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| 단일 세포 eQTL 분석× | eQTL 분석× | |
|---|---|---|
| 분야 | 생물정보학 | 생물정보학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2020 | 2001 (term coined); widely adopted after 2005 |
| 창시자≠ | Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020) | Ritsert C. Jansen & Jan-Peter Nap |
| 유형≠ | Statistical genomics pipeline | Association mapping method |
| 원전≠ | Cuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810. link ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| 별칭 | sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| 관련 | 6 | 6 |
| 요약≠ | Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages. | 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 ↗ |
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