방법 비교

	네트워크 기반 전장 후성유전체 연관 분석 (Network EWAS)	네트워크 기반 GWAS
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2010s, consolidating 2012–2018	2011–2013 (early tools); mature framework by 2015
창시자	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
유형	Integrative epigenomic analysis	Network-augmented association analysis
원전	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.	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 EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS
관련	6	6
요약	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.