Samenvatting
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-2 | English |
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Artikelnummer | 80 |
Aantal pagina's | 37 |
Tijdschrift | Genome Biology |
Volume | 24 |
DOI's | |
Status | Published - 18-apr.-2023 |
Vingerafdruk
Duik in de onderzoeksthema's van 'Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data'. Samen vormen ze een unieke vingerafdruk.Datasets
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Additional file 6 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 8-apr.-2023
DOI: 10.6084/m9.figshare.22655414.v1, https://springernature.figshare.com/articles/dataset/Additional_file_6_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655414/1
Dataset
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Additional file 4 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-apr.-2023
DOI: 10.6084/m9.figshare.22655405.v1, https://springernature.figshare.com/articles/dataset/Additional_file_4_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655405/1
Dataset