scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
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 S...
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| Format: | Article |
| Language: | English |
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BMC
2021-01-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-020-07334-y |
| _version_ | 1818580140952649728 |
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| author | Hongyu Liu N. M. Prashant Liam F. Spurr Pavlos Bousounis Nawaf Alomran Helen Ibeawuchi Justin Sein Piotr Słowiński Krasimira Tsaneva-Atanasova Anelia Horvath |
| author_facet | Hongyu Liu N. M. Prashant Liam F. Spurr Pavlos Bousounis Nawaf Alomran Helen Ibeawuchi Justin Sein Piotr Słowiński Krasimira Tsaneva-Atanasova Anelia Horvath |
| author_sort | Hongyu Liu |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-16T07:12:52Z |
| format | Article |
| id | doaj.art-e2261ab400a54bc9979697a03b05e5e8 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-16T07:12:52Z |
| publishDate | 2021-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-e2261ab400a54bc9979697a03b05e5e82022-12-21T22:39:52ZengBMCBMC Genomics1471-21642021-01-0122111610.1186/s12864-020-07334-yscReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasetsHongyu Liu0N. M. Prashant1Liam F. Spurr2Pavlos Bousounis3Nawaf Alomran4Helen Ibeawuchi5Justin Sein6Piotr Słowiński7Krasimira Tsaneva-Atanasova8Anelia Horvath9McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityDepartment of Medical Oncology, Dana-Farber Cancer InstituteMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityTranslational Research Exchange at Exeter, University of ExeterTranslational Research Exchange at Exeter, University of ExeterMcCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington UniversityAbstract 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/scReQTLhttps://doi.org/10.1186/s12864-020-07334-yeQTL, ReQTL, scReQTL, single cellVAFRNAscVAFRNAscRNA-seqSNVGenetic variation |
| spellingShingle | Hongyu Liu N. M. Prashant Liam F. Spurr Pavlos Bousounis Nawaf Alomran Helen Ibeawuchi Justin Sein Piotr Słowiński Krasimira Tsaneva-Atanasova Anelia Horvath scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets BMC Genomics eQTL, ReQTL, scReQTL, single cell VAFRNA scVAFRNA scRNA-seq SNV Genetic variation |
| title | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets |
| title_full | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets |
| title_fullStr | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets |
| title_full_unstemmed | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets |
| title_short | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets |
| title_sort | screqtl an approach to correlate snvs to gene expression from individual scrna seq datasets |
| topic | eQTL, ReQTL, scReQTL, single cell VAFRNA scVAFRNA scRNA-seq SNV Genetic variation |
| url | https://doi.org/10.1186/s12864-020-07334-y |
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