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Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects

  • Virginie ClémentEmail author
  • Bernard Bibé
  • Étienne Verrier
  • Jean-Michel Elsen
  • Eduardo Manfredi
  • Jacques Bouix
  • Éric Hanocq
Open Access
Research

Abstract

Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects. To test model adequacy, several data sets were simulated according to different underlying genetic assumptions and analysed by comparing the correct and incorrect models. Results showed that omission of one of the random effects leads to an incorrect decomposition of the other components. If maternal genetic effects exist but are neglected, direct heritability is overestimated, and sometimes more than double. The bias depends on the value of the genetic correlation between direct and maternal effects. To study the influence of data structure on the estimation of genetic parameters, several populations were simulated, with different degrees of known paternity and different levels of genetic connectedness between flocks. Results showed that the lack of connectedness affects estimates when flocks have different genetic means because no distinction can be made between genetic and environmental differences between flocks. In this case, direct and maternal heritabilities are under-estimated, whereas maternal environmental effects are overestimated. The insufficiency of pedigree leads to biased estimates of genetic parameters.

Keywords

genetic parameters animal model maternal effects simulations connectedness 

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Copyright information

© INRA, EDP Sciences 2001

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Virginie Clément
    • 1
    Email author
  • Bernard Bibé
    • 1
  • Étienne Verrier
    • 2
    • 3
  • Jean-Michel Elsen
    • 1
  • Eduardo Manfredi
    • 1
  • Jacques Bouix
    • 1
  • Éric Hanocq
    • 1
  1. 1.Station d'amélioration génétique des animauxInstitut national de la recherche agronomiqueCastanet-Tolosan CedexFrance
  2. 2.Station de génétique quantitative et appliquéeInstitut national de la recherche agronomiqueJouy-en-Josas CedexFrance
  3. 3.Département des sciences animalesInstitut national agronomique Paris-GrignonParis Cedex 05France

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