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Warped linear mixed models for the genetic analysis of transformed phenotypes

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Item Type:Article
Title:Warped linear mixed models for the genetic analysis of transformed phenotypes
Creators Name:Fusi, N. and Lippert, C. and Lawrence, N.D. and Stegle, O.
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.
Keywords:Computer Simulation, Factual Databases, Fungi, Genetic Association Studies, Genetic Models, Genome-Wide Association Study, Linear Models, Normal Distribution, Phenotype, Single Nucleotide Polymorphism, Yeasts, Animals, Mice
Source:Nature Communications
Publisher:Nature Publishing Group
Page Range:4890
Date:19 September 2014
Official Publication:https://doi.org/10.1038/ncomms5890
PubMed:View item in PubMed

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