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

• Published in: Genome Biology 2023-04, Vol.24 (1), p.80-37, Article 80
• Main 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
• Format: Article
• Language: English
• Subjects: Autoimmune diseases; Autoimmune Diseases - genetics; Blood; Cell activation; Cells; Co-expression QTLs; CRISPR; Datasets; eQTL; Gene expression; Gene Expression Regulation; Gene regulation; Genetic diversity; Genome-Wide Association Study; Genomes; Humans; Leukocytes, Mononuclear; Lymphocytes; Lymphocytes T; Peripheral blood; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Ribosomal Proteins - genetics; Sample size; scRNA-seq; Single-nucleotide polymorphism; Transcription activation; Transcription factors
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-023-02897-x

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. 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. 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.