Purchase this article with an account.
Rui Chen, Jun Wang, Xuesen Cheng, Qingnan Liang, Jun Wang, Margaret M DeAngelis, Yumei Li; Deciphering non-coding genetic variants via integrative single cell multi-omics analysis. Invest. Ophthalmol. Vis. Sci. 2022;63(7):3.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Genetic variants located at the non-coding part of the genome, such as enhancers and promoters, can have a significant impact on the cellular state and phenotype of an individual. However, identifying and characterizing functional variants in gene regulatory elements remains challenging. We sought to overcome this challenge by integrating genomic sequencing with recent developed single-cell omics technologies.
To identify causal genetic variants that affect gene expression in the retina, multi-omics profiling, including whole genome sequencing (WGS), single nuclei RNA-sequencing (snRNA-seq), and single nuclei ATAC-seq (snATAC-seq), were performed on the donor retina. Open chromatin regions in each retinal cell type and potential regulatory elements were identified. Candidate genetic variants that affect gene expression and/or chromatin accessibility in each cell type were identified through integrative analysis of single-cell expression QTLs (sc-eQTLs), single-cell chromatin accessibility QTLs (sc-caQTLs), single-cell allelic specific expression (sc-ASE), and single-cell allelic specific chromatin accessibility (sc-ASCA).
A total of 191,818 and 245,456 nuclei from 20 human donor retinae were profiled with snRNA-seq and snATAC-seq respectively. These data were organized into 11 major cell types in the retina with a total of about 500K open chromatin regions identified. Through co-accessibility and co-expression analysis, 75,477 open chromatin regions were linked to 14,053 genes as candidate regulatory elements. A significant proportion of these elements is cell type specific. Integrative analysis of WGS with the single nuclei multi-omics data leads to the identification of sc-eQTL, sc-caQTL, sc-ASE, sc-ASCA. Strikingly, the majority of these quantitative trait loci are cell type specific. Finally, integration of the single-cell genetic association analyses with GWAS studies leads to the identification of candidate causal variants and genes in cell type context.
Our results indicate that the impact of vast majority of genetic variants on gene regulation is cell context specific. Furthermore, we demonstrated that integrative analysis of genomics and single cell omics data is an effective strategy for genetic fine mapping and casual variant identification underlying human quantitative traits and complex diseases.
This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.
This PDF is available to Subscribers Only