방법 비교

	Differential eQTL Analysis	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2007–2012	2008–2010 (RNA-seq DE methodology established)
창시자	Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Statistical genomics pipeline	Quantitative genomics pipeline
원전	Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
별칭	deQTL analysis, context-specific eQTL, interaction eQTL, conditional eQTL	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	6	6
요약	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.