방법 비교

	Multi-omics eQTL Analysis	다중 오믹스 경로 농축 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)	2014–2016 (multi-omics extension of enrichment methods established ~2005)
창시자	GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)
유형	Integrative genomic association analysis	Integrative pathway analysis pipeline
원전	GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213.	Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162.
별칭	multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
관련	6	1
요약	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.	Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss.