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Efficiency of genomic selection in an established commercial layer breeding program.

Sitzenstock F, Ytournel F, Sharifi AR, Cavero D, Täubert H, Preisinger R, Simianer H - Genet. Sel. Evol. (2013)

Bottom Line: In this case, the generation interval was reduced to eight months.This increase was in all cases associated with higher breeding costs.While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Sciences, University of Göttingen, 37075 Göttingen, Germany. fsitzen@gwdg.de

ABSTRACT

Background: In breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. 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.

Results: In scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs.

Conclusions: While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

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Related in: MedlinePlus

Genetic gain for individual traits in crossbred hens in the practical environment for Scenario I. LP1: laying performance 1, LP2: laying performance 2, LP3+4: laying performance 3 and 4, EW: egg weight, ESS: egg shell strength and M: mortality; genetic gain relative to the reference scenario (set to 100%) with different numbers of genotyped cocks and different sizes of the calibration set.
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Figure 3: Genetic gain for individual traits in crossbred hens in the practical environment for Scenario I. LP1: laying performance 1, LP2: laying performance 2, LP3+4: laying performance 3 and 4, EW: egg weight, ESS: egg shell strength and M: mortality; genetic gain relative to the reference scenario (set to 100%) with different numbers of genotyped cocks and different sizes of the calibration set.

Mentions: To compare the expected genetic gain for individual traits between scenarios, we used the six traits of the crossbred hens in the practical environment, since this is the type of production that is closest to the production system for which the hens are selected. In Figure 3, the predicted genetic change for these six traits is shown relative to the predicted change for the reference scenario. The gain for laying performance increased for all laying periods, with the highest gain for the second period, for which the genetic gain was doubled when a calibration set of 2000 cocks was used and 4000 cocks were genotyped for selection. Genetic gain in egg weight decreased slightly when only a few cocks were genotyped for selection and reached the same level as in the reference scenario only when 4000 or more cocks were genotyped for selection. With 2000 animals in the calibration set, the genetic gain was greater than with 500 animals but ranking of traits remained the same. In all cases, a considerable genetic improvement in mortality was observed. Adding the genotyping of hens increased genetic gain for all traits except egg weight (data not shown). In particular, genetic gains in laying performance and mortality benefitted from the additional genomic information. The additional increase in egg shell stability was only marginal and the genetic gain in egg weight was lower than when only the cocks were genotyped.


Efficiency of genomic selection in an established commercial layer breeding program.

Sitzenstock F, Ytournel F, Sharifi AR, Cavero D, Täubert H, Preisinger R, Simianer H - Genet. Sel. Evol. (2013)

Genetic gain for individual traits in crossbred hens in the practical environment for Scenario I. LP1: laying performance 1, LP2: laying performance 2, LP3+4: laying performance 3 and 4, EW: egg weight, ESS: egg shell strength and M: mortality; genetic gain relative to the reference scenario (set to 100%) with different numbers of genotyped cocks and different sizes of the calibration set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750290&req=5

Figure 3: Genetic gain for individual traits in crossbred hens in the practical environment for Scenario I. LP1: laying performance 1, LP2: laying performance 2, LP3+4: laying performance 3 and 4, EW: egg weight, ESS: egg shell strength and M: mortality; genetic gain relative to the reference scenario (set to 100%) with different numbers of genotyped cocks and different sizes of the calibration set.
Mentions: To compare the expected genetic gain for individual traits between scenarios, we used the six traits of the crossbred hens in the practical environment, since this is the type of production that is closest to the production system for which the hens are selected. In Figure 3, the predicted genetic change for these six traits is shown relative to the predicted change for the reference scenario. The gain for laying performance increased for all laying periods, with the highest gain for the second period, for which the genetic gain was doubled when a calibration set of 2000 cocks was used and 4000 cocks were genotyped for selection. Genetic gain in egg weight decreased slightly when only a few cocks were genotyped for selection and reached the same level as in the reference scenario only when 4000 or more cocks were genotyped for selection. With 2000 animals in the calibration set, the genetic gain was greater than with 500 animals but ranking of traits remained the same. In all cases, a considerable genetic improvement in mortality was observed. Adding the genotyping of hens increased genetic gain for all traits except egg weight (data not shown). In particular, genetic gains in laying performance and mortality benefitted from the additional genomic information. The additional increase in egg shell stability was only marginal and the genetic gain in egg weight was lower than when only the cocks were genotyped.

Bottom Line: In this case, the generation interval was reduced to eight months.This increase was in all cases associated with higher breeding costs.While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Sciences, University of Göttingen, 37075 Göttingen, Germany. fsitzen@gwdg.de

ABSTRACT

Background: In breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. 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.

Results: In scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs.

Conclusions: While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

Show MeSH
Related in: MedlinePlus