方法对比

	差异性 eQTL 分析	贝叶斯eQTL分析
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2007–2012	2000s–2010s
提出者	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
类型	Statistical genomics pipeline	Probabilistic genomic association method
开创性文献	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.
别名	deQTL analysis, context-specific eQTL, interaction eQTL, conditional eQTL	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping
相关	6	6
摘要	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.