QTL-based analysis form Single cell RNA sequencing data Open Access
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Single cell RNA sequencing (scRNA-seq) presents the unique opportunities to identify new cell sub-populations, to discover unique individual cells, and to study intra-cellular molecular correlations. Specifically, with the recent technological advances, scRNA-seq allows analyses of correlations between single nucleotide variants (SNV) and gene expression, with the aim to identify SNVs with potential regulatory features.We analyzed scRNA-seq data generated on 10x Genomics Chromium platform from 26,640 human adipose-derived mesenchymal stem cells obtained from 3 healthy female donors. After SNV-aware alignment (STßAR-WASP) and variant call (GATK), we estimated allele expression from heterozygous SNV loci, which we defined as expressed Variant Allele Frequency (VAFRNA= nvar/(nvar+nref), where nvar and nref are the number of unique sequencing reads encompassing the SNV locus of interest). In parallel, we estimated the gene expression (featureCounts) and determined cell types (Seurat). We used UMI-tools to remove PCR deduplicates and Seurat to regress out batch effects across the samples and cell cycle-related heterogeneity. We next applied Quantitative loci trails (QTL)-based analysis to look for pairwise correlations between VAFRNA and gene expression. Our results suggest wide-spread and significant correlations between SNVs and gene expression in both cis (same gene) and trans (across genes) mode. We also observed concordance with known significant QTLs reported in the Genotype-Tissue Expression (GTEx) database. Our study shows that VAFRNA from scRNA-seq data can be used in QTL-based studies to assess genetic regulatory networks.