CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses

Linear mixed models have bias due to the assumed independence between random effects. Here, the authors describe a genome-based restricted maximum likelihood, CORE GREML, which estimates covariance between random effects. Application to UK Biobank data highlights this as an important parameter for m...

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Detalles Bibliográficos
Autores principales:	Xuan Zhou, Hae Kyung Im, S. Hong Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/afce71f5c58642fcbe15beea89cf3f05
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spelling	oai:doaj.org-article:afce71f5c58642fcbe15beea89cf3f052021-12-02T15:10:48ZCORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses10.1038/s41467-020-18085-52041-1723https://doaj.org/article/afce71f5c58642fcbe15beea89cf3f052020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18085-5https://doaj.org/toc/2041-1723Linear mixed models have bias due to the assumed independence between random effects. Here, the authors describe a genome-based restricted maximum likelihood, CORE GREML, which estimates covariance between random effects. Application to UK Biobank data highlights this as an important parameter for multi-omics analyses of phenotypic variance.Xuan ZhouHae Kyung ImS. Hong LeeNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xuan Zhou Hae Kyung Im S. Hong Lee CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
description	Linear mixed models have bias due to the assumed independence between random effects. Here, the authors describe a genome-based restricted maximum likelihood, CORE GREML, which estimates covariance between random effects. Application to UK Biobank data highlights this as an important parameter for multi-omics analyses of phenotypic variance.
format	article
author	Xuan Zhou Hae Kyung Im S. Hong Lee
author_facet	Xuan Zhou Hae Kyung Im S. Hong Lee
author_sort	Xuan Zhou
title	CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
title_short	CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
title_full	CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
title_fullStr	CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
title_full_unstemmed	CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
title_sort	core greml for estimating covariance between random effects in linear mixed models for complex trait analyses
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/afce71f5c58642fcbe15beea89cf3f05
work_keys_str_mv	AT xuanzhou coregremlforestimatingcovariancebetweenrandomeffectsinlinearmixedmodelsforcomplextraitanalyses AT haekyungim coregremlforestimatingcovariancebetweenrandomeffectsinlinearmixedmodelsforcomplextraitanalyses AT shonglee coregremlforestimatingcovariancebetweenrandomeffectsinlinearmixedmodelsforcomplextraitanalyses
_version_	1718387711774031872

CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses

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