Bayesian inference on genetic merit under uncertain paternity

Genetics Selection Evolution

Robert J Tempelman¹  Fernando F Cardoso¹ 

[1] Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA

关键词: reduced animal model;    Bayesian inference;    genetic merit;    multiple-sire;    uncertain paternity;   

Others  :  1094416
DOI  :  10.1186/1297-9686-35-6-469

 received in 2002-10-03, accepted in 2003-04-03,  发布年份 2003

【 摘 要 】

A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM) in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h²) for each of direct and maternal genetic effects whereas Trait 2 had a high h² attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture) for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion) decisively favored the proposed hierarchical model over the ANRM model.

【 授权许可】

   
2003 INRA, EDP Sciences