Cell type-specific eQTL analysis of COVID-19 based on single-cell transcriptomic data.
Chao Wang, Xinyu Chen, Sainan Zhang, Zijun Zhu, Meiyu Du, Guanzhi He, Senwei Tan, Hailong Li, Duoyi Zhang, Jingqiao Bai, Liang Cheng
Abstract
Open AccessGenome-wide association study analysis has revealed the significant influence of genetic factors in the progression of COVID-19. However, the impact of these genetic variants on gene expression in various cell types remains largely unexplored. Here, we profiled immune cells from 42 COVID-19 cases (involving five stages) and 11 healthy individuals, and performed a single-cell RNA-seq assay with >200 000 cells to investigate cell type-specific and stage-specific genetic effects of genetic variants. Single-cell expression quantitative trait loci analysis of eight distinct cell types showed that the expression of 2593 genes is significantly associated with common genetic polymorphisms, and that the majority of these genes show their most prominent effects in specific cell types. Furthermore, we also discovered new gene associations for COVID-19-risk variants identified from genome-wide association studies and highlighted the monocytes in which their effects are most prominent. We found that 57 genes were regulated by variants associated with COVID-19, with significant enrichment for dynamic effects. In summary, our results highlight the importance of studying context-specific genetic regulation of gene expression and provide insights into the mechanisms underlying genetic susceptibility to COVID-19.