Vertaile menetelmiä

	Verkostopohjainen GWAS	Kopiolukuvaihtelun analyysi – CNV:n havaitseminen ja tulkinta
Tieteenala	Bioinformatiikka	Bioinformatiikka
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2011–2013 (early tools); mature framework by 2015	1998–2006
Kehittäjä	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Tyyppi	Network-augmented association analysis	Genomic structural variant detection pipeline
Alkuperäislähde	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.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Rinnakkaisnimet	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Liittyvät	6	6
Tiivistelmä	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.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.