Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes

Nicolò Fusi, Christoph Lippert, Neil D. Lawrence, Oliver Stegle
Nature Communications, 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
@Article{Fusi-warped14, title = {Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes}, author = {Fusi, Nicolò and Lippert, Christoph and Lawrence, Neil D. and Stegle, Oliver}, journal = {Nature Communications}, year = {2014}, volume = {5}, number = {4890}, doi = {10.1038/ncomms5890}, pdf = {https://www.nature.com/articles/ncomms5890.pdf}, 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 Journal Article %T Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes %A Nicolò Fusi %A Christoph Lippert %A Neil D. Lawrence %A Oliver Stegle %J Nature Communications %D 2014 %F Fusi-warped14 %R 10.1038/ncomms5890 %U http://inverseprobability.com/publications/fusi-warped14.html %V 5 %N 4890 %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 - JOUR TI - Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes AU - Nicolò Fusi AU - Christoph Lippert AU - Neil D. Lawrence AU - Oliver Stegle DA - 2014/09/19 ID - Fusi-warped14 VL - 5 IS - 4890 DO - 10.1038/ncomms5890 L1 - https://www.nature.com/articles/ncomms5890.pdf 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.. (2014). Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes. Nature Communications 5(4890) doi:10.1038/ncomms5890 Available from http://inverseprobability.com/publications/fusi-warped14.html.

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