Search Results (162)

Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding. Full article

Age at first calving (AFC) and 305-day milk yield in the first lactation (MY) data of 18,545 Holstein-Friesian cows born between 2008 and 2018 in six herds were evaluated. The effects of some genetic and environmental factors, population genetic parameters, breeding value (BV), and phenotypic and genetic trends of AFC and MY traits were estimated. The GLM method (ANOVA Type III) and BLUP animal model were used for the estimations. One-way linear regression analysis was used for trend calculations. The adjusted overall mean value (±SE) of the AFC and MY traits was 25.19 ± 0.02 months and 10,287.14 ± 24.79 kg, respectively. The percentage proportion contribution of the different factors in the phenotype in the case of AFC was as follows: herd 94.41%, birth year of cow 3.26%, birth season of cow 1.39%, and sire 0.71%. For MY, the contribution was as follows: herd 89.17%, birth season of cow 5.38%, birth year of cow 4.09%, and sire 1.05%. The heritability of AFC and MY traits by two different models proved to be moderate (0.26 ± 0.02, 0.19 ± 0.01 and 0.30 ± 0.02, 0.34 ± 0.01, respectively). There were relatively small differences between the sires in the estimated BV for the traits AFC and MY. The phenotypic and genetic correlations between AFC and MY traits were weak (between −0.05 and −0.16). Based on the phenotypic trend calculation, AFC showed a decreasing direction (−0.12 months per year) and MY an increasing direction (+42.30 kg per year). However, the genetic trend was very slightly decreasing for AFC (−0.00 and −0.05 months per year) and slightly increasing for MY (+5.52 and +16.49 kg per year) over the period studied. Full article

Gilthead seabream (Sparus aurata) is a prominent aquaculture species in Europe; however, the repercussions of growth-oriented selective breeding on reproductive performance under industrial conditions have not been adequately characterised. The present study evaluated the influence of divergent Best Liner Unbiased Prediction (BLUP)-based selection for low growth (LG) (n = 49; mean weigh ± SD = 842 ± 189 g) and high growth (HG) (n = 50; mean weight ± SD = 1127 ± 407 g) on spawning quality throughout the commercial mass-spawning season. A number of significant differences were detected between the genetic lines. The LG broodstock produced substantially higher oocyte yields and numbers of fertilised eggs (26% and 25% increases, respectively), indicating greater quantitative reproductive output. In contrast, the HG line exhibited marginally higher fertilisation, egg viability, and hatching rates, indicative of enhanced early developmental efficiency. Despite these contrasting patterns, both lines exhibited similar numbers of viable eggs, larvae, and comparable larval survival. These findings demonstrate that selection for growth impacts reproductive traits through different pathways: The selection of HG results in an enhancement of developmental performance, while the selection of LG leads to an optimisation of egg production. Across the spawning period, oocyte yield was identified as the primary driver of overall spawn quality. The findings of this study offer pertinent insights into the optimisation of broodstock management and the enhancement of sustainability and efficiency in gilthead seabream aquaculture. Full article

Animal breeding has undergone profound transformations from its origins in phenotypic observation to the integration of genomic and machine learning techniques. This review paper explores the progression of livestock breeding, tracing its roots to the domestication of animals during the Neolithic Revolution. Gregor Mendel’s foundational work with pea plants established key principles of Mendelian genetics, which initially focused on discrete qualitative traits. However, the advancement of quantitative genetics has shifted the focus to continuous traits, such as body weight and milk yield, which are influenced by multiple genes. QTL mapping revolutionized breeding by shifting from phenotype- to genotype-based selection, enhancing accuracy through genomic predictions like GEBV under GBLUP. The strongest QTL associations on chromosome 18 linked local GEBV with FUK and DDX19B expression. In recent years, machine learning and artificial intelligence have transformed genomic prediction into livestock breeding by efficiently handling high-dimensional data and capturing complex genetic relationships. Notably, a deployed deep learning model achieved an average correlation of up to 0.643 between actual and predicted values. This review highlights the integration of machine learning approaches in animal breeding, showcasing advancements in milk and meat production, and the improvement of disease management through multi-omics strategies. The paper underscores the shift towards innovative methods and their impact on advancing animal breeding practices, offering insights into prospects for enhancing productivity, health, and welfare in livestock. Full article

Kernel row number (KRN) is a pivotal determinant for yield in maize breeding programs. However, the genetic basis underlying KRN remains largely elusive. To identify candidate genes regulating KRN, a population of 318 BC₄F₄ chromosomal segment substitution lines (CSSLs) was developed via backcrossing, with Baimaya (BMY) as the donor parent and B73 as the recurrent parent. Furthermore, a high-density genetic linkage map containing 2859 high-quality single-nucleotide polymorphism (SNP) markers was constructed for quantitative trait locus (QTL) mapping of KRN. Notably, 19 QTLs controlling KRN were detected across three environments and in the Best Linear Unbiased Prediction (BLUP) values; among these, a major-effect QTL (qKRN4.09-1) was consistently identified across all three environments and BLUP. Then, the integration of linkage mapping and transcriptome analysis of 5 mm immature ears from near-isogenic lines (NILs) uncovered a candidate gene, Zm00001eb205550. This gene exhibited significant downregulation in qKRN4.09-1^BMY, and two missense variants were detected between qKRN4.09-1^BMY and qKRN4.09-1^B73. Zm00001eb205550 exhibited preferential expression in developing ears. Moreover, the pyramiding of favorable alleles from the five stable QTLs significantly increased KRN in maize. These findings advance our genetic understanding of maize ear development and provide valuable genetic targets for improving KRN in maize breeding. Full article

Within the framework of this study, a genetic evaluation of milk traits was conducted in the Saanen goat breed. The focus of the research was placed on the application of more advanced models for estimating heritability and breeding values of economically important milk traits. The study included 670 Saanen goats and a total of 2155 lactations between 2010 and 2021 on a single farm located in the Autonomous Province of Vojvodina. The milk production traits included total milk yield per lactation (TMY), milk fat yield (FY), protein yield (PY), and the content of milk fat (MF) and protein (PC). The fixed effects included in the Sire and Animal models were as follows: kidding season, type of kidding, year of kidding, and lactation number. The permanent environmental effect of the doe and the animal’s additive genetic effect were considered as random effects. In the Animal model, the estimated heritability values for the traits were: 0.2216 (TMY), 0.2564 (FY), 0.2556 (PY), 0.3977 (MF), 0.2864 (PC). The heritability estimates obtained using the sire model were slightly higher: 0.2742 for TMY, 0.3256 for FY, 0.3855 for PY, 0.3925 for MF, and 0.3502 for PC. The estimation of breeding values for bucks was performed using both the Sire model and the Animal model. Breeding values for the bucks derived from the two models showed a close relationship, with correlations ranging from 0.85 for TMY to 0.90 for PC. The results of this study confirm that the application of the BLUP-Animal model provides a more accurate estimation of breeding values and represents a reliable basis for the selection of the Saanen goat breed. The findings from this study provide a practical basis for enhancing breeding programs and developing an effective strategy for genetic improvement of milk production in the population. Full article

The development of tropical maize populations with high heterosis potential is essential for sustaining genetic progress in hybrid breeding programs, yet accurate selection remains challenging due to genotype–phenotype interactions and inbreeding depression. This study evaluated the efficiency of five selection strategies in recurrent selection programs using F₂ populations derived from commercial maize hybrids: Smith–Hazel Index (SHI), Base Index (BIA), Mulamba–Mock Index (MMI), REML/BLUP for grain yield (BLUP_GY), and REML/BLUP for inbreeding depression (BLUP_ID). Consistency among methods was assessed with a heatmap, and predicted genetic gains were compared with realized field performance. Predicted gains were highest with MMI and BIA for grain yield and ear weight, although realized results revealed discrepancies, particularly for BLUP-based approaches. Notably, BLUP_GY, which had the lowest predicted yield (4025 kg ha⁻¹), achieved a realized yield of 5620 kg ha⁻¹, surpassing BIA and SHI. This indicates that additive potential was underestimated in predictions, likely due to dominance and environmental effects in early F₂ cycles. Overall, BLUP-based methods proved effective in identifying progenies with higher additive value, and their integration with phenotypic indices is recommended to combine short-term realized gains with sustained genetic improvement. Full article

Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on its genetic variability and agronomic potential. This study aimed to estimate genetic parameters, predict genotypic values, and identify superior ecotypes with desirable forage traits, integrating stability and adaptability analyses. Methods: A total of 84 ecotypes were evaluated over three consecutive years for twelve morphological and forage-related traits. Genetic parameters, genotypic values, and selection gains were estimated using mixed models (REML/BLUP). Stability was assessed through harmonic means of genotypic performance, and the multi-trait genotype–ideotype distance index (MGIDI) was applied to identify ecotypes with balanced performance across traits. Results: Substantial genetic variability was detected for most traits, particularly those related to biomass accumulation, such as total dry matter, the number of tillers, fresh matter, and leaf dry matter. These traits exhibited medium to high heritability and strong potential for selection. Ecotype N3.10 consistently showed superior performance across productivity traits while other ecotypes, such as N4.14 and N1.09, stood out for quality-related attributes and cold tolerance, respectively. The application of the MGIDI index enabled the identification of 17 ecotypes with balanced multi-trait performance, supporting the simultaneous selection for productivity, quality, and adaptability. Comparisons with P. notatum suggest that P. nicorae harbors competitive genetic potential, despite its lower level of domestication. Conclusions: The integration of REML/BLUP analyses, stability parameters, and ideotype-based multi-trait selection provided a robust framework for identifying elite P. nicorae ecotypes. These findings reinforce the strategic importance of this species as a valuable genetic resource for the development of adapted and productive forage cultivars in subtropical environments. Full article

Anthracnose, predominantly caused by Colletotrichum fructicola, severely reduces yield in Fragaria × ananassa production. We assessed ensemble machine learning (ML) frameworks to improve genomic prediction (GP) of resistance using a training population of 300 individuals from six full-sib families. Genotyping with the Axiom^® 50K FanaSNP array and phenotyping by AUDPC after artificial inoculation enabled evaluation of five algorithms—G-BLUP, LASSO, LightGBM, Random Forest, and XGBoost—combined with informed feature selection and resampling-based data augmentation (3×, 5×). Ensemble ML models consistently outperformed linear approaches, with Random Forest, LightGBM, and XGBoost achieving the highest accuracies. Marker prioritization revealed that a reduced SNP panel of ~200 markers provided near-maximal predictive performance (R² up to 0.991), demonstrating that compact subsets can support cost-efficient GP. Data augmentation, implemented through the resampling of existing observations rather than the creation of new alleles, improved statistical power and model stability under limited sample sizes. Collectively, this study demonstrates that (i) ensemble ML models deliver superior accuracy for predicting polygenic resistance, (ii) small SNP panels can achieve high efficiency, and (iii) augmentation enhances robustness in resource-constrained breeding populations. These findings establish a scalable and breeder-oriented GP pipeline to accelerate the development of anthracnose-resistant strawberry cultivars. Full article

Platonia insignis Mart. is a native Amazonian fruit tree with considerable agro-industrial potential, yet it remains underutilized due to limited domestication efforts and the absence of breeding programs or improved genetic lines. This study aimed to estimate genetic parameters based on morpho-agronomic fruit traits and to identify superior genotypes from natural coastal populations in the Brazilian Amazon. Thirteen genotypes were evaluated for 16 biometric and compositional traits. Genetic parameters were estimated using REML/BLUP (Restricted Maximum Likelihood/Best Linear Unbiased Prediction) procedures, and a rank–sum selection index was applied to identify elite individuals. The results revealed substantial phenotypic and genetic variability, with broad-sense heritability values ranging from moderate to high for key traits, including longitudinal fruit diameter (0.81), fruit fresh mass (0.66), and seed fresh mass (0.75). Selection accuracy was high (≥0.96) across most traits, indicating strong experimental reliability. Genotypic correlations highlighted favorable associations among traits related to pulp yield, sugar content, and seed reduction. Six superior genotypes (G7, G1, G6, G3, G2, and G4) exhibited optimal profiles for fruit quality and productivity. These findings provide a strong foundation for breeding strategies and support the genetic conservation and domestication of P. insignis as a native species of high economic and ecological importance. Full article

Buffaloes are a vital genetic resource for dairy production, yet advancements in improving milk production have been somewhat limited. In this study, we performed an integrated analysis of genomic sequencing data from 78 water buffaloes and their milk production traits, with a focus on 305-day milk yield (MY). Leveraging advancements in sequencing technology alongside genome-wide association study (GWAS) methods such as cBLUP, GMATs, and BayesR, we aimed to identify genetic factors that could facilitate the breeding of high-quality buffaloes. Our analysis revealed two significant SNPs associated with milk production traits. Based on these markers, four candidate genes were identified within the surrounding genomic regions. These genes showed significant enrichment in lactation-related pathways, including the prolactin signaling pathway (mitogen-activated protein kinase 10, MAPK10), IL-17 signaling pathway (MAPK10), MAPK signaling pathway (MAPK10), and adipocytokine signaling pathway (MAPK10). The identification of these candidate genes, particularly MAPK10, provides a robust theoretical basis for molecular breeding strategies aimed at enhancing milk production in buffaloes. This work paves the way for more targeted and effective breeding programs in the future. Full article

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

The screening of poplar varieties that demonstrate tolerance to low nitrogen (N) represents a promising strategy for improving nitrogen-use efficiency in trees. Such an approach could reduce reliance on N fertilizers while mitigating environmental pollution associated with their cultivation. In this study, a total of 87 poplar varieties were evaluated in a controlled greenhouse pot experiment. Under both low-nitrogen (LN) and normal-nitrogen (NN) conditions, 18 traits spanning four categories—growth performance, leaf morphology, chlorophyll fluorescence, and N isotope parameters were measured. For 13 of these traits (growth, leaf morphology, chlorophyll fluorescence), genetic variation and parameters, including genotypic values, were analyzed using best linear unbiased prediction (BLUP) within a linear mixed model (LMM). LN tolerance of tested poplar varieties was comprehensively assessed with three MGIDI strategies by integrating means, BLUPs, and low-nitrogen tolerance coefficient (LNindex) to rank poplar varieties. The results exhibited highly significant differences across all traits between LN and NN experiments, as well as among varieties. LN stress markedly inhibited growth, altered leaf morphology, and reduced chlorophyll fluorescence parameters in young poplar plants. Among the selection strategies, the MGIDI_LNindex approach demonstrated the highest selection differential percent (SD% = 10.5–35.23%). Using a selection intensity (SI) of 20%, we systematically identified 17 superior genotypes across all three strategies. In a thorough, comprehensive MGIDI-based evaluation, these varieties exhibited exceptional adaptability and stability under LN stress. The selected genotypes represent valuable genetic resources for developing improved poplar cultivars with enhanced low-nitrogen tolerance. Full article

Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period. Full article

Considering that effective selection strategies are essential for the development of new ornamental pepper cultivars, the objective of this work was to select superior partially endogamic lines (PEL) of pepper in a F_2:3 generation, using the combination of the genealogical method with mixed linear models. The experiment consisted of four cycles: parents and generations F₁, F₂ and F_2:3. Qualitative (QLD) and quantitative (QTD) descriptors were evaluated. QLD were analyzed through descriptive statistics, and QTD were analyzed through estimates of genetic parameters and prediction of genetic values by REML/BLUP. Multivariate analysis was performed to group and select individuals based on QLD and QTD simultaneously. The descriptors number of flowers per axil, flower position, mature fruit color, fruit position, fruit brightness, and capsaicin in the placenta presented no variation within the F_2:3 population. The selection accuracy varied from high to very high, denoting a high experimental precision. Higher additive genetic action was detected for descriptors, considering the individual heritability in the strict sense and the additive heritability within the progeny. Forty-eight PELs were selected quantitatively and, considering QLD and QTD descriptors simultaneously, the number of individuals was reduced from 48 to 30 PELs. The combined strategies used enabled to establish the best strategy for an efficient selection of superior PEL of ornamental pepper. Full article