An efficient technique for Bayesian modeling of family data using the BUGS software

Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sa...

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Main Authors:	Bae, Harold T., Perls, Thomas T., Sebastiani, Paola
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235415/

id	pubmed-4235415
recordtype	oai_dc
spelling	pubmed-42354152014-12-04 An efficient technique for Bayesian modeling of family data using the BUGS software Bae, Harold T. Perls, Thomas T. Sebastiani, Paola Genetics Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sampling) software because of the high-dimensional covariance matrix of the random effects. This paper describes an efficient parameterization that utilizes the singular value decomposition of the covariance matrix of random effects, includes the BUGS code for such implementation, and extends the parameterization to generalized linear mixed models. The implementation is evaluated using simulated data and an example from a large family-based study is presented with a comparison to other existing methods. Frontiers Media S.A. 2014-11-18 /pmc/articles/PMC4235415/ /pubmed/25477899 http://dx.doi.org/10.3389/fgene.2014.00390 Text en Copyright © 2014 Bae, Perls and Sebastiani. http://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) or licensor 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Bae, Harold T. Perls, Thomas T. Sebastiani, Paola
spellingShingle	Bae, Harold T. Perls, Thomas T. Sebastiani, Paola An efficient technique for Bayesian modeling of family data using the BUGS software
author_facet	Bae, Harold T. Perls, Thomas T. Sebastiani, Paola
author_sort	Bae, Harold T.
title	An efficient technique for Bayesian modeling of family data using the BUGS software
title_short	An efficient technique for Bayesian modeling of family data using the BUGS software
title_full	An efficient technique for Bayesian modeling of family data using the BUGS software
title_fullStr	An efficient technique for Bayesian modeling of family data using the BUGS software
title_full_unstemmed	An efficient technique for Bayesian modeling of family data using the BUGS software
title_sort	efficient technique for bayesian modeling of family data using the bugs software
description	Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sampling) software because of the high-dimensional covariance matrix of the random effects. This paper describes an efficient parameterization that utilizes the singular value decomposition of the covariance matrix of random effects, includes the BUGS code for such implementation, and extends the parameterization to generalized linear mixed models. The implementation is evaluated using simulated data and an example from a large family-based study is presented with a comparison to other existing methods.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4235415/
_version_	1613157899234705408

An efficient technique for Bayesian modeling of family data using the BUGS software

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