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Partitioning of genetic variation across the genome using multimarker methods in a wild bird population

Authors

  • Matthew R. Robinson,

    Corresponding author
    1. Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane 4029, Australia
    • Department of Animal and Plant Science, University of Sheffield, Western Bank, Sheffield, UK
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  • Anna W. Santure,

    1. Department of Animal and Plant Science, University of Sheffield, Western Bank, Sheffield, UK
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  • Isabelle DeCauwer,

    1. Department of Animal and Plant Science, University of Sheffield, Western Bank, Sheffield, UK
    2. Laboratoire de Génétique et Evolution des Populations Végétales, UMR CNRS 8198, Bâtiment SN2, Université des Sciences et Technologies de Lille – Lille 1, Villeneuve d'Ascq Cedex, France
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  • Ben C. Sheldon,

    1. Department of Zoology, Edward Grey Institute, University of Oxford, Oxford, UK
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  • Jon Slate

    1. Department of Animal and Plant Science, University of Sheffield, Western Bank, Sheffield, UK
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Correspondence and Present address: Matthew R. Robinson, The University of Queensland, Queensland Brain Institute (QBI), QBI Building (#79) St Lucia, QLD 4072, Australia Fax: 0044 (0)114 222 0002;

E-mail: matthew.r.robinson@sheffield.ac.uk

Abstract

The underlying basis of genetic variation in quantitative traits, in terms of the number of causal variants and the size of their effects, is largely unknown in natural populations. The expectation is that complex quantitative trait variation is attributable to many, possibly interacting, causal variants, whose effects may depend upon the sex, age and the environment in which they are expressed. A recently developed methodology in animal breeding derives a value of relatedness among individuals from high-density genomic marker data, to estimate additive genetic variance within livestock populations. Here, we adapt and test the effectiveness of these methods to partition genetic variation for complex traits across genomic regions within ecological study populations where individuals have varying degrees of relatedness. We then apply this approach for the first time to a natural population and demonstrate that genetic variation in wing length in the great tit (Parus major) reflects contributions from multiple genomic regions. We show that a polygenic additive mode of gene action best describes the patterns observed, and we find no evidence of dosage compensation for the sex chromosome. Our results suggest that most of the genomic regions that influence wing length have the same effects in both sexes. We found a limited amount of genetic variance in males that is attributed to regions that have no effects in females, which could facilitate the sexual dimorphism observed for this trait. Although this exploratory work focuses on one complex trait, the methodology is generally applicable to any trait for any laboratory or wild population, paving the way for investigating sex-, age- and environment-specific genetic effects and thus the underlying genetic architecture of phenotype in biological study systems.

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