Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2002-02

AUTHORS

Flávio S. Schenkel, Lawrence R. Schaeffer, Paul J. Boettcher

ABSTRACT

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman's rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations. More... »

PAGES

41

References to SciGraph publications

  • 1986-12. Prediction of breeding values when variances are not known in GENETICS SELECTION EVOLUTION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1297-9686-34-1-41

    DOI

    http://dx.doi.org/10.1186/1297-9686-34-1-41

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005872382

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/11929624


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