Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics
Summary: Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human physiology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference re...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | HGG Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666247724001374 |
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| author | Hanna Abe Phillip Lin Dan Zhou Douglas M. Ruderfer Eric R. Gamazon |
| author_facet | Hanna Abe Phillip Lin Dan Zhou Douglas M. Ruderfer Eric R. Gamazon |
| author_sort | Hanna Abe |
| collection | DOAJ |
| description | Summary: Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human physiology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resources from population-scale studies, data sparsity in single-cell RNA sequencing, and the complex cell state pattern of expression within individual cell types. Here, we develop genetic models of cell-type-specific and cell-state-adjusted gene expression in mid-brain neurons undergoing differentiation from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell-type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1,500 phenotypes from the UK Biobank. Using longitudinal, genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, the results of this work demonstrate the insights that can be gained into the molecular underpinnings of disease by quantifying the genetic control of gene expression at single-cell resolution. |
| format | Article |
| id | doaj-art-f39896bd548e4276961fe66ab46c635a |
| institution | Kabale University |
| issn | 2666-2477 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HGG Advances |
| spelling | doaj-art-f39896bd548e4276961fe66ab46c635a2025-01-31T05:12:27ZengElsevierHGG Advances2666-24772025-04-0162100397Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease geneticsHanna Abe0Phillip Lin1Dan Zhou2Douglas M. Ruderfer3Eric R. Gamazon4Vanderbilt University, Nashville, TN, USA; Corresponding authorDivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USADivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USADivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics and Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USADivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Clare Hall, University of Cambridge, Cambridge, UK; Corresponding authorSummary: Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human physiology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resources from population-scale studies, data sparsity in single-cell RNA sequencing, and the complex cell state pattern of expression within individual cell types. Here, we develop genetic models of cell-type-specific and cell-state-adjusted gene expression in mid-brain neurons undergoing differentiation from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell-type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1,500 phenotypes from the UK Biobank. Using longitudinal, genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, the results of this work demonstrate the insights that can be gained into the molecular underpinnings of disease by quantifying the genetic control of gene expression at single-cell resolution.http://www.sciencedirect.com/science/article/pii/S2666247724001374TWASsingle-cellPheWAScell-stateGranger-causalityschizophrenia |
| spellingShingle | Hanna Abe Phillip Lin Dan Zhou Douglas M. Ruderfer Eric R. Gamazon Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics HGG Advances TWAS single-cell PheWAS cell-state Granger-causality schizophrenia |
| title | Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics |
| title_full | Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics |
| title_fullStr | Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics |
| title_full_unstemmed | Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics |
| title_short | Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics |
| title_sort | mapping dynamic regulation of gene expression using single cell transcriptomics and application to complex disease genetics |
| topic | TWAS single-cell PheWAS cell-state Granger-causality schizophrenia |
| url | http://www.sciencedirect.com/science/article/pii/S2666247724001374 |
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