H3AGWAS: a portable workflow for genome wide association studies

BACKGROUND: Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be...

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Autores principales: Brandenburg, Jean-Tristan, Clark, Lindsay, Botha, Gerrit, Panji, Sumir, Baichoo, Shakuntala, Fields, Christopher, Hazelhurst, Scott
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675212/
https://www.ncbi.nlm.nih.gov/pubmed/36402955
http://dx.doi.org/10.1186/s12859-022-05034-w
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author Brandenburg, Jean-Tristan
Clark, Lindsay
Botha, Gerrit
Panji, Sumir
Baichoo, Shakuntala
Fields, Christopher
Hazelhurst, Scott
author_facet Brandenburg, Jean-Tristan
Clark, Lindsay
Botha, Gerrit
Panji, Sumir
Baichoo, Shakuntala
Fields, Christopher
Hazelhurst, Scott
author_sort Brandenburg, Jean-Tristan
collection PubMed
description BACKGROUND: Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. RESULTS: The H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and post-association analysis of results. CONCLUSIONS: The workflow is scalable—laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker or Singularity.
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spelling pubmed-96752122022-11-20 H3AGWAS: a portable workflow for genome wide association studies Brandenburg, Jean-Tristan Clark, Lindsay Botha, Gerrit Panji, Sumir Baichoo, Shakuntala Fields, Christopher Hazelhurst, Scott BMC Bioinformatics Software BACKGROUND: Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. RESULTS: The H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and post-association analysis of results. CONCLUSIONS: The workflow is scalable—laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker or Singularity. BioMed Central 2022-11-19 /pmc/articles/PMC9675212/ /pubmed/36402955 http://dx.doi.org/10.1186/s12859-022-05034-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Brandenburg, Jean-Tristan
Clark, Lindsay
Botha, Gerrit
Panji, Sumir
Baichoo, Shakuntala
Fields, Christopher
Hazelhurst, Scott
H3AGWAS: a portable workflow for genome wide association studies
title H3AGWAS: a portable workflow for genome wide association studies
title_full H3AGWAS: a portable workflow for genome wide association studies
title_fullStr H3AGWAS: a portable workflow for genome wide association studies
title_full_unstemmed H3AGWAS: a portable workflow for genome wide association studies
title_short H3AGWAS: a portable workflow for genome wide association studies
title_sort h3agwas: a portable workflow for genome wide association studies
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675212/
https://www.ncbi.nlm.nih.gov/pubmed/36402955
http://dx.doi.org/10.1186/s12859-022-05034-w
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