Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Single-cell GWAS× | Повногеномне асоціативне дослідження (GWAS)× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2019–2022 (rapid emergence with large-scale scRNA-seq atlases) | 2005–2007 |
| Автор методу≠ | Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022 | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Тип≠ | Integrative genomic analysis pipeline | Observational genomic association study |
| Основоположне джерело≠ | 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 ↗ | Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678. link ↗ |
| Інші назви | sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Пов'язані | 6 | 6 |
| Підсумок≠ | 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. | A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition. |
| ScholarGateНабір даних ↗ |
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