerkbasierte Genomweite Assoziationsstudien× | Gen-Satz-Anreicherungsanalyse (GSEA)× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2011–2013 (early tools); mature framework by 2015 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Urheber≠ | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Typ≠ | Network-augmented association analysis | Functional genomics / enrichment analysis |
| Wegweisende Quelle≠ | 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 ↗ | Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI ↗ |
| Aliasnamen | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold. |
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