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

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.

Unveiling the role of Tdark genes in genetic diseases and phenotypes through bioinformatics-based functional enrichment and network analyses

“…Furthermore, these eQTLs provide potential markers for further investigation into the mechanisms underlying the regulation of hub dark genes [84]. Investigating the impact of these eQTLs on gene expression and protein function can provide insights into the molecular pathways regulating hub dark genes and potentially identify therapeutic targets for diseases associated with these genes [85][86][87].…”

Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals

“…In alignment with our earlier reference to the impact of RPS26 on T lymphocytes, it has been established that rs1131017 is a prominent SNP of a trans‐regulatory site that affects multiple genes in CD4 + and CD8 + T lymphocytes 20 . In T lymphocytes, rs1131017 can influence several genes connected with T lymphocyte activation and autoimmune disease 24 , yielding a potential explanation for the linkage of this variant with type 1 diabetes mellitus and LADA.…”

“…Previous GWASs have associated these variants with vitiligo 21 , type 1 diabetes mellitus in white people 18 and rheumatoid arthritis 22 , respectively. Furthermore, rs1131017 has been identified as a lead SNP in a trans‐acting regulatory region within T lymphocytes 20 , affecting not only RPS26 expression 23 , but also the expression of genes associated with T lymphocyte activation and autoimmune disorders 24 . Although existing research suggests that the expression of the RPS26 gene might not be the molecular trait directly responsible for susceptibility to type 1 diabetes mellitus in white people 23 , the correlation between the rs1131017 polymorphism in the RPS26 gene and susceptibility to type 1 diabetes mellitus and other diabetes subtypes in the Chinese population remains unexplored, and warrants further investigation.…”

Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal new mechanisms of genome regulation

“…The top SNP for RPS26's deQTL is located in the gene's 5'UTR region, the binding site for six transcription factors (RBM39, TCF7, LEF1, KLF6, CD74 and MAF) 48 . We hypothesized that if these transcription factors (TFs) regulate the expression level of RPS26 via binding to this site, rs1131017 should be detected as a co-expression eQTL between RPS26 and these TFs in our data.…”