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Protocol for using GRPath to identify putative gene regulation paths in complex human diseases
Unfolding the “black-box” associations between genotype and phenotype is essential for understanding the molecular mechanisms of complex human diseases. Here, we describe the use of GRPath to uncover putative causal paths (pcPaths) from genetic variants to disease phenotypes. GRPath takes multiple o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664398/ https://www.ncbi.nlm.nih.gov/pubmed/36386883 http://dx.doi.org/10.1016/j.xpro.2022.101831 |
_version_ | 1784831091694108672 |
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author | Xi, Xi Li, Haochen Wei, Lei Zhang, Xuegong |
author_facet | Xi, Xi Li, Haochen Wei, Lei Zhang, Xuegong |
author_sort | Xi, Xi |
collection | PubMed |
description | Unfolding the “black-box” associations between genotype and phenotype is essential for understanding the molecular mechanisms of complex human diseases. Here, we describe the use of GRPath to uncover putative causal paths (pcPaths) from genetic variants to disease phenotypes. GRPath takes multiple omics data and summary statistics as input and identifies pcPaths that link the putative causal region (pcRegion), putative causal variant (pcVariant), putative causal gene (pcGene), noteworthy cell type, and disease phenotype. For complete details on the use and execution of this protocol, please refer to Xi et al. (2022).(1) |
format | Online Article Text |
id | pubmed-9664398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96643982022-11-15 Protocol for using GRPath to identify putative gene regulation paths in complex human diseases Xi, Xi Li, Haochen Wei, Lei Zhang, Xuegong STAR Protoc Protocol Unfolding the “black-box” associations between genotype and phenotype is essential for understanding the molecular mechanisms of complex human diseases. Here, we describe the use of GRPath to uncover putative causal paths (pcPaths) from genetic variants to disease phenotypes. GRPath takes multiple omics data and summary statistics as input and identifies pcPaths that link the putative causal region (pcRegion), putative causal variant (pcVariant), putative causal gene (pcGene), noteworthy cell type, and disease phenotype. For complete details on the use and execution of this protocol, please refer to Xi et al. (2022).(1) Elsevier 2022-11-09 /pmc/articles/PMC9664398/ /pubmed/36386883 http://dx.doi.org/10.1016/j.xpro.2022.101831 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Xi, Xi Li, Haochen Wei, Lei Zhang, Xuegong Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title | Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title_full | Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title_fullStr | Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title_full_unstemmed | Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title_short | Protocol for using GRPath to identify putative gene regulation paths in complex human diseases |
title_sort | protocol for using grpath to identify putative gene regulation paths in complex human diseases |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664398/ https://www.ncbi.nlm.nih.gov/pubmed/36386883 http://dx.doi.org/10.1016/j.xpro.2022.101831 |
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