Abstract
BackgroundThe predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.MethodsThe training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.ResultsPedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.
Highlights
The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information
According to [2], for a given effective population size, the accuracy of GEBV depends on four parameters: level of linkage disequilibrium (LD) between markers and QTL, size of the training data set, heritability, and distribution of QTL effects
Using real data on egg production and quality traits in layers, this study confirms that the accuracy of EBV based on dense marker data is on average higher than that based on pedigree
Summary
The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. The resulting marker-based or genomic estimated breeding values (GEBV) exploit associations between markers and QTL (Quantitative Trait Loci) through linkage disequilibrium (LD) and linkage, along with the capture of pedigree relationships between animals [4]. Estimated breeding values (EBV) obtained with the pedigree-based BLUP animal model rely completely decline in accuracy over generations was greater at low marker densities. Solberg et al [6] reported that higher marker densities help maintain accuracy over generations These results are in agreement with those of Habier et al [4] who showed that 1,000 to 2,000 markers are sufficient to capture most pedigree relationships and that accuracy due to LD, which requires higher densities, is more persistent. The persistency of accuracy was greater for all marker-based methods than for EBV obtained from standard pedigree-based BLUP analyses
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