Linganisha mbinu

	GWAS yenye msingi wa mtandao	Uchambuzi wa eQTL
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2011–2013 (early tools); mature framework by 2015	2001 (term coined); widely adopted after 2005
Mwanzilishi	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups	Ritsert C. Jansen & Jan-Peter Nap
Aina	Network-augmented association analysis	Association mapping method
Chanzo asilia	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.
Majina mbadala	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS	eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study
Zinazohusiana	6	6
Muhtasari	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.