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FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits
We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649398/ https://www.ncbi.nlm.nih.gov/pubmed/34927094 http://dx.doi.org/10.1016/j.xpro.2021.100985 |
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author | Dong, Ang Feng, Li Yang, Dengcheng Wu, Shuang Zhao, Jinshuai Wang, Jing Wu, Rongling |
author_facet | Dong, Ang Feng, Li Yang, Dengcheng Wu, Shuang Zhao, Jinshuai Wang, Jing Wu, Rongling |
author_sort | Dong, Ang |
collection | PubMed |
description | We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021). |
format | Online Article Text |
id | pubmed-8649398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86493982021-12-17 FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits Dong, Ang Feng, Li Yang, Dengcheng Wu, Shuang Zhao, Jinshuai Wang, Jing Wu, Rongling STAR Protoc Protocol We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021). Elsevier 2021-12-04 /pmc/articles/PMC8649398/ /pubmed/34927094 http://dx.doi.org/10.1016/j.xpro.2021.100985 Text en © 2021 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 Dong, Ang Feng, Li Yang, Dengcheng Wu, Shuang Zhao, Jinshuai Wang, Jing Wu, Rongling FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title | FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title_full | FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title_fullStr | FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title_full_unstemmed | FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title_short | FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
title_sort | fungraph: a statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649398/ https://www.ncbi.nlm.nih.gov/pubmed/34927094 http://dx.doi.org/10.1016/j.xpro.2021.100985 |
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