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	Analiza eQTL wspomagana uczeniem maszynowym	Analiza wieloomikowa eQTL
Dziedzina	Bioinformatyka	Bioinformatyka
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)	2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)
Twórca	Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward	GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)
Typ	Statistical-computational genomics pipeline	Integrative genomic association analysis
Źródło pierwotne	Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098.	GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213.
Inne nazwy	ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modeling	multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics
Pokrewne	6	6
Podsumowanie	Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation.	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.