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Detection and modelling of time-dependent QTL in animal populations

Genetics Selection Evolution volume 40, Article number: 177 (2008) Cite this article

Abstract

A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model.

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Authors and Affiliations

  1. Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, University of Aarhus, Research Center Foulum, P.O. Box 50, 8830, Tjele, Denmark

    Mogens S. Lund, Peter Sorensen & Per Madsen

  2. UR337 Station de génétique quantitative et appliquée, INRA, 78350, Jouy-en-Josas, France

    Florence Jaffrézic

Authors
  1. Mogens S. Lund
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  2. Peter Sorensen
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  3. Per Madsen
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  4. Florence Jaffrézic
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Corresponding author

Correspondence to Mogens S. Lund.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lund, M.S., Sorensen, P., Madsen, P. et al. Detection and modelling of time-dependent QTL in animal populations. Genet Sel Evol 40, 177 (2008). https://doi.org/10.1186/1297-9686-40-2-177

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  • DOI: https://doi.org/10.1186/1297-9686-40-2-177

Keywords

Genetics Selection Evolution

ISSN: 1297-9686