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| 机器学习辅助的 eQTL 分析× | 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) | 2001 (term coined); widely adopted after 2005 |
| 提出者≠ | Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward | Ritsert C. Jansen & Jan-Peter Nap |
| 类型≠ | Statistical-computational genomics pipeline | Association mapping method |
| 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 ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| 别名 | ML-assisted eQTL analysis, ML eQTL mapping, deep learning eQTL, predictive eQTL modeling | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 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. | eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions. |
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