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Breeding for a subjective goal such as flavor is challenging, as many blueberry cultivars are grown worldwide, and identifying breeding targets relating to blueberry flavor biochemistry that have a high degree of genetic control and low environmental variability are priorities. A variety of biochemical compounds and physical characters induce the sensory responses of taste, olfaction, and somatosensation, all of which interact to create what is perceived flavor. The goal of this study was to identify the flavor compounds with a larger genetic versus environmental component regulating their expression over an array of cultivars, locations, and years. Over the course of three years, consumer panelists rated overall liking, texture, sweetness, sourness, and flavor intensity of 19 southern highbush blueberry (Vaccinium corymbosum hybrids) genotypes in 30 sensory panels. Significant positive correlations to overall liking of blueberry fruit (P<0.001) were found with sweetness (R2 = 0.70), texture (R2 = 0.68), and flavor (R2 = 0.63). Sourness had a significantly negative relationship with overall liking (R2 = 0.55). The relationship between flavor and texture liking was also linear (R2 = 0.73, P<0.0001) demonstrating interaction between olfaction and somatosensation. Partial least squares analysis was used to identify sugars, acids, and volatile compounds contributing to liking and sensory intensities, and revealed strong effects of fructose, pH, and several volatile compounds upon all sensory parameters measured. To assess the feasibility of breeding for flavor components, a three year study was conducted to compare genetic and environmental influences on flavor biochemistry. Panelists could discern genotypic variation in blueberry sensory components, and many of the compounds affecting consumer favor of blueberries, such as fructose, pH, β-caryophyllene oxide and 2-heptanone, were sufficiently genetically controlled that allocating resources for their breeding is worthwhile.

Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains. Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models. While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely. This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp .

The dengue and Zika viruses are primarily transmitted by Aedes aegypti mosquitoes, which are most active during day light hours and feed both in and outside of the household. Personal protection technologies such as insecticide-treated clothing could provide individual protection. Here we assessed the efficacy of permethrin-treated clothing on personal protection in the laboratory. The effect of washing on treated clothing, skin coverage and protection against resistant and susceptible Ae. aegypti was assessed using modified WHO arm-in-cage assays. Coverage was further assessed using free-flight room tests to investigate the protective efficacy of unwashed factory-dipped permethrin-treated clothing. Clothing was worn as full coverage (long sleeves and trousers) and partial coverage (short sleeves and shorts). Residual permethrin on the skin and its effect on mosquitoes was measured using modified WHO cone assays and quantified using high-pressure liquid chromatography (HPLC) analysis. In the arm-in-cage assays, unwashed clothing reduced landing by 58.9% (95% CI 49.2-66.9) and biting by 28.5% (95% CI 22.5-34.0), but reduced to 18.5% (95% CI 14.7-22.3) and 11.1% (95% CI 8.5-13.8) respectively after 10 washes. Landing and biting for resistant and susceptible strains was not significantly different (p<0.05). In free-flight room tests, full coverage treated clothing reduced landing by 24.3% (95% CI 17.4-31.7) and biting by 91% (95% CI 82.2-95.9) with partial coverage reducing landing and biting by 26.4% (95% CI 20.3-31.2) and 49.3% (95% CI 42.1-59.1) respectively with coverage type having no significant difference on landing (p<0.05). Residual permethrin was present on the skin in low amounts (0.0041mg/cm2), but still produced a KD of >80% one hour after wearing treated clothing. Whilst partially covering the body with permethrin-treated clothing provided some protection against biting, wearing treated clothing with long sleeves and trousers provided the highest form of protection. Washing treated clothing dramatically reduced protection provided. Permethrin-treated clothing could provide protection to individuals from Ae. aegypti that show permethrin resistance. Additionally, it could continue to provide protection even after the clothing has been worn. Field trials are urgently needed to determine whether clothing can protect against dengue and Zika.

• Genome-wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta-analysis using information from independent studies. • We carried out GWAS for growth traits with six single-marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by Joint-GWAS, using gene and segment-based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. • While single-single nucleotide polymorphism (SNP) GWAS hardly detected significant associations at high-stringency in each population, gene-based Joint-GWAS revealed nine genes significantly associated with tree height. Associations detected using single-SNP GWAS, RHM and Joint-GWAS set-based models explained on average 3–20% of the phenotypic variance. Whole-genome regression, conversely, captured 64–89% of the pedigree-based heritability in all populations. Several associations independently detected for the same SNPs in different populations provided unprecedented GWAS validation results in forest trees. Rare and common associations were discovered in eight genes involved in cell wall biosynthesis and lignification. • With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, Joint-GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications.

Background The evaluation of genomic selection (GS) efficiency in forestry has primarily relied on cross-validation schemes that split the same population within a single generation for both training and validation. While useful, this approach may not be reliable for multigenerational breeding. To our knowledge, this is the first study to assess genomic prediction in Norway spruce using a large dataset spanning two generations in two environments. We trained pedigree-based (ABLUP) and marker-based (GBLUP) prediction models under three approaches: forward prediction, backward prediction, and across-environment prediction. The models were evaluated for ring-width, solid-wood and tracheid characteristics, using ~ 6,000 phenotyped and ~ 2,500 genotyped individual. Predictive ability (PA) and prediction accuracy (ACC) were estimated using an independent validation method, ensuring no individuals were shared between training and validation datasets. To assess the trade-off between comprehensive radial history and practical direct methods, we compared GBLUP models trained with cumulative area-weighted density (AWE-GBLUP) and single annual-ring density (SAD-GBLUP) from mother plus-trees. These models were validated using early and mature-stage progeny density measurements across two trials. Results Despite the smaller number of individuals used in the GBLUP models, both PA and ACC were generally comparable to those of the ABLUP model, particularly for cross-environment predictions. Overall, forward and backward predictions were significantly higher for density-related and tracheid properties, suggesting that across-generation predictions are feasible for wood properties but may be challenging for growth and low-heritability traits. Notably, SAD-GBLUP provided comparable prediction accuracies to AWE-GBLUP, supporting the use of more practical and cost-effective phenotyping methods in operational breeding programs. Conclusions Our findings highlight the need for context-specific models to improve the accuracy and reliability of genomic prediction in forest tree breeding. Future efforts might aim to expand training populations, incorporate non-additive genetic effects, and validate model performance across cambial ages while accounting for climatic variability during the corresponding growth years. Overall, this study offers a valuable foundation for implementing GS in Norway spruce breeding programs.

Background Establishing faba bean ( Vicia faba L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These traits can be improved by exploiting the extensive but largely uncharacterized faba bean germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per pod (SP), and single seed weight (SSW) in 245 faba bean accessions (CGN panel) via a Genome-Wide Association Study (GWAS). The accessions were grown in 2021 and 2022 in the Netherlands. Additionally, we developed genomic selection (GS) models to predict the genetic merit within large germplasm collections for the mentioned traits, as well as yield (YLD). Results The CGN panel was an optimal panel for performing high-resolution GWAS, showing large phenotypic variation, high narrow-sense heritability for all traits, and minimal genetic relatedness among accessions. Population structure analysis revealed the presence of four genetic groups. GWAS uncovered 33 SNP-trait associations in 2021 and 17 in 2022. We identified one stable QTL for FT and four for SSW over the two years, representing key molecular markers for testing in breeding applications. Short linkage disequilibrium decay (~ 268 Kbp) facilitated the identification of several important candidate genes with interesting homologs in other crops. Ten SNPs in 2021 and five in 2022 were predicted to be intra-genic missense variants, potentially altering protein function. Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. Predictive ability ranged from 0.58 to 0.81 in 2021, and 0.47 to 0.85 in 2022 for different traits. Additionally, across-year predictions showed stable PA. Conclusion GWAS revealed promising QTLs for use in molecular breeding and highlighted new candidate genes. Interestingly, the prediction of intra-genic SNPs categorized 15 SNPs as putatively affecting protein function. Moreover, we demonstrated for the first time in faba bean that GS has the potential to unlock untapped diversity in genebank collections and accelerate trait integration into faba bean breeding programs.

Female mosquitoes display preferences for certain individuals over others, which is determined by differences in volatile chemicals produced by the human body and detected by mosquitoes. Body odour can be controlled genetically but the existence of a genetic basis for differential attraction to insects has never been formally demonstrated. This study investigated heritability of attractiveness to mosquitoes by evaluating the response of Aedes aegypti (=Stegomyia aegypti) mosquitoes to odours from the hands of identical and non-identical twins in a dual-choice assay. Volatiles from individuals in an identical twin pair showed a high correlation in attractiveness to mosquitoes, while non-identical twin pairs showed a significantly lower correlation. Overall, there was a strong narrow-sense heritability of 0.62 (SE 0.124) for relative attraction and 0.67 (0.354) for flight activity based on the average of ten measurements. The results demonstrate an underlying genetic component detectable by mosquitoes through olfaction. Understanding the genetic basis for attractiveness could create a more informed approach to repellent development.

Background Some people produce specific body odours that make them more attractive than others to mosquitoes, and consequently are at higher risk of contracting vector-borne diseases. The skin microbiome can break down carbohydrates, fatty acids and peptides on the skin into volatiles that mosquitoes can differentiate. Results Here, we examined how skin microbiome composition of women differs in relation to level of attractiveness to Anopheles coluzzii mosquitoes, to identify volatiles in body odour and metabolic pathways associated with individuals that tend to be poorly-attractive to mosquitoes. We used behavioural assays to measure attractiveness of participants to An. coluzzii mosquitoes, 16S rRNA amplicon sequencing of the bacteria sampled from the skin and gas chromatography of volatiles in body odour. We found differences in skin microbiome composition between the poorly- and highly-attractive groups, particularly eight Amplicon Sequence Variants (ASVs) belonging to the Proteobacteria, Actinobacteria and Firmicutes phyla. Staphylococcus 2 ASVs are four times as abundant in the highly-attractive compared to poorly-attractive group. Associations were found between these ASVs and volatiles known to be attractive to Anopheles mosquitoes. Propanoic pathways are enriched in the poorly-attractive participants compared to those found to be highly-attractive. Conclusions Our findings suggest that variation in attractiveness of people to mosquitoes is related to the composition of the skin microbiota, knowledge that could improve odour-baited traps or other next generation vector control tools.

Norway spruce has a wide natural distribution range, harboring substantial physiological and genetic variation. There are three altitudinal ecotypes described in this species. Each ecotype has been shaped by natural selection and retains morphological and physiological characteristics. Foliar spectral reflectance is readily used in evaluating the physiological status of crops and forest ecosystems. However, underlying genetics of foliar spectral reflectance and pigment content in forest trees has rarely been investigated. We assessed the reflectance in a clonal bank comprising three ecotypes in two dates covering different vegetation season conditions. Significant seasonal differences in spectral reflectance among Norway spruce ecotypes were manifested in a wide-ranging reflectance spectrum. We estimated significant heritable variation and uncovered phenotypic and genetic correlations among growth and physiological traits through bivariate linear models utilizing spatial corrections. We confirmed the relative importance of the red edge within the context of the study site’s ecotypic variation. When interpreting these findings, growth traits such as height, diameter, crown length, and crown height allowed us to estimate variable correlations across the reflectance spectrum, peaking in most cases in wavelengths connected to water content in plant tissues. Finally, significant differences among ecotypes in reflectance and other correlated traits were detected.