bGWAS: an R package to perform Bayesian genome wide association studies

SUMMARY: Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging G...

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Autores principales: Mounier, Ninon, Kutalik, Zoltán
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520046/
https://www.ncbi.nlm.nih.gov/pubmed/32470106
http://dx.doi.org/10.1093/bioinformatics/btaa549
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author Mounier, Ninon
Kutalik, Zoltán
author_facet Mounier, Ninon
Kutalik, Zoltán
author_sort Mounier, Ninon
collection PubMed
description SUMMARY: Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. AVAILABILITY AND IMPLEMENTATION: bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-75200462020-09-30 bGWAS: an R package to perform Bayesian genome wide association studies Mounier, Ninon Kutalik, Zoltán Bioinformatics Applications Notes SUMMARY: Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. AVAILABILITY AND IMPLEMENTATION: bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-29 /pmc/articles/PMC7520046/ /pubmed/32470106 http://dx.doi.org/10.1093/bioinformatics/btaa549 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Mounier, Ninon
Kutalik, Zoltán
bGWAS: an R package to perform Bayesian genome wide association studies
title bGWAS: an R package to perform Bayesian genome wide association studies
title_full bGWAS: an R package to perform Bayesian genome wide association studies
title_fullStr bGWAS: an R package to perform Bayesian genome wide association studies
title_full_unstemmed bGWAS: an R package to perform Bayesian genome wide association studies
title_short bGWAS: an R package to perform Bayesian genome wide association studies
title_sort bgwas: an r package to perform bayesian genome wide association studies
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520046/
https://www.ncbi.nlm.nih.gov/pubmed/32470106
http://dx.doi.org/10.1093/bioinformatics/btaa549
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