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.

In this study, a metabolomic investigation was presented to correlate single polyphenolic compounds in apple pulp with quality characteristics such as antioxidant activity and content of phenolic compounds and anthocyanins in apple skin. Since the concentration of these compounds is influenced by environmental factors, the twenty-two apple cultivars originate from the same site. The polyphenolic compounds were analyzed by ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QqQ-MS/MS). The antioxidant activity, phenolic content, and anthocyanins were evaluated on the sunny and the shady sides of apple skin by spectrometric assays. In old apple varieties, the measured parameters were higher than in the commercial and red-fleshed varieties. By contrast, the profile of flavan-3-ols and anthocyanins was variable amongst commercial and red-fleshed varieties. The partial least square (PLS) method was applied to investigate the association between the skin proprieties and the metabolic profile of the pulp. The highest coefficients of determination in prediction (Q2) were obtained for compounds quantified in old cultivars. These results provided information to define the old apple varieties as a reliable group based on the pathway of the antioxidant compounds and anthocyanins content. Our results show the possibility to find cultivars with promising health features based on their content of polyphenols suitable for commercialization or breeding.

The aromatic profile of apples constitutes important information for the characterization and description of local products. Apple flavor is determined by perception in mouth and aroma; while the first is mainly defined by sugars and organic acids, aroma is a complex mixture of many volatile organic compounds (VOCs) whose composition is often specific to the variety. Headspace-solid phase microextraction gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) allows for the detection of detailed information of volatile constituents. In this study, eleven apple varieties (Braeburn, Fuji, Gala, Golden Delicious, Coop 39-Crimson Crisp^®, Dalinette-Choupette^®, Fujion, CIV323-Isaaq^®, Coop43-Juliet^®, SQ159-Natyra^®, UEB32642-Opal^®) grown in two pedoclimatic locations at different altitudes in South Tyrol (Italy) (ca. 225 m and ca. 650 m a.s.l.) were investigated. Thirty-eight VOCs were identified and combined with sensory analysis results (from 11 trained panelist) to characterize the aroma of new and standard apple varieties with a special focus on pedoclimatic location differences. The study shows strong diversification of the varieties based on their VOC profiles and sensory attributes, as expected. Moreover, investigating how the pedoclimatic location at different altitudes can influence the apple aroma profile, we identified twelve VOCs involved in these differences and provided a deeper investigation on how different altitudes can influence the apple aroma composition and perceptions combining the analytical and sensory parameters.

Realization of the PeachRefPop, the international multisite reference collection in peach, provides an invaluable tool for scientific studies in perennial speci

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard genomic best linear unbiased predictor (G-BLUP). Including nonadditive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model. The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.