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

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

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

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

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

[This corrects the article DOI: 10.3389/fpls.2021.644799.].

Genome-Wide Association Study of Brown Rot (Monilinia spp.) Tolerance in Peach.

Brown rot, caused by Monilinia spp., is one of the most important diseases on stone fruit worldwide. Severe yield loss can be caused by pre- and post-harvest fruit decay. Although some degree of tolerance has been reported in peach and almond, the genetic resistance in peach cultivars is still lacking. To date, only few genomic regions associated with brown rot response in fruit skin and flesh have been detected in peach. Previous studies suggested brown rot tolerance in peach being a polygenic quantitative trait. More information is needed to uncover the genetics behind brown rot tolerance in peach. To identify the genomic regions in peach associated with this trait, 26 cultivars and progeny from 9 crosses with 'Bolinha' sources of tolerance, were phenotyped across two seasons (2015 and 2016) for brown rot disease severity index in wounded and non-wounded fruits and genotyped using a newly developed 9+9K peach SNP array. Genome wide association study using single- and multi-locus methods by GAPIT version 3, mrMLM 4.0, GAPIT and G Model, revealed 14 reliable SNPs significantly associated with brown rot infection responses in peach skin (10) and flesh (4) across whole genome except for chromosome 3. Candidate gene analysis within the haplotype regions of the detected markers identified 25 predicted genes associated with pathogen infection response/resistance. Results presented here facilitate further understanding of genetics behind brown rot tolerance in peach and provide an important foundation for DNA-assisted breeding.

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

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

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

BACKGROUND: Environmental adaptation and expanding harvest seasons are primary goals of most peach [Prunus persica (L.) Batsch] breeding programs. Breeding perennial crops is a challenging task due to their long breeding cycles and large tree size. Pedigree-based analysis using pedigreed families followed by haplotype construction creates a platform for QTL and marker identification, validation, and the use of marker-assisted selection in breeding programs. RESULTS: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. Three QTLs were discovered for bloom date (BD) and mapped on linkage group 1 (LG1) (172-182 cM), LG4 (48-54 cM), and LG7 (62-70 cM), explaining 17-54%, 11-55%, and 11-18% of the phenotypic variance, respectively. The QTL for ripening date (RD) and fruit development period (FDP) on LG4 was co-localized at the central part of LG4 (40-46 cM) and explained between 40 and 75% of the phenotypic variance. Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles and the presence of multiple functional alleles with different effects for a single locus for RD and FDP. CONCLUSIONS: A multiple pedigree-linked families approach validated major QTLs for the three key phenological traits which were reported in previous studies across diverse materials, geographical distributions, and QTL mapping methods. Haplotype characterization of these genomic regions differentiates this study from the previous QTL studies. Our results will provide the peach breeder with the haplotypes for three BD QTLs and one RD/FDP QTL to create predictive DNA-based molecular marker tests to select parents and/or seedlings that have desired QTL alleles and cull unwanted genotypes in early seedling stages.

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

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

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

BACKGROUND: Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (Prunus persica (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time. RESULTS: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40-46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (D-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles. CONCLUSIONS: A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow.

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

High-density multi-population consensus genetic linkage map for peach.

Highly saturated genetic linkage maps are extremely helpful to breeders and are an essential prerequisite for many biological applications such as the identification of marker-trait associations, mapping quantitative trait loci (QTL), candidate gene identification, development of molecular markers for marker-assisted selection (MAS) and comparative genetic studies. Several high-density genetic maps, constructed using the 9K SNP peach array, are available for peach. However, each of these maps is based on a single mapping population and has limited use for QTL discovery and comparative studies. A consensus genetic linkage map developed from multiple populations provides not only a higher marker density and a greater genome coverage when compared to the individual maps, but also serves as a valuable tool for estimating genetic positions of unmapped markers. In this study, a previously developed linkage map from the cross between two peach cultivars 'Zin Dai' and 'Crimson Lady' (ZC2) was improved by genotyping additional progenies. In addition, a peach consensus map was developed based on the combination of the improved ZC2 genetic linkage map with three existing high-density genetic maps of peach and a reference map of Prunus. A total of 1,476 SNPs representing 351 unique marker positions were mapped across eight linkage groups on the ZC2 genetic map. The ZC2 linkage map spans 483.3 cM with an average distance between markers of 1.38 cM/marker. The MergeMap and LPmerge tools were used for the construction of a consensus map based on markers shared across five genetic linkage maps. The consensus linkage map contains a total of 3,092 molecular markers, consisting of 2,975 SNPs, 116 SSRs and 1 morphological marker associated with slow ripening in peach (SR). The consensus map provides valuable information on marker order and genetic position for QTL identification in peach and other genetic studies within Prunus and Rosaceae.

Deletion of the miR172 target site in a TOE-type gene is a strong candidate variant for dominant double-flower trait in Rosaceae.

Double flowers with supernumerary petals have been selected by humans for their attractive appearance and commercial value in several ornamental plants, including Prunus persica (peach), a recognized model for Rosaceae genetics and genomics. Despite the relevance of this trait, knowledge of the underlying genes is limited. Of two distinct loci controlling the double-flower phenotype in peach, we focused on the dominant Di2 locus. High-resolution linkage mapping in five segregating progenies delimited Di2 to an interval spanning 150 858 bp and 22 genes, including Prupe.6G242400 encoding an euAP2 transcription factor. Analyzing genomic resequencing data from single- and double-flower accessions, we identified a deletion spanning the binding site for miR172 in Prupe.6G242400 as a candidate variant for the double-flower trait, and we showed transcript expression for both wild-type and deleted alleles. Consistent with the proposed role in controlling petal number, Prupe.6G242400 is expressed in buds at critical times for floral development. The indelDi2 molecular marker designed on this sequence variant co-segregated with the phenotype in 621 progenies, accounting for the dominant inheritance of the Di2 locus. Further corroborating the results in peach, we identified a distinct but similar mutation in the ortholog of Prupe.6G242400 in double-flower roses. Phylogenetic analysis showed that these two genes belong to a TARGET OF EAT (TOE)-type clade not represented in Arabidopsis, indicating a divergence of gene functions between AP2-type and TOE-type factors in Arabidopsis and other species. The identification of orthologous candidate genes for the double-flower phenotype in two important Rosaceae species provides valuable information to understand the genetic control of this trait in other major ornamental plants.

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies.

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

Whole-Genome Analysis of Diversity and SNP-Major Gene Association in Peach Germplasm.

Peach was domesticated in China more than four millennia ago and from there it spread world-wide. Since the middle of the last century, peach breeding programs have been very dynamic generating hundreds of new commercial varieties, however, in most cases such varieties derive from a limited collection of parental lines (founders). This is one reason for the observed low levels of variability of the commercial gene pool, implying that knowledge of the extent and distribution of genetic variability in peach is critical to allow the choice of adequate parents to confer enhanced productivity, adaptation and quality to improved varieties. With this aim we genotyped 1,580 peach accessions (including a few closely related Prunus species) maintained and phenotyped in five germplasm collections (four European and one Chinese) with the International Peach SNP Consortium 9K SNP peach array. The study of population structure revealed the subdivision of the panel in three main populations, one mainly made up of Occidental varieties from breeding programs (POP1OCB), one of Occidental landraces (POP2OCT) and the third of Oriental accessions (POP3OR). Analysis of linkage disequilibrium (LD) identified differential patterns of genome-wide LD blocks in each of the populations. Phenotypic data for seven monogenic traits were integrated in a genome-wide association study (GWAS). The significantly associated SNPs were always in the regions predicted by linkage analysis, forming haplotypes of markers. These diagnostic haplotypes could be used for marker-assisted selection (MAS) in modern breeding programs.

Fine mapping and identification of a candidate gene for a major locus controlling maturity date in peach.

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