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Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection

In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal va...

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Detalles Bibliográficos
Autores principales: Lu, Xi, Fan, Kun, Ren, Jie, Wu, Cen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693717/
https://www.ncbi.nlm.nih.gov/pubmed/34956304
http://dx.doi.org/10.3389/fgene.2021.667074
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author Lu, Xi
Fan, Kun
Ren, Jie
Wu, Cen
author_facet Lu, Xi
Fan, Kun
Ren, Jie
Wu, Cen
author_sort Lu, Xi
collection PubMed
description In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.
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spelling pubmed-86937172021-12-23 Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection Lu, Xi Fan, Kun Ren, Jie Wu, Cen Front Genet Genetics In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications. Frontiers Media S.A. 2021-12-08 /pmc/articles/PMC8693717/ /pubmed/34956304 http://dx.doi.org/10.3389/fgene.2021.667074 Text en Copyright © 2021 Lu, Fan, Ren and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Lu, Xi
Fan, Kun
Ren, Jie
Wu, Cen
Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title_full Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title_fullStr Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title_full_unstemmed Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title_short Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
title_sort identifying gene–environment interactions with robust marginal bayesian variable selection
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693717/
https://www.ncbi.nlm.nih.gov/pubmed/34956304
http://dx.doi.org/10.3389/fgene.2021.667074
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