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.

An integrated approach for increasing breeding efficiency in apple and peach in Europe

Despite the availability of whole genome sequences of apple and peach,there has been a considerable gap between genomics and breeding.To bridge the gap,the European Union funded the FruitBreedomics project(March 2011 to August 2015)involving 28 research institutes and private companies.Three complementary approaches were pursued:(i)tool and software development,(ii)deciphering genetic control of main horticultural traits taking into account allelic diversity and(iii)developing plant materials,tools and methodologies for breeders.Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding,development of new,dense SNP arrays in apple and peach,new phenotypic methods for some complex traits,software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis(PBA).This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies(GWAS)on several European genebank collections.FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities.Through FruitBreedomics,significant progresses were made in the field of apple and peach breeding,genetics,genomics and bioinformatics of which advantage will be made by breeders,germplasm curators and scientists.A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public.This review covers the scientific discoveries made in this major endeavour,and perspective in the apple and peach breeding and genomics in Europe and beyond.

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.

The apple REFPOP—a reference population for genomics-assisted breeding in apple

Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates.Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs,and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions.Here we present an apple reference population:the apple REFPOP,a large collection formed of 534 genotypes planted in six European countries,as a unique tool to accelerate apple breeding.The population consisted of 269 accessions and 265 progeny from 27 parental combinations,representing the diversity in cultivated apple and current European breeding material,respectively.A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95.Based on the genotypic data,linkage disequilibrium was low and population structure was weak.Two well-studied phenological traits of horticultural importance were measured.We found marker–trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date,respectively.With decreasing SNP density,the detection of significant marker–trait associations varied depending on trait architecture.Regardless of the trait,10,000 SNPs sufficed to maximize genomic prediction ability.We confirm the suitability of the apple REFPOP design for genomics-assisted breeding,especially for breeding programs using related germplasm,and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era.

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.