Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation

Show full item record



Permalink

http://hdl.handle.net/10138/209574

Citation

Ronnegard , L , McFarlane , S E , Husby , A , Kawakami , T , Ellegren , H & Qvarnstrom , A 2016 , ' Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation ' , Methods in Ecology and Evolution , vol. 7 , no. 7 , pp. 792-799 . https://doi.org/10.1111/2041-210X.12535

Title: Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation
Author: Ronnegard, Lars; McFarlane, S. Eryn; Husby, Arild; Kawakami, Takeshi; Ellegren, Hans; Qvarnstrom, Anna
Contributor: University of Helsinki, Biosciences
Date: 2016-07
Language: eng
Number of pages: 8
Belongs to series: Methods in Ecology and Evolution
ISSN: 2041-210X
URI: http://hdl.handle.net/10138/209574
Abstract: 1. 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. 2. 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. 3. 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. 4. 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.
Subject: Ficedula albicollis
genomic relationship
hierarchical generalized linear model
single-nucleotide polymorphisms
FLYCATCHER FICEDULA-ALBICOLLIS
COLLARED FLYCATCHER
MIXED-MODEL
LONGITUDINAL DATA
MATE CHOICE
WILD
EVOLUTION
DIVERGENCE
SENESCENCE
SELECTION
1181 Ecology, evolutionary biology
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
R_nneg_rd_et_al ... _Ecology_and_Evolution.pdf 207.9Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record