Genet. Sel. Evol. 35 (2003) 469-487
DOI: 10.1051/gse:2003035

Bayesian inference on genetic merit under uncertain paternity

Fernando F. Cardoso and Robert J. Tempelman

Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
(Received 3 October 2002; accepted 3 April 2003)

Abstract
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.

Key words: uncertain paternity / multiple-sire / genetic merit / Bayesian inference / reduced animal model

Correspondence and reprints: Fernando F. Cardoso
    e-mail: cardosof@msu.edu

© INRA, EDP Sciences 2003