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

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 forGWAS 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. Ourmethod fits fixed SNP effects in a linearmixed model that can include both randompolygenic effects and permanent environmental effects. In this way, the model can correct for population structure andmodel repeated measures. The covariance structure of the linearmixed 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-termstudy 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 repeatedmeasurements 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 facilitatemodelling of longitudinal data for studies of both animal and human populations.

Keywords

Ficedula albicollis; genomic relationship; hierarchical generalized linear model; single-nucleotide polymorphisms

Published in

Methods in Ecology and Evolution
2016, Volume: 7, number: 7, pages: 792-799

SLU Authors

• Rönnegård, Lars

  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Genetics and Breeding


Publication identifier

DOI: https://doi.org/10.1111/2041-210X.12535

Permanent link to this page (URI)

https://res.slu.se/id/publ/79477