Порівняння методів

	Мультиоміксний аналіз eQTL	Байєсівський аналіз eQTL
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)	2000s–2010s
Автор методу	GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context
Тип	Integrative genomic association analysis	Probabilistic genomic association method
Основоположне джерело	GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.
Інші назви	multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping
Пов'язані	6	6
Підсумок	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.	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.