Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Netwerkgebaseerde Epigenoom-Brede Associatiestudie (Network EWAS)× | Netwerkgebaseerde GWAS× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2010s, consolidating 2012–2018 | 2011–2013 (early tools); mature framework by 2015 |
| Grondlegger≠ | Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008) | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups |
| Type≠ | Integrative epigenomic analysis | Network-augmented association analysis |
| Oorspronkelijke bron≠ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. link ↗ | 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 ↗ |
| Aliassen | network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS |
| Verwant | 6 | 6 |
| Samenvatting≠ | Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways. | 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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