Background: Maturity date (MD) is a crucial factor for marketing of fresh fruit, especially those with limited shelf-life such as peach (Prunus persica L. Batsch): selection of several cultivars with differing MD would be advantageous to cover and extend the marketing season. Aims of this work were the fine mapping and identification of candidate genes for the major maturity date locus previously identified on peach linkage group 4. To improve genetic resolution of the target locus two F₂ populations derived from the crosses Contender x Ambra (CxA, 306 individuals) and PI91459 (NJ Weeping) x Bounty (WxBy, 103 individuals) were genotyped with the Sequenom and 9K Illumina Peach Chip SNP platforms, respectively. Results: Recombinant individuals from the WxBy F₂ population allowed the localisation of maturity date locus to a 220 kb region of the peach genome. Among the 25 annotated genes within this interval, functional classification identified ppa007577m and ppa008301m as the most likely candidates, both encoding transcription factors of the NAC (NAM/ATAF1, 2/CUC2) family. Re-sequencing of the four parents and comparison with the reference genome sequence uncovered a deletion of 232 bp in the upstream region of ppa007577m that is homozygous in NJ Weeping and heterozygous in Ambra, Bounty and the WxBy F₁ parent. However, this variation did not segregate in the CxA F₂ population being the CxA F₁ parent homozygous for the reference allele. The second gene was thus examined as a candidate for maturity date. Re-sequencing of ppa008301m, showed an in-frame insertion of 9 bp in the last exon that co-segregated with the maturity date locus in both CxA and WxBy F₂ populations. Conclusions: Using two different segregating populations, the map position of the maturity date locus was refined from 3.56 Mb to 220 kb. A sequence variant in the NAC gene ppa008301m was shown to co-segregate with the maturity date locus, suggesting this gene as a candidate controlling ripening time in peach. If confirmed on other genetic materials, this variant may be used for marker-assisted breeding of new cultivars with differing maturity date.

Peach was domesticated in China more than four millennia ago and from there it spread world-wide. Since the middle of the last century, peach breeding programs have been very dynamic generating hundreds of new commercial varieties, however, in most cases such varieties derive from a limited collection of parental lines (founders). This is one reason for the observed low levels of variability of the commercial gene pool, implying that knowledge of the extent and distribution of genetic variability in peach is critical to allow the choice of adequate parents to confer enhanced productivity, adaptation and quality to improved varieties. With this aim we genotyped 1,580 peach accessions (including a few closely related Prunus species) maintained and phenotyped in five germplasm collections (four European and one Chinese) with the International Peach SNP Consortium 9K SNP peach array. The study of population structure revealed the subdivision of the panel in three main populations, one mainly made up of Occidental varieties from breeding programs (POP1OC_B), one of Occidental landraces (POP2OC_T) and the third of Oriental accessions (POP3OR). Analysis of linkage disequilibrium (LD) identified differential patterns of genome-wide LD blocks in each of the populations. Phenotypic data for seven monogenic traits were integrated in a genome-wide association study (GWAS). The significantly associated SNPs were always in the regions predicted by linkage analysis, forming haplotypes of markers. These diagnostic haplotypes could be used for marker-assisted selection (MAS) in modern breeding programs.

Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach. A repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3–5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0.35, SC:0.48, TA:0.53, on average) and repeatability (FW:0.56, SC:0.63, TA:0.62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0.60, SC:0.72, TA:0.65, on average). This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0.65, but could be as high as 0.84 for fruit weight or 0.83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.

Background: Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (Prunus persica(L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time.Results: Phenotypic data of seven F₁low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40–46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (D-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles.Conclusions: A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.