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| eQTL 분석× | RNA-seq 차등 발현× | |
|---|---|---|
| 분야 | 생물정보학 | 생물정보학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2001 (term coined); widely adopted after 2005 | 2008–2010 (RNA-seq DE methodology established) |
| 창시자≠ | Ritsert C. Jansen & Jan-Peter Nap | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| 유형≠ | Association mapping method | Quantitative genomics pipeline |
| 원전≠ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| 별칭 | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| 관련 | 6 | 6 |
| 요약≠ | 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. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGate데이터셋 ↗ |
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