Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз eQTL на рівні окремих клітин× | Повногеномне асоціативне дослідження (GWAS)× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2020 | 2005–2007 |
| Автор методу≠ | Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020) | Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007) |
| Тип≠ | Statistical genomics pipeline | Observational genomic association study |
| Основоположне джерело≠ | 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 ↗ | 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-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL | GWAS, genome-wide association analysis, whole-genome association study, WGAS |
| Пов'язані | 6 | 6 |
| Підсумок≠ | 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. | 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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