방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Multi-omics eQTL Analysis× | eQTL 분석× | |
|---|---|---|
| 분야 | 생물정보학 | 생물정보학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017) | 2001 (term coined); widely adopted after 2005 |
| 창시자≠ | GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020) | Ritsert C. Jansen & Jan-Peter Nap |
| 유형≠ | Integrative genomic association analysis | Association mapping method |
| 원전≠ | 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 ↗ |
| 별칭 | multi-omics molQTL, multi-layer eQTL, integrated eQTL analysis, xQTL multi-omics | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 관련 | 6 | 6 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|