مقایسهٔ روش‌ها

	تحلیل چند-اُمیکس eQTL	تحلیل غنی‌سازی مسیر چند-اُمیکس
حوزه	زیست‌اطلاعاتی	زیست‌اطلاعاتی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)	2014–2016 (multi-omics extension of enrichment methods established ~2005)
پدیدآور	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)
نوع	Integrative genomic association analysis	Integrative pathway analysis pipeline
منبع بنیادین	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.
نام‌های دیگر	multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
مرتبط	6	1
خلاصه	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.