@article{a431631ba23a4ea69602034a958b7db4,
title = "H3AGWAS: a portable workflow for genome wide association studies",
abstract = "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.",
keywords = "Association testing, Docker, Genome-wide association study, Nextflow, Pipeline, Post-association analysis, Quality control, Singularity, Workflow",
author = "Brandenburg, {Jean Tristan} and Lindsay Clark and Gerrit Botha and Sumir Panji and Shakuntala Baichoo and Christopher Fields and Scott Hazelhurst",
note = "The work is supported by National Human Genome Research Institute/National Institutes of Health: JTB is supported by the AWI-Gen Collaborative Centre (U54HG006938) and all other authors and the Wits Core Cluster are supported by the Pan-African Bioinformatics Network for H3Africa (U24HG006941). The views expressed are solely those of the authors and not that of the NIH. Funders played no roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript The authors thank all AWI-Gen collaborators for use of their data set and more particularly Mich{\`e}le Ramsay Principal Investigator and all participants of AWI-Gen. Our SBIMB colleagues have made many useful and generous contributions: Carl Wenlong Chen, Palwend{\'e} R. Boua, Vivien Chebii and Shaun Aron in particular. Many people contributed to the workflow and we especially thank Lerato Magosi, Rob Clucas and Eugene de Beste whose effort at the start of the project was so important. We thank Professor Nicola Mulder from the University of Cape Town whose leadership of H3ABioNet made the work possible.",
year = "2022",
month = dec,
doi = "10.1186/s12859-022-05034-w",
language = "English (US)",
volume = "23",
journal = "BMC bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",
number = "1",
}