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The open targets post-GWAS analysis pipeline

MOTIVATION: Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug ta...

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Autores principales: Peat, Gareth, Jones, William, Nuhn, Michael, Marugán, José Carlos, Newell, William, Dunham, Ian, Zerbino, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203748/
https://www.ncbi.nlm.nih.gov/pubmed/31930349
http://dx.doi.org/10.1093/bioinformatics/btaa020
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author Peat, Gareth
Jones, William
Nuhn, Michael
Marugán, José Carlos
Newell, William
Dunham, Ian
Zerbino, Daniel
author_facet Peat, Gareth
Jones, William
Nuhn, Michael
Marugán, José Carlos
Newell, William
Dunham, Ian
Zerbino, Daniel
author_sort Peat, Gareth
collection PubMed
description MOTIVATION: Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. RESULTS: We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. AVAILABILITY AND IMPLEMENTATION: The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.
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spelling pubmed-72037482020-05-11 The open targets post-GWAS analysis pipeline Peat, Gareth Jones, William Nuhn, Michael Marugán, José Carlos Newell, William Dunham, Ian Zerbino, Daniel Bioinformatics Applications Notes MOTIVATION: Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. RESULTS: We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. AVAILABILITY AND IMPLEMENTATION: The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io. Oxford University Press 2020-05-01 2020-01-13 /pmc/articles/PMC7203748/ /pubmed/31930349 http://dx.doi.org/10.1093/bioinformatics/btaa020 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Peat, Gareth
Jones, William
Nuhn, Michael
Marugán, José Carlos
Newell, William
Dunham, Ian
Zerbino, Daniel
The open targets post-GWAS analysis pipeline
title The open targets post-GWAS analysis pipeline
title_full The open targets post-GWAS analysis pipeline
title_fullStr The open targets post-GWAS analysis pipeline
title_full_unstemmed The open targets post-GWAS analysis pipeline
title_short The open targets post-GWAS analysis pipeline
title_sort open targets post-gwas analysis pipeline
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203748/
https://www.ncbi.nlm.nih.gov/pubmed/31930349
http://dx.doi.org/10.1093/bioinformatics/btaa020
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