Sammenlign metoder

	Differential eQTL Analyse	Single-cell eQTL-analyse
Fagområde	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2007–2012	2020
Ophavsperson	Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012	Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)
Type	Statistical genomics pipeline	Statistical genomics pipeline
Oprindelig kilde	Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.	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.
Aliasser	deQTL analysis, context-specific eQTL, interaction eQTL, conditional eQTL	sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL
Relaterede	6	6
Resumé	Differential eQTL analysis identifies genetic variants — expression quantitative trait loci — whose regulatory effect on gene expression varies systematically across biological conditions such as tissue types, disease states, developmental stages, or treatment groups. By testing for statistical interactions between genotype and condition, the method pinpoints loci where the same allele has different transcriptional consequences depending on context, revealing the molecular basis of condition-specific gene regulation.	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.