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

Autor: Strober BJ; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Tayeb K; Department of Human Genetics, University of Chicago, Chicago, IL, USA., Popp J; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA., Qi G; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA., Gordon MG; Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA.; Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA.; Institute for Human Genetics, University of California, San Francisco, CA, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA., Perez R; Institute for Human Genetics, University of California, San Francisco, CA, USA., Ye CJ; Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA.; Institute for Human Genetics, University of California, San Francisco, CA, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.; Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA.; Chan-Zuckerberg Biohub, San Francisco, CA, USA., Battle A; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2024 Jan 22; Vol. 25 (1), pp. 28. Date of Electronic Publication: 2024 Jan 22.
DOI: 10.1186/s13059-023-03152-z
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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