Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
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Abstract
Genome-wide association studies have identified thousands of genetic variants that are associated with disease 1 . Most of these variants have small effect sizes, but their downstream expression effects, so-called expression quantitative trait loci (eQTLs), are often large 2 and celltype-specific 3-5. 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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The eQTL framework has clear limitations, including reliance on naturally occurring human genetic variation, linkage disequilibrium, and restriction to cell types and tissues practically obtainable from large numbers of individuals.
“On the other hand, the framework has clear limitations, including its reliance on naturally occurring human genetic variation (most enhancers do not harbour common variants that substantially perturb their activity), linkage disequilibrium [G] (multiple variants in a haplotype block may equivalently explain an association), and restriction to cell types and tissues that can be practically obtained”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [138]
The main text (page unavailable) reports near-perfect linkage disequilibrium between an eQTL SNP and a diabetes-associated SNP, supporting the cited point that linkage disequilibrium is relevant to interpreting associations. It does not state that multiple variants in a haplotype block may equivalently explain an association. The Online Methods describe analysis of common variants in donor blood cells, but do not establish the claim about most enhancers or a general restriction to practically obtainable tissues. (LLM, conf=0.76)
Experimental — accuracy not yet measuredEarlier studies limited differential expression testing to predefined candidate genes or grouped genes into signatures.
“differential gene expression testing was restricted to pre-defined candidate genes7,9,10 or genes were grouped into gene signatures3,4,6,8.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [10]
In our own data set and published studies, doublet rates are slightly higher than shown in reference 77.
“However, we observe in our own data set as well as in published studies47,78 slightly higher doublet rates than shown in77.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [78]
The default doublet rate is based on 10X Genomics reference values and is a lower bound compared to doublet rates estimated for our own data and reported by other studies.
“The default for the doublet rate is based on reference values from 10X Genomics and is a lower bound compared to the doublet rates we estimate for our own data and to rates reported by other studies47,78.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [78]
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- Authors
- Study, L. C., van der Wijst, M. G. P., BIOS Consortium, Brugge, H., de Vries, D. H., Deelen, P., Swertz, M. A., & Franke, L.
- Affiliations
- University Medical Center Groningen (NL)
- Corresponding author
- Franke, Lude
- Venue
- Nature Genetics
- Published
- 2018-04
- Volume
- 50
- Issue
- 4
- Pages
- 493-497
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5905669
- OpenAlex
- W2794458614
- Cited by
- 399
- References
- 38
- Citations by year
- 2026: 32 · 2025: 52 · 2024: 61 · 2023: 52 · 2022: 48 · 2021: 68 · 2020: 46 · 2019: 27 · 2018: 11 · 2017: 1 · 2013: 1
- FWCI
- 23.77
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- ZonMw (184.021.007, 917.14.374)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (184021007, 917.164.455, 917.14.374, VIDI 917.164.455, 637640, NWO 184.021.007, ZonMW-VIDI 917.14.374)
- Universitair Medisch Centrum Groningen
- H2020 European Research Council (637640)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- cis-eQTL, single-cell RNA sequencing, peripheral blood mononuclear cells, co-expression network, gene regulatory networks, genetic variants, expression quantitative trait loci
- MeSH terms
- Epistasis, Genetic, Humans, Leukocytes, Mononuclear, Genetic Variation, Sequence Analysis, RNA, Quantitative Trait Loci, Gene Regulatory Networks, Genome-Wide Association Study, Single-Cell Analysis, Transcriptome
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