Abstract
Simple SummaryCommercial genotyping has become accessible at a relatively low cost and nowadays it is widely used by breeders to predict production and economic traits. Many studies explored the benefits of using DNA information in breeding programs, and many methods have been established to optimize the use of such information. To date, however, very few studies have explored how prediction accuracies change across generations. Here we present a short evaluation across five generations in two pig breeds and evaluate the accuracy of the prediction of relevant production traits using different generational groups.Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.
Highlights
The results suggested that, there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes
Comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection
When genomic prediction is performed over successive generations of the same population, the prediction accuracy is expected to decay if the reference population is not updated, even if the base population initially genotyped was large, due to the decline in the extent of the relationships between reference and test individuals, and the breakdown of the linkage disequilibrium (LD) between single nucleotide polymorphism (SNP) and the quantitative trait loci (QTL) across generations [11]
Summary
Genomic models that incorporate dense single nucleotide polymorphism (SNP) marker information are widely used for the prediction of genomic values [1] to select animals and plants in breeding programs [2,3,4,5] or to predict susceptibility to diseases in humans [6,7].Within a genotyped population, the extent of the genomic relationships [8,9], linkage disequilibrium (LD), and co-segregation of QTL with markers [10] contribute to the accuracy of genomic predictions.In livestock populations such as beef and dairy cattle, sheep, and pigs, the level of relatedness between individuals is higher compared to the relatedness observed in other populations, such as humans.Genomic relationships are higher within families, rather than across families, and genomic prediction in breeding populations generally relies on the genotypes and phenotypes from ancestralAnimals 2019, 9, 672; doi:10.3390/ani9090672 www.mdpi.com/journal/animalsAnimals 2019, 9, 672 individuals to predict the breeding values of subsequent generations. The extent of the genomic relationships [8,9], linkage disequilibrium (LD), and co-segregation of QTL with markers [10] contribute to the accuracy of genomic predictions. In livestock populations such as beef and dairy cattle, sheep, and pigs, the level of relatedness between individuals is higher compared to the relatedness observed in other populations, such as humans. Animals 2019, 9, 672 individuals to predict the breeding values of subsequent generations This practice of using information from previous generations to predict the one has been in use for many years and predates the genomic technologies. Only the known family relationships from the current and previous generations (in the form of a pedigree relationship matrix) were combined with recorded phenotypes, to predict breeding values
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