方法对比
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| 多组学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≠ | 2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017) | 2001 (term coined); widely adopted after 2005 |
| 提出者≠ | GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020) | Ritsert C. Jansen & Jan-Peter Nap |
| 类型≠ | Integrative genomic association analysis | Association mapping method |
| 开创性文献≠ | GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213. 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 | multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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