Methoden vergelijken

	Netwerkgebaseerde GWAS	Pathway-verrijkingsanalyse
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2011–2013 (early tools); mature framework by 2015	2003–2005
Grondlegger	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Network-augmented association analysis	Statistical functional annotation method
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.	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.
Aliassen	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Verwant	6	6
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.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.