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Such a formulation can be interpreted in terms of variable selection in a linear model, which has been a topic of frequent research aiming at reducing over-parameterisation in statistical models for GS [ 19, 20], as well as making the implementation of a genomic breeding program more cost-effective [ 21].
We economically optimized via simulation a genomic breeding program for a population of approximately 120,000 milk-recorded cows, corresponding to the Israeli Holstein population.
This strategy is competitive with a 4-pathway genomic breeding program if the fraction of selected male calves for the artificial insemination program is below 1% and if selection is focused on functionality, thus pointing to substantial insufficiencies caused by low reliabilities of breeding values for cows for such traits in conventional bull dam selection schemes.
For breeding companies, it is basically a business decision whether an investment in a genomic breeding program is expected to pay off through additional returns from a higher genetic quality of the marketable products, or an increase in the market share in a competitive market.
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For example, in simulation studies, Heffner et al. (2010) and Bernardo and Yu (2007) found genomic breeding programs to be clearly superior over traditional recurrent selection programs for complex traits in maize.
Hence, genomic breeding programs should focus on recording functional traits within progeny groups, preferably in cooperator herds.
This non-genomic reference scenario was compared to two genomic breeding programs to determine the best strategy for implementing genomic information in layer breeding programs.
These costs were fixed costs for the genomic breeding programs and were divided by the time horizon of ten years.
Genetic variants associated with feed efficiency may be used in genomic breeding programs to select more efficient layers.
This study focused on the implementation and short-term effects of incorporating genomic information in a layer breeding program in the process transition from traditional to genomic breeding programs.
The index accuracy of genomic breeding programs using a PB reference population differed from a CB reference population with 5.2% for rPC = 0.7 and 14.8% (0.434 vs. 0.498) for rPC = 0.5.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com