| Title: |
Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data |
| Authors: |
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; BIOS Consortium, sc-eQTLgen Consortium; Cardiologie; Gezonde Vaten; Circulatory Health |
| Publication Year: |
2023 |
| Subject Terms: |
Co-expression QTLs; eQTL; scRNA-seq; Ecology; Evolution; Behavior and Systematics; Genetics; Cell Biology |
| 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text/plain |
| Language: |
English |
| ISSN: |
1474-7596 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/459058 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/459058 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.FF0DD0EF |
| Database: |
BASE |