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The database contains MRI images and histological slices of the enrolled cases. A comparison table between visually- and automatically-selected pairs is also reported., The study was supported by the Italian Ministry of Health grant RF-2016-02362195

Additional file 9: Supplementary Table S2. Analysis of allelic variant on WGS data retrieved from NCBI SRA database of 41 peach accessions. The presence of di1/di1, di2/di2 and di1/di2 genotypes or of PET alleles in Prupe.6G242400 was assessed. References to phenotype information about double-flower and other ornamental traits are also indicated. Accessions with unclear genotype at either di or Di2 loci were also included.

Additional file 7: Supplementary File S1. DNA sequence of the diΔ, di1 and di2 variants. Prupe.2G237700 is highlighted in green with the boxed pre-miR172 sequence. The deletion is indicated in diΔ with strikethrough text while the insertions in di1 and di2 are highlighted in orange and yellow, respectively. Primers used for genotyping are marked as bold, underlined sequences. Primers used for RT-qPCR analysis are shown on a portion of the pre-miR172d sequence.

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., This work has been funded under the EU seventh Framework Programme by the FruitBreedomics project No. 265582: Integrated Approach for increasing breeding efficiency in fruit tree crops (http://www.fruitbreedomics.com/).

Table S3. SRA accession number of assembled Illumina Whole-Genome libraries. (DOCX 13Â kb)

Table S4. List of candidate genes identified on the chromosome 6 region comprised between SNP_IGA_652659 (13,743,178Â bp) and SNP_IGA_534275 (15,609,595Â bp). (DOCX 17Â kb)

Table S2. List of primers used for quantitative PCR analyses. (DOCX 12Â kb)

Table S1. List of analyzed peach accessions and respective texture phenotype. (DOCX 19Â kb)

List of candidate genes identified on the chromosome 2 region associated to SNP_IGA_214703 (from 8.5 to 9.1Â Mb) (DOCX 9 kb)

MareyMap plot of TxE linkage map. Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 236Â kb)

Figure S2. QTLs for Fusarium ear rot (FER), fumonisin B1 contamination (FB1) and days to silking (DTS). Marker names are listed on the right side of each LG and the corresponding genetic distances (in centiMorgan) on the left side. QTLs are drawn at the right of each LG and are represented as central bars indicating the 1-LOD confidence interval and external thin lines indicating the 2-LOD confidence interval. QTLs are identified by the trait code, followed by the year (2011/2012), the sowing time (A/B) and the inoculation technique (F/T) for which the QTL was detected. QTLs for FER are reported with red bars, FB1 in black and DTS in green. (PPTX 152Â kb)

Figure S1. Weekly means of A) temperature, B) relative humidity and C) weekly sums of rain precipitation in years 2011 and 2012. First week corresponds to the first 7Â days of flowering in the early sowing of both 2011 and 2012. Last week corresponds to the week of the harvest day for both 2011 and 2012, in the early and late sowings. (PPTX 72Â kb)

Coefficient of variation of the predictive ability vs heritability. Figure reporting, for each trait, the coefficient of variation of predictive ability as function of the heritability of the trait in each progeny. Fruit weight in red, Sugar content in green and Titratable acidity in blue. (PDF 5 kb)

MareyMap plot of TxE linkage map. Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 236Â kb)

List of candidate genes identified on the chromosome 3 region associated to the SNP_IGA_366639 (from 26.2 to 26.5Â Mb) (DOCX 10 kb)

List of primers used in this study. (DOCX 8 kb)

Anchoring of the peach scaffolds to the three genetic maps. Colored bars represent the 8 linkage groups: pink for PxF, purple for TxE and blue for CxA. WGS scaffolds were positioned in each pseudomolecule (Pp01 to Pp08) with the corresponding genetic markers and are depicted in three different colors (dark blue, pink and pale blue); genetic markers are in the same colors of the corresponding WGS scaffolds. The zero (0) denotes the six scaffolds placed with random orientation along the pseudomolecules. The asterisk (*) indicates the two scaffolds with random order. The crosshatch (#) indicates the two scaffolds with the wrong order in Peach v2.0 that need to be inverted in a future release. (PDF 495Â kb)

Effect of sample size. Figure reporting, for each trait, the coefficient of variation of heritability, predictive ability, and repeatability as functions of sample size. (PDF 6 kb)

MareyMap plot of CxA linkage map. Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 233Â kb)

SRA accession number (DOCX 8 kb)

MareyMap plot of CxA linkage map. Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 233Â kb)

MareyMap plot of PxF linkage maps (including the F1 and recurrent parent maps). Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 405Â kb)

Figure S3. Rating scale of Fusarium ear rot severity on the F. verticillioides inoculated maize ears. (PPTX 1340Â kb)

Candidate genes identified on the chromosome 2 associated to the SNP_IGA_185608 (from 5.6 to 6.0Â Mb) (DOCX 9 kb)

Effect of phenotypic variability. Figure reporting, for each trait, the coefficient of variation of heritability, predictive ability, and repeatability as functions of the coefficient of variation of each phenotipic trait. (PDF 6 kb)

MareyMap plot of PxF linkage maps (including the F1 and recurrent parent maps). Vertical bars indicate the putative position of the centromere. The solid line represents the recombination rate plotted along the 8 pseudomolecules calculated using the cubic spline method. (PDF 405Â kb)

Candidate genes identified on the chromosome 2 associated to the SNP_IGA_185608 (from 5.6 to 6.0Â Mb) (DOCX 9 kb)

List of primers used in this study. (DOCX 8 kb)

[Background]: Peach (Prunus persica (L.) Batsch) is a major temperate fruit crop with an intense breeding activity. Breeding is facilitated by knowledge of the inheritance of the key traits that are often of a quantitative nature. QTLs have traditionally been studied using the phenotype of a single progeny (usually a full-sib progeny) and the correlation with a set of markers covering its genome. This approach has allowed the identification of various genes and QTLs but is limited by the small numbers of individuals used and by the narrow transect of the variability analyzed. In this article we propose the use of a multi-progeny mapping strategy that used pedigree information and Bayesian approaches that supports a more precise and complete survey of the available genetic variability., [Results]: Seven key agronomic characters (data from 1 to 3 years) were analyzed in 18 progenies from crosses between occidental commercial genotypes and various exotic lines including accessions of other Prunus species. A total of 1467 plants from these progenies were genotyped with a 9 k SNP array. Forty-seven QTLs were identified, 22 coinciding with major genes and QTLs that have been consistently found in the same populations when studied individually and 25 were new. A substantial part of the QTLs observed (47%) would not have been detected in crosses between only commercial materials, showing the high value of exotic lines as a source of novel alleles for the commercial gene pool. Our strategy also provided estimations on the narrow sense heritability of each character, and the estimation of the QTL genotypes of each parent for the different QTLs and their breeding value., [Conclusions]: The integrated strategy used provides a broader and more accurate picture of the variability available for peach breeding with the identification of many new QTLs, information on the sources of the alleles of interest and the breeding values of the potential donors of such valuable alleles. These results are first-hand information for breeders and a step forward towards the implementation of DNA-informed strategies to facilitate selection of new cultivars with improved productivity and quality., This work has been funded under the EU seventh Framework Programme by the FruitBreedomics project Nu. 265582: Integrated Approach for increasing breeding efficiency in fruit tree crops. IRTA-CRAG group received financial support from the Spanish Ministry of Economy and Competitiveness, through the “Severo Ochoa Programme for Centres of Excellence in R&D” 2016–2019 (SEV-2015-0533)” and through the project AGL2015-68329-R, and from the CERCA Programme/Generalitat de Catalunya.