title: Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation.
abstract: Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful.Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden.The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response.The method is available in the r package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.
authors.affiliation: Department of Clinical Sciences Swedish University of Agricultural Sciences SE-75007 Uppsala Sweden.
authors.forename: Lars
authors.initials: L
authors.lastname: Rönnegård
authors.affiliation: Department of Animal Ecology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden.
authors.forename: S Eryn
authors.initials: SE
authors.lastname: McFarlane
authors.affiliation: Department of Biosciences Metapopulation Research Centre University of Helsinki PO Box 65FI-00014 Helsinki Finland; Department of Biology Centre for Biodiversity Dynamics Norwegian University of Science and Technology N-7491 Trondheim Norway.
authors.forename: Arild
authors.initials: A
authors.lastname: Husby
authors.affiliation: Department of Evolutionary Biology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kawakami
authors.affiliation: Department of Evolutionary Biology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden.
authors.forename: Hans
authors.initials: H
authors.lastname: Ellegren
authors.affiliation: Department of Animal Ecology Evolutionary Biology Centre (EBC) Uppsala University Norbyvägen 18D SE-75236 Uppsala Sweden.
authors.forename: Anna
authors.initials: A
authors.lastname: Qvarnström
keywords: Ficedula albicollis; genomic relationship; hierarchical generalized linear model; single‐nucleotide polymorphisms