Abstract
Genome-wide association studies have identified thousands of genetic variants that are associated with disease1. Most of these variants have small effect sizes, but their downstream expression effects, so-called expression quantitative trait loci (eQTLs), are often large2 and celltype-specific3,4,5. To identify these celltype-specific eQTLs using an unbiased approach, we used single-cell RNA sequencing to generate expression profiles of ~25,000 peripheral blood mononuclear cells from 45 donors. We identified previously reported cis-eQTLs, but also identified new celltype-specific cis-eQTLs. Finally, we generated personalized co-expression networks and identified genetic variants that significantly alter co-expression relationships (which we termed ‘co-expression QTLs’). Single-cell eQTL analysis thus allows for the identification of genetic variants that impact regulatory networks.
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References
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
Brown, C. D., Mangravite, L. M. & Engelhardt, B. E. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 9, e1003649 (2013).
Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).
Fu, J. et al. Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet. 8, e1002431 (2012).
Kasela, S. et al. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLoS Genet. 13, e1006643 (2017).
Naranbhai, V. et al. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6, 7545 (2015).
Ishigaki, K. et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat. Genet. 49, 1120–1125 (2017).
Westra, H. J. et al. Cell specific eQTL analysis without sorting cells. PLoS Genet. 11, e1005223 (2015).
Venet, D., Pecasse, F., Maenhaut, C. & Bersini, H. Separation of samples into their constituents using gene expression data. Bioinformatics 17 (Suppl. 1), S279–S287 (2001).
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).
Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573, https://doi.org/10.1126/science.aah4573 (2017).
Wills, Q. F. et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat. Biotechnol. 31, 748–752 (2013).
Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).
Zhernakova, D. V. et al. DeepSAGE reveals genetic variants associated with alternative polyadenylation and expression of coding and non-coding transcripts. PLoS Genet. 9, e1003594 (2013).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).
Flutre, T., Wen, X., Pritchard, J. & Stephens, M. A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet. 9, e1003486 (2013).
Sul, J. H., Han, B., Ye, C., Choi, T. & Eskin, E. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS Genet. 9, e1003491 (2013).
Duong, D. et al. Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes. Bioinformatics 33, i67–i74 (2017).
Knowles, D. A. et al. Allele-specific expression reveals interactions between genetic variation and environment. Nat. Methods 14, 699–702 (2017).
van Dijk, D. et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. Preprint at bioRxiv https://doi.org/10.1101/111591 (2017).
Huang, M. et al. Gene expression recovery for single cell RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/138677 (2017).
Li, W. V. & Li, J. J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9, 997 (2018).
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
Simpson, E. H. The interpretation of interaction in contingency tables. J. R. Stat. Soc. Ser. B Methodol. 13, 238–241 (1951).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Rosser, Z. H., Balaresque, P. & Jobling, M. A. Gene conversion between the X chromosome and the male-specific region of the Y chromosome at a translocation hotspot. Am. J. Hum. Genet. 85, 130–134 (2009).
Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).
van de Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Acknowledgements
We are very grateful to all the volunteers who participated in this study. Moreover, we thank J. Dekens for arranging informed consent and contact with LifeLines. We thank A. Maatman and M. Platteel for their assistance in the lab. M.A.S. and L.F. are supported by grants from the Dutch Research Council (ZonMW-VIDI 917.164.455 to M.S. and ZonMW-VIDI 917.14.374 to L.F.), and L.F. is supported by an ERC Starting Grant, grant agreement 637640 (ImmRisk). The Biobank-Based Integrative Omics Studies (BIOS) Consortium is funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).
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M.G.P.v.d.W. generated the scRNA-seq data. M.G.P.v.d.W., H.B., and D.H.d.V. performed bioinformatics and statistical analyses. P.D. and the BIOS Consortium performed replication of co-expression QTLs. M.G.P.v.d.W. and L.F. designed the study and wrote the manuscript. M.A.S. and the LifeLines Cohort Study provided biomaterials, genotype data, and computational resources. All authors discussed the results and commented on the manuscript.
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Supplementary Figures 1–5, Supplementary Tables 5–7 and Supplementary Notes 1 and 2
Supplementary Table 1
scRNA-seq eQTL analysis and concordance check confined to previously reported top-eQTLs from whole blood DeepSAGE and RNA-seq data.
Supplementary Table 2
Genome-wide scRNA-seq eQTL analysis and replication of previously reported top-eQTLs from whole blood RNA-seq data.
Supplementary Table 3
Replication in purified cell type RNA-seq data of 19 eQTLs not found in the bulk-like PBMC scRNA-seq or whole blood RNA-seq data.
Supplementary Table 4
Co-expression QTLs in the CD4+ T cells.
Supplementary Table 8
Sample metadata (including sample name, sample batch/lane of chip, gender and age).
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van der Wijst, M.G.P., Brugge, H., de Vries, D.H. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat Genet 50, 493–497 (2018). https://doi.org/10.1038/s41588-018-0089-9
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DOI: https://doi.org/10.1038/s41588-018-0089-9
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