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Epigenome-Wide Association Study (Network EWAS) berasaskan Rangkaian×GWAS berasaskan rangkaian×
BidangBioinformatikBioinformatik
KeluargaProcess / pipelineProcess / pipeline
Tahun asal2010s, consolidating 2012–20182011–2013 (early tools); mature framework by 2015
PengasasAdapted 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
JenisIntegrative epigenomic analysisNetwork-augmented association analysis
Sumber perintisRakyan, 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. link ↗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 ↗
Aliasnetwork EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysisnetwork GWAS, gene network GWAS, network-informed GWAS, NbGWAS
Berkaitan66
RingkasanNetwork-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.
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ScholarGateBandingkan kaedah: Network-based epigenome-wide association study · Network-based GWAS. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare