方法对比

	网络驱动的 eQTL 分析	全基因组关联研究 (GWAS)
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2008–2013 (network-integrated extensions of eQTL mapping)	2005–2007
提出者	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	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
类型	Statistical genomics / network analysis pipeline	Observational genomic association study
开创性文献	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.	Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.
别名	network eQTL, network-integrated eQTL mapping, graph-based eQTL analysis, eQTL network analysis	GWAS, genome-wide association analysis, whole-genome association study, WGAS
相关	5	6
摘要	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.	A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition.