Salīdzināt metodes

	Diferenciālā eQTL analīze	Ģenoma plaša asociācijas pētījums (GWAS)
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2007–2012	2005–2007
Autors	Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012	Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)
Tips	Statistical genomics pipeline	Observational genomic association study
Pirmavots	Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.	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.
Citi nosaukumi	deQTL analysis, context-specific eQTL, interaction eQTL, conditional eQTL	GWAS, genome-wide association analysis, whole-genome association study, WGAS
Saistītās	6	6
Kopsavilkums	Differential eQTL analysis identifies genetic variants — expression quantitative trait loci — whose regulatory effect on gene expression varies systematically across biological conditions such as tissue types, disease states, developmental stages, or treatment groups. By testing for statistical interactions between genotype and condition, the method pinpoints loci where the same allele has different transcriptional consequences depending on context, revealing the molecular basis of condition-specific gene regulation.	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.