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Li, S.* ; Schmid, K. ; de Vries, D.H.* ; Korshevniuk, M.* ; Losert, C. ; Oelen, R.* ; van Blokland, I.V.* ; Groot, H.E.* ; Swertz, M.A.* ; van der Harst, P.* ; Westra, H.J.* ; van der Wijst, M.G.P.* ; Heinig, M. ; Franke, L.*

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.

Genome Biol. 24:80 (2023)

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	Open Access Gold

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BACKGROUND: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Co-expression Qtls ; Eqtl ; Scrna-seq; Proteins; Challenges; Thousands; Drivers

ISSN (print) / ISBN 1474-760X

e-ISSN 1465-6906

Journal Genome Biology

Quellenangaben Volume: 24, Issue: 1, Article Number: 80

Publisher BioMed Central

Publishing Place Campus, 4 Crinan St, London N1 9xw, England

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants
Federal Ministry of Education and Research (BMBF) within the German Center for Cardiovascular Research (DZHK)
Chan Zuckerberg Initiative
NWO-VIDI
ZonMW-VICI
ZonMW-VIDI
Horizon2020
NWO-VENI
Projekt DEAL.
Netherlands Organization for Scientific research (NWO)