Genetic architecture and genomic predictive ability of apple quantitative traits across environments.

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. Recently, the multi-environment apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic predictive ability, and studying genotype by environment interactions (G × E). So far, only two phenological traits were investigated using the apple REFPOP, although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding. Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic predictive abilities of 0.18-0.88 were estimated using main-effect univariate, main-effect multivariate, multi-environment univariate, and multi-environment multivariate models. The G × E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

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.

Ten Years of VINQUEST: First Insight for Breeding New Apple Cultivars With Durable Apple Scab Resistance.

Apple scab, caused by Venturia inaequalis, is a major fungal disease worldwide. Cultivation of scab-resistant cultivars would reduce the chemical footprint of apple production. However, new apple cultivars carrying durable resistances should be developed to prevent or at least slow the breakdown of resistance against races of V. inaequalis. One way to achieve durable resistance is to pyramid multiple scab resistance genes in a cultivar. The choice of the resistance genes to be combined in the pyramids should take into account the frequency of resistance breakdown and the geographical distribution of apple scab isolates able to cause such breakdowns. In order to acquire this information and to make it available to apple breeders, the VINQUEST project (www.vinquest.ch) was initiated in 2009. Ten years after launching this project, 24 partners from 14 countries regularly contribute data. From 2009 to 2018, nearly 9,000 data points have been collected. This information has been used to identify the most promising apple scab resistance genes for developing cultivars with durable resistance, which to date are: Rvi5, Rvi11, Rvi12, Rvi14, and Rvi15. As expected, Rvi1, together with Rvi3 and Rvi8, were often overcome, and have little value for scab resistance breeding. Rvi10 may also belong to this group. On the other hand, Rvi2, Rvi4, Rvi6, Rvi7, Rvi9, and Rvi13 are still useful for breeding, but their use is recommended only in extended pyramids of ≥3 resistance genes.

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 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.