Prediction of genetic value for sweet cherry fruit maturity among environments using a 6K SNP array.

The timing of fruit maturity is an important trait in sweet cherry production and breeding. Phenotypic variation for phenology of fruit maturity in sweet cherry appears to be under strong genetic control, but that control might be complicated by phenotypic instability across environments. Although such genotype-by-environment interaction (G × E) is a common phenomenon in crop plants, knowledge about it is lacking for fruit maturity timing and other sweet cherry traits. In this study, 1673 genome-wide SNP markers were used to estimate genomic relationships among 597 weakly pedigree-connected individuals evaluated over two seasons at three locations in Europe and one location in the USA, thus sampling eight 'environments'. The combined dataset enabled a single meta-analysis to investigate the environmental stability of genomic predictions. Linkage disequilibrium among marker loci declined rapidly with physical distance, and ordination of the relationship matrix suggested no strong structure among germplasm. The most parsimonious G × E model allowed heterogeneous genetic variance and pairwise covariances among environments. Narrow-sense genomic heritability was very high (0.60-0.83), as was accuracy of predicted breeding values (>0.62). Average correlation of additive effects among environments was high (0.96) and breeding values were highly correlated across locations. Results indicated that genomic models can be used in cherry to accurately predict date of fruit maturity for untested individuals in new environments. Limited G × E for this trait indicated that phenotypes of individuals will be stable across similar environments. Equivalent analyses for other sweet cherry traits, for which multiple years of data are commonly available among breeders and cultivar testers, would be informative for predicting performance of elite selections and cultivars in new environments.

A collection of European sweet cherry phenology data for assessing climate change.

Professional and scientific networks built around the production of sweet cherry (Prunus avium L.) led to the collection of phenology data for a wide range of cultivars grown in experimental sites characterized by highly contrasted climatic conditions. We present a dataset of flowering and maturity dates, recorded each year for one tree when available, or the average of several trees for each cultivar, over a period of 37 years (1978-2015). Such a dataset is extremely valuable for characterizing the phenological response to climate change, and the plasticity of the different cultivars' behaviour under different environmental conditions. In addition, this dataset will support the development of predictive models for sweet cherry phenology exploitable at the continental scale, and will help anticipate breeding strategies in order to maintain and improve sweet cherry production in Europe.