Warped linear mixed models for the genetic analysis of transformed phenotypes

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and...

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Main Authors:	Fusi, Nicolo, Lippert, Christoph, Lawrence, Neil D., Stegle, Oliver
Format:	Online
Language:	English
Published:	Nature Pub. Group 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199105/

id	pubmed-4199105
recordtype	oai_dc
spelling	pubmed-41991052014-10-17 Warped linear mixed models for the genetic analysis of transformed phenotypes Fusi, Nicolo Lippert, Christoph Lawrence, Neil D. Stegle, Oliver Article Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction. Nature Pub. Group 2014-09-19 /pmc/articles/PMC4199105/ /pubmed/25234577 http://dx.doi.org/10.1038/ncomms5890 Text en Copyright © 2014, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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	Fusi, Nicolo Lippert, Christoph Lawrence, Neil D. Stegle, Oliver
spellingShingle	Fusi, Nicolo Lippert, Christoph Lawrence, Neil D. Stegle, Oliver Warped linear mixed models for the genetic analysis of transformed phenotypes
author_facet	Fusi, Nicolo Lippert, Christoph Lawrence, Neil D. Stegle, Oliver
author_sort	Fusi, Nicolo
title	Warped linear mixed models for the genetic analysis of transformed phenotypes
title_short	Warped linear mixed models for the genetic analysis of transformed phenotypes
title_full	Warped linear mixed models for the genetic analysis of transformed phenotypes
title_fullStr	Warped linear mixed models for the genetic analysis of transformed phenotypes
title_full_unstemmed	Warped linear mixed models for the genetic analysis of transformed phenotypes
title_sort	warped linear mixed models for the genetic analysis of transformed phenotypes
description	Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.
publisher	Nature Pub. Group
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199105/
_version_	1613145454492516352

Warped linear mixed models for the genetic analysis of transformed phenotypes

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