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Likelihood and Bayesian analyses reveal major genes affecting body composition, carcass, meat quality and the number of false teats in a Chinese European pig line

  • Sanchez Marie-PierreEmail author
  • Jean-Pierre Bidanel
  • Siqing Zhang
  • Jean Naveau
  • Thierry Burlot
  • Pascale Le Roy
Open Access
Research

Abstract

Segregation analyses were performed using both maximum likelihood – via a Quasi Newton algorithm – (ML-QN) and Bayesian – via Gibbs sampling – (Bayesian-GS) approaches in the Chinese European Tiameslan pig line. Major genes were searched for average ultrasonic backfat thickness (ABT), carcass fat (X2 and X4) and lean (X5) depths, days from 20 to 100 kg (D20100), Napole technological yield (NTY), number of false (FTN) and good (GTN) teats, as well as total teat number (TTN). The discrete nature of FTN was additionally considered using a threshold model under ML methodology. The results obtained with both methods consistently suggested the presence of major genes affecting ABT, X2, NTY, GTN and FTN. Major genes were also suggested for X4 and X5 using ML-QN, but not the Bayesian-GS, approach. The major gene affecting FTN was confirmed using the threshold model. Genetic correlations as well as gene effect and genotype frequency estimates suggested the presence of four different major genes. The first gene would affect fatness traits (ABT, X2 and X4), the second one a leanness trait (X5), the third one NTY and the last one GTN and FTN. Genotype frequencies of breeding animals and their evolution over time were consistent with the selection performed in the Tiameslan line.

Keywords

segregation analysis likelihood Bayesian major gene pig 

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Copyright information

© INRA, EDP Sciences 2003

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

  • Sanchez Marie-Pierre
    • 1
    Email author
  • Jean-Pierre Bidanel
    • 1
  • Siqing Zhang
    • 1
  • Jean Naveau
    • 2
  • Thierry Burlot
    • 2
  • Pascale Le Roy
    • 1
  1. 1.Institut national de la recherche agronomiqueStation de génétique quantitative et appliquéeJouy-en-Josas CedexFrance
  2. 2.PEN AR LANMaxentFrance

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