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. 2011 Jan 21;43(1):5.
doi: 10.1186/1297-9686-43-5.

Breeding Value Prediction for Production Traits in Layer Chickens Using Pedigree or Genomic Relationships in a Reduced Animal Model

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Free PMC article

Breeding Value Prediction for Production Traits in Layer Chickens Using Pedigree or Genomic Relationships in a Reduced Animal Model

Anna Wolc et al. Genet Sel Evol. .
Free PMC article

Abstract

Background: Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.

Methods: The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.

Results: Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

Figures

Figure 1
Figure 1
Accuracy of predicted breeding values and parental average (PA) breeding values from three methods: pedigree-based BLUP (PBLUP), marker-based BLUP (GBLUP), and Bayesian variable selection prediction (Bayes-C-π) in the early selection scenario. Accuracy is the correlation between predicted breeding values and hatch-corrected phenotype in the validation set divided by square root of heritability from Table 2.
Figure 2
Figure 2
Accuracy of predicted breeding values and parental average (PA) breeding values from three methods: pedigree-based BLUP (PBLUP), marker-based BLUP (GBLUP), and Bayesian variable selection prediction (Bayes-C-π) in the late selection scenario. Accuracy is the correlation between predicted breeding values and hatch-corrected phenotype in the validation set divided by square root of heritability from Table 2.
Figure 3
Figure 3
Pedigree and marker based relationships in the studied population.

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