Uncovering context-specific genetic-regulation of gene expression from single-cell RNA-sequencing using latent-factor models

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-...

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Bibliographic Details
Published inbioRxiv
Main Authors Strober, Benjamin J, Tayeb, Karl, Popp, Joshua, Guanghao Qi, Gordon, Mary Grace, Perez, Richard, Chun Jimmie Ye, Battle, Alexis
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 23.12.2022
Subjects
Cell culture
Environmental conditions
Gene expression
Peripheral blood
Quantitative trait loci
Transcriptomics
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Summary: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.Competing Interest StatementAB is a shareholder of Alphabet, Inc and a consultant for Third Rock Ventures.
DOI:10.1101/2022.12.22.521678