scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
dc.contributor.author | Liu, H | |
dc.contributor.author | Prashant, NM | |
dc.contributor.author | Spurr, LF | |
dc.contributor.author | Bousounis, P | |
dc.contributor.author | Alomran, N | |
dc.contributor.author | Ibeawuchi, H | |
dc.contributor.author | Sein, J | |
dc.contributor.author | Słowiński, P | |
dc.contributor.author | Tsaneva-Atanasova, K | |
dc.contributor.author | Horvath, A | |
dc.date.accessioned | 2021-01-11T08:27:03Z | |
dc.date.issued | 2021-01-08 | |
dc.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 | en_GB |
dc.description.sponsorship | McCormick Genomic and Proteomic Center (MGPC), The George Washington University | en_GB |
dc.identifier.citation | Vol. 22, article 40 | en_GB |
dc.identifier.doi | 10.1186/s12864-020-07334-y | |
dc.identifier.grantnumber | MGPC_PG2019 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/124347 | |
dc.language.iso | en | en_GB |
dc.publisher | BMC | en_GB |
dc.relation.url | https://github.com/HorvathLab/NGS/tree/master/scReQTL | en_GB |
dc.rights | © The Author(s). 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_GB |
dc.subject | eQTL | en_GB |
dc.subject | ReQTL | en_GB |
dc.subject | scReQTL | en_GB |
dc.subject | single cell | en_GB |
dc.subject | VAF_RNA | en_GB |
dc.subject | scVAF_RNA | en_GB |
dc.subject | scRNA-seq | en_GB |
dc.subject | SNV | en_GB |
dc.subject | Genetic variation | en_GB |
dc.subject | RNA-seq | en_GB |
dc.subject | single cell RNA sequencing | en_GB |
dc.subject | single cell RNA-seq | en_GB |
dc.title | scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-01-11T08:27:03Z | |
dc.identifier.issn | 1471-2164 | |
dc.description | This is the final version. Available from BMC via the DOI in this record. | en_GB |
dc.description | All data generated or analyzed during this study are included in this published article and its supplementary information files. | en_GB |
dc.identifier.journal | BMC Genomics | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-12-16 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-12-16 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-01-10T10:26:43Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2021-01-11T08:27:10Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © The Author(s). 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.