Uncovering context-specific genetic-regulation of gene expression from single-cell RNA-sequencing using latent-factor models
Strober, B. J.; Tayeb, K.; Popp, J.; Qi, G.; Gordon, M. G.; Perez, R.; Ye, C. J.; Battle, A.
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Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.
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