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機械学習支援型 eQTL解析×マルチオミクスeQTL解析×
分野バイオインフォマティクスバイオインフォマティクス
系統Process / pipelineProcess / pipeline
提唱年2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)
提唱者Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onwardGTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)
種類Statistical-computational genomics pipelineIntegrative genomic association analysis
原典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. link ↗GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213. link ↗
別名ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modelingmulti-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics
関連66
概要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.
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ScholarGate手法を比較: Machine learning-assisted expression quantitative trait loci analysis · Multi-omics eQTL analysis. 2026-06-17に以下より取得 https://scholargate.app/ja/compare