CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq
Abstract Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. Howev...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2022-08-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.202110663 |
| _version_ | 1827014862662795264 |
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| author | Anna S E Cuomo Tobias Heinen Danai Vagiaki Danilo Horta John C Marioni Oliver Stegle |
| author_facet | Anna S E Cuomo Tobias Heinen Danai Vagiaki Danilo Horta John C Marioni Oliver Stegle |
| author_sort | Anna S E Cuomo |
| collection | DOAJ |
| description | Abstract Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants. |
| first_indexed | 2024-03-07T16:51:31Z |
| format | Article |
| id | doaj.art-dd1f4450ca5c4d6f8d42bc6a9a7ce3e3 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2025-02-18T14:14:41Z |
| publishDate | 2022-08-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-dd1f4450ca5c4d6f8d42bc6a9a7ce3e32024-10-28T09:19:05ZengSpringer NatureMolecular Systems Biology1744-42922022-08-0118811410.15252/msb.202110663CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seqAnna S E Cuomo0Tobias Heinen1Danai Vagiaki2Danilo Horta3John C Marioni4Oliver Stegle5European Bioinformatics Institute (EMBL‐EBI)Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ)Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ)European Bioinformatics Institute (EMBL‐EBI)European Bioinformatics Institute (EMBL‐EBI)European Bioinformatics Institute (EMBL‐EBI)Abstract Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.https://doi.org/10.15252/msb.202110663cell‐type specificityeQTLgenetic interactionsingle‐cell sequencing |
| spellingShingle | Anna S E Cuomo Tobias Heinen Danai Vagiaki Danilo Horta John C Marioni Oliver Stegle CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq Molecular Systems Biology cell‐type specificity eQTL genetic interaction single‐cell sequencing |
| title | CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq |
| title_full | CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq |
| title_fullStr | CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq |
| title_full_unstemmed | CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq |
| title_short | CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq |
| title_sort | cellregmap a statistical framework for mapping context specific regulatory variants using scrna seq |
| topic | cell‐type specificity eQTL genetic interaction single‐cell sequencing |
| url | https://doi.org/10.15252/msb.202110663 |
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