Biology subjects

Benevenuto, J.

Publications and source records attributed to Benevenuto, J..

7 recordsLinked to original sources

bioRxiv2025

Genome-wide association study reveals candidate loci for resistance to anthracnose in blueberry

Anthracnose, caused by Colletotrichum gloeosporioides, poses a significant threat to blueberries, necessitating a deeper understanding of the genetic mechanisms underlying resistance to develop efficient breeding strategies. Here, we conducted a genome-wide association study on two distinct populations, comprising 355 advanced selections from the University of Florida Blueberry Breeding and Genomics Program. Visual scores and image analyses were used for assessing disease severity. The population was genotyped using Capture-Seq, detecting 38,379 single nucleotide polymorphisms. The study revealed a moderate narrow-sense heritability estimate ([~]0.5) for anthracnose resistance in blueberries. Minor additive loci contributing to anthracnose resistance were identified on chromosomes 2, 3, 5, 6, 8, 9, 10, 11 and 12, spanning different populations. Image analyses demonstrated heightened sensitivity, detecting more associations within both populations compared to the visual approach. Candidate gene mining flanking significant associations unveiled key defense-related proteins, such as serine/threonine protein kinases, pentatricopeptide repeat-containing proteins, E3 ubiquitin ligases that have been well-known for their roles in plant defense signaling pathways. The dissection of speculated defense-related proteins into distinct layers offers further understanding into the intricate defense responses against C. gloeosporioides. Our findings highlight the complex and quantitative resistance mechanism for anthracnose in blueberry, providing insights for breeding strategies and sustainable disease management.

genomics↗

bioRxiv2025

THE HAPLOTYPE-RESOLVED AND CHROMOSOME-SCALE GENOME OF VACCINIUM STAMINEUM: A NEW SOURCE OF GENETIC VARIABILITY FOR BLUEBERRY BREEDING

The Vaccinium genus comprises several commercially important fruit crops, such as blueberry, lingonberry, bilberry, and cranberry. However, past breeding efforts have primarily focused on a limited number of wild relatives as sources of genetic variability, leaving a vast genetic reservoir untapped. In this study, we present the first haplotype-phased reference genome for V. stamineum, a blueberry wild relative with potential for de novo domestication and introgression into breeding programs. V. stamineum is particularly notable for several agronomic traits of interest, such as high soluble sugars, unique flavor profile, and its unique anthocyanin accumulation in fruit pulp, a trait absent in cultivated blueberries.Our assemblies revealed 12 pseudomolecules corresponding to the base chromosome number of Vaccinium species, with a genome size of 529.16 Mb and 493.82 Mb for primary and secondary haplotypes, respectively. Despite a slightly smaller genome than other Vaccinium species, V. stamineum exhibited a higher number of predicted protein-coding genes, while the repetitive elements comprised 39.77% and 42.38% of the primary and secondary haplotypes, respectively. BUSCO analysis indicated 97% transcriptome completeness, supporting the accuracy of gene annotation. Genome-wide alignments showed that V. stamineum haplotypes were highly collinear to each other, as well as to V. corymbosum. However, further validation is required to resolve a putative translocation in chromosome 1 of the primary haplotype. Altogether, this study established a genomic framework that will facilitate the introgression of traits of interest into blueberry breeding programs and support the potential domestication of V. stamineum as a novel fruit crop.

genomics↗

bioRxiv2023

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

genomics↗

bioRxiv2023

Using visual scores and categorical data for genomic prediction of complex traits in breeding programs

Most genomic prediction methods are based on assumptions of normality due to their simplicity, robustness, and ease of implementation. However, in plant and animal breeding, target traits are often collected as categorical data, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of crucial genetic parameters. In this study, we examined the main challenges of categorical phenotypes in genomic prediction and genetic parameter estimation using mixed models, Bayesian approaches, and machine learning techniques. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1 to 3 and 1 to 5 scores) is the best strategy, even considering errors and subjectivity associated with visual scores; (ii) in the context of genomic prediction, Linear Mixed Models and Bayesian Linear Regression Models are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification technique; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry, which can guide breeders decisions; and (v) a comparison of continuous and categorical phenotype testing for complex traits with low heritability, found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using categorical traits to explore genetic information in breeding programs, and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. Key messageAn approach for handling categorical data with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making.

genetics↗

bioRxiv2022

Chloroplast genome assemblies and comparative analyses of major Vaccinium berry crops

BackgroundVaccinium is an economically important genus of berry crops in the family Ericaceae. Given the numerous hybridizations and polyploidization events among Vaccinium species, the taxonomy of this genus has remained uncertain and the subject of long debate. Therefore, the availability of more genomic resources for Vaccinium can provide useful tools for phylogenetic resolution, species identification, authentication of berry food products, and a framework for genetic engineering. ResultsIn this study, we assembled five Vaccinium chloroplast sequences representing the following berry types: northern highbush blueberry (V. corymbosum), southern highbush blueberry (V. corymbosum hybrids), rabbiteye blueberry (V. virgatum), lowbush blueberry (V. angustifolium), and bilberry (V. myrtillus). Two complete plastid genomes were achieved using long-read PacBio sequencing, while three draft sequences were obtained using short-read Illumina sequencing. Comparative analyses also included other previously available Vaccinium chloroplast sequences, especially the commercially important species V. macrocarpon (cranberry). The Vaccinium chloroplast genomes exhibited a circular quadripartite structure, with an overall highly conserved synteny and sequence identity among them. Despite their high similarity, we identified some polymorphic regions in terms of expansion/contraction of inverted repeats, gene copy number variation, simple sequence repeats, and single nucleotide polymorphisms. Phylogenetic analysis revealed multiple origins of highbush blueberry plastomes, likely due to the hybridization events during northern and southern highbush blueberry domestication. ConclusionsOur results enrich the genomic data availability for new Vaccinium species by sequencing and assembling the chloroplast DNA of major economically important berry types. Additional whole plastome analyses including more samples and wild species will be useful to obtain a refined knowledge of the maternal breeding history of blueberries and increase phylogenetic resolution at low taxonomic levels.

plant biology↗

bioRxiv2021

Genomic Prediction In An Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop, with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to the expansion of production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on phenotypic recurrent selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberrys breeding cycles are costly and time-consuming, which results in low genetic gains per unit of time. Motivated by the application of molecular markers for a more accurate selection in early stages of breeding, we performed pioneering genomic prediction studies and optimization for implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based geno- typing and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic prediction studies and described the current achievements in the crop. In this paper, our contribution for genomic prediction in an autotetraploid crop is three-fold: i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for the use of genomic selection in blueberry, with potential to be applied to other polyploid species of a similar background.

genomics↗

bioRxiv2020

High-Resolution Linkage Map and QTL Analyses for Machine Harvest Traits in Autotetraploid Blueberry

Blueberry (Vaccinium corymbosum and hybrids) is an autotetraploid crop whose commercial relevance has been growing steadily during the last twenty years. However, the ever-increasing cost of labor for hand-picking blueberry is one main constraint in competitive marketing of the fruit. Machine harvestability is, therefore, a key trait for the blueberry industry. Understanding the genetic architecture of traits through quantitative trait locus (QTL) mapping is the first step towards implementation of molecular breeding for faster genetic gains. Despite recent advances in software development for autotetraploid genetic mapping, a high-resolution map is still not available for blueberry. In this study, we crafted a map for autotetraploid low-chill highbush blueberry containing 11,292 SNP markers and a total size of 1,953.97 cM (average density of 5.78 markers/cM). This map was subsequently used to perform QTL analyses for traits relevant to machine harvesting: firmness, firmness retention, and fruit detachment force. Significant QTL peaks were identified for all the traits. The QTL intervals were further explored for putative candidate genes. Genes related to cell wall remodeling were highlighted in the firmness and firmness retention intervals. For fruit detachment force, transcription factors involved in fruit abscission were detected. Altogether, our findings provide the basis for future fine-mapping and molecular breeding efforts for machine harvesting in blueberry.

genomics↗