方法对比
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| 机器学习辅助的 eQTL 分析× | 多组学eQTL分析× | |
|---|---|---|
| 领域 | 生物信息学 | 生物信息学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001) | 2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017) |
| 提出者≠ | 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) |
| 类型≠ | Statistical-computational genomics pipeline | Integrative genomic association analysis |
| 开创性文献≠ | 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 ↗ |
| 别名 | 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 |
| 相关 | 6 | 6 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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