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

Benjamin J Strober; Karl Tayeb; Joshua Popp; Guanghao Qi; M Grace Gordon; Richard Perez; Chun Jimmie Ye; Alexis Battle

doi:10.1186/s13059-023-03152-z

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

Genome Biol. 2024 Jan 22;25(1):28. doi: 10.1186/s13059-023-03152-z.

Authors

Benjamin J Strober¹, Karl Tayeb², Joshua Popp³, Guanghao Qi³, M Grace Gordon^{4

5

6

7}, Richard Perez⁶, Chun Jimmie Ye^{5

6

7

8

9}, Alexis Battle^{10

11

12}

Affiliations

¹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
² Department of Human Genetics, University of Chicago, Chicago, IL, USA.
³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
⁴ Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA.
⁵ Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA.
⁶ Institute for Human Genetics, University of California, San Francisco, CA, USA.
⁷ Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
⁸ Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA.
⁹ Chan-Zuckerberg Biohub, San Francisco, CA, USA.
¹⁰ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
¹¹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
¹² Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.

Abstract

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.

Keywords: Single-cell transcriptomics; eQTL.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Profiling*
Gene Expression Regulation*
Quantitative Trait Loci
Sequence Analysis, RNA
Transcriptome

Abstract

Publication types

MeSH terms

Grants and funding