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Variant to function mapping at single-cell resolution through network propagation
Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, par...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646486/ https://www.ncbi.nlm.nih.gov/pubmed/35668323 http://dx.doi.org/10.1038/s41587-022-01341-y |
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author | Yu, Fulong Cato, Liam D. Weng, Chen Liggett, L. Alexander Jeon, Soyoung Xu, Keren Chiang, Charleston W. K. Wiemels, Joseph L. Weissman, Jonathan S. de Smith, Adam J. Sankaran, Vijay G. |
author_facet | Yu, Fulong Cato, Liam D. Weng, Chen Liggett, L. Alexander Jeon, Soyoung Xu, Keren Chiang, Charleston W. K. Wiemels, Joseph L. Weissman, Jonathan S. de Smith, Adam J. Sankaran, Vijay G. |
author_sort | Yu, Fulong |
collection | PubMed |
description | Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, particularly in the analysis of single-cell epigenomic data. To overcome these challenges, we present SCAVENGE, a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation, applying the method to blood traits at distinct stages of human hematopoiesis, to monocyte subsets that increase the risk for severe Coronavirus Disease 2019 (COVID-19) and to intermediate lymphocyte developmental states that predispose to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data. |
format | Online Article Text |
id | pubmed-9646486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96464862022-11-15 Variant to function mapping at single-cell resolution through network propagation Yu, Fulong Cato, Liam D. Weng, Chen Liggett, L. Alexander Jeon, Soyoung Xu, Keren Chiang, Charleston W. K. Wiemels, Joseph L. Weissman, Jonathan S. de Smith, Adam J. Sankaran, Vijay G. Nat Biotechnol Article Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, particularly in the analysis of single-cell epigenomic data. To overcome these challenges, we present SCAVENGE, a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation, applying the method to blood traits at distinct stages of human hematopoiesis, to monocyte subsets that increase the risk for severe Coronavirus Disease 2019 (COVID-19) and to intermediate lymphocyte developmental states that predispose to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data. Nature Publishing Group US 2022-06-06 2022 /pmc/articles/PMC9646486/ /pubmed/35668323 http://dx.doi.org/10.1038/s41587-022-01341-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yu, Fulong Cato, Liam D. Weng, Chen Liggett, L. Alexander Jeon, Soyoung Xu, Keren Chiang, Charleston W. K. Wiemels, Joseph L. Weissman, Jonathan S. de Smith, Adam J. Sankaran, Vijay G. Variant to function mapping at single-cell resolution through network propagation |
title | Variant to function mapping at single-cell resolution through network propagation |
title_full | Variant to function mapping at single-cell resolution through network propagation |
title_fullStr | Variant to function mapping at single-cell resolution through network propagation |
title_full_unstemmed | Variant to function mapping at single-cell resolution through network propagation |
title_short | Variant to function mapping at single-cell resolution through network propagation |
title_sort | variant to function mapping at single-cell resolution through network propagation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646486/ https://www.ncbi.nlm.nih.gov/pubmed/35668323 http://dx.doi.org/10.1038/s41587-022-01341-y |
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