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.

One of the challenge fruit growers are facing is to balance between tree production and vegetative growth from year to year. To investigate the existence of genetic determinism for reproductive behaviour in olive tree, we studied an olive segregating population derived from a cross between ‘Olivière’ and ‘Arbequina’ cultivars. Our strategy was based on (i) an annual assessment of individual trees yield, and (ii) a decomposition of adult growth units at the crown periphery into quantitative variables related to both flowering and fruiting process in relation to their growth and branching. Genetic models, including the year, genotype effects and their interactions, were built with variance function and correlation structure of residuals when necessary. Among the progeny, trees were either ‘ON’ or ‘OFF’ for a given year and patterns of regular vs. irregular bearing were revealed. Genotype effect was significant on yield but not for flowering traits at growth unit (GU) scale, whereas the interaction between genotype and year was significant for both traits. A strong genetic effect was found for all fruiting traits without interaction with the year. Based on the new constructed genetic map, QTLs with small effects were detected, revealing multigenic control of the studied traits. Many were associated to alleles from ‘Arbequina’. Genetic correlations were found between Yield and Fruit set at GU scale suggesting a common genetic control, even though QTL co-localisations were in spe`cific years only. Most QTL were associated to flowering traits in specific years, even though reproductive traits at GU scale did not capture the bearing status of the trees in a given year. Results were also interpreted with respect to ontogenetic changes of growth and branching, and an alternative sampling strategy was proposed for capturing tree fruiting behaviour. Regular bearing progenies were identified and could constitute innovative material for selection programs.