Accuracy and responses of genomic selection on key traits in apple breeding

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.

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

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’בPink 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.

A high-density, multi-parental SNP genetic map on apple validates a new mapping approach for outcrossing species

Quantitative trait loci(QTL)mapping approaches rely on the correct ordering of molecular markers along the chromosomes,which can be obtained from genetic linkage maps or a reference genome sequence.For apple(Malus domestica Borkh),the genome sequence v1 and v2 could not meet this need;therefore,a novel approach was devised to develop a dense genetic linkage map,providing the most reliable marker-loci order for the highest possible number of markers.The approach was based on four strategies:(i)the use of multiple full-sib families,(ii)the reduction of missing information through the use of HaploBlocks and alternative calling procedures for single-nucleotide polymorphism(SNP)markers,(iii)the construction of a single backcross-type data set including all families,and(iv)a two-step map generation procedure based on the sequential inclusion of markers.The map comprises 15417 SNP markers,clustered in 3 K HaploBlock markers spanning 1267 cM,with an average distance between adjacent markers of 0.37 cM and a maximum distance of 3.29 cM.Moreover,chromosome 5 was oriented according to its homoeologous chromosome 10.This map was useful to improve the apple genome sequence,design the Axiom Apple 480 K SNP array and perform multifamily-based QTL studies.Its collinearity with the genome sequences v1 and v3 are reported.To our knowledge,this is the shortest published SNP map in apple,while including the largest number of markers,families and individuals.This result validates our methodology,proving its value for the construction of integrated linkage maps for any outbreeding species.