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A method for the prediction of multitrait breeding values for use in stochastic simulation to compare progeny‐testing schemes, with large progeny groups for proven sires
Author(s) -
Eikje L.S.,
Schaeffer L.R.,
Ådnøy T.,
Klemetsdal G.,
Ødegård J.
Publication year - 2012
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/j.1439-0388.2011.00952.x
Subject(s) - selection (genetic algorithm) , statistics , multivariate statistics , progeny testing , covariance matrix , variance (accounting) , mathematics , covariance , biology , computer science , machine learning , accounting , business
Summary A method of approximating estimated breeding values (EBV) from a multivariate distribution of true breeding values (TBV) and EBV is proposed for use in large‐scale stochastic simulation of alternative breeding schemes with a complex breeding goal. The covariance matrix of the multivariate distributions includes the additive genetic (co)variances and approximated prediction error (co)variances at different selection stages in the life of the animal. The prediction error (co)variance matrix is set up for one animal at a time, utilizing information on the selection candidate and its offspring, the parents, as well as paternal and maternal half‐ sibs. The EBV are a regression on TBV taking individual uncertainty into account, but with additional ‘free’ variation drawn at random. With the current information included in the calculation of the prediction error variance of a selection candidate, it is concluded that the method can be used to optimize progeny‐testing schemes, where the progeny‐tested sires are utilized with large progeny groups, e.g. through artificial insemination.