The application of genomic selection in fruit tree crops is expected to enhance breeding eficiency by increasing prediction accuracy,increasing selection intensity and decreasing generation interval.The objectives of ...The application of genomic selection in fruit tree crops is expected to enhance breeding eficiency by increasing prediction accuracy,increasing selection intensity and decreasing generation interval.The objectives of this study were to assess the accuracy of prediction and selection response in commercial apple breeding programmes for key traits.The training population comprised 977 individuals derived from 20 pedigreed fllsib families.Historic phenotypic data were available on 10 traits related to productivity and fruit external appearance and genotypic data for 7829 SNPs obtained with an llumina 20K SNP array.From these data,a genome-wide prediction model was built and subsequently used to calculate genomic breeding values of five application fllsib families.The application families had genotypes at 364 SNPs from a dedicated 512 SNP array,and these genotypic data were extended to the high-density level by imputation.These five families were phenotyped for 1 year and their phenotypes were compared to the predicted breeding values.Accuracy of genomic prediction across the 10 traits reached a maximum value of 0.5 and had a median value of 0.19.The accuracies were strongly affected by the phenotypic distribution and heritability of traits.In the largest family,significant selection response was observed for traits with high heritability and symmetric phenotypic distribution.Traits that showed non-significant response often had reduced and skewed phenotypic variation or low heritability.Among the five application families the accuracies were uncorrelated to the degree of relatedness to the training population.The results underline the potential of genomic prediction to accelerate breeding progress in outbred fruit tree crops that still need to overcome long generation intervals and extensive phenotyping costs.展开更多
基金This work has been funded under the EU seventh Framework Programme by the FruitBreedomics project No.265582:Integrated Approach for increasing breeding efficiency in fruit tree crops(http://www.fruitbreedomics.com/).
文摘The application of genomic selection in fruit tree crops is expected to enhance breeding eficiency by increasing prediction accuracy,increasing selection intensity and decreasing generation interval.The objectives of this study were to assess the accuracy of prediction and selection response in commercial apple breeding programmes for key traits.The training population comprised 977 individuals derived from 20 pedigreed fllsib families.Historic phenotypic data were available on 10 traits related to productivity and fruit external appearance and genotypic data for 7829 SNPs obtained with an llumina 20K SNP array.From these data,a genome-wide prediction model was built and subsequently used to calculate genomic breeding values of five application fllsib families.The application families had genotypes at 364 SNPs from a dedicated 512 SNP array,and these genotypic data were extended to the high-density level by imputation.These five families were phenotyped for 1 year and their phenotypes were compared to the predicted breeding values.Accuracy of genomic prediction across the 10 traits reached a maximum value of 0.5 and had a median value of 0.19.The accuracies were strongly affected by the phenotypic distribution and heritability of traits.In the largest family,significant selection response was observed for traits with high heritability and symmetric phenotypic distribution.Traits that showed non-significant response often had reduced and skewed phenotypic variation or low heritability.Among the five application families the accuracies were uncorrelated to the degree of relatedness to the training population.The results underline the potential of genomic prediction to accelerate breeding progress in outbred fruit tree crops that still need to overcome long generation intervals and extensive phenotyping costs.
