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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...

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Detalles Bibliográficos
Autores principales: Xi, Xi, Li, Haochen, Wei, Lei, Zhang, Xuegong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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
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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)
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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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