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An algorithm for efficient constrained mate selection.

Kinghorn BP - Genet. Sel. Evol. (2011)

Bottom Line: Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level.The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers.The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Environmental and Rural Science, Universiy of New England, Armidale, NSW 2350, Australia. bkinghor@une.edu.au

ABSTRACT

Background: Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level. The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers.

Methods: This paper describes a mate selection algorithm that is widely used and presents an extension that makes it possible to apply constraints on certain matings, as dictated through a group mating permission matrix.

Results: This full algorithm leads to simpler applications, and to computing speed for the scenario tested, which is several hundred times faster than the previous strategy of penalising solutions that break constraints.

Conclusions: The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.

Show MeSH
An example frontier response surface involving Progeny Index and Parental Coancestry. See text for details; from the MateSel tool in Pedigree Viewer, available at http://www-personal.une.edu.au/~bkinghor/pedigree.htm.
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Figure 2: An example frontier response surface involving Progeny Index and Parental Coancestry. See text for details; from the MateSel tool in Pedigree Viewer, available at http://www-personal.une.edu.au/~bkinghor/pedigree.htm.

Mentions: The relative emphasis on the mean index versus coancestry was set in the light of their response surface (Figure 2). The curved frontier in this figure shows the range of possible outcomes of optimal contributions (number of matings allocated to each candidate), with each point reflecting a different relative weighting on mean progeny index versus parental coancestry [see [10]]. However in this case, the frontier accommodates the grouping constraints in Table 3, using the GroupFix algorithm for all treatments, so that the same conditions prevail for each treatment during its main run.


An algorithm for efficient constrained mate selection.

Kinghorn BP - Genet. Sel. Evol. (2011)

An example frontier response surface involving Progeny Index and Parental Coancestry. See text for details; from the MateSel tool in Pedigree Viewer, available at http://www-personal.une.edu.au/~bkinghor/pedigree.htm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037843&req=5

Figure 2: An example frontier response surface involving Progeny Index and Parental Coancestry. See text for details; from the MateSel tool in Pedigree Viewer, available at http://www-personal.une.edu.au/~bkinghor/pedigree.htm.
Mentions: The relative emphasis on the mean index versus coancestry was set in the light of their response surface (Figure 2). The curved frontier in this figure shows the range of possible outcomes of optimal contributions (number of matings allocated to each candidate), with each point reflecting a different relative weighting on mean progeny index versus parental coancestry [see [10]]. However in this case, the frontier accommodates the grouping constraints in Table 3, using the GroupFix algorithm for all treatments, so that the same conditions prevail for each treatment during its main run.

Bottom Line: Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level.The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers.The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Environmental and Rural Science, Universiy of New England, Armidale, NSW 2350, Australia. bkinghor@une.edu.au

ABSTRACT

Background: Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level. The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers.

Methods: This paper describes a mate selection algorithm that is widely used and presents an extension that makes it possible to apply constraints on certain matings, as dictated through a group mating permission matrix.

Results: This full algorithm leads to simpler applications, and to computing speed for the scenario tested, which is several hundred times faster than the previous strategy of penalising solutions that break constraints.

Conclusions: The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.

Show MeSH