Genome Biology (Apr 2023)

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

  • Shuang Li,
  • Katharina T. Schmid,
  • Dylan H. de Vries,
  • Maryna Korshevniuk,
  • Corinna Losert,
  • Roy Oelen,
  • Irene V. van Blokland,
  • BIOS Consortium, sc-eQTLgen Consortium,
  • Hilde E. Groot,
  • Morris A. Swertz,
  • Pim van der Harst,
  • Harm-Jan Westra,
  • Monique G.P. van der Wijst,
  • Matthias Heinig,
  • Lude Franke

DOI
https://doi.org/10.1186/s13059-023-02897-x
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 37

Abstract

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Abstract 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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