Limits...
Response and inbreeding from a genomic selection experiment in layer chickens.

Wolc A, Zhao HH, Arango J, Settar P, Fulton JE, O'Sullivan NP, Preisinger R, Stricker C, Habier D, Fernando RL, Garrick DJ, Lamont SJ, Dekkers JC - Genet. Sel. Evol. (2015)

Bottom Line: We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year.At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50011-3150, USA. awolc@iastate.edu.

ABSTRACT

Background: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken.

Methods: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.

Results: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line.

Conclusions: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

No MeSH data available.


Numbers of contributing sires and dams and total number of selection candidates in the experimental breeding program, including training generations, three generations of the pedigree sub-line and five generations of the genomic sub-line
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Fig1: Numbers of contributing sires and dams and total number of selection candidates in the experimental breeding program, including training generations, three generations of the pedigree sub-line and five generations of the genomic sub-line

Mentions: Different population structures were implemented for the two sub-lines, with numbers of contributing sires and dams and the total number of selection candidates shown on Fig. 1. In the pedigree line, 1000 male and 3000 female candidates were produced at each generation from 60 male and 360 female parents that were selected on a multi-trait index of phenotype-BLUP EBV, after records on female candidates were collected, with a restriction on the number of full-sibs selected. Selected males and females were mated in a hierarchical manner (six females per male), with some restriction to avoid matings between full- or half-sibs.Fig. 1


Response and inbreeding from a genomic selection experiment in layer chickens.

Wolc A, Zhao HH, Arango J, Settar P, Fulton JE, O'Sullivan NP, Preisinger R, Stricker C, Habier D, Fernando RL, Garrick DJ, Lamont SJ, Dekkers JC - Genet. Sel. Evol. (2015)

Numbers of contributing sires and dams and total number of selection candidates in the experimental breeding program, including training generations, three generations of the pedigree sub-line and five generations of the genomic sub-line
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4492088&req=5

Fig1: Numbers of contributing sires and dams and total number of selection candidates in the experimental breeding program, including training generations, three generations of the pedigree sub-line and five generations of the genomic sub-line
Mentions: Different population structures were implemented for the two sub-lines, with numbers of contributing sires and dams and the total number of selection candidates shown on Fig. 1. In the pedigree line, 1000 male and 3000 female candidates were produced at each generation from 60 male and 360 female parents that were selected on a multi-trait index of phenotype-BLUP EBV, after records on female candidates were collected, with a restriction on the number of full-sibs selected. Selected males and females were mated in a hierarchical manner (six females per male), with some restriction to avoid matings between full- or half-sibs.Fig. 1

Bottom Line: We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year.At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

View Article: PubMed Central - PubMed

Affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50011-3150, USA. awolc@iastate.edu.

ABSTRACT

Background: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken.

Methods: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.

Results: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line.

Conclusions: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

No MeSH data available.