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Fig. 3 | Genome Biology

Fig. 3

From: SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Fig. 3

SURGE applied to PBMC single-cell eQTL data. A SURGE latent context loadings of pseudocells (y-axis) stratified by cell type (color) according to marker gene expression profiles for each of the SURGE latent context 1, 2, and 4 (x-axis). B Colocalization between SURGE latent context 4 interaction eQTL variant chr6:26370572:C:T for BTN3A2 and GWAS signal for SLE. C Number of colocalizations identified (PPH4 > .95; y-axis) between various 14 independent GWAS studies (x-axis) and eQTLs identified from pseudocells. The number of colocalizations using standard eQTLs shown in grey, the number of unique colocalizations using expression PC interaction eQTLs aggregated across the top 6 expression PC shown in yellow, and the number of unique colocalizations using SURGE interaction eQTLs, aggregated across the 6 SURGE latent contexts, shown in blue. D, E S-LDSC enrichment (y-axis) of squared standard eQTL effect sizes (black line) and SURGE predicted squared eQTL effect size at a specific SURGE latent context value (pink line at a specific x-axis position) within D monocyte count and E celiac disease heritability. SURGE predicted eQTL effect sizes at a particular SURGE latent context value was calculated at 200 equally spaced positions along the range of SURGE latent context values. Black dashed line represents 95% confidence on the standard eQTL S-LDSC enrichment. Light pink region depicts 95% confidence on the SURGE predicted eQTL S-LDSC enrichment

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