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.

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.

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (G×E). Here we show contrasting genetic architecture and genomic prediction accuracies 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 prediction accuracies of 0.18–0.88 were estimated using single-environment univariate, single-environment 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.

Fruit morphology description for variety registration or evaluation is mostly based on human visual inspection. However, the development of an objective and efficient method for evaluating apple fruit shape would be of significant value. Furthermore, if this method can provide a comprehensive assessment of the multiple attributes encompassed by the term “shape”, it would have great potential for genomic studies. Here, we investigated the potential of a shape analyzer software originally developed to study tomato fruits (Tomato Analyzer) for the morphometric description of apple fruits. We conducted an analysis of 12,920 images of apple sections from 364 genotypes, collected across three harvest seasons. Also, we assigned the images into classes by visual inspection. The software detected the contour of the fruits in most of the images, but with some degree of imprecision, particularly in the stalk and calyx regions. After manual correction of the contours, we obtained 15 measurements of shape and size attributes. In general, size traits had higher heritability (H2) than shape traits (0.72 vs 0.45 in average, respectively). A Random Forest model was used to identify the most important variables determining fruit shape. The fruit shape index external I (FSII) outstood in importance, followed by the fruit shape triangle (FST), the distal angle Macro (DAMa), the eccentricity (ECC), and the proximal angle macro (PAMa). Incorporating these parameters into fruit description guides could provide more precise descriptions of apple cultivars. Additionally, this data will be useful to investigate the potential genetic control of these traits through genomic studies.

Japanese plums exhibit wide diversity of fruit coloration. The red to black hues are caused by the accumulation of anthocyanins, while their absence results in yellow, orange or green fruits. In Prunus, MYB10 genes are determinants for anthocyanin accumulation. In peach, QTLs for red plant organ traits map in an LG3 region with three MYB10 copies (PpMYB10.1, PpMYB10.2 and PpMYB10.3). In Japanese plum the gene copy number in this region differs with respect to peach: there are at least three copies of PsMYB10.1, with the expression of one of them (PsMYB10.1a) correlating with fruit skin color. The objective of this study was to determine a possible role of LG3-PsMYB10 genes in the natural variability of the flesh color trait and to develop a molecular marker for marker-assisted selection (MAS). We explored the variability within the LG3-PsMYB10 region using long-range sequences obtained in previous studies through CRISPR-Cas9 enrichment sequencing. We found that the PsMYB10.2 gene was only expressed in red flesh fruits. Its role in promoting anthocyanin biosynthesis was validated by transient overexpression in Japanese plum fruits. The analysis of long-range sequences identified an LTR retrotransposon in the promoter of the expressed PsMYB10.2 gene that explained the trait in 93.1% of the 145 individuals analyzed. We hypothesize that the LTR retrotransposon may promote the PsMYB10.2 expression and activate the anthocyanin biosynthesis pathway. We propose for the first time the PsMYB10.2 gene as candidate for the flesh color natural variation in Japanese plum and provide a molecular marker for MAS.