Title 30 31 Prediction of fruit texture with training population optimization for efficient genomic selection in apple 32 33Running title 34Genomic prediction for apple texture 35Keywords 36Apple, genomic prediction, rrBLUP, multi-trait, fruit texture, relatedness, training set 37 optimization 38
Abbreviations 39Abstract 47Texture plays a major role in the determination of fruit quality in apple. Due to its 48 physiological and economic relevance, this trait has been largely investigated, leading to the 49 fixation of the major gene PG1 controlling firmness in elite cultivars. To further improve fruit 50 texture, the targeting of an undisclosed reservoir of loci with minor effects is compelling. In 51 this work, we aimed to unlock this potential with a genomic selection approach by predicting 52 fruit acoustic and mechanical features as obtained with a TA.XTplus texture analyzer in 537 53 individuals genotyped with 8,294 SNP markers. The best prediction accuracies following 54 cross-validations within the training set (TRS) of 259 individuals were obtained for the 55 acoustic linear distance (0.64). Prediction accuracy was further improved through the 56 optimization of TRS size and composition according to the test set. With this strategy, a 57 maximal accuracy of 0.81 was obtained when predicting the synthetic trait PC1 in the family 58 'Gala ൈ Pink Lady'. We discuss the impact of genetic relatedness and clustering on trait 59 variability and predictability. Moreover, we demonstrated the need for a comprehensive 60 dissection of the complex texture phenotype and the potentiality of using genomic selection to 61 improve fruit quality in apple. 62 63 This work was co-funded by the EU seventh Framework Programme by the FruitBreedomics 583 Project No. 265582: Integrated Approach for increasing breeding efficiency in fruit tree crops 584 19 (www.FruitBreedomics.com). The views expressed in this work are the sole responsibility of 585 the authors and do not necessarily reflect the views of the European Commission. 586 References Akdemir D, Isidro-Sánchez J. 2019. Design of training populations for selective phenotyping in genomic prediction. Scientific Reports 9: 1446. Akdemir D, Sánchez JI, Jannink J-L. 2015. Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution 47: 38. Amyotte B, Bowen AJ, Banks T, Rajcan I, Somers DJ. 2017. Mapping the sensory perception of apple using descriptive sensory evaluation in a genome wide association study. PLoS ONE 12: e0171710. Atkinson RG, Sutherland PW, Johnston SL, Gunaseelan K, Hallett IC, Mitra D, Brummell DA, Schröder R, Johnston JW, Schaffer RJ. 2012. Down-regulation of POLYGALACTURONASE1 alters firmness, tensile strength and water loss in apple (Malus × domestica) fruit. BMC Plant Biology 12: 129. Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67. Bianco L, Cestaro A, Linsmith G, et al. 2016. Development and validation of the Axiom ® Apple48...