ASHS 2024 Conference

Muscadine grapes (Muscadinia rotundifolia) are perennial vines grown commercially in the Southeast United States for the fresh market and as wine and juice grapes. Two highly sought-after traits in fresh market muscadine cultivars are stenospermocarpic seedlessness and perfect-flowered vines. However, the genetic disparity between Vitis and Muscadinia subgenera, coupled with differing chromosome numbers (Vitis=38 chromosomes, Muscadinia=40 chromosomes), presents challenges in introgression of stenospermocarpy from V. vinifera to M. rotundifolia. Although conventional breeding has introduced stenospermocarpy into M. rotundifolia, no molecular markers for this trait have been validated in muscadines. Recently, two Kompetitive Allele Specific PCR (KASP) markers targeting candidate genes for male sterility (VviINP1) and stenospermocarpy (VviAGL11) in V. vinifera have shown promise. Sequence comparisons with published V. rotundifolia genomes suggest that these markers might be effective across diverse Vitis, Muscadinia, and wide hybrid germplasm. In this study, we validated the predictive ability of KASP markers for flower sex and stenospermocarpy across thirteen Vitis x Muscadinia hybrid seedling populations and 191 diverse genotypes. In 2023, 891 seedlings were evaluated for seedlessness, with an additional 214 seedlings assessed for flower sex. Furthermore, 191 diverse accessions underwent evaluation for both flower sex and seedlessness. Of the 891 seedlings, 66 were seedless, 490 were seeded, and 335 could not be phenotyped due to fruit absence. Among the 214 seedlings assessed for flower sex, 88 were perfect, 106 were female, and 20 could not be phenotyped due to flower absence. The stenospermocarpy marker accurately predicted 771 of 783 seedlings and diverse material, while the flower sex marker matched 366 of 383 seedlings and diverse accessions. Discrepancies between marker predictions and observed phenotypes may be due to human error or pollen sterility. Notably, most fruitless seedlings were predicted to be stenospermocarpic, indicating potential issues with partial sterility or cold hardiness in seedless hybrids. We intend to reevaluate the populations for flower sex and seedlessness in summer 2024 to address discrepancies. Overall, the KASP markers developed in V. vinifera exhibited excellent predictive ability across diverse germplasm, offering valuable insights for muscadine breeding programs.

Genetic maps are essential tools for gene positional cloning and marker-assisted breeding. A pecan mapping population of 119 F 1 trees was derived from a cross of the widely planted cultivars Pawnee and Elliott. Whereas ‘Pawnee’ is susceptible, ‘Elliott’ has long- standing resistance to pecan scab caused by the fungal pathogen Venturia effusa. Molecular markers were developed using genotyping-by-sequencing, and linkage maps were constructed for each parent following the two-way pseudo-test-cross strategy used for cross-pollinated species. The ‘Pawnee’ and ‘Elliott’ maps contain 1,347 and 1,050 single nucleotide polymorphism markers spanning a genetic distance of 4,493.0 and 3,758.4 cM, respectively. While these map lengths are likely inflated due to genotyping errors, a high level of synteny between genetic and physical distances of the markers in both parental maps was achieved. Scab resistance was evaluated through controlled inoculations in the greenhouse using two scab isolates, and a significant quantitative trait locus (QTL) for scab resistance was identified on chromosome 5 in ‘Elliott’. Candidate gene searches within the 2-logarithm of the odds interval of the scab-resistant QTL identified a number of disease resistance related genes, including genes encoding wall-associated receptor kinases, cytochrome P450s, leucine-rich repeats receptor-like serine/threonine-protein kinases, a pectinesterase inhibitor, a cellulose synthase, a flavonol synthase, a 4-coumarate-CoA ligase, a caffeic acid 3-O-methyltransferase, and a MYB domain transcription factor.

Mango (Mangifera indica L.) is a popular fruit produced widely along tropical climates for fresh consumption. In this study, mature fruits from a collection of 189 mango cultivars were evaluated for fruit weight and sugar content at horticultural maturity. Subsequently, a total of 10958 single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing (GBS) were used to identify quantitative trait nucleotides (QTNs) associated with fruit weight and sugar content through genome wide association studies (GWAS). Fruit weight over three seasons averaged 454 g. ‘Lancetilla’ and ‘Webber’ cultivars produced the heaviest fruits averaging 1127.5 g and 1108.5 g respectively and the lowest average fruit weights (166.16 g, 184.5 g, and 189.5 g) were observed in ‘Itamaraca’, ‘13-1' and ‘Fralan’ cultivars respectively. The mango cultivars had a mean degrees Brix (°Bx) value of 14.8 with ‘Venus,’ ‘Peach Cobbler’ and ‘Julie’ displaying the highest degrees Brix (°Bx) values of 25.6, 22.4 and 20.6 respectively. Three QTNs in chromosomes 5, 8 and 10 were significantly associated with fruit weight using Fixed and random model Circulating Probability Unification (FarmCPU) association model, while two QTNs in chromosome 2 and chromosome 20 were significantly associated with sugar content using Bayesian-information, Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model. Genetic characterization of loci associated with these two traits in mango provides a solid foundation for SNP marker assisted selection (MAS) to accelerate molecular screening of segregating populations and germplasm in a mango breeding program.

Red raspberry (Rubus idaeus L.) is a high-value crop, acclaimed for its fruit quality characteristics and putative health benefits. Among fruit quality characteristics, color is a critical trait in determining market acceptability and consumer preference. Red raspberry fruit encompasses a wide array of colors, notably yellow, orange, and red. Red fruited cultivars are the most common in commercial settings and market selection is dependent on the color intensity: while the processing industry needs dark berries for most applications, the fresh market requires bright red and non-darkening fruit. Anthocyanins, a group of water-soluble phenolic compounds, are regarded as the major contributors to raspberry red fruit color. Knowledge on the genetics of raspberry fruit pigmentation would be valuable for breeding programs, but to date the genetic control of the different red intensities of raspberries remains elusive. This research aims to map the genetic regions underlying the red shades of raspberry fruit through a genome-wide association study (GWAS). Fruits from 765 red raspberry cultivars and selections – including red-, orange-, and yellow-fruited genotypes – were harvested over four seasons (2018-2021) and analyzed for total anthocyanin content and color. Total anthocyanins were measured through the pH differential methods and color was assessed using a high-throughput digital phenotyping protocol. Leaves from all genotypes were harvested in summer 2022 and used for genomic DNA extraction. Whole-genome sequencing of DNA samples was achieved through Illumina NovaSeq6000, with an average coverage of 30×. Sequences were aligned to the ‘Malling Jewel’ reference genome using BWA-MEM and single nucleotide polymorphisms (SNPs) were identified following the GATK pipeline. Total anthocyanin content ranged between zero (yellow genotypes) and 113.21 mg/100 g fresh weight (FW) of peonidin-3-O-glucoside equivalents (P3OG eq.) and averaged 42.09 mg/100 g FW P3OG eq. Color coordinates L* (lightness) varied between 11.99 and 48.21 and averaged 21.21, a* (red-green) spanned -1.18 and 38.38 and averaged 26.85, b* (yellow-blue) ranged between 4.57 and 30.04 and averaged 18.05. The association between the detected genetic variants and the phenotypic data (fruit color and total anthocyanins) will enable the identification of SNP markers that explain the variation in observed red shades of berries. Such markers will be used in raspberry breeding programs to facilitate the development of cultivars with desired fruit color.

The renowned horticultural artist and plant breeder Luther Burbank worked with many different species of plants. During his 50-year career, he introduced over 800 cultivars, including more than 150 accessions of plums (Prunus spp.) in the late 1800’s and early 1900’s. Burbank preferred utilizing wide, interspecific crosses to create a vast range of phenotypic variation and then artificially select from the extremes. While a very great artist, Burbank was a substandard scientist because he was derelict in pedigree note-taking. Though many of his introductions are extinct, hobbyists, enthusiasts, and international collections retain nearly a third of the economically viable cultivars he bred. For a century, many of his hybridizations remained inscrutable mysteries until modern genomic and computational tools developed their resolution and statistical power. Today, genotyping by sequencing (GBS) is a useful tool for pedigree reconstruction in the absence of reliable records. GBS can inform principal component analyses (PCA), identity by descent (IBD) kinship, and phylogenetic admixture, revealing complex relationships among taxa. In this study, whole genome sequencing was performed on 53 Prunus taxa used by Luther Burbank in his breeding experiments in the most comprehensive genetic survey of his work to date. Exact parent-offspring relationships between this population may be impossible to discern due to years of back crossing, sibling mating, and open pollination. However, the proportion of genomic similarity amongst these taxa provides information on the relatedness of the genotypes in Burbank’s Prunus experiments, defining four primary lineages within his breeding population. These lineages are comprised primarily of P. salicina and P. simonii, but also have influences from P. americana, P. cerasifera, P. domestica, and P. rivularis. The prevalence of P. simonii in Burbank’s Prunus introductions appears to have been vastly underreported, indicating that some of the seedstock founders of his breeding population could have been P. salicina x P. simonii hybrids at the inception of his career. This research has implications for pedigree reconstruction and prioritizing conservation in collections curation for future studies.

The global blueberry market has been expanding vastly driven by consumer demand for healthier food. As a top blueberry producer, United States generated a revenue of $932 million in 2020. A profitable blueberry industry relies on continued cultivar improvement. One challenge faced by blueberry breeders, researchers, and growers, is yield data collection. Measuring blueberry yield by manual sampling is labor-intensive and time-consuming. We developed a smartphone application leveraging deep learning techniques to automate yield prediction and maturity assessment for different blueberry cultivars under field conditions. State of the art YOLOv8 models were fine-tuned and evaluated using a dataset of side-view images of various southern highbush and rabbiteye blueberry cultivars. The best performing DL model of YOLOv8-x achieved a mean average precision of 0.708 and 0.372 under 0.5 and 0.5-0.95 Intersection over Union thresholds on validation datasets, respectively. Blueberry yield was predicted using non-linear regression-based machine learning models using the image-derived mature berry count multiplied by user-defined average berry weight and cultivar as explanatory variables with satisfactory accuracy. This proposed smartphone app can enable image-based yield prediction for blueberry growers and breeders, which is valuable for management decision making and accelerated selection for high-yielding cultivars.

Phenotyping remains a bottleneck in many breeding programs, including sweet cherry. Current fruit evaluation protocols require extensive manual sorting and visual evaluation, which reduces throughput and is subject to evaluator bias and fatigue. The Washington State University Cherry Breeding Program is seeking more efficient methods of evaluating fruit quality. In 2023, the program acquired an optical fruit sorter. Our objective was to customize the sorter parameters according to breeding program needs and compare the results of the sorter with traditional methods. Our Tomra InVision 2 sorter has the same optics, software and computer hardware as a commercial sorter, but operates on a single lane. Fruit are loaded onto an infeed system which passes fruit in single file into the detection area. A combination of fruit rotation, multiple cameras and mirrors is designed to image the entire surface of individual fruit. Both visual and infrared images are captured, generally > 24 images per fruit at a rate of approximately 15 fruit per second. The sorter software identifies fruit and classifies them according to a set of tunable quality parameters or grades. Air-actuated valves then eject the fruit into one of four grade-determined exits. The sorter generates reports that include the fruit size profile as well as the percentage of fruit sorted into the various exits and/or grades. The sorter shipped with a pre-loaded map (sorting algorithm), which we modified by updating with data from representative images of various quality parameters. We then used the sorter to grade fruit from Phase 2 variety trials. We analyzed 50-fruit subsamples in the traditional manner for size and defects. All remaining fruit from each sample were analyzed via the sorter. Out of 20 samples evaluated, the average number of fruit per sample evaluated by the sorter was 154, vs. 50 for manual evaluation. Overall, the sorter detected a lower percentage of cracking, doubles (polycarpy), and pitting vs. manual evaluation, and a higher percentage of skin blemishes. Continued testing will be required to determine whether these differences are due to the effects of small sample size or bias due to the methods themselves (human evaluator vs. sorter). While the sorter required a similar number of personnel as for manual evaluation, it required less time to evaluate each sample even though more fruit were analyzed. We will expanding the use and testing of the sorter in 2024, including evaluation of postharvest quality.