방법 비교

	Differential eQTL Analysis	베이지안 eQTL 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2007–2012	2000s–2010s
창시자	Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context
유형	Statistical genomics pipeline	Probabilistic genomic association method
원전	Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
별칭	deQTL analysis, context-specific eQTL, interaction eQTL, conditional eQTL	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping
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.	Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled fine-mapping of causal variants and coherent uncertainty quantification across thousands of gene-SNP pairs simultaneously.