Salīdzināt metodes

	Tīklā balstīta GWAS	eQTL analīze
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2011–2013 (early tools); mature framework by 2015	2001 (term coined); widely adopted after 2005
Autors	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups	Ritsert C. Jansen & Jan-Peter Nap
Tips	Network-augmented association analysis	Association mapping method
Pirmavots	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.	Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.
Citi nosaukumi	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS	eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study
Saistītās	6	6
Kopsavilkums	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.	eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions.