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Warped linear mixed models for the genetic analysis of transformed phenotypes.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Fusi, Nicolo 
Lippert, Christoph 
Lawrence, Neil David  ORCID logo  https://orcid.org/0000-0001-9258-1030
Stegle, Oliver 

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.

Description

Keywords

Animals, Computer Simulation, Databases, Factual, Fungi, Genetic Association Studies, Genome-Wide Association Study, Humans, Linear Models, Mice, Models, Genetic, Normal Distribution, Phenotype, Polymorphism, Single Nucleotide, Yeasts

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

5

Publisher

Springer Science and Business Media LLC