方法对比

	多组学eQTL分析	RNA-seq差异表达
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)	2008–2010 (RNA-seq DE methodology established)
提出者	GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Integrative genomic association analysis	Quantitative genomics pipeline
开创性文献	GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213.	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.
别名	multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	6	6
摘要	Multi-omics eQTL analysis maps genetic variants (SNPs or structural variants) to molecular phenotypes simultaneously across multiple omics layers — transcriptome, epigenome, proteome, and metabolome — in the same cohort. By linking genotype to gene expression and then tracing those effects through downstream molecular layers, the approach reveals how genetic variation propagates through the molecular machinery of a cell, yielding mechanistic insight that no single-omics eQTL study can provide.	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.