Biology subjects

Munoz, P. R.

Publications and source records attributed to Munoz, P. R..

10 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↗

bioRxiv2024

Genotypic variation in blueberry flower morphology and nectar reward content affects pollinator attraction in a diverse breeding population

Pollination is crucial to obtain optimal blueberry yield and fruit quality. Despite substantial investments in seasonal beekeeping services, blueberry producers consistently report suboptimal pollinator visitation and fruit set in some cultivars. Flower morphology and floral rewards are among the key factors that have shown to contribute to pollinator attraction, however little is known about their relative importance for improving yield in the context of plant breeding. Clarifying the relationships between flower morphology, nectar reward content, pollinator recruitment, and pollination outcomes, as well as their genetic components, can inform breeding priorities for enhancing yield. In the present study, we measured ten flower and nectar traits and indices of successful pollination, including fruit set, seed count, and fruit weight in 38 southern highbush blueberry genotypes. Additionally, we assessed pollinator visitation frequency and foraging behavior over two growing seasons. Several statistical models were tested to optimize the prediction of pollinator visitation and pollination success, including partial least squares, BayesB, ridge-regression, and random forest. Random forest models obtained high predictive abilities for pollinator visitation frequency, with values of 0.54, 0.52, and 0.66 for honey bee, bumble bee, and total pollinator visits, respectively. The BayesB model provided the most consistent prediction of fruit set, fruit weight, and seed set, with predictive abilities of 0.07, -0.08, and 0.42, respectively. Variable importance analysis revealed that genotypic differences in nectar volume had the greatest impact on honey bee and bumble bee recruitment, although preferences for flower morphological traits varied depending on the foraging task. Flower density was a major driving factor attracting nectar-foraging honey bees and bumble bees, while pollen-foraging bumble bees were most influenced by flower accessibility, specifically corolla length and the length-to-width ratio. Corolla length and the length-to-width ratio were also identified as the main predictors of fruit set, fruit weight, and seed count, suggesting that bumble bees and their foraging preferences may play a pivotal role in fruit production. Moderate to high narrow-sense heritability values (ranging from 0.30 to 0.77) were obtained for all floral traits, indicating that selective breeding efforts may enhance cultivar attractiveness to pollinators.

plant biology↗

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↗

bioRxiv2021

Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability

Among the main features treasured by blueberry consumers, flavor is the most important. Human perception of food flavors can primarily be divided into two main sensory inputs, taste and aroma. Through retronasal olfaction, a group of metabolites called volatile organic compounds (VOCs) emitted from the fruit are able to produce the sensation of aroma, creating the myriad of flavors experienced during our life. In blueberry, breeders have noticed some genotypes with unique floral and sweet flavor notes that, ultimately, enhance human aroma perception. Despite the importance, both the understanding of which chemicals are mediating this variation across phenotypes and the potential impact on consumer preferences remains largely unknown. In this study we dissected the main components underlying blueberry aroma and associated it with consumer predilections by paring metabolomics with sensory analysis. Our contribution in this study is four-fold: (i) first, based on a representative blueberry germplasm cultivated at the University of Florida, we differentiated genotypes with floral and sweet aromatic notes and confirmed that such unique characteristics are preferred by consumers; (ii) at the chemical level, we showed that a group of eight terpene volatiles constitute the primary metabolic group associated with aroma sensation; (iii) we demonstrated that aromatic genotypes can be classified using information from a group of a few key volatiles; and finally, (iv) we combined pedigree and metabolomic information in a single predictive framework and showed the importance of metabolomic data for flavor-assisted selection. For the blueberry community, our findings open new venues to explore flavor. Broadly, we present an emerging view about flavor and provide a detailed blueprint of how this target could be addressed in fruits and vegetables.

genetics↗

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

Metabolomic Selection for Enhanced Fruit Flavor

Although they are staple foods in cuisines globally, commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, many breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of varieties, previously limited by the low-throughput and high cost of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation and the accuracies of 18 different models are assessed. The best performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. The prediction accuracies were high for most attributes in both blueberries and tomatoes. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.

plant biology↗

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↗

bioRxiv2018

Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction

Estimation of allele dosage in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Here we propose and compare the use of next generation sequencing read depth as continuous parameterization for autotetraploid genomic prediction of breeding values, using blueberry (Vaccinium corybosum spp.) as a model. Additionally, we investigated the influence of different sources of information to build relationship matrices in phenotype prediction; no relationship, pedigree, and genomic information, considering either diploid or tetraploid parameterizations. A real breeding population composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Analyses were based on extensive pedigree (since 1908) and high-density marker data (86K markers). Our results show that marker-based matrices can yield significantly better prediction than pedigree for most of the traits, based on model fitting and expected genetic gain. Continuous genotypic based models performed as well as the current best models and presented a significantly better goodness-of-fit for all traits analyzed. This approach also reduces the computational time required for marker calling and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. Accuracies are encouraging for application of genomic selection (GS) for blueberry breeding. Conservatively, GS could reduce the time for cultivar release by three years. GS could increase the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection.

genomics↗

bioRxiv2018

Integration of Dominance and Marker x Environment Interactions into Maize Genomic Prediction Models

Hybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferred in silico based on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.

genomics↗