Hongyu Liu1,2, N M Prashant1, Liam F Spurr3,4,5, Pavlos Bousounis1, Nawaf Alomran1, Helen Ibeawuchi1, Justin Sein1, Piotr Słowiński6,7, Krasimira Tsaneva-Atanasova6,7,8,9, Anelia Horvath10,11. 1. McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA. 2. Chinese Medicine Toxicological Laboratory, Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, 150040, People's Republic of China. 3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. 4. Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. 5. Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, IL, 60637, USA. 6. Translational Research Exchange at Exeter, University of Exeter, Exeter, EX4 4QJ, UK. 7. EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QJ, UK. 8. Department of Mathematics & Living Systems Institute, University of Exeter, Stocker Road, Exeter, EX4 4QD, UK. 9. Dept. of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str, 1113, Sofia, Bulgaria. 10. McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20037, USA. horvatha@gwu.edu. 11. Department of Biochemistry and Molecular Medicine, Department of Biostatistics and Bioinformatics School of Medicine and Health Sciences, George Washington University, Washington, DC, 20037, USA. horvatha@gwu.edu.
Abstract
BACKGROUND: Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. RESULTS: Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. CONCLUSION: ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. AVAILABILITY: https://github.com/HorvathLab/NGS/tree/master/scReQTL.
BACKGROUND: Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. RESULTS: Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. CONCLUSION: ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. AVAILABILITY: https://github.com/HorvathLab/NGS/tree/master/scReQTL.
Entities:
Keywords:
Genetic variation; RNA-seq; SNV; VAFRNA; eQTL, ReQTL, scReQTL, single cell; scRNA-seq; scVAFRNA; single cell RNA sequencing, single cell RNA-seq
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