手法を比較

	Single-cell eQTL Analysis	シングルセルGWAS
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2020	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)
提唱者	Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)	Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022
種類	Statistical genomics pipeline	Integrative genomic analysis pipeline
原典	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.	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.
別名	sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL	sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis
関連	6	6
概要	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.	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.