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

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-eQ...

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Autores principales: Li, Shuang, Schmid, Katharina T., de Vries, Dylan H., Korshevniuk, Maryna, Losert, Corinna, Oelen, Roy, van Blokland, Irene V., Groot, Hilde E., Swertz, Morris A., van der Harst, Pim, Westra, Harm-Jan, van der Wijst, Monique G.P., Heinig, Matthias, Franke, Lude
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111756/
https://www.ncbi.nlm.nih.gov/pubmed/37072791
http://dx.doi.org/10.1186/s13059-023-02897-x
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author Li, Shuang
Schmid, Katharina T.
de Vries, Dylan H.
Korshevniuk, Maryna
Losert, Corinna
Oelen, Roy
van Blokland, Irene V.
Groot, Hilde E.
Swertz, Morris A.
van der Harst, Pim
Westra, Harm-Jan
van der Wijst, Monique G.P.
Heinig, Matthias
Franke, Lude
author_facet Li, Shuang
Schmid, Katharina T.
de Vries, Dylan H.
Korshevniuk, Maryna
Losert, Corinna
Oelen, Roy
van Blokland, Irene V.
Groot, Hilde E.
Swertz, Morris A.
van der Harst, Pim
Westra, Harm-Jan
van der Wijst, Monique G.P.
Heinig, Matthias
Franke, Lude
author_sort Li, Shuang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02897-x.
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spelling pubmed-101117562023-04-19 Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data Li, Shuang Schmid, Katharina T. de Vries, Dylan H. Korshevniuk, Maryna Losert, Corinna Oelen, Roy van Blokland, Irene V. Groot, Hilde E. Swertz, Morris A. van der Harst, Pim Westra, Harm-Jan van der Wijst, Monique G.P. Heinig, Matthias Franke, Lude Genome Biol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02897-x. BioMed Central 2023-04-18 /pmc/articles/PMC10111756/ /pubmed/37072791 http://dx.doi.org/10.1186/s13059-023-02897-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Shuang
Schmid, Katharina T.
de Vries, Dylan H.
Korshevniuk, Maryna
Losert, Corinna
Oelen, Roy
van Blokland, Irene V.
Groot, Hilde E.
Swertz, Morris A.
van der Harst, Pim
Westra, Harm-Jan
van der Wijst, Monique G.P.
Heinig, Matthias
Franke, Lude
Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title_full Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title_fullStr Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title_full_unstemmed Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title_short Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
title_sort identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111756/
https://www.ncbi.nlm.nih.gov/pubmed/37072791
http://dx.doi.org/10.1186/s13059-023-02897-x
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