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Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple

  • Roth, Morgane1, 2
  • Muranty, Hélène3
  • Di Guardo, Mario4, 5
  • Guerra, Walter6
  • Patocchi, Andrea1
  • Costa, Fabrizio4, 7
  • 1 Agroscope, Wädenswil, Zurich, Switzerland , Zurich (Switzerland)
  • 2 GAFL, INRAE, Montfavet, 84140, France , Montfavet (France)
  • 3 IRHS, INRAE, Agrocampus-Ouest, Université d’Angers, SFR 4207 QuaSaV, Beaucouzé, France , Beaucouzé (France)
  • 4 Fondazione Edmund Mach (FEM), Via E. Mach 1, San Michele all’Adige, 38010, Italy , San Michele all’Adige (Italy)
  • 5 University of Catania, Catania, Italy , Catania (Italy)
  • 6 Research Centre Laimburg, Laimburg 6, Auer, 39040, Italy , Auer (Italy)
  • 7 University of Trento, Via Mach 1, San Michele all’Adige, 38010, Italy , San Michele all’Adige (Italy)
Published Article
Horticulture Research
Nature Publishing Group UK
Publication Date
Sep 01, 2020
DOI: 10.1038/s41438-020-00370-5
Springer Nature


Texture is a complex trait and a major component of fruit quality in apple. While the major effect of MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the ‘Gala’ × ‘Pink Lady’ family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a ‘tailored training population’ can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.

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