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

Abstract

Gene expression levels can vary substantially across cells, even in a seemingly homogeneous cell population. Identifying the relationships between genetic variation and gene expression is critical for understanding the mechanisms of genome regulation. However, the genetic control of gene expression variability among the cells within individuals has yet to be extensively examined. This is primarily due to the statistical challenges, such as the need for sufficiently powered cohorts and adjusting mean-variance dependence. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a novel statistical framework to identify genetic effects on the gene expression variability (sc-veQTL) accounting for the mean-variance dependence. Using single-cell RNA-seq data of 1.2 million peripheral blood mononuclear cells from 980 human donors, we identified 14 - 3,488 genes with significant sc-veQTLs (study-wide q-value < 0.05) across different blood cell types, 2,103 of which were shared across more than one cell type. We further detected 55 SNP-gene pairs (in 34 unique genes) by directly linking genetic variations with gene expression dispersion (sc-deQTL) regardless of mean-variance dependence, and these genes were enriched in biological processes relevant to immune response and viral infection. An example is rs1131017 (p<9.08x10-52), a sc-veQTL in the 5 UTR of RPS26, which shows a ubiquitous dispersion effect across cell types, with higher dispersion levels associated with lower auto-immune disease risk, including rheumatoid arthritis and type 1 diabetes. Another example is LYZ, which is associated with antibacterial activity against bacterial species and was only detected with a monocyte-specific deQTL (rs1384) located at the 3 UTR region (p=1.48x10-11) and replicated in an independent cohort. Our results demonstrate an efficient and robust statistical method to identify genetic effects on gene expression variability and how these associations and their involved pathways confer auto-immune disease risk. This analytical framework provides a new approach to unravelling the genetic regulation of gene expression at the single-cell resolution, advancing our understanding of complex biological processes.