Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Enkeltcelle-GWAS× | eQTL-analyse× | |
|---|---|---|
| Fagfelt | Bioinformatikk | Bioinformatikk |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 2019–2022 (rapid emergence with large-scale scRNA-seq atlases) | 2001 (term coined); widely adopted after 2005 |
| Opphavsperson≠ | Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022 | Ritsert C. Jansen & Jan-Peter Nap |
| Type≠ | Integrative genomic analysis pipeline | Association mapping method |
| Opprinnelig kilde≠ | Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234. link ↗ | Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391. DOI ↗ |
| Alias | sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis | eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study |
| Relaterte | 6 | 6 |
| Sammendrag≠ | Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects. | 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. |
| ScholarGateDatasett ↗ |
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