Genetics Selection Evolution

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Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

Anna Wolc1,2*, Chris Stricker3, Jesus Arango4, Petek Settar4, Janet E Fulton4, Neil P O'Sullivan4, Rudolf Preisinger5, David Habier2, Rohan Fernando2, Dorian J Garrick2, Susan J Lamont2 and Jack CM Dekkers2

Author Affiliations

1 Department of Genetics and Animal Breeding, University of Life Sciences in Poznan, Wołyńska st. 33, 60-637 Poznan, Poland

2 Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA

3 Applied Genetics Network, Börtjstrasse 8b, 7260 Davos, Switzerland

4 Hy-Line International, Dallas Center, IA 50063, USA

5 Lohmann Tierzucht GmbH, 27472 Cuxhaven, Germany

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Genetics Selection Evolution 2011, 43:5 doi:10.1186/1297-9686-43-5

Published: 21 January 2011

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.