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.
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.
Sanchez
Marie-Pierre
MP
Institut national de la recherche agronomique, Station de génétique quantitative et appliquée, 78352 Jouy-en-Josas Cedex, France. sanchez@dga2.jouy.inra.fr
Bidanel
Jean-Pierre
JP
Zhang
Siqing
S
Naveau
Jean
J
Burlot
Thierry
T
Le Roy
Pascale
P
D000818:Animals; D001499:Bayes Theorem; D001823:Body Composition / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D040582:Inheritance Patterns / Q000235:genetics; D016013:Likelihood Functions; D008460:Meat; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D013552:Swine / Q000235:genetics*
D005819:Genetic Markers