Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies

Multivariate linear mixed models (mvLMMs) are powerful tools for testing SNP associations with multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present computationally-efficient algorithms for fitting mvLMMs and computing likelihoo...

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Detalles Bibliográficos
Autores principales: Zhou, Xiang, Stephens, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2014
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211878/
https://www.ncbi.nlm.nih.gov/pubmed/24531419
http://dx.doi.org/10.1038/nmeth.2848
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author Zhou, Xiang
Stephens, Matthew
author_facet Zhou, Xiang
Stephens, Matthew
author_sort Zhou, Xiang
collection PubMed
description Multivariate linear mixed models (mvLMMs) are powerful tools for testing SNP associations with multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present computationally-efficient algorithms for fitting mvLMMs and computing likelihood ratio tests that improve on existing approximate methods in i) computation speed, ii) power/p value calibration, iii) ability to deal with more than two phenotypes. We illustrate these features on real and simulated data.
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spelling pubmed-42118782014-10-28 Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies Zhou, Xiang Stephens, Matthew Nat Methods Article Multivariate linear mixed models (mvLMMs) are powerful tools for testing SNP associations with multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present computationally-efficient algorithms for fitting mvLMMs and computing likelihood ratio tests that improve on existing approximate methods in i) computation speed, ii) power/p value calibration, iii) ability to deal with more than two phenotypes. We illustrate these features on real and simulated data. 2014-02-16 2014-04 /pmc/articles/PMC4211878/ /pubmed/24531419 http://dx.doi.org/10.1038/nmeth.2848 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Zhou, Xiang
Stephens, Matthew
Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title_full Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title_fullStr Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title_full_unstemmed Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title_short Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies
title_sort efficient algorithms for multivariate linear mixed models in genome-wide association studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211878/
https://www.ncbi.nlm.nih.gov/pubmed/24531419
http://dx.doi.org/10.1038/nmeth.2848
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