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A computational approach for identification of core modules from a co-expression network and GWAS data
This protocol describes the application of the “omnigenic” model of the genetic architecture of complex traits to identify novel “core” genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol lever...
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/PMC8385446/ https://www.ncbi.nlm.nih.gov/pubmed/34467232 http://dx.doi.org/10.1016/j.xpro.2021.100768 |
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author | Sabik, Olivia L. Ackert-Bicknell, Cheryl L. Farber, Charles R. |
author_facet | Sabik, Olivia L. Ackert-Bicknell, Cheryl L. Farber, Charles R. |
author_sort | Sabik, Olivia L. |
collection | PubMed |
description | This protocol describes the application of the “omnigenic” model of the genetic architecture of complex traits to identify novel “core” genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with “core-like” characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020). |
format | Online Article Text |
id | pubmed-8385446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83854462021-08-30 A computational approach for identification of core modules from a co-expression network and GWAS data Sabik, Olivia L. Ackert-Bicknell, Cheryl L. Farber, Charles R. STAR Protoc Protocol This protocol describes the application of the “omnigenic” model of the genetic architecture of complex traits to identify novel “core” genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with “core-like” characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020). Elsevier 2021-08-21 /pmc/articles/PMC8385446/ /pubmed/34467232 http://dx.doi.org/10.1016/j.xpro.2021.100768 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Protocol Sabik, Olivia L. Ackert-Bicknell, Cheryl L. Farber, Charles R. A computational approach for identification of core modules from a co-expression network and GWAS data |
title | A computational approach for identification of core modules from a co-expression network and GWAS data |
title_full | A computational approach for identification of core modules from a co-expression network and GWAS data |
title_fullStr | A computational approach for identification of core modules from a co-expression network and GWAS data |
title_full_unstemmed | A computational approach for identification of core modules from a co-expression network and GWAS data |
title_short | A computational approach for identification of core modules from a co-expression network and GWAS data |
title_sort | computational approach for identification of core modules from a co-expression network and gwas data |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385446/ https://www.ncbi.nlm.nih.gov/pubmed/34467232 http://dx.doi.org/10.1016/j.xpro.2021.100768 |
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