Identifying genetically driven clinical phenotypes using linear mixed models

Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region ar...

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Autores principales:	Jonathan D. Mosley, John S. Witte, Emma K. Larkin, Lisa Bastarache, Christian M. Shaffer, Jason H. Karnes, C. Michael Stein, Elizabeth Phillips, Scott J. Hebbring, Murray H. Brilliant, John Mayer, Zhan Ye, Dan M. Roden, Joshua C. Denny
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5401bbf278ce480b9e71837f9f3eca2c
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spelling	oai:doaj.org-article:5401bbf278ce480b9e71837f9f3eca2c2021-12-02T14:38:34ZIdentifying genetically driven clinical phenotypes using linear mixed models10.1038/ncomms114332041-1723https://doaj.org/article/5401bbf278ce480b9e71837f9f3eca2c2016-04-01T00:00:00Zhttps://doi.org/10.1038/ncomms11433https://doaj.org/toc/2041-1723Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region are associated with 44 phenotypes, 5 phenotypes which were not previously reported in GWASes.Jonathan D. MosleyJohn S. WitteEmma K. LarkinLisa BastaracheChristian M. ShafferJason H. KarnesC. Michael SteinElizabeth PhillipsScott J. HebbringMurray H. BrilliantJohn MayerZhan YeDan M. RodenJoshua C. DennyNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-8 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jonathan D. Mosley John S. Witte Emma K. Larkin Lisa Bastarache Christian M. Shaffer Jason H. Karnes C. Michael Stein Elizabeth Phillips Scott J. Hebbring Murray H. Brilliant John Mayer Zhan Ye Dan M. Roden Joshua C. Denny Identifying genetically driven clinical phenotypes using linear mixed models
description	Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region are associated with 44 phenotypes, 5 phenotypes which were not previously reported in GWASes.
format	article
author	Jonathan D. Mosley John S. Witte Emma K. Larkin Lisa Bastarache Christian M. Shaffer Jason H. Karnes C. Michael Stein Elizabeth Phillips Scott J. Hebbring Murray H. Brilliant John Mayer Zhan Ye Dan M. Roden Joshua C. Denny
author_facet	Jonathan D. Mosley John S. Witte Emma K. Larkin Lisa Bastarache Christian M. Shaffer Jason H. Karnes C. Michael Stein Elizabeth Phillips Scott J. Hebbring Murray H. Brilliant John Mayer Zhan Ye Dan M. Roden Joshua C. Denny
author_sort	Jonathan D. Mosley
title	Identifying genetically driven clinical phenotypes using linear mixed models
title_short	Identifying genetically driven clinical phenotypes using linear mixed models
title_full	Identifying genetically driven clinical phenotypes using linear mixed models
title_fullStr	Identifying genetically driven clinical phenotypes using linear mixed models
title_full_unstemmed	Identifying genetically driven clinical phenotypes using linear mixed models
title_sort	identifying genetically driven clinical phenotypes using linear mixed models
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/5401bbf278ce480b9e71837f9f3eca2c
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Identifying genetically driven clinical phenotypes using linear mixed models

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