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.

Transcriptomics reveal the genetic coordination of early defense to Armillaria root rot (ARR) in Prunus spp.

Armillaria root rot (ARR) poses a significant threat to the long-term productivity of stone-fruit and nut crops in the predominant production area of the United States. To mitigate this issue, the development of ARR-resistant and horticulturally-acceptable rootstocks is a crucial step towards the maintenance of production sustainability. To date, genetic resistance to ARR has been found in exotic plum germplasm and a peach/plum hybrid rootstock, 'MP-29'. However, the widely-used peach rootstock Guardian® is susceptible to the pathogen. To understand the molecular defense mechanisms involved in ARR resistance in Prunus rootstocks, transcriptomic analyses of one susceptible and two resistant Prunus spp. were performed using two causal agents of ARR, including Armillaria mellea and Desarmillaria tabescens. The results of in vitro co-culture experiments revealed that the two resistant genotypes showed different temporal response dynamics and fungus-specific responses, as seen in the genetic response. Gene expression analysis over time indicated an enrichment of defense-related ontologies, including glucosyltransferase activity, monooxygenase activity, glutathione transferase activity, and peroxidase activity. Differential gene expression and co-expression network analysis highlighted key hub genes involved in the sensing and enzymatic degradation of chitin, GSTs, oxidoreductases, transcription factors, and biochemical pathways likely involved in Armillaria resistance. These data provide valuable resources for the improvement of ARR resistance in Prunus rootstocks through breeding.

Ppe.CR.1 DNA test for predicting chilling requirement in peach.

Chilling requirement (CR) is an important agronomic trait controlling the floral bud break for proper flowering in peach. Even though it has been widely researched and several peach CR quantitative trait loci (QTLs) have been identified, no diagnostic DNA tests validated in the U.S. peach breeding germplasm are available for this trait. Breeders and growers need a simple DNA test to predict the CR of peach cultivars for their particular environment. Therefore, we developed a quick and reliable Kompetitive Allele Specific PCR (KASP) DNA test using haplotype information from 9K IPSC genotype data of the U.S. peach germplasm integrating four CR-associated SNP markers from the previously reported CR QTL region on linkage group 1. Four KASP assays (Ppe.CR.1-1 to -4) were developed and validated on 77 peach cultivars, and nine accessions from two F2 populations, with 96 and 74% accuracy in determining expected CR genotype (compared to SNP array) and predicting phenotype, respectively. Furthermore, the Ppe.CR.1 showed 80% accuracy in predicting the precise CR phenotype in the Clemson University peach breeding material. Only one Ppe.CR.1 KASP assay (Ppe.CR.1-1) is needed to distinguish between haplotypes with CR lower and higher than 800 chilling hours, and two Ppe.CR.1 assays (Pp.CR.1-1 and -4), are capable of distinguishing low, moderate, and high CR alleles. Coupled with the crude DNA extraction, the Ppe.CR.1 DNA test provides a low-cost option for breeders and growers to predict CR in peach material with more than 70% accuracy.

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.

Identification of Key Genes Related to Dormancy Control in Prunus Species by Meta-Analysis of RNAseq Data.

Bud dormancy is a genotype-dependent mechanism observed in Prunus species in which bud growth is inhibited, and the accumulation of a specific amount of chilling (endodormancy) and heat (ecodormancy) is necessary to resume growth and reach flowering. We analyzed publicly available transcriptome data from fifteen cultivars of four Prunus species (almond, apricot, peach, and sweet cherry) sampled at endo- and ecodormancy points to identify conserved genes and pathways associated with dormancy control in the genus. A total of 13,018 genes were differentially expressed during dormancy transitions, of which 139 and 223 were of interest because their expression profiles correlated with endo- and ecodormancy, respectively, in at least one cultivar of each species. The endodormancy-related genes comprised transcripts mainly overexpressed during chilling accumulation and were associated with abiotic stresses, cell wall modifications, and hormone regulation. The ecodormancy-related genes, upregulated after chilling fulfillment, were primarily involved in the genetic control of carbohydrate regulation, hormone biosynthesis, and pollen development. Additionally, the integrated co-expression network of differentially expressed genes in the four species showed clusters of co-expressed genes correlated to dormancy stages and genes of breeding interest overlapping with quantitative trait loci for bloom time and chilling and heat requirements.

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

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

Peach [Prunus persica (L.) Batsch] Cultivars Differ in Apparent Base Temperature and Growing Degree Hour Requirement for Floral Bud Break.

Bud break timing in peach [Prunus persica (L.) Batsch] is determined by the sequential fulfillment of a chilling requirement (CR) and a heat requirement (HR) for development. Genotypic variation in CR has been well characterized in peach. Adapting peaches to low chilling environment through reduced CR can make them susceptible to crop destroying spring frosts, if bloom occurs too early. Potential variation in HR between accessions has received less attention due to the methodological difficulty in assessing HR independently of CR. HR could vary in the magnitude of growing degree hours (GDHs) and/or the base temperature at which GDH accumulation begins. Characterizing HR traits in peach accessions could allow improved bloom time modeling and selection of phenotypes with improved spring frost avoidance through delayed bloom. We estimated GDH and apparent base temperature for floral bud break by observing time to floral bud break at several constant forcing temperatures. We evaluated 54 peach accessions (representing a range of CR) in which chilling had been saturated after >1,700 h at 3°C. Accessions differed widely in both the GDH requirement (2,015 to 11,191°C⋅h) and apparent base temperature (-1.85 to 8.69°C) for GDH accumulation. GDH and apparent base temperature were negatively correlated. A simulation exercise was performed to assess relative importance of varying base temperature vs. GDH for delaying bloom at different chilling accumulations at three locations in the southeastern United States using 30 years of historical weather data. The aim of this study was to determine whether there may be unrecognized diversity in peach germplasm for two HR traits (base temperature and thermal time) to enable breeding efforts to delay floral bud break and reduce the frost exposure risk of developing flowers and fruits. Our results suggest that selecting cultivars for increased GDH would be a safer, more reliable strategy for delaying bloom than increasing base temperature for GDH accumulation.

Ppe.XapF: High throughput KASP assays to identify fruit response to Xanthomonas arboricola pv. pruni (Xap) in peach.

Bacterial spot, caused by Xanthomonas arboricola pv. pruni (Xap), is a serious peach disease with symptoms that traverse severe defoliation and black surface pitting, cracking or blemishes on peach fruit with global economic impacts. A management option for control and meeting consumer demand for chemical-free, environmentally friendly fruit production is the development of resistant or tolerant cultivars. We developed simple, accurate, and efficient DNA assays (Ppe.XapF) based on SNP genotyping with KASP technology to quickly test for bacterial spot resistance alleles in peach fruit that allows breeders to cull seedlings at the greenhouse stage. The objective of this research was to validate newly developed DNA tests that target the two major QTLs for fruit resistance in peach with diagnostic utility in predicting fruit response to bacterial spot infection. Our study confirms that with only two Ppe.XapF DNA tests, Ppe.XapF1-1 and Ppe.XapF6-2, individuals carrying susceptible alleles can be identified. Use of these efficient and accurate Ppe.XapF KASP tests resulted in 44% reduction in seedling planting rate in the Clemson University peach breeding program.

The Breeding Information Management System (BIMS): an online resource for crop breeding.

In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/).

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.

The cherry 6+9K SNP array: a cost-effective improvement to the cherry 6K SNP array for genetic studies.

Cherry breeding and genetic studies can benefit from genome-wide genetic marker assays. Currently, a 6K SNP array enables genome scans in cherry; however, only a third of these SNPs are informative, with low coverage in many genomic regions. Adding previously detected SNPs to this array could provide a cost-efficient upgrade with increased genomic coverage across the 670 cM/352.9 Mb cherry whole genome sequence. For sweet cherry, new SNPs were chosen following a focal point strategy, grouping six to eight SNPs within 10-kb windows with an average of 0.6 cM (627 kb) between focal points. Additional SNPs were chosen to represent important regions. Sweet cherry, the fruticosa subgenome of sour cherry, and cherry organellar genomes were targeted with 6942, 2020, and 38 new SNPs, respectively. The +9K add-on provided 2128, 1091, and 70 new reliable, polymorphic SNPs for sweet cherry and the avium and the fruticosa subgenomes of sour cherry, respectively. For sweet cherry, 1241 reliable polymorphic SNPs formed 237 informative focal points, with another 2504 SNPs in-between. The +9K SNPs increased genetic resolution and genome coverage of the original cherry SNP array and will help increase understanding of the genetic control of key traits and relationships among individuals in cherry.

Preventative Root-Collar Excavation Reduces Peach Tree Mortality Caused By Armillaria Root Rot On Replant Sites.

In the southeastern United States, Armillaria root rot (ARR) is caused by Desarmillaria tabescens and has become the primary cause of premature mortality in peach orchards. Most rootstocks used in commercial orchards are susceptible and management options are limited. A postinfection practice known as root-collar excavation (RCE), which involves permanent removal of the soil from the base of the trunk, has been shown to improve yields and prolong the productive life of symptomatic trees. However, symptomatic trees already have an advanced infection at the base of the trunk. This study evaluated the efficacy of preventative RCE on the progression of tree mortality in two orchards that were planted in infested replant sites. To provide convincing data for growers, the study was carried out in a commercial orchard and an experimental orchard for 8 years. Furthermore, representative enterprise budgets and net present value (NPV) analysis were utilized to compare the profitability of the two approaches. Trees were planted shallow on berms (45 by 90 cm) to facilitate RCE with hoes and AirSpade 2 years later. Tree mortality in the RCE treatment of the experimental orchard was first observed in year 6 and increased 8% on average per year thereafter. In contrast, tree mortality in the "Grower Standard" treatment was first observed in year 4 and increased 12.7% on average per year thereafter. At the commercial orchard, tree mortality in the RCE treatment was first observed in year 7 and increased 1.9% on average thereafter, while tree mortality in the Grower Standard treatment first appeared in year 5 and increased 4.3% on average thereafter. The delayed onset of ARR-associated tree mortality and the lower annual tree mortality rate in the RCE treatment led to higher NPVs in both locations. There were no negative effects on yield or fruit quality. However, the new planting system can create horticultural challenges, including the formation of a proper berm, uneven ground around the tree interfering with tree care and harvest, increased erosion due to channeling of rainwater, and increased rootstock suckering. The RCE is a valid option for southeastern growers needing to manage high ARR disease pressure on replant sites or on sites only recently cleared from ARR-infected forest land.

Genome-wide SNP identification in Prunus rootstocks germplasm collections using Genotyping-by-Sequencing: phylogenetic analysis, distribution of SNPs and prediction of their effect on gene function.

Genotyping-by-Sequencing (GBS) was applied in a set of 53 diploid Prunus rootstocks and five scion cultivars from three subgenera (Amygdalus, Prunus and Cerasus) for genome-wide SNP identification and to assess genetic diversity of both Chilean and Spanish germplasm collections. A group of 45,382 high quality SNPs (MAF >0.05; missing data <5%) were selected for analysis of this group of 58 accessions. These SNPs were distributed in genic and intergenic regions in the eight pseudomolecules of the peach genome (Peach v2.0), with an average of 53% located in exonic regions. The genetic diversity detected among the studied accessions divided them in three groups, which are in agreement with their current taxonomic classification. SNPs were classified based on their putative effect on annotated genes and KOG analysis was carried out to provide a deeper understanding of the function of 119 genes affected by high-impact SNPs. Results demonstrate the high utility for Prunus rootstocks identification and studies of diversity in Prunus species. Also, given the high number of SNPs identified in exonic regions, this strategy represents an important tool for finding candidate genes underlying traits of interest and potential functional markers for use in marker-assisted selection.

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.

Prunus genetics and applications after de novo genome sequencing: achievements and prospects.

Prior to the availability of whole-genome sequences, our understanding of the structural and functional aspects of Prunus tree genomes was limited mostly to molecular genetic mapping of important traits and development of EST resources. With public release of the peach genome and others that followed, significant advances in our knowledge of Prunus genomes and the genetic underpinnings of important traits ensued. In this review, we highlight key achievements in Prunus genetics and breeding driven by the availability of these whole-genome sequences. Within the structural and evolutionary contexts, we summarize: (1) the current status of Prunus whole-genome sequences; (2) preliminary and ongoing work on the sequence structure and diversity of the genomes; (3) the analyses of Prunus genome evolution driven by natural and man-made selection; and (4) provide insight into haploblocking genomes as a means to define genome-scale patterns of evolution that can be leveraged for trait selection in pedigree-based Prunus tree breeding programs worldwide. Functionally, we summarize recent and ongoing work that leverages whole-genome sequences to identify and characterize genes controlling 22 agronomically important Prunus traits. These include phenology, fruit quality, allergens, disease resistance, tree architecture, and self-incompatibility. Translationally, we explore the application of sequence-based marker-assisted breeding technologies and other sequence-guided biotechnological approaches for Prunus crop improvement. Finally, we present the current status of publically available Prunus genomics and genetics data housed mainly in the Genome Database for Rosaceae (GDR) and its updated functionalities for future bioinformatics-based Prunus genetics and genomics inquiry.

15 years of GDR: New data and functionality in the Genome Database for Rosaceae.

The Genome Database for Rosaceae (GDR, https://www.rosaceae.org) is an integrated web-based community database resource providing access to publicly available genomics, genetics and breeding data and data-mining tools to facilitate basic, translational and applied research in Rosaceae. The volume of data in GDR has increased greatly over the last 5 years. The GDR now houses multiple versions of whole genome assembly and annotation data from 14 species, made available by recent advances in sequencing technology. Annotated and searchable reference transcriptomes, RefTrans, combining peer-reviewed published RNA-Seq as well as EST datasets, are newly available for major crop species. Significantly more quantitative trait loci, genetic maps and markers are available in MapViewer, a new visualization tool that better integrates with other pages in GDR. Pathways can be accessed through the new GDR Cyc Pathways databases, and synteny among the newest genome assemblies from eight species can be viewed through the new synteny browser, SynView. Collated single-nucleotide polymorphism diversity data and phenotypic data from publicly available breeding datasets are integrated with other relevant data. Also, the new Breeding Information Management System allows breeders to upload, manage and analyze their private breeding data within the secure GDR server with an option to release data publicly.