Methoden vergelijken

	Single-cell GWAS	eQTL-analyse
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)	2001 (term coined); widely adopted after 2005
Grondlegger	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
Oorspronkelijke bron	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.	Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.
Aliassen	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
Verwant	6	6
Samenvatting	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.