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Decision rules and variance of response in breeding schemes

Published online by Cambridge University Press:  02 September 2010

J. A. Woolliams  and
T. H. E. Meuwissen
J. A. Woolliams
Affiliation:
Institute of Animal Physiology and Genetics Research, Edinburgh Research Station, Roslin, Midlothian EH26 9PS
T. H. E. Meuwissen
Affiliation:
Research Institute for Animal Production ‘Schoonoord’, PO Box 501, 3700 AM Zeist, The Netherlands
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Abstract

Selection decisions in breeding schemes can involve choices between candidates evaluated to different accuracies. A Bayesian framework is put forward for choosing among candidates, and it is shown that attaching loss functions for estimation errors makes this process different from selecting upon best linear unbiased predictions alone. Examples are given using both linear and quadratic loss to show that when estimation errors are penalized, the selection process tends to select more unrelated and more accurately evaluated individuals. In a dairy cattle breeding scheme response was only slightly lower than that from selection on expected breeding values but with a much reduced coefficient of variation. However, if prediction errors are preferred, with the hope of selecting individuals whose breeding value are higher than expected, extra genetic progress could be obtained by favouring the selection of individuals with low accuracy. This requires consideration of more than a single generation.

With discrete generations and equal measurements on candidates the decision framework was shown to be equivalent to a single quadratic restriction on the selection scores of parents in the previous generation.

A framework based on Bayes decision theory could be simply applied to produce a flexible means for producers to select according to their individual risk preferences.

Keywords

Bayesian theorybreeding programmesdairy cattleselection
Type
Research Article
Information
Animal Production , Volume 56 , Issue 2 , April 1993 , pp. 179 - 186
Copyright
Copyright © British Society of Animal Science 1993

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