Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Netwerkgebaseerde GWAS× | Netwerk-gebaseerde eQTL-analyse× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2011–2013 (early tools); mature framework by 2015 | 2008–2013 (network-integrated extensions of eQTL mapping) |
| Grondlegger≠ | Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups | Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others |
| Type≠ | Network-augmented association analysis | Statistical genomics / network analysis pipeline |
| Oorspronkelijke bron≠ | 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 ↗ | Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638. link ↗ |
| Aliassen | network GWAS, gene network GWAS, network-informed GWAS, NbGWAS | network eQTL, network-integrated eQTL mapping, graph-based eQTL analysis, eQTL network analysis |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | 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. | Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts. |
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