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CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses
As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named COR...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442840/ https://www.ncbi.nlm.nih.gov/pubmed/32826890 http://dx.doi.org/10.1038/s41467-020-18085-5 |
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author | Zhou, Xuan Im, Hae Kyung Lee, S. Hong |
author_facet | Zhou, Xuan Im, Hae Kyung Lee, S. Hong |
author_sort | Zhou, Xuan |
collection | PubMed |
description | As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for example, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p-value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p-value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers. |
format | Online Article Text |
id | pubmed-7442840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74428402020-09-02 CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses Zhou, Xuan Im, Hae Kyung Lee, S. Hong Nat Commun Article As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for example, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p-value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p-value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers. Nature Publishing Group UK 2020-08-21 /pmc/articles/PMC7442840/ /pubmed/32826890 http://dx.doi.org/10.1038/s41467-020-18085-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhou, Xuan Im, Hae Kyung Lee, S. Hong CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442840/ https://www.ncbi.nlm.nih.gov/pubmed/32826890 http://dx.doi.org/10.1038/s41467-020-18085-5 |
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