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Analisis eQTL Sel Tunggal×Analisis Asosiasi Genetik Spesifik Tipe Sel Single-cell GWAS×
BidangBioinformatikaBioinformatika
KeluargaProcess / pipelineProcess / pipeline
Tahun asal20202019–2022 (rapid emergence with large-scale scRNA-seq atlases)
PencetusCuomo 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
TipeStatistical genomics pipelineIntegrative genomic analysis pipeline
Sumber perintisCuomo, 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 ↗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. link ↗
Aliassc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTLsc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis
Terkait66
RingkasanSingle-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.
ScholarGateSet data
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  1. v1
  2. 2 Sumber
  3. PUBLISHED

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ScholarGateBandingkan metode: Single-cell eQTL analysis · Single-cell GWAS. Diakses 2026-06-18 dari https://scholargate.app/id/compare