Bandingkan kaedah

	Epigenome-Wide Association Study (Network EWAS) berasaskan Rangkaian	Epigenome-Wide Association Study (EWAS)
Bidang	Bioinformatik	Bioinformatik
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2010s, consolidating 2012–2018	2008–2011 (term and framework established c. 2011)
Pengasas	Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)	Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application
Jenis	Integrative epigenomic analysis	Population-scale epigenomic association study
Sumber perintis	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.	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.
Alias	network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis	EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study
Berkaitan	6	5
Ringkasan	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.	An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.