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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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 Text and Figures
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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