A study of Genomic Prediction across Generations of Two Korean Pig Populations

SIMPLE SUMMARY: Commercial 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. ABSTRACT: 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.