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

Abstract

BackgroundGenomic 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.MethodsThe 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.ResultsUsing 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.

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

“…If the accuracy of the parent average is low, then genomic information may be useful for predicting parent average as well as Mendelian sampling, so the distinction becomes less important. Mendelian sampling term accuracy can also be predicted by comparison of accuracies of GEBVs predicted from average genotypes of the parents and actual individual genotypes, as shown by Wolc et al (2011), or by correlating the residuals of GEBV and predictand when both are corrected for the parent average estimated breeding values. In the future the contribution of genomic information to evaluating the accuracy of the Mendelian sampling term needs to become more prominent in the validation of genomic prediction.…”

“…An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals. G ENOMIC information is transforming animal and plant breeding (e.g., Dekkers and Hospital 2002; Bernardo and Yu 2007;Goddard and Hayes 2009;Hayes et al 2009a;Heffner et al 2009;VanRaden et al 2009a;Calus 2010;Crossa et al 2010;Daetwyler et al 2010a;Jannink et al 2010;Wolc et al 2011). Genomic selection can increase the rates of genetic gain through increased accuracy of estimated breeding values, reduction of generation intervals, and better utilization of available genetic resources through genome-guided mate selection (e.g., Sonesson et al 2010;Schierenbeck et al 2011;Pryce et al 2012b).…”

Advances in genomic selection in domestic animals

“…Although the theoretical issues of GS in swine [50,51], sheep [52,53], and chicken [54][55][56][57] have been discussed, and HD SNP panels for swine (Illumina PorcineSNP60), sheep (Illumina OvineSNP50), horse (Illumina Equine SNP50), and chicken (Illumina iSelect 18K Custom genotype) are now available, the reported implementation of GS in these species is still very limited. In sheep, a theoretical calculation revealed that, compared with traditional selection methods, GS could increase the overall response for a terminal sire index by about 30% and for a fine wool merino index by about 40% .…”

“…In broilers, Gonzalez-Recio et al [57] found that GS could increase the accuracy of selection for food conversion rates up to an almost 4-fold relative to a pedigree index. Applying GS in layers has been shown to increase the accuracy of selection by up to two-fold at an early age and by up to 88% when applied again at a later age [56]. An important reason for the limited application on GS in these species is that the cost of GS is relatively high when compared with GS in dairy cattle.…”

Incorporação de informações de marcadores genéticos em programas de melhoramento genético de bovinos de corte

“…A primeira proposta para estimação do efeito dos marcadores em características quantitativas foi pelo método dos mínimos quadrados (GELDERMANN, 1975 Diversas estratégias e metodologias foram publicadas na literatura para a implantação e avaliação do impacto da seleção assistida por marcadores em programas de melhoramento animal (GODDARD, 1992;VAN ARENDONK;TIER;KINGHORN, 1994;GODDARD, 1996;HALEY;VISSCHER, 1998 VAN RADEN et al, 2009;AGUILAR;MISZTAL, 2009;WOLC et al, 2011).…”