Abstract
The application of genomic selection in fruit tree crops is expected to enhance breeding efficiency 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 full-sib 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 Illumina 20K SNP array. From these data, a genome-wide prediction model was built and subsequently used to calculate genomic breeding values of five application full-sib 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.
Highlights
Developing new fruit tree cultivars is a time-consuming process for two main reasons
Plant material The training population, developed in the EU-funded HiDRAS project[25] for Pedigree Based quantitative trait locus (QTL) analysis,[26] consisted of 20 full-sib (FS) families with a total of 977 individuals. These FS families were obtained from breeding programmes from four research institutes (INRA, France; JKI, Germany; UNIBO and LFW, Italy) at the start of the HiDRAS project and resulted from crosses among 24 parents (Supplementary Table S1) which were related to each other via common ancestors
Distribution of trait phenotypes In the training population the distributions of phenotypes (5 genotypic best linear unbiased predictions (BLUPs) values) varied greatly among the 10 traits considered in this study (Figure 1)
Summary
Developing new fruit tree cultivars is a time-consuming process for two main reasons. The long juvenile phase delays the acquisition of phenotypic data that are necessary to identify genotypes that will produce marketable fruits satisfying both farmer and consumer demands.[1] Second, breeding programmes are often organized in two or more successive steps. The initial large diversity step involves phenotyping a large number of candidates over two or three years at a single location. Those candidates presenting favourable characters are identified and enter the cultivar evaluation step comprising extensive phenotypic evaluation over a longer period at multiple contrasting locations. The early identification of promising genotypes at the large diversity step would enable fruit tree breeders to enhance genetic gain per year and react more efficiently to changing demands by reducing breeding cycle length.[2,3]
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