方法对比

	时间序列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	2010s–2019 (concept established earlier; dynamic framework formalized ~2019)	2005–2007
提出者	Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019)	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
类型	Genetic mapping method	Observational genomic association study
开创性文献	Fair, B. J., et al. (2020). Gene expression variability in human and chimpanzee populations share common determinants. eLife, 9, e59929.	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	dynamic eQTL analysis, longitudinal eQTL mapping, ts-eQTL, temporal eQTL	GWAS, genome-wide association analysis, whole-genome association study, WGAS
相关	2	6
摘要	Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time points — revealing the temporal architecture of the genome's regulatory program in processes such as differentiation, disease progression, and environmental response.	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.