A quantitative survey of the blueberry (Vaccinium spp.) culturable nectar microbiome: variation between cultivars, locations, and farm management approaches.

Microbes in floral nectar can impact both their host plants and floral visitors, yet little is known about the nectar microbiome of most pollinator-dependent crops. In this study, we examined the abundance and composition of the fungi and bacteria inhabiting Vaccinium spp. nectar, as well as nectar volume and sugar concentrations. We compared wild V. myrsinites with two field-grown V. corymbosum cultivars collected from two organic and two conventional farms. Differences in nectar traits and microbiomes were identified between V. corymbosum cultivars but not Vaccinium species. The microbiome of cultivated plants also varied greatly between farms, whereas management regime had only subtle effects, with higher fungal populations detected under organic management. Nectars were hexose-dominant, and high cell densities were correlated with reduced nectar sugar concentrations. Bacteria were more common than fungi in blueberry nectar, although both were frequently detected and co-occurred more often than would be predicted by chance. "Cosmopolitan" blueberry nectar microbes that were isolated in all plants, including Rosenbergiella sp. and Symmetrospora symmetrica, were identified. This study provides the first systematic report of the blueberry nectar microbiome, which may have important implications for pollinator and crop health.

Genomic-assisted breeding for climate-smart coffee.

Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

Using visual scores for genomic prediction of complex traits in breeding programs.

KEY MESSAGE: An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. 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 (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes 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 visual scores 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.

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.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

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.

AGHmatrix: genetic relationship matrices in R.

MOTIVATION: The resemble between relatives computed from pedigree and genomic data is an important resource for geneticists and ecologists, who are interested in understanding how genes influence phenotypic variation, fitness adaptation, and population dynamics. RESULTS: The AGHmatrix software is an R package focused on the construction of pedigree (A matrix) and/or molecular markers (G matrix), with the possibility of building a combined matrix of pedigree corrected by molecular markers (H matrix). Designed to estimate the relationships for any ploidy level, the software also includes auxiliary functions related to filtering molecular markers, and checks pedigree errors in large data sets. After computing the relationship matrices, results from the AGHmatrix can be used in different contexts, including on prediction of (genomic) estimated breeding values and genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: AGHmatrix v2.1.0 is available under GPL-3 license in CRAN at https://cran.r-project.org/web/packages/AGHmatrix/index.html and also in GitHub at https://github.com/rramadeu/AGHmatrix. It has a comprehensive tutorial, and it follows with real data examples.

Genomic selection for genotype performance and environmental stability in Coffea canephora.

Coffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated.

Chloroplast genome assemblies and comparative analyses of commercially important Vaccinium berry crops.

Vaccinium is a large genus of shrubs that includes a handful of economically important berry crops. Given the numerous hybridizations and polyploidization events, the taxonomy of this genus has remained the subject of long debate. In addition, berries and berry-based products are liable to adulteration, either fraudulent or unintentional due to misidentification of species. The availability of more genomic information could help achieve higher phylogenetic resolution for the genus, provide molecular markers for berry crops identification, and a framework for efficient genetic engineering of chloroplasts. Therefore, in this study we assembled five Vaccinium chloroplast sequences representing the economically relevant 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). Comparative analyses showed that the Vaccinium chloroplast genomes exhibited an overall highly conserved synteny and sequence identity among them. Polymorphic regions included the expansion/contraction of inverted repeats, gene copy number variation, simple sequence repeats, indels, and single nucleotide polymorphisms. Based on their in silico discrimination power, we suggested variants that could be developed into molecular markers for berry crops identification. Phylogenetic analysis revealed multiple origins of highbush blueberry plastomes, likely due to the hybridization events that occurred during northern and southern highbush blueberry domestication.

Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.

Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.

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

Flavor is among the most important traits valued by consumers of fresh fruits. Human perception of flavor occurs primarily through two main sensory inputs, taste and aroma. Through retronasal olfaction, volatile organic compounds (VOCs) emitted by the fruit are able to produce the sensation of aroma which when combined with gustatory inputs from the tongue together underly our perception of the thousands of flavors we experience throughout our lives. In blueberry, breeders have observed that some genotypes possess berries with unique 'floral' and 'sweet' flavor and aroma notes. The potential impact these characteristics might have on consumer acceptability is largely unknown and represents an opportunity to better understand how aroma attributes affect the perception of blueberry flavor. In this study, we dissected the main components of blueberry aroma and associated it with consumer predilections by pairing metabolomics with sensory analysis. Our contribution in this study is four-fold: (i) first, we differentiated genotypes with floral and sweet aroma notes and confirmed that such characteristics are preferred by consumers; (ii) at the chemical level, we showed that a group of eight terpenoid volatiles (p-cymene, myrtenal, linalool, L-carvenol, geranyl acetone, geranyl acetate, D-limonene and ß-myrcene) constitute the primary metabolic group associated with these aroma sensations; (iii) we demonstrated that aromatic genotypes can be classified using metabolomics; and finally, (iv) we combined pedigree and metabolomic information and showed the importance of metabolomic data for flavor-assisted selection. Our findings open new avenues to explore the phenomenon of flavor in blueberries and also allow us to present an emerging view about flavor and provide a detailed blueprint of how this targeted trait could be addressed in fruit and vegetable breeding.

There and back again; historical perspective and future directions for Vaccinium breeding and research studies.

The genus Vaccinium L. (Ericaceae) contains a wide diversity of culturally and economically important berry crop species. Consumer demand and scientific research in blueberry (Vaccinium spp.) and cranberry (Vaccinium macrocarpon) have increased worldwide over the crops' relatively short domestication history (~100 years). Other species, including bilberry (Vaccinium myrtillus), lingonberry (Vaccinium vitis-idaea), and ohelo berry (Vaccinium reticulatum) are largely still harvested from the wild but with crop improvement efforts underway. Here, we present a review article on these Vaccinium berry crops on topics that span taxonomy to genetics and genomics to breeding. We highlight the accomplishments made thus far for each of these crops, along their journey from the wild, and propose research areas and questions that will require investments by the community over the coming decades to guide future crop improvement efforts. New tools and resources are needed to underpin the development of superior cultivars that are not only more resilient to various environmental stresses and higher yielding, but also produce fruit that continue to meet a variety of consumer preferences, including fruit quality and health related traits.

Metabolomic selection for enhanced fruit flavor.

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, 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 genotypes, previously limited by the low throughput 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 were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. 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.

Genomic features of a multidrug-resistant and mercury-tolerant environmental Escherichia coli recovered after a mining dam disaster in South America.

Mining dam disasters contribute to the contamination of aquatic environments, impacting associated ecosystems and wildlife. A multidrug-resistant Escherichia coli strain (B2C) was isolated from a river water sample in Brazil after the Mariana mining dam disaster. The genome was sequenced using the Illumina MiSeq platform, and de novo assembled using Unicycler. Resistome, virulome, and plasmidome were predicted using bioinformatics tools. Data analysis revealed that E. coli B2C belonged to sequence type ST219 and phylogroup E. Strikingly, a broad resistome (antibiotics, hazardous heavy metals, and biocides) was predicted, including the presence of the clinically relevant blaCTX-M-2 extended-spectrum ß-lactamase (ESBL) gene, qacE∆1 efflux pump gene, and the mer (mercury resistance) operon. SNP-based analysis revealed that environmental E. coli B2C was clustered along to ESBL-negative E. coli strains of ST219 isolated between 1980 and 2021 from livestock in the United States of America. Acquisition of clinically relevant genes by ST219 seems to be a recent genetic event related to anthropogenic activities, where polluted water environments may contribute to its dissemination at the human-animal-environment interface. In addition, the presence of genes conferring resistance to heavy metals could be related to environmental pollution from mining activities. Antimicrobial resistance genes could be essential biomarkers of environmental exposure to human and mining pollution.

Genome-Wide Association Study of Healthful Flavonoids among Diverse Mandarin Accessions.

Mandarins have many unique flavonoids with documented health benefits and that help to prevent chronic human diseases. Flavonoids are difficult to measure and cannot be phenotyped without the use of specialized equipment; consequently, citrus breeders have not used flavonoid contents as selection criteria to develop cultivars with increased benefits for human health or increased tolerance to diseases. In this study, peel, pulp, and seed samples collected from many mandarin accessions and their hybrids were analyzed for the presence of selected flavonoids with documented human health benefits. A genome-wide association study (GWAS) was used to identify SNPs associated with biosynthesis of flavonoids in these mandarin accessions, and there were 420 significant SNPs were found to be associated with 28 compounds in peel, pulp, or seed samples. Four candidate genes involved in flavonoid biosynthesis were identified by enrichment analysis. SNPs that were found to be associated with compounds in pulp samples have the potential to be used as markers to select mandarins with improved phytonutrient content to benefit human health. Mandarin cultivars bred with increased flavonoid content may provide value to growers and consumers.

Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover.

Local adaptation is common in plants, yet characterization of its underlying genetic basis is rare in herbaceous perennials. Moreover, while many plant species exhibit intraspecific chemical defence polymorphisms, their importance for local adaptation remains poorly understood. We examined the genetic architecture of local adaptation in a perennial, obligately-outcrossing herbaceous legume, white clover (Trifolium repens). This widespread species displays a well-studied chemical defence polymorphism for cyanogenesis (HCN release following tissue damage) and has evolved climate-associated cyanogenesis clines throughout its range. Two biparental F2  mapping populations, derived from three parents collected in environments spanning the U.S. latitudinal species range (Duluth, MN, St. Louis, MO and Gainesville, FL), were grown in triplicate for two years in reciprocal common garden experiments in the parental environments (6,012 total plants). Vegetative growth and reproductive fitness traits displayed trade-offs across reciprocal environments, indicating local adaptation. Genetic mapping of fitness traits revealed a genetic architecture characterized by allelic trade-offs between environments, with 100% and 80% of fitness QTL in the two mapping populations showing significant QTL×E interactions, consistent with antagonistic pleiotropy. Across the genome there were three hotspots of QTL colocalization. Unexpectedly, we found little evidence that the cyanogenesis polymorphism contributes to local adaptation. Instead, divergent life history strategies in reciprocal environments were major fitness determinants: selection favoured early investment in flowering at the cost of multiyear survival in the southernmost site versus delayed flowering and multiyear persistence in the northern environments. Our findings demonstrate that multilocus genetic trade-offs contribute to contrasting life history characteristics that allow for local adaptation in this outcrossing herbaceous perennial.

Haplotype reconstruction in connected tetraploid F1 populations.

In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequence-based genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3 × 3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations.

Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.).

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.

QTL mapping in outbred tetraploid (and diploid) diallel populations.

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

Chemical Characterization, Nutritional and Bioactive Properties of Physalis peruviana Fruit from High Areas of the Atacama Desert.

Physalis peruviana L. belongs to the Solanaceae family and produces a spherical fruit used to treat various diseases. However, the chemical composition, nutritional characterization, and bioactive properties of the P. peruviana growing in the Andean region of the Atacama Desert have not been conducted so far. The results showed clear differences in the nutritional and bioactive characteristics of the fruits grown in arid environmental conditions, which were comparable to those from countries with a production tradition. The fruits studied showed a higher Ca, Cu, Mn, P, and Zn content and bioactive compounds such as flavonoids and tannins than those reported in the literature. UHPLC was performed to determine the main phenols. Gallic acid was identified as the predominant phenolic compound in this species (303.63 mg/100 g FW), of which to our knowledge no previous study has reported similar concentrations in this species. Moreover, Cape gooseberry extract has antioxidant and antimicrobial activity against Gram-positive and Gram-negative bacteria. Pseudomonas syringae (MIC 0.313 mg/mL and MBC 1.25 mg/mL) was the most susceptible bacterium. Meanwhile, Erwinia rhapontici was the most resistant bacterium (MIC and MIB 5.00 mg/mL). Furthermore, it was found to inhibit α-amylase activity with an IC50 value (39.28 µg/mL) similar to that of acarbose (35.74 µg/mL). These results expand the knowledge of the species cultivated in arid environmental conditions and suggest an alternative for the potential use of this fruit to manage chronic diseases such as diabetes.