So sánh phương pháp

	GWAS dựa trên mạng	Phân tích eQTL dựa trên mạng
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2011–2013 (early tools); mature framework by 2015	2008–2013 (network-integrated extensions of eQTL mapping)
Người khởi xướng	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups	Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others
Loại	Network-augmented association analysis	Statistical genomics / network analysis pipeline
Công trình gốc	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.	Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638.
Tên gọi khác	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS	network eQTL, network-integrated eQTL mapping, graph-based eQTL analysis, eQTL network analysis
Liên quan	6	5
Tóm tắt	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.	Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts.