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	Analiza Bayesowska eQTL	Analiza eQTL pojedynczych komórek
Dziedzina	Bioinformatyka	Bioinformatyka
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2000s–2010s	2020
Twórca	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context	Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)
Typ	Probabilistic genomic association method	Statistical genomics pipeline
Źródło pierwotne	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.	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.
Inne nazwy	Bayesian eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping	sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL
Pokrewne	6	6
Podsumowanie	Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled fine-mapping of causal variants and coherent uncertainty quantification across thousands of gene-SNP pairs simultaneously.	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.