Warped linear mixed models for the genetic analysis of transformed phenotypes

Nicoló FusiChristoph LippertNeil D. LawrenceOliver Stegle
,  5(4890), 2014.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-fusi-warped14, title = {Warped linear mixed models for the genetic analysis of transformed phenotypes}, author = {Nicoló Fusi and Christoph Lippert and Neil D. Lawrence and Oliver Stegle}, year = {}, editor = {}, volume = {5}, number = {4890}, url = {http://inverseprobability.com/publications/fusi-warped14.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Warped linear mixed models for the genetic analysis of transformed phenotypes %A Nicoló Fusi %A Christoph Lippert %A Neil D. Lawrence %A Oliver Stegle %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-fusi-warped14 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1038/ncomms5890 %U http://inverseprobability.com %V %N 4890 %W PMLR %X 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.
RIS
TY - CPAPER TI - Warped linear mixed models for the genetic analysis of transformed phenotypes AU - Nicoló Fusi AU - Christoph Lippert AU - Neil D. Lawrence AU - Oliver Stegle BT - PY - DA - ED - ID - pmlr-v-fusi-warped14 PB - PMLR SP - DP - PMLR EP - DO - 10.1038/ncomms5890 L1 - UR - http://inverseprobability.com/publications/fusi-warped14.html AB - 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. ER -
APA
Fusi, N., Lippert, C., Lawrence, N.D. & Stegle, O.. (). Warped linear mixed models for the genetic analysis of transformed phenotypes. , in PMLR (4890):-

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