Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach

Genetic and QTL analyses of sugarand acid content in sweet cherry (Prunus avium L.)

Sweet cherry is very appreciated by consumers because of its attractive appearance and taste, which is determined by the balanced sweet-sour flavor. In this work, the genetics of soluble solid content (SSC), titratable acidity (TA), sugars, and organic acids was investigated in sweet cherry to facilitate breeding improvement for fruit quality. The fruits of five sweet cherry populations (N = 372), three F1 and two F2, were sampled over two years to evaluate SSC, TA, and the content of individual sugars (glucose, fructose, sorbitol, and sucrose) and organic acids (malic, quinic, oxalic, citric, and shikimic) by ultra-performance liquid chromatography. Glucose, followed by fructose, was the most abundant sugar, while malic acid was the predominant acid. Sorbitol and malic acid were the most stable compounds between years, and had the highest heritability, being also the best correlated to SSC and TA, respectively, revealing their relevance for breeding. Significantly positive correlations were observed among sugars and SSC, and acids and TA, but high interannual variability between years was observed for all traits. Quantitative trait loci (QTL) mapping for SSC, sugars, TA, and organic acids was performed using a multi-family approach with FlexQTL™. Twenty QTLs were detected consistently during the two phenotyped years, and several relevant regions with overlapping QTLs for sugars and acids were also identified. The results confirmed major stable SSC and TA QTLs on the linkage groups 4 and 6, respectively. Within the main LG4 SSC QTL region, where maturity and fruit development time QTLs have been previously detected, three stable sugar (glucose, sorbitol, and sucrose) and two acid (quinic, shikimic) QTLs were also identified, suggesting a pleiotropic effect of ripening date on the content of these compounds. The major malic acid QTL overlapped with TA QTL on LG6; thus, TA QTL mapping on LG6 may correspond to malic acid QTLs. Haplotype analyses of major SSC and sugars QTL on LG4, and TA and malic acid on LG6 revealed haplotypes of breeding interest. Several candidate genes previously identified in other Prunus fruit species, like peach, were found to collocate with the QTLs detected herein. This work reports QTLs regions and haplotypes of sugar and acid content in a Prunus nonclimacteric stone fruit for the first time.

Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects

Modern breeding technologies and the development of quantitative trait locus (QTL) mapping have brought about a new era in peach breeding. This study examines the complex genetic structure that underlies the morphology of peach fruits, paying special attention to the interaction between genome editing, genomic selection, and marker-assisted selection. Breeders now have access to precise tools that enhance crop resilience, productivity, and quality, facilitated by QTL mapping, which has significantly advanced our understanding of the genetic determinants underlying essential traits such as fruit shape, size, and firmness. New technologies like CRISPR/Cas9 and genomic selection enable the development of cultivars that can withstand climate change and satisfy consumer demands with unprecedented precision in trait modification. Genotype-environment interactions remain a critical challenge for modern breeding efforts, which can be addressed through high-throughput phenotyping and multi-environment trials. This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. This study outlines a roadmap that prioritizes the development of superior cultivars utilizing cutting-edge methods and technologies in order to address evolving agricultural and environmental challenges.

De novo gene integration into regulatory networks via interaction with conserved genes in peach

De novo genes can evolve "from scratch" from noncoding sequences, acquiring novel functions in organisms and integrating into regulatory networks during evolution to drive innovations in important phenotypes and traits. However, identifying de novo genes is challenging, as it requires high-quality genomes from closely related species. According to the comparison with nine closely related Prunus genomes, we determined at least 178 de novo genes in P. persica "baifeng". The distinct differences were observed between de novo and conserved genes in gene characteristics and expression patterns. Gene ontology enrichment analysis suggested that Type I de novo genes originated from sequences related to plastid modification functions, while Type II genes were inferred to have derived from sequences related to reproductive functions. Finally, transcriptome sequencing across different tissues and developmental stages suggested that de novo genes have been evolutionarily recruited into existing regulatory networks, playing important roles in plant growth and development, which was also supported by WGCNA analysis and quantitative trait loci data. This study lays the groundwork for future research on the origins and functions of genes in Prunus and related taxa.

First Report of the Peach Leaf Spot Caused by Nigrospora sphaerica in China

Peach (Prunus persica L.) is a globally significant fruit valued for its high edible and ornamental qualities. Peach leaf spot disease has become increasingly prevalent in recent years, negatively affecting fruit quality and esthetic appeal. During the summer of 2024, symptoms of leaf spots were observed on peach trees in an orchard in Bazhong City, Sichuan Province, China. Leaf samples displaying typical spot symptoms were collected from peach orchards, and the pathogenic agents were isolated. Based on their morphological characteristics and multi-locus phylogenetic analysis, the isolated and purified fungus SCBZPP6 was identified as Nigrospora sphaerica. Furthermore, the pathogenicity of the isolated fungus was verified via Koch’s postulates. To our knowledge, this is the first report of N. sphaerica causing leaf spot on peach in China.

Fruit sugar hub: gene regulatory network associated with soluble solids content (SSC) in Prunus persica

Chilean peach growers have achieved worldwide recognition for their high-quality fruit products. Among the main factors influencing peach fruit quality, sweetness is pivotal for maintaining the market's competitiveness. Numerous studies have been conducted in different peach-segregating populations to unravel SSC regulation. However, different cultivars may also have distinct genetic conformation, and other factors, such as environmental conditions, can significantly impact SSC. Using a transcriptomic approach with a gene co-expression network analysis, we aimed to identify the regulatory mechanism that controls the sugar accumulation process in an 'O × N' peach population. This population was previously studied through genomic analysis, associating LG5 with the genetic control of the SSC trait. The results obtained in this study allowed us to identify 91 differentially expressed genes located on chromosome 5 of the peach genome as putative new regulators of sugar accumulation in peach, together with a regulatory network that involves genes directly associated with sugar transport (PpSWEET15), cellulose biosynthesis (PpCSLG2), flavonoid biosynthesis (PpPAL1), pectin modifications (PpPG, PpPL and PpPMEi), expansins (PpEXPA1 and PpEXPA8) and several transcription factors (PpC3H67, PpHB7, PpRVE1 and PpCBF4) involved with the SSC phenotype. These results contribute to a better understanding of the genetic control of the SSC trait for future breeding programs in peaches.

Ppe.RPT/SSC-1: from QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach

Genomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F1 and F2 families from the Clemson University peach breeding program (CUPBP). RPT and SSC QTLs were consistently identified in two seasons (2011 and 2012) and the average datasets (average of two seasons). A target region spanning 10,981,971-11,298,736 bp on chromosome 4 of peach reference genome used for haplotype analysis revealed four haplotypes with significant differences in trait values among different diplotype combinations. Favorable alleles at the target region for both RPT and SSC were determined and a DNA test for predicting RPT and SSC was developed. Two Kompetitive Allele Specific PCR (KASP) assays were validated on 84 peach cultivars and 163 seedlings from the CUPBP, with only one assay (Ppe.RPT/SSC-1) needed to predict between early and late-season ripening cultivars and low and high SSC. These results advance our understanding of the genetic basis of RPT and SSC and facilitate selection of new peach cultivars with the desired RPT and SSC.

Marker-Assisted Selection in Breeding for Fruit Trait Improvement: A Review

Breeding fruit species is time-consuming and expensive. With few exceptions, trees are likely the worst species to work with in terms of genetics and breeding. Most are characterized by large trees, long juvenile periods, and intensive agricultural practice, and environmental variability plays an important role in the heritability evaluations of every single important trait. Although vegetative propagation allows for the production of a significant number of clonal replicates for the evaluation of environmental effects and genotype × environment interactions, the spaces required for plant cultivation and the intensity of work necessary for phenotypic surveys slow down the work of researchers. Fruit breeders are very often interested in fruit traits: size, weight, sugar and acid content, ripening time, fruit storability, and post-harvest practices, among other traits relevant to each individual species. The translation of trait loci and whole-genome sequences into diagnostic genetic markers that are effective and affordable for use by breeders, who must choose genetically superior parents and subsequently choose genetically superior individuals among their progeny, is one of the most difficult tasks still facing tree fruit geneticists. The availability of updated sequencing techniques and powerful software tools offered the opportunity to mine tens of fruit genomes to find out sequence variants potentially useful as molecular markers. This review is devoted to analysing what has been the role of molecular markers in assisting breeders in selection processes, with an emphasis on the fruit traits of the most important fruit crops for which examples of trustworthy molecular markers have been developed, such as the MDo.chr9.4 marker for red skin colour in apples, the CCD4-based marker CPRFC1, and LG3_13.146 marker for flesh colour in peaches, papayas, and cherries, respectively.

Comparative Anatomical and Transcriptomics Reveal the Larger Cell Size as a Major Contributor to Larger Fruit Size in Apricot

Fruit size is one of the essential quality traits and influences the economic value of apricots. To explore the underlying mechanisms of the formation of differences in fruit size in apricots, we performed a comparative analysis of anatomical and transcriptomics dynamics during fruit growth and development in two apricot cultivars with contrasting fruit sizes (large-fruit Prunus armeniaca 'Sungold' and small-fruit P. sibirica 'F43'). Our analysis identified that the difference in fruit size was mainly caused by the difference in cell size between the two apricot cultivars. Compared with 'F43', the transcriptional programs exhibited significant differences in 'Sungold', mainly in the cell expansion period. After analysis, key differentially expressed genes (DEGs) most likely to influence cell size were screened out, including genes involved in auxin signal transduction and cell wall loosening mechanisms. Furthermore, weighted gene co-expression network analysis (WGCNA) revealed that PRE6/bHLH was identified as a hub gene, which interacted with 1 TIR1, 3 AUX/IAAs, 4 SAURs, 3 EXPs, and 1 CEL. Hence, a total of 13 key candidate genes were identified as positive regulators of fruit size in apricots. The results provide new insights into the molecular basis of fruit size control and lay a foundation for future breeding and cultivation of larger fruits in apricot.

Deducing genotypes for loci of interest from SNP array data via haplotype sharing, demonstrated for apple and cherry

Breeders, collection curators, and other germplasm users require genetic information, both genome-wide and locus-specific, to effectively manage their genetically diverse plant material. SNP arrays have become the preferred platform to provide genome-wide genetic profiles for elite germplasm and could also provide locus-specific genotypic information. However, genotypic information for loci of interest such as those within PCR-based DNA fingerprinting panels and trait-predictive DNA tests is not readily extracted from SNP array data, thus creating a disconnect between historic and new data sets. This study aimed to establish a method for deducing genotypes at loci of interest from their associated SNP haplotypes, demonstrated for two fruit crops and three locus types: quantitative trait loci Ma and Ma3 for acidity in apple, apple fingerprinting microsatellite marker GD12, and Mendelian trait locus Rf for sweet cherry fruit color. Using phased data from an apple 8K SNP array and sweet cherry 6K SNP array, unique haplotypes spanning each target locus were associated with alleles of important breeding parents. These haplotypes were compared via identity-by-descent (IBD) or identity-by-state (IBS) to haplotypes present in germplasm important to U.S. apple and cherry breeding programs to deduce target locus alleles in this germplasm. While IBD segments were confidently tracked through pedigrees, confidence in allele identity among IBS segments used a shared length threshold. At least one allele per locus was deduced for 64-93% of the 181 individuals. Successful validation compared deduced Rf and GD12 genotypes with reported and newly obtained genotypes. Our approach can efficiently merge and expand genotypic data sets, deducing missing data and identifying errors, and is appropriate for any crop with SNP array data and historic genotypic data sets, especially where linkage disequilibrium is high. Locus-specific genotypic information extracted from genome-wide SNP data is expected to enhance confidence in management of genetic resources.

Pedigree-based analysis in multi-parental diploid rose populations reveals QTLs for cercospora leaf spot disease resistance

Cercospora leaf spot (CLS) (Cercospora rosicola) is a major fungal disease of roses (Rosa sp.) in the southeastern U.S. Developing CLS-resistant cultivars offers a potential solution to reduce pesticide use. Yet, no work has been performed on CLS resistance. This study aimed to identify QTLs and to characterize alleles for resistance to CLS. The study used pedigree-based QTL analysis to dissect the genetic basis of CLS resistance using two multi-parental diploid rose populations (TX2WOB and TX2WSE) evaluated across five years in two Texas locations. A total 38 QTLs were identified across both populations and distributed over all linkage groups. Three QTLs on LG3, LG4, and LG6 were consistently mapped over multiple environments. The LG3 QTL was mapped in a region between 18.9 and 27.8 Mbp on the Rosa chinensis genome assembly. This QTL explained 13 to 25% of phenotypic variance. The LG4 QTL detected in the TX2WOB population spanned a 35.2 to 39.7 Mbp region with phenotypic variance explained (PVE) up to 48%. The LG6 QTL detected in the TX2WSE population was localized to 17.9 to 33.6 Mbp interval with PVE up to 36%. Also, this study found multiple degrees of favorable allele effects (q-allele) associated with decreasing CLS at major loci. Ancestors 'OB', 'Violette', and PP-M4-4 were sources of resistance q-alleles. These results will aid breeders in parental selection to develop CLS-resistant rose cultivars. Ultimately, high throughput DNA tests that target major loci for CLS could be developed for routine use in a DNA-informed breeding program.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

QTL mapping and characterization of black spot disease resistance using two multi-parental diploid rose populations

Black spot disease (BSD) (Diplocarpon rosae) is the most common and damaging fungal disease in garden roses (Rosa sp.). Although qualitative resistance to BSD has been extensively investigated, the research on quantitative resistance lags behind. The goal of this research was to study the genetic basis of BSD resistance in two multi-parental populations (TX2WOB and TX2WSE) through a pedigree-based analysis approach (PBA). Both populations were genotyped and evaluated for BSD incidence over five years in three locations in Texas. A total of 28 QTLs, distributed over all linkage groups (LGs), were detected across both populations. Consistent minor effect QTLs included two on LG1 and LG3 (TX2WOB and TX2WSE), two on LG4 and LG5 (TX2WSE), and one QTL on LG7 (TX2WOB). In addition, one major QTL detected in both populations was consistently mapped on LG3. This QTL was localized to an interval ranging from 18.9 to 27.8 Mbp on the Rosa chinensis genome and explained 20 and 33% of the phenotypic variation. Furthermore, haplotype analysis showed that this QTL had three distinct functional alleles. The parent PP-J14-3 was the common source of the LG3 BSD resistance in both populations. Taken together, this research presents the characterization of new SNP-tagged genetic determinants of BSD resistance, the discovery of marker-trait associations to enable parental choice based on their BSD resistance QTL haplotypes, and substrates for the development of trait-predictive DNA tests for routine use in marker-assisted breeding for BSD resistance.

Identification of QTLs for Reduced Susceptibility to Rose Rosette Disease in Diploid Roses

Resistance to rose rosette disease (RRD), a fatal disease of roses (Rosa spp.), is a high priority for rose breeding. As RRD resistance is time-consuming to phenotype, the identification of genetic markers for resistance could expedite breeding efforts. However, little is known about the genetics of RRD resistance. Therefore, we performed a quantitative trait locus (QTL) analysis on a set of inter-related diploid rose populations phenotyped for RRD resistance and identified four QTLs. Two QTLs were found in multiple years. The most consistent QTL is qRRV_TX2WSE_ch5, which explains approximately 20% and 40% of the phenotypic variation in virus quantity and severity of RRD symptoms, respectively. The second, a QTL on chromosome 1, qRRD_TX2WSE_ch1, accounts for approximately 16% of the phenotypic variation for severity. Finally, a third QTL on chromosome 3 was identified only in the multiyear analysis, and a fourth on chromosome 6 was identified in data from one year only. In addition, haplotypes associated with significant changes in virus quantity and severity were identified for qRRV_TX2WSE_ch5 and qRRD_TX2WSE_ch1. This research represents the first report of genetic determinants of resistance to RRD. In addition, marker trait associations discovered here will enable better parental selection when breeding for RRD resistance and pave the way for marker-assisted selection for RRD resistance.

Integration of genomics, transcriptomics and metabolomics identifies candidate loci underlying fruit weight in loquat

Fruit weight is an integral part of fruit-quality traits and directly influences commodity values and economic returns of fruit crops. Despite its importance, the molecular mechanisms underlying fruit weight remain understudied, especially for perennial fruit tree crops such as cultivated loquat (Eriobotrya japonica Lindl.). Auxin is known to regulate fruit development, whereas its role and metabolism in fruit development remain obscure in loquat. In this study, we applied a multi-omics approach, integrating whole-genome resequencing-based quantitative trait locus (QTL) mapping with an F1 population, population genomics analysis using germplasm accessions, transcriptome analysis, and metabolic profiling to identify the genomic regions potentially associated with fruit weight in loquat. We identified three major loci associated with fruit weight, supported by both QTL mapping and comparative genomic analysis between small- and big-fruited loquat cultivars. Comparison between two genotypes with contrasting fruit weight performance through transcriptomic and metabolic profiling revealed an important role of auxin in regulating fruit development, especially at the fruit enlarging stage. The multi-omics approach identified two homologs of ETHYLENE INSENSITIVE 4 (EjEIN4) and TORNADO 1 (EjTRN1) as promising candidates controlling fruit weight. Moreover, three single nucleotide polymorphism (SNP) markers were closely associated with fruit weight. Results from this study provided insights from multiple perspectives into the genetic and metabolic controls of fruit weight in loquat. The candidate genomic regions, genes, and sequence variants will facilitate understanding the molecular basis of fruit weight and lay a foundation for future breeding and manipulation of fruit weight in loquat.

High-density linkage map construction and identification of loci regulating fruit quality traits in blueberry

Fruit quality traits play a significant role in consumer preferences and consumption in blueberry (Vaccinium corymbosum L). The objectives of this study were to construct a high-density linkage map and to identify the underlying genetic basis of fruit quality traits in blueberry. A total of 287 F1 individuals derived from a cross between two southern highbush blueberry cultivars, 'Reveille' and 'Arlen', were phenotyped over three years (2016-2018) for fruit quality-related traits, including titratable acidity, pH, total soluble solids, and fruit weight. A high-density linkage map was constructed using 17k single nucleotide polymorphisms markers. The linkage map spanned a total of 1397 cM with an average inter-loci distance of 0.08 cM. The quantitative trait loci interval mapping based on the hidden Markov model identified 18 loci for fruit quality traits, including seven loci for fruit weight, three loci for titratable acidity, five loci for pH, and three loci for total soluble solids. Ten of these loci were detected in more than one year. These loci explained phenotypic variance ranging from 7 to 28% for titratable acidity and total soluble solid, and 8-13% for pH. However, the loci identified for fruit weight did not explain more than 10% of the phenotypic variance. We also reported the association between fruit quality traits and metabolites detected by Proton nuclear magnetic resonance analysis directly responsible for these fruit quality traits. Organic acids, citric acid, and quinic acid were significantly (P < 0.05) and positively correlated with titratable acidity. Sugar molecules showed a strong and positive correlation with total soluble solids. Overall, the study dissected the genetic basis of fruit quality traits and established an association between these fruit quality traits and metabolites.

Application of a multilayer perceptron artificial neural network for identification of peach cultivars based on physical characteristics

In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swelling) were evaluated for differences in skin color, firmness, and size. Additionally, a multilayer perceptron (MLP) artificial neural network was applied for identification of the cultivars according to these attributes. The MLP was trained with an input layer including six input nodes, a single hidden layer with six hidden nodes, and an output layer with nine output nodes. A hyperbolic tangent activation function was used in the hidden layer and the cross entropy error was given because the softmax activation function was functional to the output layer. Results showed that the cross entropy error was 0.165. The peach identification process was significantly affected by the following variables in order of contribution (normalized importance): polar diameter (100%), L ∗ (89.0), b ∗ (88.0%), a ∗ (78.5%), firmness (71.3%), and cross diameter (37.5.3%). The MLP was found to be a viable method of peach cultivar identification and classification because few identifying attributes were required and an overall classification accuracy of 100% was achieved in the testing phase. Measurements and quantitative discrimination of peach properties are provided in this research; these data may help enhance the processing efficiency and quality of processed peaches.

Population-scale peach genome analyses unravel selection patterns and biochemical basis underlying fruit flavor

A narrow genetic basis in modern cultivars and strong linkage disequilibrium in peach (Prunus persica) has restricted resolution power for association studies in this model fruit species, thereby limiting our understanding of economically important quality traits including fruit flavor. Here, we present a high-quality genome assembly for a Chinese landrace, Longhua Shui Mi (LHSM), a representative of the Chinese Cling peaches that have been central in global peach genetic improvement. We also map the resequencing data for 564 peach accessions to this LHSM assembly at an average depth of 26.34× per accession. Population genomic analyses reveal a fascinating history of convergent selection for sweetness yet divergent selection for acidity in eastern vs. western modern cultivars. Molecular-genetics and biochemical analyses establish that PpALMT1 (aluminum-activated malate transporter 1) contributes to their difference of malate content and that increases fructose content accounts for the increased sweetness of modern peach fruits, as regulated by PpERDL16 (early response to dehydration 6-like 16). Our study illustrates the strong utility of the genomics resources for both basic and applied efforts to understand and exploit the genetic basis of fruit quality in peach.

Mapping and characterization QTLs for phenological traits in seven pedigree-connected peach families

Background

Environmental adaptation and expanding harvest seasons are primary goals of most peach [Prunus persica (L.) Batsch] breeding programs. Breeding perennial crops is a challenging task due to their long breeding cycles and large tree size. Pedigree-based analysis using pedigreed families followed by haplotype construction creates a platform for QTL and marker identification, validation, and the use of marker-assisted selection in breeding programs.

Results

Phenotypic data of seven F₁ low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. Three QTLs were discovered for bloom date (BD) and mapped on linkage group 1 (LG1) (172–182 cM), LG4 (48–54 cM), and LG7 (62–70 cM), explaining 17–54%, 11–55%, and 11–18% of the phenotypic variance, respectively. The QTL for ripening date (RD) and fruit development period (FDP) on LG4 was co-localized at the central part of LG4 (40–46 cM) and explained between 40 and 75% of the phenotypic variance. Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles and the presence of multiple functional alleles with different effects for a single locus for RD and FDP.

Conclusions

A multiple pedigree-linked families approach validated major QTLs for the three key phenological traits which were reported in previous studies across diverse materials, geographical distributions, and QTL mapping methods. Haplotype characterization of these genomic regions differentiates this study from the previous QTL studies. Our results will provide the peach breeder with the haplotypes for three BD QTLs and one RD/FDP QTL to create predictive DNA-based molecular marker tests to select parents and/or seedlings that have desired QTL alleles and cull unwanted genotypes in early seedling stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07483-8.

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome

Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

Molecular Bases of Fruit Quality in Prunus Species: An Integrated Genomic, Transcriptomic, and Metabolic Review with a Breeding Perspective

In plants, fruit ripening is a coordinated developmental process that requires the change in expression of hundreds to thousands of genes to modify many biochemical and physiological signal cascades such as carbohydrate and organic acid metabolism, cell wall restructuring, ethylene production, stress response, and organoleptic compound formation. In Prunus species (including peaches, apricots, plums, and cherries), fruit ripening leads to the breakdown of complex carbohydrates into sugars, fruit firmness reductions (softening by cell wall degradation and cuticle properties alteration), color changes (loss of green color by chlorophylls degradation and increase in non-photosynthetic pigments like anthocyanins and carotenoids), acidity decreases, and aroma increases (the production and release of organic volatile compounds). Actually, the level of information of molecular events at the transcriptional, biochemical, hormonal, and metabolite levels underlying ripening in Prunus fruits has increased considerably. However, we still poorly understand the molecular switch that occurs during the transition from unripe to ripe fruits. The objective of this review was to analyze of the molecular bases of fruit quality in Prunus species through an integrated metabolic, genomic, transcriptomic, and epigenetic approach to better understand the molecular switch involved in the ripening process with important consequences from a breeding point of view.

Non-structural Carbohydrate Metabolism in the Flesh of Stone Fruits of the Genus Prunus (Rosaceae) - A Review

Non-structural carbohydrates are abundant constituents of the ripe flesh of all stone fruits. The bulk of their content comprises sucrose, glucose, fructose and sorbitol. However, the abundance of each of these carbohydrates in the flesh differs between species, and also with its stage of development. In this article the import, subcellular compartmentation, contents, metabolism and functions of non-structural carbohydrates in the flesh of commercially cultivated stone fruits of the family Rosaceae are reviewed.

RosBREED: bridging the chasm between discovery and application to enable DNA-informed breeding in rosaceous crops

The Rosaceae crop family (including almond, apple, apricot, blackberry, peach, pear, plum, raspberry, rose, strawberry, sweet cherry, and sour cherry) provides vital contributions to human well-being and is economically significant across the U.S. In 2003, industry stakeholder initiatives prioritized the utilization of genomics, genetics, and breeding to develop new cultivars exhibiting both disease resistance and superior horticultural quality. However, rosaceous crop breeders lacked certain knowledge and tools to fully implement DNA-informed breeding-a "chasm" existed between existing genomics and genetic information and the application of this knowledge in breeding. The RosBREED project ("Ros" signifying a Rosaceae genomics, genetics, and breeding community initiative, and "BREED", indicating the core focus on breeding programs), addressed this challenge through a comprehensive and coordinated 10-year effort funded by the USDA-NIFA Specialty Crop Research Initiative. RosBREED was designed to enable the routine application of modern genomics and genetics technologies in U.S. rosaceous crop breeding programs, thereby enhancing their efficiency and effectiveness in delivering cultivars with producer-required disease resistances and market-essential horticultural quality. This review presents a synopsis of the approach, deliverables, and impacts of RosBREED, highlighting synergistic global collaborations and future needs. Enabling technologies and tools developed are described, including genome-wide scanning platforms and DNA diagnostic tests. Examples of DNA-informed breeding use by project participants are presented for all breeding stages, including pre-breeding for disease resistance, parental and seedling selection, and elite selection advancement. The chasm is now bridged, accelerating rosaceous crop genetic improvement.