방법 비교

	네트워크 기반 복사본 수 변이 분석	네트워크 기반 GWAS
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2011–2015	2011–2013 (early tools); mature framework by 2015
창시자	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups
유형	Computational network analysis pipeline	Network-augmented association analysis
원전	Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.	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.
별칭	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS
관련	6	6
요약	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.