title: Warped linear mixed models for the genetic analysis of transformed phenotypes.
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.
authors.affiliation: eScience Group, Microsoft Research, Los Angeles, California 90024, USA.
authors.forename: Nicolo
authors.initials: N
authors.lastname: Fusi
authors.affiliation: eScience Group, Microsoft Research, Los Angeles, California 90024, USA.
authors.forename: Christoph
authors.initials: C
authors.lastname: Lippert
authors.affiliation: Department of Computer Science, University of Sheffield, Sheffield S10 2HQ, UK.
authors.forename: Neil D
authors.initials: ND
authors.lastname: Lawrence
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, UK.
authors.forename: Oliver
authors.initials: O
authors.lastname: Stegle
mesh_terms: D000818:Animals; D003198:Computer Simulation; D016208:Databases, Factual; D005658:Fungi / Q000235:genetics / Q000378:metabolism; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D051379:Mice; D008957:Models, Genetic; D016011:Normal Distribution; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D015003:Yeasts