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

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

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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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.

Originele taal-2English
Aantal pagina's37
TijdschriftGenome Biology
StatusE-pub ahead of print - 18-apr.-2023

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