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PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies
Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing co...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956664/ https://www.ncbi.nlm.nih.gov/pubmed/35338232 http://dx.doi.org/10.1038/s42003-022-03194-2 |
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author | Coll, Francesc Gouliouris, Theodore Bruchmann, Sebastian Phelan, Jody Raven, Kathy E. Clark, Taane G. Parkhill, Julian Peacock, Sharon J. |
author_facet | Coll, Francesc Gouliouris, Theodore Bruchmann, Sebastian Phelan, Jody Raven, Kathy E. Clark, Taane G. Parkhill, Julian Peacock, Sharon J. |
author_sort | Coll, Francesc |
collection | PubMed |
description | Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing collections of bacterial genomes. First, a sub-sampling approach was undertaken to reduce the allele frequency and effect size of a known and detectable genotype-phenotype relationship by modifying phenotype labels. Second, a phenotype-simulation approach was conducted to simulate phenotypes from existing genetic variants. We implemented both approaches into a computational pipeline (PowerBacGWAS) that supports power calculations for burden testing, pan-genome and variant GWAS; and applied it to collections of Enterococcus faecium, Klebsiella pneumoniae and Mycobacterium tuberculosis. We used this pipeline to determine sample sizes required to detect causal variants of different minor allele frequencies (MAF), effect sizes and phenotype heritability, and studied the effect of homoplasy and population diversity on the power to detect causal variants. Our pipeline and user documentation are made available and can be applied to other bacterial populations. PowerBacGWAS can be used to determine sample sizes required to find statistically significant associations, or the associations detectable with a given sample size. We recommend to perform power calculations using existing genomes of the bacterial species and population of study. |
format | Online Article Text |
id | pubmed-8956664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89566642022-04-20 PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies Coll, Francesc Gouliouris, Theodore Bruchmann, Sebastian Phelan, Jody Raven, Kathy E. Clark, Taane G. Parkhill, Julian Peacock, Sharon J. Commun Biol Article Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing collections of bacterial genomes. First, a sub-sampling approach was undertaken to reduce the allele frequency and effect size of a known and detectable genotype-phenotype relationship by modifying phenotype labels. Second, a phenotype-simulation approach was conducted to simulate phenotypes from existing genetic variants. We implemented both approaches into a computational pipeline (PowerBacGWAS) that supports power calculations for burden testing, pan-genome and variant GWAS; and applied it to collections of Enterococcus faecium, Klebsiella pneumoniae and Mycobacterium tuberculosis. We used this pipeline to determine sample sizes required to detect causal variants of different minor allele frequencies (MAF), effect sizes and phenotype heritability, and studied the effect of homoplasy and population diversity on the power to detect causal variants. Our pipeline and user documentation are made available and can be applied to other bacterial populations. PowerBacGWAS can be used to determine sample sizes required to find statistically significant associations, or the associations detectable with a given sample size. We recommend to perform power calculations using existing genomes of the bacterial species and population of study. Nature Publishing Group UK 2022-03-25 /pmc/articles/PMC8956664/ /pubmed/35338232 http://dx.doi.org/10.1038/s42003-022-03194-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Coll, Francesc Gouliouris, Theodore Bruchmann, Sebastian Phelan, Jody Raven, Kathy E. Clark, Taane G. Parkhill, Julian Peacock, Sharon J. PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title | PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title_full | PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title_fullStr | PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title_full_unstemmed | PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title_short | PowerBacGWAS: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
title_sort | powerbacgwas: a computational pipeline to perform power calculations for bacterial genome-wide association studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956664/ https://www.ncbi.nlm.nih.gov/pubmed/35338232 http://dx.doi.org/10.1038/s42003-022-03194-2 |
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