A computational approach for identification of core modules from a co-expression network and GWAS data
Summary: 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 proto...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166721004743 |
| _version_ | 1819132605059039232 |
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| author | Olivia L. Sabik Cheryl L. Ackert-Bicknell Charles R. Farber |
| author_facet | Olivia L. Sabik Cheryl L. Ackert-Bicknell Charles R. Farber |
| author_sort | Olivia L. Sabik |
| collection | DOAJ |
| description | Summary: 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). |
| first_indexed | 2024-12-22T09:34:03Z |
| format | Article |
| id | doaj.art-56b6628c126f443fab0e810cd2f7d203 |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-12-22T09:34:03Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-56b6628c126f443fab0e810cd2f7d2032022-12-21T18:30:53ZengElsevierSTAR Protocols2666-16672021-09-0123100768A computational approach for identification of core modules from a co-expression network and GWAS dataOlivia L. Sabik0Cheryl L. Ackert-Bicknell1Charles R. Farber2Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908 USA; Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908 USADepartment of Orthopedics, Anschutz Medical Campus, University of Colorado, Aurora, CO, USACenter for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908 USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA; Corresponding authorSummary: 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).http://www.sciencedirect.com/science/article/pii/S2666166721004743BioinformaticsGeneticsGenomicsRNAseqSystems biology |
| spellingShingle | Olivia L. Sabik Cheryl L. Ackert-Bicknell Charles R. Farber A computational approach for identification of core modules from a co-expression network and GWAS data STAR Protocols Bioinformatics Genetics Genomics RNAseq Systems biology |
| 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 | Bioinformatics Genetics Genomics RNAseq Systems biology |
| url | http://www.sciencedirect.com/science/article/pii/S2666166721004743 |
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