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

Ppe.RPT/SSC-1: From QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ppe.RPT/SSC-1: From QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach Cassia Silva Linge, Wanfang Fu, Alejandro Calle, Zena Rawandoozi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3569064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Genomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F 1 and F 2 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. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Peach [ Prunus persica (L.) Batsch] is the third most cultivated temperate tree fruit in the world after apple and pear 1 . The United States is the fifth largest peach producer in the world with an approximate production of 730 thousand tons, behind China, Spain, Italy, and Turkey 2 . Despite high production levels, the variability in fruit quality has resulted in a significant decrease in the US peach consumption in the last decades 3 . One of the main consumer complaints about peaches is the lack of sensory quality, which is mostly caused by harvesting fruits at an inadequate ripening stage 4 . The process of fruit ripening plays an important role in improving peach organoleptic quality, including increases in soluble solids concentration (SSC) to acid ratio, softening, and aroma 5,6 . SSC, measured as °Brix, is often used to express sugar content and represents one of the main drivers affecting peach consumer acceptance 7 . The amount of total SSC in ripe fruits combined with additional active compounds, such as pectin, salts, and organic acids, significantly increases consumer satisfaction in peaches 8,9 . Medium and late season cultivars have a greater capacity to accumulate sugar compared to early season cultivars 4 . Ripening time, however, affects the farm gate value of the crop with early ripening cultivars getting higher price despite the SSC levels 10 . Therefore, breeders aim at developing new peach cultivars tailored to specific ripening periods and with enhanced sensory characteristics that balance sensory quality and maturity at harvest to ensure consumer satisfaction and encourage consumption. New genetic resources for peach have enabled several recent studies on genetic dissection of important traits including ripening time (RPT) and SSC. Quantitative Trait Loci (QTLs) and associated signals have been identified using different approaches such as bi-parental QTL mapping 11–14 , pedigree-based analysis (PBA) 15–17 and genome-wide association studies 18 . In addition, genomic prediction models focusing on SSC were developed 19,20 . A large-effect QTL on chromosome 4, discovered using a low-density linkage map, suggested a pleiotropic effect between SSC and RPT 11 . Further studies also highlighted a QTL hotspot in the same region using different genetic backgrounds and mapping approaches 15,18 . Although several QTLs for several traits including SSC and RPT have been discovered, the conversion into practical breeding tools such as DNA tests is still scarce. Trait-predictive DNA tests could be useful for rapidly identifying valuable parents as well as for removing inferior seedlings that do not possess a target characteristic. Thus, applying these genetic assays in breeding programs could significantly enhance the efficiency of the cultivar development process 21,22 . In peach, rapid and accurate DNA tests for predicting bacterial spot severity in fruit and chilling requirement were recently developed 23,24 . Additional tests for peach skin blush 25 , fruit pubescence 26 , subacid trait 27 and flesh color 28 are also available. Most of the existing tests were developed using PCR-based simple sequence repeat (SSR) technology. Single nucleotide polymorphism (SNP) markers have replaced SSRs and other types of molecular markers due to the considerable cost-efficiency for genotyping and their precision, stability, and easy detectability 29–31 . Kompetitive allele-specific PCR (KASP) assays are affordable, rapid, and robust tools to genotype SNPs of interest and are widely used due to improved cost-effectiveness compared to other technologies 23,29,31,32 . The objective of this work was to showcase the whole process from QTL discovery to DNA test development for routine use in peach breeding for the simultaneous prediction of RPT and SSC. First, we mapped and validated the previously discovered large effect RPT and SSC QTLs on chromosome 4 within Clemson University peach breeding germplasm using a pedigree-based analysis (PBA) approach. Subsequently, we developed and validated a rapid KASP test using the target region obtained by the QTL mapping. Results Phenotypic data The breeding population containing 288 seedlings from the CUPBP was evaluated for RPT and SSC over two seasons (2011–2012). The RPT ranged from 150 to 235 JD with a mean of 195 and 182 in 2011 and 2012, respectively. SSC varied from 7.4 to 19.8 ºBrix with means of 12.3 and 11.7 in 2011 and 2012 (Supplementary Table S6). According to the Shapiro Wilk test, RPT did not follow a normal distribution in both seasons, whereas SSC exhibited a normal distribution only in 2012 (Supplementary Table S7). According to the non-parametric Wilcoxon signed rank test, significant differences were observed in the RPT and SSC averages between seasons with a delay in the RPT and a higher SSC mean value observed in 2011. Spearman’s rank correlation analysis demonstrated significant positive coefficients between RPT and SSC in 2011 (0.67) and 2012 (0.62). Considering the correlation between seasons, RPT had the highest coefficient (0.90) and SSC the lowest (0.41) (Supplementary Fig. S1 ). The narrow- sense heritability was 0.84 and 0.24 for RPT and SSC, respectively. QTL analysis We performed the QTL analysis using a Bayesian, pedigree-based QTL analysis (PBA) implemented in FlexQTL™ using the RPT and SSC phenotypic data from 2011 (RPT_2011 and SSC_2011), 2012 (RPT_2012 and SSC_2012) as well as the average values of both seasons (RPT_Ave and SSC_Ave). The QTL analysis identified major QTLs associated with both RPT and SSC (Table 2 ; Supplementary Fig. S2 and S3). The RPT QTL, qRPT_SC_4 , was consistently mapped with decisive evidence (BF > 10) on LG4 in the overlapped genetic interval of 44-51cM in both 2011 and 2012. Peak position of the qRPT_SC_4.1 was at 45–48 cM. The SSC QTL, qSSC_SC_4.1 , was also consistently mapped in both seasons in a wider genetic interval (38–52 cM and 43–58 cM in 2011 and 2012, respectively) and exhibited a peak position varying from 44 to 49 cM. Overall, the qRPT_SC_4.1 and the qSSC_SC_4.1 were mapped in the same genetic interval forming a QTL cluster (Supplementary Fig. S3). Based on the peak position mapped in both seasons and traits, an initial genetic interval comprising eight SNPs spanning from 44 to 49 cM (physical position from 10,981,971 to 12,523,245 bp) was highlighted and subsequently used for the PVE. The qRPT_SC_4.1 explained approximately 53% and 59% of the phenotypic variance in RPT 2011 and 2012, respectively, while the PVE for qSSC_SC_4.1 ranged from 25% (2011) to 35% (2012). Datasets using the average of two seasons for both traits (RPT_Ave and SSC_Ave) were used in an independent FlexQTL™ runs and the overlapped peak position was detected at 45 cM, explaining approximately 53% and 36% of the phenotypic variance observed in RPT and SSC, respectively (Table 2 ). Thus, we targeted the genetic interval from 44 to 45 cM (spanning a physical position from 10,981,971 to 11,298,736 bp) for further analysis of diplotype/genotype effects. Table 2 Summary of QTLs mapped on linkage group 4 in the Clemson University peach breeding program using FlexQTL™. Trait QTL name LG Dataset BF Interval(cM) Interval (bp) Peak (cM) Additive PVE RPT qRPT_SC_4.1 4 2011 10.2 44–51 10,981,971 − 13,633,831 48 18.44 59 qRPT_SC_4.1 4 2012 27.2 44–50 10,981,971 − 12,971,285 46 16.2 53.2 qRPT_SC_4.1 4 Ave 28.9 44–47 10,981,971 − 11,654,504 45 17.4 53.8 SSC qSSC_SC_4.1 4 2011 28.7 43–58 10,760,086 − 17,176,059 49 1.68 25.9 qSSC_SC_4.1 4 2012 31.7 38–52 9,617,585 − 13,882,450 44 1.47 34.5 qSSC_SC_4.1 4 Ave 29.9 41–49 10,280,095 − 12,546,297 45 1.51 36 RPT: Ripening time; SSC: Soluble Solids Concentration; LG: Linkage group; BF: Bayes factor; Additive effect; PVE: Percentage of variance explained. The QTL analysis for SSC_Ave using the RPT_Ave information as a cofactor revealed that both the BF (evidence of the presence of QTL) and PVE decreased in all three datasets containing the cofactor compared to the one where no cofactors were included (Supplementary Table S8). The dataset using no cofactor showed a BF of 29.7 and explained approximately 35% of the observed phenotypic variance. The dataset using the phenotypic average of RPT as a cofactor had the lowest BF (8.4) and PVE (20.8), while the dataset incorporating the haplotype information of the RPT QTL interval exhibited the highest BF (29.4) and PVE (27.2). QTL haplotype, genotype and predictive markers Three SNPs were located within the target region of the QTL interval from 44 cM (10,981,971 bp) to 45 cM (11,298,736 bp), which was detected in the average datasets (RPT_Ave and SSC_Ave). Out of the possible six we observed four unique haplotypes (H1-H4) defined by three SNPs within the region in the analyzed material (Table 3 ; Fig. 1 ). The sources of each haplotype are shown in the Supplementary Table S9. The haplotype H4 was associated with an early ripening, lower SSC values and was attributed to the q allele. The H1-H3 haplotypes were associated with increase of both traits and were assigned to the Q allele. The SNP_IGA_411637 was determined as the predictive marker for distinguishing the Q and q alleles (Table 3 ). Three statistically different QTL genotypes ( qq, Qq, QQ ) were detected in the breeding population used for QTL mapping (Fig. 1 ). The allele Q was associated with late ripening and a higher SSC and the allele q with early ripening and lower SSC (Fig. 1 ). The seedlings carrying the QQ genotype (H3H2 or H2H2 diplotypes) averaged 199 and 213 JD and 13.2 ºBrix, while the seedlings with Qq genotypes (H4H2 or H1H4 diplotypes) ripened on average at 192 and 194 JD and had on average 12.4 and 12.5 ºBrix. Lastly, seedlings with the qq genotype (H4H4) ripened on average at 180 JD and had an average of 11.1 °Brix (Fig. 1 ). The most frequent diplotype was H4H4, followed by H1H4. Seedlings carrying the Q allele with H3H2 and H2H2 diplotypes were less frequent in the population (Fig. 1 ). Although three SNPs were selected to distinguish haplotypes in the QTL region, only one, SNP_IGA_411637, was necessary to discriminate early, mid, or late-ripening individuals (Fig. 2 ). Single linear regression (SLR) analysis of RPT and SSC at SNP_IGA_411637 revealed 27.7 and 16.6% PVE, respectively (Table 3 ). Thus, to further validate our findings, we compared the RPT and SSC for the three different genotypes (AA, AB, and BB) of the SNP_IGA_411637 in Arkansas (AR), South Carolina (SC), and Texas (TX) breeding materials (Fig. 2 ). The comparison of the phenotypic performance of different genotypes revealed significant differences ( p < 0.05) in all three peach breeding programs with individuals homozygous for the ‘B’ allele showing the earliest RPT and lowest SSC, and individuals homozygous for the ‘A’ allele having the latest RPT and highest SSC (Fig. 2 ; Supplementary Table S10). On average, individuals homozygous for the ‘B’ allele ripened 12 (SC), 16 (TX) and 17 (AR) days earlier than heterozygous individuals, and individuals homozygous for ‘A’ allele ripened 9 (SC) to 12 (AR) days later than heterozygous individuals (Supplementary Table S10). The AA genotypes were not observed in the TX material. For SSC, individuals homozygous for the ‘B’ allele had decreased SSC by 0.48 (TX) to 0.64 (SC) in comparison to the heterozygous individuals (AB). In contrast, individuals homozygous for the ‘A’ allele had increased SSC by 0.2 (SC) to 1.3 (AR) in comparison to individuals with the AB genotype (Fig. 2 ; Supplementary Table S10). Table 3 QTL peach genotypes of ripening time (RPT) and soluble solids concentration (SSC) target region spanning 44cM (10,981,971 bp) to 45cM (11,298,736). SNP names, position in bp and cM, haplotype names. Shaded = predictive SNP. *Illumina (A|B) nucleotide designation and actual nucleotides. SNP Nucleotides* Position PVE (%)* Haplotypes Physical (bp) Genetic (cM) RPT SSC H1 H2 H3 H4 SNP_IGA_411637 A|B (T|G) 10,981,971 44.1 27.7 16.6 A A A B SNP_IGA_412338 A|B (A|G) 11,208,736 45.0 5.7 4.7 A B B B SNP_IGA_412662 A|B (A|G) 11,298,736 45.0 12.5 6.6 B A B A QTL allele Q Q Q q PVE: calculated by each marker using single linear regression analysis during the KASP test validation. KASP assay development and validation In order to confirm the predictive SNP and reconstruct haplotypes (possible six and observed four), all three SNP markers (SNP_IGA_422637, SNP_IGA_412338 and SNP_IGA_412662) were used in the KASP assay development and validation. The two KASP assays, Ppe.RPT/SSC-1.1 (SNP_IGA_411637) and Ppe.RPT/SSC-1.2 (SNP_IGA_412338), were developed and successfully amplified and distinguished alleles in validation material (Table 1 , Fig. 3 ). The SNP_IGA_412662 was unsuitable for conversion into a KASP assay and was subsequently discarded. Using 84 peach cultivars, the assay validation revealed four different clusters corresponding to each genotype (AA, AB, and BB) and non-template controls (Fig. 3 ; Supplementary Table S3). The KASP had 93.1% accuracy in correct allele assignment compared to the 9K SNP array, with the discrepancies observed in four cultivars (China Pearl, Fantasia, Le Grand, and Saturn) (Supplementary Table S3). Additionally, end-point PCR for the same 84 peach cultivars showed similar performance to real - time PCR (Fig. 3 ), with only 3.2% samples needing to be repeated due to the lack of amplification. Similarly, to the haplotype phenotype analyses above, the analysis of the phenotypic effect of the SNP_IGA_411637 ( Ppe.RPT/SSC-1.1 ) in the 84 peach cultivars suggested that the ‘A’ allele was associated with a delay of RPT and an increase of SSC, while the ‘B’ allele was related to the advancement of RPT and a decrease of SSC. The comparison between observed and predicted mean RPT (early, mid and late) in the validation material (84 cultivars) explained 20% of the phenotypic variance observed and revealed a significant difference (Tukey test, p < 0.05) between early and late-ripening individuals (Fig. 4 a). Individuals predicted as early and mid-season ripened on average 18 and 9 days before ‘Elberta’, respectively (Supplementary Table S11). In contrast, individuals predicted as late season had, on average, a delay of 16 days compared to ‘Elberta’ (Supplementary Table S11). The comparison between observed and predicted mean of SSC, showed that 12% of phenotypic variance can be explained by Ppe.RPT/SSC-1.1 . Individuals with low and mid predicted SSC revealed a significantly (Turkey, p < 0.05) lower mean of SSC when compared with individuals with high predicted SSC (Fig. 4 b). No significant differences were observed between individuals with low and mid predicted SSC; however, individuals predicted to have low SSC exhibited lower observed SSC levels than those with mid predicted SSC. The same patterns were observed for the 163 individuals from the CUPBP and 26 commercial cultivars with known RPT and SSC in five evaluated seasons. In these materials, individuals with the AA genotype (predicted to have late RPT and high SSC) had the latest average RPT and highest average SSC in all five seasons (Fig. 5 ). These individuals ripened on average three and four weeks later than mid (AB genotype) and early (BB genotypes) season cultivars, respectively (Fig. 5 and Supplementary Table S12). Regarding SSC, less significant differences according to Tukey test (p < 0.05) were observed in the CUPBP material between the three genotypes of Ppe.RD/SSC-1.1 , with late-ripening individuals (genotype AA) having 1.0 and 0.6 ºBrix more than early and mid-season cultivars, respectively (Fig. 5 and Supplementary Table S12). Discussion We have analyzed a pedigree-connected germplasm containing 288 seedlings from multiple F 1 and F 2 families of the CUPBP for RPT and SSC using datasets from the two seasons (2011 and 2012) and the average dataset (average of the two seasons). The phenotypic mean values of RPT observed in this study were higher than those reported by Rawandoozi et al. 17 and Nuñez-Lillo et al. 13 and in agreement with the mean values observed by Eduardo et al. 10 . On the other hand, the mean values of SSC reported in this study were slightly lower than the values described by Nuñez-Lillo et al. 12 and similar to the mean values observed by Rawandoozi et al. 15 . We observed significant differences between seasons in the mean values of RPT and SSC. However, moderate (0.41) to high (0.90) correlation coefficients between seasons were recorded for SSC and RPT, respectively. Pedigree based QTL mapping using FlexQTL™ enabled the successful mapping of stable QTLs with decisive evidence (BF > 10) for RPT and SSC. The qRPT.1 and qSSC_4.1 detected in both seasons in an overlapped genetic interval formed a QTL cluster on LG4 and confirmed the QTLs and associated signals identified in previous studies using different germplasm and approaches 11,13–15,18 . The RPT and SSC QTL cluster initially covered a genetic region from 44 to 49 cM (from 10,981,971 bp to 12,523,245 bp) and comprised eight SNPs that could be used for haplotype analysis. However, the genetic region was narrowed down to 44–45 cM (physical position from 10,981,971 bp to 11,298,736 bp) and three SNPs when the peak position of the RPT and SSC QTLs in the FlexQTL™ outputs in the average datasets was taken into account. Average datasets have been frequently used for determining the genetic region for further haplotype analysis using FlexQTL™ 16,17,44 . The target region comprised three SNPs and allowed the identification of QTL genotypes with different effects of increasing/decreasing RPT and SSC (Fig. 1 ; Table 3 ). In addition, a predictive marker (SNP_IGA_411637) distinguished the allele associated with increasing (A) and decreasing (B) of the RPT and SSC in the breeding populations. These findings suggested that the marker-assisted selection (MAS) with the SNP_IGA_411637 could efficiently be used for selecting parents/seedlings for targeted RD and SSC. Previous RPT and SSC QTL mapping in peach focused mainly on identifying marker-trait association with candidate genes analysis in the mapped QTL interval. In this study, our primary goal was not only to map or validate QTLs, but also to convert the findings into a diagnostic DNA test for routine application in breeding. The complexity of RPT and SSC traits due to their polygenic nature and environmental influence 45 made development of breeding tools for MAS difficult. However, quantitative traits were recently targeted in peach for DNA test development resulting in tools such as the Ppe.CR.1 designed for predicting chilling requirement 24 . RPT and SSC display similar patterns to chilling requirements, where multiple genes control the phenotype and the major QTL explained more than 30% of the phenotypic variation. Thus, the findings obtained from the QTL mapping and haplotype analysis performed in this study suggested that it was feasible to develop a DNA test for predicting RPT and SSC in peach. The Ppe.RPT/SSC-1 was designed using a recently reported KASP approach for fruit response to Xanthomonas arboricola pv. pruni 23 and chilling requirement 24 prediction in peach. In combination with crude DNA extraction 41 , this technology provides an inexpensive, trustworthy, and user-friendly tool for accurately predicting SNP genotypes 29,46 . The validation of the Ppe.RPT/SSC-1 breeding tool revealed concurrence between the outcomes of real-time and endpoint PCRs, indicating that endpoint PCRs can be utilized to improve throughput. Additionally, the Ppe.RPT/SSC-1 assay developed for SNP discrimination successfully predicted the genotypes previously obtained from the 9K SNP array with disagreement in only four accessions. From those, two accessions (‘Fantasia’ and ‘Saturn’) also demonstrated discrepancies between the SNP array and KASP genotypes when Ppe.CR.1 for prediction of chilling requirement was validated 24 . This result supports the hypothesis that the discrepancies could be due to the incorrectly called array genotypes or different DNA sources used in the array (genotyping carried out by the RosBREED project 22 ) and KASP genotyping. Although two KASP assays were developed to distinguish haplotypes in the QTL target region (10,981,971 bp to 11,298,736 bp), only one ( Ppe.RPT/SSC-1.1 ) was needed to distinguish the early and late-season ripening cultivars, confirming the results obtained from the haplotype analysis that indicated the SNP_IGA_411637 as a predictive marker. The predictive ability of Ppe.RPT/SSC-1.1 for RPT of materials from different genetic backgrounds and environments (AR, SC, and TX) highlighted the usefulness of this tool for breeding. Moderate to high correlation between RPT and SSC were detected in this work. However, the predictive ability of Ppe.RPT/SSC-1 was lower for SSC in comparison to RPT. Possible explanations for the lower prediction values could be due to the majority of the QTL effect observed in the SSC being attributed to RPT. This hypothesis was confirmed by the SSC QTLs runs where the RPT was used as cofactor and resulted in a lower BF (evidence of the presence of QTL) and the PVE. Effect of other QTL regions on different chromosomes associated with the genetic control of SSC 13,15,18, 47 could also affect prediction. For example, the major QTL for SSC was mapped on the chromosome 5 16 in the Texas material, where the lowest prediction of SSC was observed. The Ppe.RPT/SSC-1.1 developed in this study, did not account for other regions, which could explain the lower SSC prediction when compared to RPT. Lastly, the strong influence of environmental conditions on the final sugar content 45 or the possibility that fruits in different ripening stages (under-ripened) could have skewed SSC values as only five fruits were considered for juice extraction and SSC determination. Thus, further studies focusing on DNA tool development accounting for other chromosomal regions associated with SSC and the appropriate ripening stage of the fruits selected for °Brix determination are required to obtain a high predictive accuracy for SSC. Conclusion We successfully mapped/validated a stable QTL cluster on LG4 associated with RPT and SSC using a pedigree-connected germplasm comprising 288 individuals. In addition, we identified statistically different QTL genotypes in the target genetic interval selected for haplotype analysis. We also developed an accurate KASP assay for RPT and SSC identifying the correct genotype for 93% of samples with known genotypes and validated the assay on individuals from different genetic backgrounds and environments (AR, SC and TX). Furthermore, a prediction accuracy of the KASP assay and an ability to distinguish between early and late-season ripening and low and high SSC cultivars was obtained. The low-cost and quick crude DNA extraction combined with the KASP approach reported here provide relevant information to breeders for large-scale genotyping in breeding programs, saving time, and resources and efficiently accelerating the selection of desired individuals. Moreover, the results of this work will be extremely helpful for strengthening the bridge between academic research and breeding applications and, consequently, for peach genetic improvement. Methods Plant Material The material used in this study was comprised of pedigree connected germplasm with cultivars, advanced selections, and seedlings from Clemson University peach breeding program (CUPBP), which were previously assembled under the RosBREED project 18,22 . A breeding population containing a total of 288 seedlings, which included multiple F 1 and F 2 families obtained from 19 parents (Supplementary Table S1 ), was chosen for mapping analyses. The parents and seedlings were maintained at the Clemson University Musser Fruit Research Center, in Seneca, South Carolina (Latitude: 34.639038, Longitude: -82.935244, Altitude 210 msl), under warm, humid, temperate climate and standard commercial practices for irrigation, fertilization, and pest and disease control. The trees were grafted on ‘Guardian®’ rootstock, and either planted at 4 × 6 m and trained to open center or at 1.5 × 4 m and trained to perpendicular V. Phenotypic data Phenotypic data for QTL mapping and DNA test development were recorded over two seasons (2011–2012). Ripening time (RPT), in Julian days (JD), was determined when 20% of fruits were at commercial harvest by visually inspecting the presence of a few soft fruits in the field for maturity twice per week. A composite sample of one approximately 2 cm wide longitudinal slice from each of five fruits was used to extract juice with a juicer to measure SSC (°Brix) using a digital refractometer. Statistical analysis (descriptive, normality test, Spearman’s rank correlation, and Wilcoxon signed rank test) was performed using R Statistical Software (version 4.1.2). The narrow-sense heritability (h 2 ) were estimated considering the two seasons (2011–2012) using the R package Sommer 33 and the vpredict function: $$vpredict\left(object, transform\right)$$ where: object represents a model fitted with the mmer function; transform is the formula to calculate the function. $$mix<-mmer(Trait\sim Year,$$ $$random=\sim vsr(Selection,Gu=K),$$ $$rcov=\sim vsr\left(dsr\left(Year\right),units\right),$$ $$data=Trait)$$ Where: “K” refers to the additive relationship matrix. The formula included in the function was: $${h}^{2}= VA/VP$$ where: “VA” is the additive genetic variance; “VP” is the phenotypic variance. Genotyping and linkage map Samples were genotyped with the IPSC peach 9K SNP array v1 34 . The SNP data curation was performed using the workflow for high-resolution genetic marker data described in Vanderzande et al. 21 . In order to reduce the time needed for analysis, a total of 1487 informative SNPs were retained for pedigree-based QTL mapping (Supplementary Table S2 ). The genetic positions of the retained SNPs were calculated using the physical position of the peach reference genome v2.0 35 and a conversion factor where every 1 Mb corresponded to 4 cM, as described by Vanderzande et al. 21 . QTL mapping The QTL mapping was performed using FlexQTL™ software (version 0.1.0.42) 36 which implements pedigree-based QTL analysis via Markov Chain Monte Carlo (MCMC) simulation. The analysis was run at least twice for the two seasons (2011 and 2012), as well as for the datasets RPT_Ave and SSC_Ave (average of the two seasons), until reaching the effective chain size (ECS) criterion for convergence (≥ 100). The MCMC length for RPT ranged from 700,000 to 900,000 iterations to store one thousand samples with a thinning between 700 and 900 under mixed genetic model. However, 200,000 iterations with a thinning of 200 under additive genetic model were adequate to achieve ECS ≥ 100 for SSC. Inference on the number of QTLs was based on a pairwise comparison of models (1/0, 2/1, 3/2, and so on) using twice the natural log of the Bayes factor (2lnBF) statistic. The Bayes factor (BF) parameter was interpreted as: non-significant (0–2), positive ( 2 – 5 ), strong ( 5 – 10 ), or decisive (> 10) evidence for the presence of QTLs 37 . In this study, we reported only stable QTLs. A QTL was considered “stable” when: the BF was decisive (> 10) and the significant effect was located in overlapping positions between the two seasons. The stable QTLs were named as “ q ” + trait name abbreviation + data set + scaffold + number of the chronological QTL for this trait reported on this chromosome (e.g. qRPT_SC_4.1 ). To narrow down and re-define the QTL intervals, further analysis was carried out in FlexQTL™ using the ‘MQTRegions.new’ file considering the supporting data files ‘Post_genome.csv’ and ‘marker map’. The new generated output files were used to recalculate the phenotypic variance explained (PVE) for the stable QTLs and to update information for QTL intensity, interval, and mode positions. FlexQTL™ obtained the additive variance (σ 2 A(trt) ) for each trait by subtracting the residual variance (σ 2 e ) from the phenotypic variance (σ 2 P ). The phenotypic variance explained (PVE) for a particular QTL considering an additive model was calculated using the following equation: \(PVE additive model= \frac{{\sigma }_{A\left(qtl\right)}^{2}}{{\sigma }_{P}^{2}}\times 100\) where: \({\sigma }_{A\left(qtl\right)}^{2}\) is the additive variance of QTL. Regarding the mixed model, genetic variance ( \({\sigma }_{G}^{2}\) ), was calculated by subtracting the residual variance ( \({\sigma }_{e}^{2}\) ), from the phenotypic variance ( \({\sigma }_{P}^{2}\) ), and the PVE was calculated as follows: \(PVE mixed model= \frac{{\sigma }_{A\left(qtl\right)}^{2}+ {\sigma }_{D\left(qtl\right)}^{2}}{{\sigma }_{P}^{2}}\times 100\) where: \({\sigma }_{A\left(qtl\right)}^{2} \text{i}\text{s} \text{t}\text{h}\text{e} \text{a}\text{d}\text{d}\text{i}\text{t}\text{i}\text{v}\text{e} \text{v}\text{a}\text{r}\text{i}\text{a}\text{n}\text{c}\text{e} \text{o}\text{f} \text{Q}\text{T}\text{L}\) and \({\sigma }_{D\left(qtl\right)}^{2} \text{i}\text{s} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{i}\text{n}\text{a}\text{n}\text{t} \text{v}\text{a}\text{r}\text{i}\text{a}\text{n}\text{c}\text{e} \text{o}\text{f} \text{Q}\text{T}\text{L}.\) A pleiotropic effect of RPT and other quality traits, including SSC, has been previously reported 11 . Therefore, QTL analysis for SSC_Ave (average of the two seasons) was also performed using the RPT information as a covariate, which was integrated as a nuisance variable in the data file used in FlexQTL™. Separate runs using a mixed model, with the same number of seeds and MCMC length (1,200,000), were carried out in FlexQTL™ using the RPT information in four different datasets: 1. Dataset where no covariate was included; 2. Dataset where the corresponding phenotypic average of RPT (in Julian days) was included as a cofactor; 3. Dataset where the haplotype information of the RPT QTL interval was included as a cofactor; 4. Dataset where both the phenotypic average of RPT (in Julian days) and haplotype information of the RPT QTL interval were included as cofactors. Haplotype analysis As described by Rawandoozi et al. 16 , the haplotype analysis was carried out by selecting the SNPs within the significant QTL interval considering the RPT and SSC average datasets. We used the output files ‘MQTRegionsGTP.csv’ and ‘mhaplotypes.csv’ generated by FlexQTL™ to perform the haplotype analysis. Haplotypes were constructed in the dataset using PediHaplotyper R package 38 . The effects were determined from combinations of diplotypes. The nonparametric multiple comparison Steele–Dwass test ( p < 0.05) was applied to assess significant differences between diplotype effects. QTL allele genotypes ( Q or q ) were assigned to haplotypes based on the direction of their effects (increasing or decreasing RPT and SSC, respectively). The statistical analysis was performed using the JMP Pro Version 13.2 (SAS Institute Inc., Cary, NC, 2016). Illumina codes for nucleotides, where A = A or T, and B = C or G, were used as marker designation in haplotypes 34 Phenotypic variation explained (PVE) by each informative SNP within the significant QTL interval was estimated using single linear regression (SLR) analysis in R Statistical Software (version 4.1.2). The phenotypic performance of different genotypes of the predictive marker was further validated in peach breeding populations comprising 128, 290 and 139 individuals from Arkansas (AR), South Carolina (SC) and Texas (TX), respectively. KASP marker development and validation Two informative SNPs capable of distinguishing haplotypes for the main RPT and SSC QTL on LG4 (10,981,971 to 11,298,736 bp) were used for developing the Ppe.RPT/SSC-1 KASP assay. The SNP_IGA_412662 was unsuitable for conversion into a KASP assay and was subsequently discarded. Primers were designed for each of the two SNPs (Table 1 ) and reaction mixtures and PCR conditions for the KASP assays were determined following Fleming et al. 22 . Eighty-four peach cultivars representing the diversity of fresh-market US germplasm, 51 of which had known genotypes from the 9K SNP array 34 , were chosen for the development and validation of the assay (Supplementary Table S3). Phenotypic data for RPT were obtained from literature 39 , while the SSC data were obtained from two databases: Clemson University Variety evaluations database ( https://www.clemsonpeach.org/ ), and the Genome Database for Rosaceae 40 . The variety evaluation data from Musser fruit research farm, were collected within 2015–2021, and the publicly available GDR peach data (GRIN_PEACH and Peach RosBREED Public), were collected during three and seven seasons, within RosBREED 22 project and GRIN, respectively. The average SSC for each cultivar was calculated and used in the validation of the assay. DNA for these cultivars was extracted using the protocol described in Edge-Garza et al. 41 . Three replicates of non-template controls and positive controls for homozygous (AA and BB) and heterozygous (AB) genotypes were tested in a 96 well-plate for each assay. Positive controls were selected from the DNA samples with known genotypes from the 9K SNP array (Supplementary Table S4). Amplifications were conducted in a Bio-Rad CFX Connect Real-Time PCR thermocycler under a standard protocol (15 min at 94°C; 10 cycles of 20 s at 94°C and 60 s at 61°C with a 0.6°C decrease in temperature per cycle; 40 cycles at 94°C for 20 s; 60 s at 55°C, and 30 s at 23°C), and cycle 25 of real-time PCR was selected for each KASP assay to maximize separation between genotypes. A template spreadsheet designed by Fleming et al. 23 was used to assign the genotypes to each sample based on the relative fluorescence unit values from this cycle. For routine use, end-point PCR was tested on Bio-Rad T100 thermocycler using the same cultivars and positive controls for all assays, with slightly modified protocol. The number of cycles at the SNP specific temperature was decreased to 25 and the last cycle of the standard protocol (30 s at 23°C) and plate reading were omitted. End-point reactions were read on the Bio-Rad CFX Connect Real-Time PCR thermocycler using Bio-Rad CFX Maestro™ software. The newly developed Ppe.RPT/SSC-1 KASP assays were validated by screening 163 seedlings from the CUPBP and 26 commercial cultivars (Supplementary Table S5) with end-point PCR. The validation set included 15 seedlings from QTL mapping germplasm. Phenotypic data was collected in the CUPB program. The best linear unbiased predictions (BLUPs) for each individual were obtained for the RPT and SSC values recorded across five seasons (2017 to 2021) using the R package 'lme4' 42 with year selected as a random effect: $${Y}_{ij}= \mu +{g}_{i}+ {y}_{j}+ {gy}_{ij} + \epsilon$$ Where: Y ij is the trait of interest, µ is the overall mean, g i is the genetic effect of i th genotype, y j is the effect of the j th year, and gy ij as the interaction effect of i th genotype with j th year, ɛ is the residual of the model. DNA samples for these individuals were obtained using a rapid crude DNA extraction protocol described by Noh et al. 43 . The template spreadsheet designed by Fleming et al. 23 was employed to automatically assign genotypes. Statistical differences between classes were evaluated using one-way ANOVA, followed by the Tukey test for multiple comparisons in SPSS. Declarations Acknowledgements The authors would like to thank Ralph Burrell and Musser Fruit Research Farm staff at Clemson University for their help with orchard maintenance and phenotypic data acquisition. Author contributions statement CDSL: formal analysis and writing – original draft. WF and AC: KASP test analysis and writing. ZR: haplotyping analysis and review. LC: SNP data curation and review. MW and DB: resources and writing - review and editing. KG: conceptualization, funding acquisition, resources, supervision, and writing – review & editing. All authors have read and approved the final manuscript. Additional information Data availability The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: www.rosaceae.org. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was funded by USDA’s National Institute of Food and Agriculture-Specialty Crop Research Initiative Projects, “RosBREED: Enabling marker-assisted breeding in Rosaceae” (2009-51181-05858) and “RosBREED: Combining disease resistance and horticultural quality in new rosaceous cultivars” (2014-51181-22378). Experiments involving plants Relevant guidelines and regulations have been followed for all experiments involving plants. Plants subject to these experiments were created in the co-authors breeding programs and belong to them so the permission or license to use them is not needed. References Bassi, D., Mignani, I., Spinardi, A. & Tura, D. Chapter 23 - PEACH (Prunus persica (L.) 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An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genomics 21 , 382 (2020). Tang, W. et al. Selection and Validation of 48 KASP Markers for Variety Identification and Breeding Guidance in Conventional and Hybrid Rice (Oryza sativa L.). Rice 15 , 48 (2022). Shikari, A.B. et al. KASP™ based markers reveal a population sub-structure in temperate rice (Oryza sativa L.) germplasm and local landraces grown in the Kashmir valley, north-western Himalayas. Genetic Resources and Crop Evolution 68 , 821 - 834 (2020). Covarrubias-Pazaran, G. Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer. PLOS ONE 11 , e0156744 (2016). Verde, I. et al. Development and evaluation of a 9k snp array for peach by internationally coordinated snp detection and validation in breeding germplasm. PLoS ONE 7 (2012). Verde, I. et al. 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A high-throughput and cost-efficient DNA extraction protocol for the tree fruit crops of apple, sweet cherry, and peach relying on silica beads during tissue sampling. Molecular Breeding 34 , 2225-2228 (2014). Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67 , 1 - 48 (2015). Noh, Y.-H., Lee, S., Whitaker, V.M., Cearley, K.R. & Cha, J.-S. A High-throughput Marker-assisted Selection System Combining Rapid DNA Extraction High-resolution Melting and Simple Sequence Repeat Analysis: Strawberry as a Model for Fruit Crops. Journal of Berry Research 7 , 23-31 (2017). Verma, S. et al. Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study. Tree Genetics & Genomes 15 , 18 (2019). Lopresti, J., Goodwin, I., McGlasson, B., Holford, P. & Golding, J. Variability in Size and Soluble Solids Concentration in Peaches and Nectarines. in Horticultural Reviews: Volume 42 253-312 (2014). 46. He, C., Holme, J. & Anthony, J. SNP Genotyping: The KASP Assay. in Crop Breeding: Methods and Protocols (eds. Fleury, D. & Whitford, R.) 75-86 (Springer New York, New York, NY, 2014). Zeballos, J.L. et al. Mapping QTLs associated with fruit quality traits in peach [Prunus persica (L.) Batsch] using SNP maps. Tree Genetics & Genomes 12 , 37 (2016). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures11022023.pdf SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Dec, 2023 Reviews received at journal 11 Dec, 2023 Reviews received at journal 20 Nov, 2023 Reviewers agreed at journal 20 Nov, 2023 Reviewers agreed at journal 19 Nov, 2023 Reviewers invited by journal 19 Nov, 2023 Editor assigned by journal 14 Nov, 2023 Editor invited by journal 11 Nov, 2023 Submission checks completed at journal 11 Nov, 2023 First submitted to journal 06 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3569064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":248217707,"identity":"f77f36a8-9a4c-4797-9353-cc1c14c390c7","order_by":0,"name":"Cassia Silva Linge","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cassia","middleName":"Silva","lastName":"Linge","suffix":""},{"id":248217709,"identity":"ec6b49cb-b193-40cc-b3a1-e6f4a08c4f74","order_by":1,"name":"Wanfang Fu","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanfang","middleName":"","lastName":"Fu","suffix":""},{"id":248217710,"identity":"0c3e58e2-9b78-499b-8d35-3cee5d2fbd69","order_by":2,"name":"Alejandro Calle","email":"","orcid":"","institution":"Institut de Recerca i Tecnologia Agroalimentàries (IRTA)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Calle","suffix":""},{"id":248217711,"identity":"da5f6671-816b-4c39-91a6-bc86f863fe9b","order_by":3,"name":"Zena Rawandoozi","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zena","middleName":"","lastName":"Rawandoozi","suffix":""},{"id":248217714,"identity":"9cca477d-3442-4de5-a755-6738639ef110","order_by":4,"name":"Lichun Cai","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lichun","middleName":"","lastName":"Cai","suffix":""},{"id":248217719,"identity":"e4f84dc3-fbad-4ee2-a148-27c190064019","order_by":5,"name":"David H. 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Effects were based on diplotypes observed for \u003cem\u003eqRPT_SC_4.1\u003c/em\u003eand \u003cem\u003eqSSC_SC_4.1\u003c/em\u003e QTLs. Only diplotypes represented by eight or more individuals were included. Significantly different (Steele-Dwass, p \u0026lt; 0.05) phenotypic means are identified by different letters\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/c5b72edde5eaad1466cf677d.png"},{"id":46441634,"identity":"76198f18-4f5e-4aa7-b4bd-c98c4938c5e3","added_by":"auto","created_at":"2023-11-14 19:03:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":709666,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of observed ripening time (a) and soluble solid concentration (b) for the three different genotypes (AA, AB, and BB) of SNP_IGA_411637 (\u003cem\u003ePpe.RPT/SSC_1.1\u003c/em\u003e) in Arkansas (AR), South Carolina (SC) and Texas (TX) peach breeding populations. Phenotypic means that were significantly different (Tukey and Student T tests, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for three and two classes, respectively) are represented by different letters. White triangles and black horizontal lines represent mean and median phenotypic values, respectively, in each peach breeding population. R\u003csup\u003e2\u003c/sup\u003e represents the proportion of variance in the dependent variable explained by the independent variable.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/9218fbc78a95a1a991ef67ce.png"},{"id":46441996,"identity":"89d1432f-db09-45b7-b28d-70e37ec0356d","added_by":"auto","created_at":"2023-11-14 19:11:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":547588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePpe.RPT/SSC\u003c/em\u003e KASP assays validation in 84 peach samples using real-time (a) and endpoint (b) PCRs. Yellow triangles, diamonds, squares, and circles indicate genotypes AA, AB, BB, and non-template controls, respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/15956831599e41874bf37a67.png"},{"id":46441635,"identity":"2cb26142-b66e-41d5-b821-a222a072fd8c","added_by":"auto","created_at":"2023-11-14 19:03:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272009,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the predicted and observed phenotypes for ripening time (RPT) (a) and soluble solid concentration (SSC) (b) in 84 peach cultivars used for assay development and validation. Phenotypic means that were significantly different (Tukey, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are represented by different letters. White triangles represent mean phenotypic values. Black horizontal lines represent median phenotypic values.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/8fbe6b0ff7bb4c5d9afc3a32.png"},{"id":46441639,"identity":"025f2473-94ea-4ad9-9faa-76b73720da37","added_by":"auto","created_at":"2023-11-14 19:03:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":565883,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the predicted and observed phenotypes for ripening time (a) and soluble solid concentration (b) in the Clemson University peach breeding program’s breeding materials across five different seasons and BLUP values. Phenotypic means that were significantly different (Tukey test, p \u0026lt; 0.05) are represented by different letters. Black triangles represent mean observed trait values, and horizontal lines represent median observed trait values.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/6d7bf3dcd49b84dbc56c4dba.png"},{"id":49979000,"identity":"16695fd1-692e-4d16-94d2-30f495d00161","added_by":"auto","created_at":"2024-01-22 15:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1288505,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/b3c9961a-b78d-4301-8b4c-5fa6e6f1c2c8.pdf"},{"id":46441636,"identity":"283ac6da-7c3f-4c27-9a82-54588ea2290b","added_by":"auto","created_at":"2023-11-14 19:03:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":599836,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures11022023.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/287b605127ddbf8f9b1c63e6.pdf"},{"id":46441638,"identity":"0ce0dec0-402f-4499-89c2-bb00ac478f3f","added_by":"auto","created_at":"2023-11-14 19:03:36","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":118738,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3569064/v1/88e52585c4c3015ca9eb60da.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ppe.RPT/SSC-1: From QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeach [\u003cem\u003ePrunus persica\u003c/em\u003e (L.) Batsch] is the third most cultivated temperate tree fruit in the world after apple and pear\u003csup\u003e1\u003c/sup\u003e. The United States is the fifth largest peach producer in the world with an approximate production of 730 thousand tons, behind China, Spain, Italy, and Turkey\u003csup\u003e2\u003c/sup\u003e. Despite high production levels, the variability in fruit quality has resulted in a significant decrease in the US peach consumption in the last decades\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the main consumer complaints about peaches is the lack of sensory quality, which is mostly caused by harvesting fruits at an inadequate ripening stage\u003csup\u003e4\u003c/sup\u003e. The process of fruit ripening plays an important role in improving peach organoleptic quality, including increases in soluble solids concentration (SSC) to acid ratio, softening, and aroma\u003csup\u003e5,6\u003c/sup\u003e. SSC, measured as \u0026deg;Brix, is often used to express sugar content and represents one of the main drivers affecting peach consumer acceptance\u003csup\u003e7\u003c/sup\u003e. The amount of total SSC in ripe fruits combined with additional active compounds, such as pectin, salts, and organic acids, significantly increases consumer satisfaction in peaches\u003csup\u003e8,9\u003c/sup\u003e. Medium and late season cultivars have a greater capacity to accumulate sugar compared to early season cultivars\u003csup\u003e4\u003c/sup\u003e. Ripening time, however, affects the farm gate value of the crop with early ripening cultivars getting higher price despite the SSC levels\u003csup\u003e10\u003c/sup\u003e. Therefore, breeders aim at developing new peach cultivars tailored to specific ripening periods and with enhanced sensory characteristics that balance sensory quality and maturity at harvest to ensure consumer satisfaction and encourage consumption.\u003c/p\u003e \u003cp\u003eNew genetic resources for peach have enabled several recent studies on genetic dissection of important traits including ripening time (RPT) and SSC. Quantitative Trait Loci (QTLs) and associated signals have been identified using different approaches such as bi-parental QTL mapping\u003csup\u003e11\u0026ndash;14\u003c/sup\u003e, pedigree-based analysis (PBA)\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e and genome-wide association studies\u003csup\u003e18\u003c/sup\u003e. In addition, genomic prediction models focusing on SSC were developed \u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA large-effect QTL on chromosome 4, discovered using a low-density linkage map, suggested a pleiotropic effect between SSC and RPT\u003csup\u003e11\u003c/sup\u003e. Further studies also highlighted a QTL hotspot in the same region using different genetic backgrounds and mapping approaches\u003csup\u003e15,18\u003c/sup\u003e. Although several QTLs for several traits including SSC and RPT have been discovered, the conversion into practical breeding tools such as DNA tests is still scarce. Trait-predictive DNA tests could be useful for rapidly identifying valuable parents as well as for removing inferior seedlings that do not possess a target characteristic. Thus, applying these genetic assays in breeding programs could significantly enhance the efficiency of the cultivar development process\u003csup\u003e21,22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn peach, rapid and accurate DNA tests for predicting bacterial spot severity in fruit and chilling requirement were recently developed\u003csup\u003e23,24\u003c/sup\u003e. Additional tests for peach skin blush\u003csup\u003e25\u003c/sup\u003e, fruit pubescence\u003csup\u003e26\u003c/sup\u003e, subacid trait\u003csup\u003e27\u003c/sup\u003e and flesh color\u003csup\u003e28\u003c/sup\u003e are also available. Most of the existing tests were developed using PCR-based simple sequence repeat (SSR) technology. Single nucleotide polymorphism (SNP) markers have replaced SSRs and other types of molecular markers due to the considerable cost-efficiency for genotyping and their precision, stability, and easy detectability\u003csup\u003e29\u0026ndash;31\u003c/sup\u003e. Kompetitive allele-specific PCR (KASP) assays are affordable, rapid, and robust tools to genotype SNPs of interest and are widely used due to improved cost-effectiveness compared to other technologies\u003csup\u003e23,29,31,32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe objective of this work was to showcase the whole process from QTL discovery to DNA test development for routine use in peach breeding for the simultaneous prediction of RPT and SSC. First, we mapped and validated the previously discovered large effect RPT and SSC QTLs on chromosome 4 within Clemson University peach breeding germplasm using a pedigree-based analysis (PBA) approach. Subsequently, we developed and validated a rapid KASP test using the target region obtained by the QTL mapping.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePhenotypic data\u003c/p\u003e \u003cp\u003eThe breeding population containing 288 seedlings from the CUPBP was evaluated for RPT and SSC over two seasons (2011\u0026ndash;2012). The RPT ranged from 150 to 235 JD with a mean of 195 and 182 in 2011 and 2012, respectively. SSC varied from 7.4 to 19.8 \u0026ordm;Brix with means of 12.3 and 11.7 in 2011 and 2012 (Supplementary Table S6). According to the Shapiro Wilk test, RPT did not follow a normal distribution in both seasons, whereas SSC exhibited a normal distribution only in 2012 (Supplementary Table S7). According to the non-parametric Wilcoxon signed rank test, significant differences were observed in the RPT and SSC averages between seasons with a delay in the RPT and a higher SSC mean value observed in 2011. Spearman\u0026rsquo;s rank correlation analysis demonstrated significant positive coefficients between RPT and SSC in 2011 (0.67) and 2012 (0.62). Considering the correlation between seasons, RPT had the highest coefficient (0.90) and SSC the lowest (0.41) (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The narrow- sense heritability was 0.84 and 0.24 for RPT and SSC, respectively.\u003c/p\u003e \u003cp\u003eQTL analysis\u003c/p\u003e \u003cp\u003eWe performed the QTL analysis using a Bayesian, pedigree-based QTL analysis (PBA) implemented in FlexQTL\u0026trade; using the RPT and SSC phenotypic data from 2011 (RPT_2011 and SSC_2011), 2012 (RPT_2012 and SSC_2012) as well as the average values of both seasons (RPT_Ave and SSC_Ave).\u003c/p\u003e \u003cp\u003eThe QTL analysis identified major QTLs associated with both RPT and SSC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and S3). The RPT QTL, \u003cem\u003eqRPT_SC_4\u003c/em\u003e, was consistently mapped with decisive evidence (BF\u0026thinsp;\u0026gt;\u0026thinsp;10) on LG4 in the overlapped genetic interval of 44-51cM in both 2011 and 2012. Peak position of the \u003cem\u003eqRPT_SC_4.1\u003c/em\u003e was at 45\u0026ndash;48 cM. The SSC QTL, \u003cem\u003eqSSC_SC_4.1\u003c/em\u003e, was also consistently mapped in both seasons in a wider genetic interval (38\u0026ndash;52 cM and 43\u0026ndash;58 cM in 2011 and 2012, respectively) and exhibited a peak position varying from 44 to 49 cM. Overall, the \u003cem\u003eqRPT_SC_4.1\u003c/em\u003e and the \u003cem\u003eqSSC_SC_4.1\u003c/em\u003e were mapped in the same genetic interval forming a QTL cluster (Supplementary Fig. S3). Based on the peak position mapped in both seasons and traits, an initial genetic interval comprising eight SNPs spanning from 44 to 49 cM (physical position from 10,981,971 to 12,523,245 bp) was highlighted and subsequently used for the PVE. The \u003cem\u003eqRPT_SC_4.1\u003c/em\u003e explained approximately 53% and 59% of the phenotypic variance in RPT 2011 and 2012, respectively, while the PVE for \u003cem\u003eqSSC_SC_4.1\u003c/em\u003e ranged from 25% (2011) to 35% (2012).\u003c/p\u003e \u003cp\u003eDatasets using the average of two seasons for both traits (RPT_Ave and SSC_Ave) were used in an independent FlexQTL\u0026trade; runs and the overlapped peak position was detected at 45 cM, explaining approximately 53% and 36% of the phenotypic variance observed in RPT and SSC, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, we targeted the genetic interval from 44 to 45 cM (spanning a physical position from 10,981,971 to 11,298,736 bp) for further analysis of diplotype/genotype effects.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1699984007.png\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of QTLs mapped on linkage group 4 in the Clemson University peach breeding program using FlexQTL\u0026trade;.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterval(cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePeak (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqRPT_SC_4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u0026ndash;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e10,981,971\u0026thinsp;\u0026minus;\u0026thinsp;13,633,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqSSC_SC_4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u0026ndash;52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e9,617,585\u0026thinsp;\u0026minus;\u0026thinsp;13,882,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eqSSC_SC_4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e10,280,095\u0026thinsp;\u0026minus;\u0026thinsp;12,546,297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRPT: Ripening time; SSC: Soluble Solids Concentration; LG: Linkage group; BF: Bayes factor; Additive effect; PVE: Percentage of variance explained.\u003c/p\u003e \u003cp\u003eThe QTL analysis for SSC_Ave using the RPT_Ave information as a cofactor revealed that both the BF (evidence of the presence of QTL) and PVE decreased in all three datasets containing the cofactor compared to the one where no cofactors were included (Supplementary Table S8). The dataset using no cofactor showed a BF of 29.7 and explained approximately 35% of the observed phenotypic variance. The dataset using the phenotypic average of RPT as a cofactor had the lowest BF (8.4) and PVE (20.8), while the dataset incorporating the haplotype information of the RPT QTL interval exhibited the highest BF (29.4) and PVE (27.2).\u003c/p\u003e \u003cp\u003eQTL haplotype, genotype and predictive markers\u003c/p\u003e \u003cp\u003eThree SNPs were located within the target region of the QTL interval from 44 cM (10,981,971 bp) to 45 cM (11,298,736 bp), which was detected in the average datasets (RPT_Ave and SSC_Ave). Out of the possible six we observed four unique haplotypes (H1-H4) defined by three SNPs within the region in the analyzed material (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sources of each haplotype are shown in the Supplementary Table S9. The haplotype H4 was associated with an early ripening, lower SSC values and was attributed to the \u003cem\u003eq\u003c/em\u003e allele. The H1-H3 haplotypes were associated with increase of both traits and were assigned to the \u003cem\u003eQ\u003c/em\u003e allele. The SNP_IGA_411637 was determined as the predictive marker for distinguishing the \u003cem\u003eQ\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e alleles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Three statistically different QTL genotypes (\u003cem\u003eqq, Qq, QQ\u003c/em\u003e) were detected in the breeding population used for QTL mapping (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The allele \u003cem\u003eQ\u003c/em\u003e was associated with late ripening and a higher SSC and the allele \u003cem\u003eq\u003c/em\u003e with early ripening and lower SSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The seedlings carrying the \u003cem\u003eQQ\u003c/em\u003e genotype (H3H2 or H2H2 diplotypes) averaged 199 and 213 JD and 13.2 \u0026ordm;Brix, while the seedlings with \u003cem\u003eQq\u003c/em\u003e genotypes (H4H2 or H1H4 diplotypes) ripened on average at 192 and 194 JD and had on average 12.4 and 12.5 \u0026ordm;Brix. Lastly, seedlings with the \u003cem\u003eqq\u003c/em\u003e genotype (H4H4) ripened on average at 180 JD and had an average of 11.1 \u0026deg;Brix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most frequent diplotype was H4H4, followed by H1H4. Seedlings carrying the \u003cem\u003eQ\u003c/em\u003e allele with H3H2 and H2H2 diplotypes were less frequent in the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough three SNPs were selected to distinguish haplotypes in the QTL region, only one, SNP_IGA_411637, was necessary to discriminate early, mid, or late-ripening individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Single linear regression (SLR) analysis of RPT and SSC at SNP_IGA_411637 revealed 27.7 and 16.6% PVE, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, to further validate our findings, we compared the RPT and SSC for the three different genotypes (AA, AB, and BB) of the SNP_IGA_411637 in Arkansas (AR), South Carolina (SC), and Texas (TX) breeding materials (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The comparison of the phenotypic performance of different genotypes revealed significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in all three peach breeding programs with individuals homozygous for the \u0026lsquo;B\u0026rsquo; allele showing the earliest RPT and lowest SSC, and individuals homozygous for the \u0026lsquo;A\u0026rsquo; allele having the latest RPT and highest SSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table S10). On average, individuals homozygous for the \u0026lsquo;B\u0026rsquo; allele ripened 12 (SC), 16 (TX) and 17 (AR) days earlier than heterozygous individuals, and individuals homozygous for \u0026lsquo;A\u0026rsquo; allele ripened 9 (SC) to 12 (AR) days later than heterozygous individuals (Supplementary Table S10). The AA genotypes were not observed in the TX material. For SSC, individuals homozygous for the \u0026lsquo;B\u0026rsquo; allele had decreased SSC by 0.48 (TX) to 0.64 (SC) in comparison to the heterozygous individuals (AB). In contrast, individuals homozygous for the \u0026lsquo;A\u0026rsquo; allele had increased SSC by 0.2 (SC) to 1.3 (AR) in comparison to individuals with the AB genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table S10).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTL peach genotypes of ripening time (RPT) and soluble solids concentration (SSC) target region spanning 44cM (10,981,971 bp) to 45cM (11,298,736). SNP names, position in bp and cM, haplotype names. Shaded\u0026thinsp;=\u0026thinsp;predictive SNP. *Illumina (A|B) nucleotide designation and actual nucleotides.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNucleotides*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePVE (%)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eHaplotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenetic (cM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRPT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP_IGA_411637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA|B (T|G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,981,971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP_IGA_412338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA|B (A|G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,208,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP_IGA_412662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA|B (A|G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,298,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eQTL allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePVE: calculated by each marker using single linear regression analysis during the KASP test validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKASP assay development and validation\u003c/p\u003e \u003cp\u003eIn order to confirm the predictive SNP and reconstruct haplotypes (possible six and observed four), all three SNP markers (SNP_IGA_422637, SNP_IGA_412338 and SNP_IGA_412662) were used in the KASP assay development and validation. The two KASP assays, Ppe.RPT/SSC-1.1 (SNP_IGA_411637) and Ppe.RPT/SSC-1.2 (SNP_IGA_412338), were developed and successfully amplified and distinguished alleles in validation material (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The SNP_IGA_412662 was unsuitable for conversion into a KASP assay and was subsequently discarded. Using 84 peach cultivars, the assay validation revealed four different clusters corresponding to each genotype (AA, AB, and BB) and non-template controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table S3). The KASP had 93.1% accuracy in correct allele assignment compared to the 9K SNP array, with the discrepancies observed in four cultivars (China Pearl, Fantasia, Le Grand, and Saturn) (Supplementary Table S3). Additionally, end-point PCR for the same 84 peach cultivars showed similar performance to real - time PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with only 3.2% samples needing to be repeated due to the lack of amplification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, to the haplotype phenotype analyses above, the analysis of the phenotypic effect of the SNP_IGA_411637 (\u003cem\u003ePpe.RPT/SSC-1.1\u003c/em\u003e) in the 84 peach cultivars suggested that the \u0026lsquo;A\u0026rsquo; allele was associated with a delay of RPT and an increase of SSC, while the \u0026lsquo;B\u0026rsquo; allele was related to the advancement of RPT and a decrease of SSC. The comparison between observed and predicted mean RPT (early, mid and late) in the validation material (84 cultivars) explained 20% of the phenotypic variance observed and revealed a significant difference (Tukey test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between early and late-ripening individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Individuals predicted as early and mid-season ripened on average 18 and 9 days before \u0026lsquo;Elberta\u0026rsquo;, respectively (Supplementary Table S11). In contrast, individuals predicted as late season had, on average, a delay of 16 days compared to \u0026lsquo;Elberta\u0026rsquo; (Supplementary Table S11). The comparison between observed and predicted mean of SSC, showed that 12% of phenotypic variance can be explained by \u003cem\u003ePpe.RPT/SSC-1.1\u003c/em\u003e. Individuals with low and mid predicted SSC revealed a significantly (Turkey, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) lower mean of SSC when compared with individuals with high predicted SSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). No significant differences were observed between individuals with low and mid predicted SSC; however, individuals predicted to have low SSC exhibited lower observed SSC levels than those with mid predicted SSC. The same patterns were observed for the 163 individuals from the CUPBP and 26 commercial cultivars with known RPT and SSC in five evaluated seasons. In these materials, individuals with the AA genotype (predicted to have late RPT and high SSC) had the latest average RPT and highest average SSC in all five seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These individuals ripened on average three and four weeks later than mid (AB genotype) and early (BB genotypes) season cultivars, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary Table S12). Regarding SSC, less significant differences according to Tukey test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in the CUPBP material between the three genotypes of \u003cem\u003ePpe.RD/SSC-1.1\u003c/em\u003e, with late-ripening individuals (genotype AA) having 1.0 and 0.6 \u0026ordm;Brix more than early and mid-season cultivars, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary Table S12).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have analyzed a pedigree-connected germplasm containing 288 seedlings from multiple F\u003csub\u003e1\u003c/sub\u003e and F\u003csub\u003e2\u003c/sub\u003e families of the CUPBP for RPT and SSC using datasets from the two seasons (2011 and 2012) and the average dataset (average of the two seasons). The phenotypic mean values of RPT observed in this study were higher than those reported by Rawandoozi et al.\u003csup\u003e17\u003c/sup\u003e and Nu\u0026ntilde;ez-Lillo et al.\u003csup\u003e13\u003c/sup\u003e and in agreement with the mean values observed by Eduardo et al.\u003csup\u003e10\u003c/sup\u003e. On the other hand, the mean values of SSC reported in this study were slightly lower than the values described by Nu\u0026ntilde;ez-Lillo et al.\u003csup\u003e12\u003c/sup\u003e and similar to the mean values observed by Rawandoozi et al.\u003csup\u003e15\u003c/sup\u003e. We observed significant differences between seasons in the mean values of RPT and SSC. However, moderate (0.41) to high (0.90) correlation coefficients between seasons were recorded for SSC and RPT, respectively.\u003c/p\u003e \u003cp\u003ePedigree based QTL mapping using FlexQTL\u0026trade; enabled the successful mapping of stable QTLs with decisive evidence (BF\u0026thinsp;\u0026gt;\u0026thinsp;10) for RPT and SSC. The \u003cem\u003eqRPT.1\u003c/em\u003e and \u003cem\u003eqSSC_4.1\u003c/em\u003e detected in both seasons in an overlapped genetic interval formed a QTL cluster on LG4 and confirmed the QTLs and associated signals identified in previous studies using different germplasm and approaches\u003csup\u003e11,13\u0026ndash;15,18\u003c/sup\u003e. The RPT and SSC QTL cluster initially covered a genetic region from 44 to 49 cM (from 10,981,971 bp to 12,523,245 bp) and comprised eight SNPs that could be used for haplotype analysis. However, the genetic region was narrowed down to 44\u0026ndash;45 cM (physical position from 10,981,971 bp to 11,298,736 bp) and three SNPs when the peak position of the RPT and SSC QTLs in the FlexQTL\u0026trade; outputs in the average datasets was taken into account. Average datasets have been frequently used for determining the genetic region for further haplotype analysis using FlexQTL\u0026trade;\u003csup\u003e16,17,44\u003c/sup\u003e. The target region comprised three SNPs and allowed the identification of QTL genotypes with different effects of increasing/decreasing RPT and SSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, a predictive marker (SNP_IGA_411637) distinguished the allele associated with increasing (A) and decreasing (B) of the RPT and SSC in the breeding populations. These findings suggested that the marker-assisted selection (MAS) with the SNP_IGA_411637 could efficiently be used for selecting parents/seedlings for targeted RD and SSC.\u003c/p\u003e \u003cp\u003ePrevious RPT and SSC QTL mapping in peach focused mainly on identifying marker-trait association with candidate genes analysis in the mapped QTL interval. In this study, our primary goal was not only to map or validate QTLs, but also to convert the findings into a diagnostic DNA test for routine application in breeding. The complexity of RPT and SSC traits due to their polygenic nature and environmental influence\u003csup\u003e45\u003c/sup\u003e made development of breeding tools for MAS difficult. However, quantitative traits were recently targeted in peach for DNA test development resulting in tools such as the \u003cem\u003ePpe.CR.1\u003c/em\u003e designed for predicting chilling requirement\u003csup\u003e24\u003c/sup\u003e. RPT and SSC display similar patterns to chilling requirements, where multiple genes control the phenotype and the major QTL explained more than 30% of the phenotypic variation. Thus, the findings obtained from the QTL mapping and haplotype analysis performed in this study suggested that it was feasible to develop a DNA test for predicting RPT and SSC in peach. The \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e was designed using a recently reported KASP approach for fruit response to \u003cem\u003eXanthomonas arboricola\u003c/em\u003e pv. \u003cem\u003epruni\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e and chilling requirement\u003csup\u003e24\u003c/sup\u003e prediction in peach. In combination with crude DNA extraction\u003csup\u003e41\u003c/sup\u003e, this technology provides an inexpensive, trustworthy, and user-friendly tool for accurately predicting SNP genotypes\u003csup\u003e29,46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe validation of the \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e breeding tool revealed concurrence between the outcomes of real-time and endpoint PCRs, indicating that endpoint PCRs can be utilized to improve throughput. Additionally, the \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e assay developed for SNP discrimination successfully predicted the genotypes previously obtained from the 9K SNP array with disagreement in only four accessions. From those, two accessions (\u0026lsquo;Fantasia\u0026rsquo; and \u0026lsquo;Saturn\u0026rsquo;) also demonstrated discrepancies between the SNP array and KASP genotypes when \u003cem\u003ePpe.CR.1\u003c/em\u003e for prediction of chilling requirement was validated\u003csup\u003e24\u003c/sup\u003e. This result supports the hypothesis that the discrepancies could be due to the incorrectly called array genotypes or different DNA sources used in the array (genotyping carried out by the RosBREED project\u003csup\u003e22\u003c/sup\u003e) and KASP genotyping.\u003c/p\u003e \u003cp\u003eAlthough two KASP assays were developed to distinguish haplotypes in the QTL target region (10,981,971 bp to 11,298,736 bp), only one (\u003cem\u003ePpe.RPT/SSC-1.1\u003c/em\u003e) was needed to distinguish the early and late-season ripening cultivars, confirming the results obtained from the haplotype analysis that indicated the SNP_IGA_411637 as a predictive marker. The predictive ability of \u003cem\u003ePpe.RPT/SSC-1.1\u003c/em\u003e for RPT of materials from different genetic backgrounds and environments (AR, SC, and TX) highlighted the usefulness of this tool for breeding. Moderate to high correlation between RPT and SSC were detected in this work. However, the predictive ability of \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e was lower for SSC in comparison to RPT. Possible explanations for the lower prediction values could be due to the majority of the QTL effect observed in the SSC being attributed to RPT. This hypothesis was confirmed by the SSC QTLs runs where the RPT was used as cofactor and resulted in a lower BF (evidence of the presence of QTL) and the PVE. Effect of other QTL regions on different chromosomes associated with the genetic control of SSC\u003csup\u003e13,15,18, 47\u003c/sup\u003e could also affect prediction. For example, the major QTL for SSC was mapped on the chromosome 5\u003csup\u003e16\u003c/sup\u003e in the Texas material, where the lowest prediction of SSC was observed. The \u003cem\u003ePpe.RPT/SSC-1.1\u003c/em\u003e developed in this study, did not account for other regions, which could explain the lower SSC prediction when compared to RPT. Lastly, the strong influence of environmental conditions on the final sugar content\u003csup\u003e45\u003c/sup\u003e or the possibility that fruits in different ripening stages (under-ripened) could have skewed SSC values as only five fruits were considered for juice extraction and SSC determination. Thus, further studies focusing on DNA tool development accounting for other chromosomal regions associated with SSC and the appropriate ripening stage of the fruits selected for \u0026deg;Brix determination are required to obtain a high predictive accuracy for SSC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe successfully mapped/validated a stable QTL cluster on LG4 associated with RPT and SSC using a pedigree-connected germplasm comprising 288 individuals. In addition, we identified statistically different QTL genotypes in the target genetic interval selected for haplotype analysis. We also developed an accurate KASP assay for RPT and SSC identifying the correct genotype for 93% of samples with known genotypes and validated the assay on individuals from different genetic backgrounds and environments (AR, SC and TX). Furthermore, a prediction accuracy of the KASP assay and an ability to distinguish between early and late-season ripening and low and high SSC cultivars was obtained. The low-cost and quick crude DNA extraction combined with the KASP approach reported here provide relevant information to breeders for large-scale genotyping in breeding programs, saving time, and resources and efficiently accelerating the selection of desired individuals. Moreover, the results of this work will be extremely helpful for strengthening the bridge between academic research and breeding applications and, consequently, for peach genetic improvement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePlant Material\u003c/p\u003e\n\u003cp\u003eThe material used in this study was comprised of pedigree connected germplasm with cultivars, advanced selections, and seedlings from Clemson University peach breeding program (CUPBP), which were previously assembled under the RosBREED project\u003csup\u003e18,22\u003c/sup\u003e. A breeding population containing a total of 288 seedlings, which included multiple F\u003csub\u003e1\u003c/sub\u003e and F\u003csub\u003e2\u003c/sub\u003e families obtained from 19 parents (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), was chosen for mapping analyses. The parents and seedlings were maintained at the Clemson University Musser Fruit Research Center, in Seneca, South Carolina (Latitude: 34.639038, Longitude: -82.935244, Altitude 210 msl), under warm, humid, temperate climate and standard commercial practices for irrigation, fertilization, and pest and disease control. The trees were grafted on \u0026lsquo;Guardian\u0026reg;\u0026rsquo; rootstock, and either planted at 4 \u0026times; 6 m and trained to open center or at 1.5 \u0026times; 4 m and trained to perpendicular V.\u003c/p\u003e\n\u003cp\u003ePhenotypic data\u003c/p\u003e\n\u003cp\u003ePhenotypic data for QTL mapping and DNA test development were recorded over two seasons (2011\u0026ndash;2012). Ripening time (RPT), in Julian days (JD), was determined when 20% of fruits were at commercial harvest by visually inspecting the presence of a few soft fruits in the field for maturity twice per week. A composite sample of one approximately 2 cm wide longitudinal slice from each of five fruits was used to extract juice with a juicer to measure SSC (\u0026deg;Brix) using a digital refractometer. Statistical analysis (descriptive, normality test, Spearman\u0026rsquo;s rank correlation, and Wilcoxon signed rank test) was performed using R Statistical Software (version 4.1.2).\u003c/p\u003e\n\u003cp\u003eThe narrow-sense heritability (h\u003csup\u003e2\u003c/sup\u003e) were estimated considering the two seasons (2011\u0026ndash;2012) using the R package Sommer\u003csup\u003e33\u003c/sup\u003e and the \u003cem\u003evpredict\u003c/em\u003e function:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$vpredict\\left(object, transform\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere: object represents a model fitted with the mmer function; transform is the formula to calculate the function.\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$mix\u0026lt;-mmer(Trait\\sim Year,$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$random=\\sim vsr(Selection,Gu=K),$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$rcov=\\sim vsr\\left(dsr\\left(Year\\right),units\\right),$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$data=Trait)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhere: \u0026ldquo;K\u0026rdquo; refers to the additive relationship matrix. The formula included in the function was:\u003c/p\u003e\n\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$${h}^{2}= VA/VP$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere: \u0026ldquo;VA\u0026rdquo; is the additive genetic variance; \u0026ldquo;VP\u0026rdquo; is the phenotypic variance.\u003c/p\u003e\n\u003cp\u003eGenotyping and linkage map\u003c/p\u003e\n\u003cp\u003eSamples were genotyped with the IPSC peach 9K SNP array v1\u003csup\u003e34\u003c/sup\u003e. The SNP data curation was performed using the workflow for high-resolution genetic marker data described in Vanderzande et al.\u003csup\u003e21\u003c/sup\u003e. In order to reduce the time needed for analysis, a total of 1487 informative SNPs were retained for pedigree-based QTL mapping (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The genetic positions of the retained SNPs were calculated using the physical position of the peach reference genome v2.0\u003csup\u003e35\u003c/sup\u003e and a conversion factor where every 1 Mb corresponded to 4 cM, as described by Vanderzande et al.\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eQTL mapping\u003c/p\u003e\n\u003cp\u003eThe QTL mapping was performed using FlexQTL\u0026trade; software (version 0.1.0.42)\u003csup\u003e36\u003c/sup\u003e which implements pedigree-based QTL analysis via Markov Chain Monte Carlo (MCMC) simulation. The analysis was run at least twice for the two seasons (2011 and 2012), as well as for the datasets RPT_Ave and SSC_Ave (average of the two seasons), until reaching the effective chain size (ECS) criterion for convergence (\u0026ge;\u0026thinsp;100). The MCMC length for RPT ranged from 700,000 to 900,000 iterations to store one thousand samples with a thinning between 700 and 900 under mixed genetic model. However, 200,000 iterations with a thinning of 200 under additive genetic model were adequate to achieve ECS\u0026thinsp;\u0026ge;\u0026thinsp;100 for SSC. Inference on the number of QTLs was based on a pairwise comparison of models (1/0, 2/1, 3/2, and so on) using twice the natural log of the Bayes factor (2lnBF) statistic. The Bayes factor (BF) parameter was interpreted as: non-significant (0\u0026ndash;2), positive (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e), strong (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e), or decisive (\u0026gt;\u0026thinsp;10) evidence for the presence of QTLs\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we reported only stable QTLs. A QTL was considered \u0026ldquo;stable\u0026rdquo; when: the BF was decisive (\u0026gt;\u0026thinsp;10) and the significant effect was located in overlapping positions between the two seasons. The stable QTLs were named as \u0026ldquo;\u003cem\u003eq\u003c/em\u003e\u0026rdquo; + trait name abbreviation\u0026thinsp;+\u0026thinsp;data set\u0026thinsp;+\u0026thinsp;scaffold\u0026thinsp;+\u0026thinsp;number of the chronological QTL for this trait reported on this chromosome (e.g. \u003cem\u003eqRPT_SC_4.1\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eTo narrow down and re-define the QTL intervals, further analysis was carried out in FlexQTL\u0026trade; using the \u0026lsquo;MQTRegions.new\u0026rsquo; file considering the supporting data files \u0026lsquo;Post_genome.csv\u0026rsquo; and \u0026lsquo;marker map\u0026rsquo;. The new generated output files were used to recalculate the phenotypic variance explained (PVE) for the stable QTLs and to update information for QTL intensity, interval, and mode positions.\u003c/p\u003e\n\u003cp\u003eFlexQTL\u0026trade; obtained the additive variance (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eA(trt)\u003c/sub\u003e) for each trait by subtracting the residual variance (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e) from the phenotypic variance (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eP\u003c/sub\u003e). The phenotypic variance explained (PVE) for a particular QTL considering an additive model was calculated using the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(PVE additive model= \\frac{{\\sigma }_{A\\left(qtl\\right)}^{2}}{{\\sigma }_{P}^{2}}\\times 100\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e where:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{A\\left(qtl\\right)}^{2}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e is the additive variance of QTL.\u003c/p\u003e\n\u003cp\u003eRegarding the mixed model, genetic variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e), was calculated by subtracting the residual variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e), from the phenotypic variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{P}^{2}\\)\u003c/span\u003e\u003c/span\u003e), and the PVE was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(PVE mixed model= \\frac{{\\sigma }_{A\\left(qtl\\right)}^{2}+ {\\sigma }_{D\\left(qtl\\right)}^{2}}{{\\sigma }_{P}^{2}}\\times 100\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003ewhere:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{A\\left(qtl\\right)}^{2} \\text{i}\\text{s} \\text{t}\\text{h}\\text{e} \\text{a}\\text{d}\\text{d}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e} \\text{v}\\text{a}\\text{r}\\text{i}\\text{a}\\text{n}\\text{c}\\text{e} \\text{o}\\text{f} \\text{Q}\\text{T}\\text{L}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{D\\left(qtl\\right)}^{2} \\text{i}\\text{s} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{i}\\text{n}\\text{a}\\text{n}\\text{t} \\text{v}\\text{a}\\text{r}\\text{i}\\text{a}\\text{n}\\text{c}\\text{e} \\text{o}\\text{f} \\text{Q}\\text{T}\\text{L}.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA pleiotropic effect of RPT and other quality traits, including SSC, has been previously reported\u003csup\u003e11\u003c/sup\u003e. Therefore, QTL analysis for SSC_Ave (average of the two seasons) was also performed using the RPT information as a covariate, which was integrated as a nuisance variable in the data file used in FlexQTL\u0026trade;. Separate runs using a mixed model, with the same number of seeds and MCMC length (1,200,000), were carried out in FlexQTL\u0026trade; using the RPT information in four different datasets: 1. Dataset where no covariate was included; 2. Dataset where the corresponding phenotypic average of RPT (in Julian days) was included as a cofactor; 3. Dataset where the haplotype information of the RPT QTL interval was included as a cofactor; 4. Dataset where both the phenotypic average of RPT (in Julian days) and haplotype information of the RPT QTL interval were included as cofactors.\u003c/p\u003e\n\u003cp\u003eHaplotype analysis\u003c/p\u003e\n\u003cp\u003eAs described by Rawandoozi et al.\u003csup\u003e16\u003c/sup\u003e, the haplotype analysis was carried out by selecting the SNPs within the significant QTL interval considering the RPT and SSC average datasets. We used the output files \u0026lsquo;MQTRegionsGTP.csv\u0026rsquo; and \u0026lsquo;mhaplotypes.csv\u0026rsquo; generated by FlexQTL\u0026trade; to perform the haplotype analysis. Haplotypes were constructed in the dataset using PediHaplotyper R package\u003csup\u003e38\u003c/sup\u003e. The effects were determined from combinations of diplotypes. The nonparametric multiple comparison Steele\u0026ndash;Dwass test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was applied to assess significant differences between diplotype effects. QTL allele genotypes (\u003cem\u003eQ\u003c/em\u003e or \u003cem\u003eq\u003c/em\u003e) were assigned to haplotypes based on the direction of their effects (increasing or decreasing RPT and SSC, respectively). The statistical analysis was performed using the JMP Pro Version 13.2 (SAS Institute Inc., Cary, NC, 2016). Illumina codes for nucleotides, where A\u0026thinsp;=\u0026thinsp;A or T, and B\u0026thinsp;=\u0026thinsp;C or G, were used as marker designation in haplotypes\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePhenotypic variation explained (PVE) by each informative SNP within the significant QTL interval was estimated using single linear regression (SLR) analysis in R Statistical Software (version 4.1.2). The phenotypic performance of different genotypes of the predictive marker was further validated in peach breeding populations comprising 128, 290 and 139 individuals from Arkansas (AR), South Carolina (SC) and Texas (TX), respectively.\u003c/p\u003e\n\u003cp\u003eKASP marker development and validation\u003c/p\u003e\n\u003cp\u003eTwo informative SNPs capable of distinguishing haplotypes for the main RPT and SSC QTL on LG4 (10,981,971 to 11,298,736 bp) were used for developing the \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e KASP assay. The SNP_IGA_412662 was unsuitable for conversion into a KASP assay and was subsequently discarded. Primers were designed for each of the two SNPs (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and reaction mixtures and PCR conditions for the KASP assays were determined following Fleming et al.\u003csup\u003e22\u003c/sup\u003e. Eighty-four peach cultivars representing the diversity of fresh-market US germplasm, 51 of which had known genotypes from the 9K SNP array\u003csup\u003e34\u003c/sup\u003e, were chosen for the development and validation of the assay (Supplementary Table S3). Phenotypic data for RPT were obtained from literature\u003csup\u003e39\u003c/sup\u003e, while the SSC data were obtained from two databases: Clemson University Variety evaluations database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.clemsonpeach.org/\u003c/span\u003e\u003c/span\u003e), and the Genome Database for Rosaceae\u003csup\u003e40\u003c/sup\u003e. The variety evaluation data from Musser fruit research farm, were collected within 2015\u0026ndash;2021, and the publicly available GDR peach data (GRIN_PEACH and Peach RosBREED Public), were collected during three and seven seasons, within RosBREED\u003csup\u003e22\u003c/sup\u003e project and GRIN, respectively. The average SSC for each cultivar was calculated and used in the validation of the assay. DNA for these cultivars was extracted using the protocol described in Edge-Garza et al.\u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThree replicates of non-template controls and positive controls for homozygous (AA and BB) and heterozygous (AB) genotypes were tested in a 96 well-plate for each assay. Positive controls were selected from the DNA samples with known genotypes from the 9K SNP array (Supplementary Table S4). Amplifications were conducted in a Bio-Rad CFX Connect Real-Time PCR thermocycler under a standard protocol (15 min at 94\u0026deg;C; 10 cycles of 20 s at 94\u0026deg;C and 60 s at 61\u0026deg;C with a 0.6\u0026deg;C decrease in temperature per cycle; 40 cycles at 94\u0026deg;C for 20 s; 60 s at 55\u0026deg;C, and 30 s at 23\u0026deg;C), and cycle 25 of real-time PCR was selected for each KASP assay to maximize separation between genotypes. A template spreadsheet designed by Fleming et al.\u003csup\u003e23\u003c/sup\u003e was used to assign the genotypes to each sample based on the relative fluorescence unit values from this cycle. For routine use, end-point PCR was tested on Bio-Rad T100 thermocycler using the same cultivars and positive controls for all assays, with slightly modified protocol. The number of cycles at the SNP specific temperature was decreased to 25 and the last cycle of the standard protocol (30 s at 23\u0026deg;C) and plate reading were omitted. End-point reactions were read on the Bio-Rad CFX Connect Real-Time PCR thermocycler using Bio-Rad CFX Maestro\u0026trade; software.\u003c/p\u003e\n\u003cp\u003eThe newly developed \u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e KASP assays were validated by screening 163 seedlings from the CUPBP and 26 commercial cultivars (Supplementary Table S5) with end-point PCR. The validation set included 15 seedlings from QTL mapping germplasm. Phenotypic data was collected in the CUPB program. The best linear unbiased predictions (BLUPs) for each individual were obtained for the RPT and SSC values recorded across five seasons (2017 to 2021) using the R package \u0026apos;lme4\u0026apos;\u003csup\u003e42\u003c/sup\u003e with year selected as a random effect:\u003c/p\u003e\n\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e$${Y}_{ij}= \\mu +{g}_{i}+ {y}_{j}+ {gy}_{ij} + \\epsilon$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere: \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the trait of interest, \u003cem\u003e\u0026micro;\u003c/em\u003e is the overall mean, \u003cem\u003eg\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the genetic effect of \u003cem\u003ei\u003c/em\u003eth genotype, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the effect of the \u003cem\u003ej\u003c/em\u003eth year, and \u003cem\u003egy\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e as the interaction effect of \u003cem\u003ei\u003c/em\u003eth genotype with \u003cem\u003ej\u003c/em\u003eth year, \u003cem\u003eɛ\u003c/em\u003e is the residual of the model.\u003c/p\u003e\n\u003cp\u003eDNA samples for these individuals were obtained using a rapid crude DNA extraction protocol described by Noh et al.\u003csup\u003e43\u003c/sup\u003e. The template spreadsheet designed by Fleming et al.\u003csup\u003e23\u003c/sup\u003e was employed to automatically assign genotypes. Statistical differences between classes were evaluated using one-way ANOVA, followed by the Tukey test for multiple comparisons in SPSS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Ralph Burrell and Musser Fruit Research Farm staff at Clemson University for their help with orchard maintenance and phenotypic data acquisition.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions statement\u003c/h2\u003e\n\u003cp\u003eCDSL: formal analysis and writing \u0026ndash; original draft. WF and AC: KASP test analysis and writing. ZR: haplotyping analysis and review. LC: SNP data curation and review. MW and DB: resources and writing - review and editing. KG: conceptualization, funding acquisition, resources, supervision, and writing \u0026ndash; review \u0026amp; editing. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: www.rosaceae.org.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was funded by USDA\u0026rsquo;s National Institute of Food and Agriculture-Specialty Crop Research Initiative Projects, \u0026ldquo;RosBREED: Enabling marker-assisted breeding in Rosaceae\u0026rdquo; (2009-51181-05858) and \u0026ldquo;RosBREED: Combining disease resistance and horticultural quality in new rosaceous cultivars\u0026rdquo; (2014-51181-22378).\u003c/p\u003e\n\u003ch3\u003eExperiments involving plants\u003c/h3\u003e\n\u003cp\u003eRelevant guidelines and regulations have been followed for all experiments involving plants. Plants subject to these experiments were created in the co-authors breeding programs and belong to them so the permission or license to use them is not needed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBassi, D., Mignani, I., Spinardi, A. \u0026amp; Tura, D. Chapter 23 - PEACH (Prunus persica (L.) Batsch). in \u003cem\u003eNutritional Composition of Fruit Cultivars\u003c/em\u003e (eds. Simmonds, M.S.J. \u0026amp; Preedy, V.R.) 535-571 (Academic Press, San Diego, 2016).\u003c/li\u003e\n \u003cli\u003e \u0026nbsp;FAOSTAT. Food and Agriculture Organization of the United.\u0026nbsp;FAOSTAT database: Data. Available via http://www.fao.org/faostat/en/#data/QV (2023).\u003c/li\u003e\n \u003cli\u003e \u0026nbsp;Anthony, B.M. \u0026amp; Minas, I.S. 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SNP Genotyping: The KASP Assay. in \u003cem\u003eCrop Breeding: Methods and Protocols\u003c/em\u003e (eds. Fleury, D. \u0026amp; Whitford, R.) 75-86 (Springer New York, New York, NY, 2014).\u003c/li\u003e\n \u003cli\u003eZeballos, J.L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Mapping QTLs associated with fruit quality traits in peach [Prunus persica (L.) Batsch] using SNP maps. \u003cem\u003eTree Genetics \u0026amp; Genomes\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 37 (2016).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3569064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3569064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F\u003csub\u003e1\u003c/sub\u003e and F\u003csub\u003e2\u003c/sub\u003e 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\u0026thinsp;\u0026minus;\u0026thinsp;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 (\u003cem\u003ePpe.RPT/SSC-1\u003c/em\u003e) 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.\u003c/p\u003e","manuscriptTitle":"Ppe.RPT/SSC-1: From QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-14 19:03:31","doi":"10.21203/rs.3.rs-3569064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-12-12T06:44:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-12-11T22:37:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-11-20T22:51:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6c4c3782-6ce4-4045-8c93-4b6d658877b1","date":"2023-11-20T16:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1c747300-cbaf-4895-8718-9d0f51dba1a0","date":"2023-11-19T21:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-11-19T21:05:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-14T12:07:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-11-11T07:27:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-11T07:23:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-11-06T15:37:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68f6033d-9cb0-4286-9474-6e2a2587d744","owner":[],"postedDate":"November 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-22T15:07:44+00:00","versionOfRecord":{"articleIdentity":"rs-3569064","link":"https://doi.org/10.1038/s41598-024-51599-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-01-17 15:00:56","publishedOnDateReadable":"January 17th, 2024"},"versionCreatedAt":"2023-11-14 19:03:31","video":"","vorDoi":"10.1038/s41598-024-51599-2","vorDoiUrl":"https://doi.org/10.1038/s41598-024-51599-2","workflowStages":[]},"version":"v1","identity":"rs-3569064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3569064","identity":"rs-3569064","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}