Search Results (110)

Progressing climate change necessitates the development of drought-tolerant crops, and understanding the temporal dynamics of genotype x environment interactions (GxE) is crucial. This study aimed to test established phenotyping methods (chlorophyll a fluorescence (ChlF) and hyperspectral (HS) imaging) to investigate the variability in 165 inbred maize lines’ responses to progressive drought stress. The inbred maize lines were grown under controlled conditions and were challenged with water withholding. Fifteen ChlF and HS indices were measured at three consecutive time points (M1, M2, and M3). Mixed models were employed to estimate the GxT interaction effects via Best Linear Unbiased Predictors (BLUPs) for each variable. A Principal Component Analysis (PCA) performed on the GxT BLUPs from each time point revealed a highly dynamic interaction structure. While the primary axis of GxT variation (PC1) was consistently associated with HI, which is related to plant vigor, across all measurement times, its importance intensified under severe stress (M3). The secondary axis (PC2) shifted markedly over time: after initial variations at M1, it was dominated by GxT effects in specific ChlF parameters related to photosynthetic regulation under moderate stress (M2), before shifting again under severe stress (M3) to reflect the GxT effects on indices potentially related to pigment degradation and other stress indicators. Full article

Phenolic compounds are important targets in olive breeding due to their health benefits and impact on fruit and oil quality. Fruit phenolic profiling enables efficient screening of large germplasm collections without oil extraction, but environmental variability, especially year-to-year differences, affects their expression. The aim of this study was to assess the genotypic influence on fruit phenolic composition, based on a three-year evaluation of 10 wild olive genotypes and 75 cultivars from an olive core collection. Each genotype was sampled in at least two seasons, with 1 to 3 trees analyzed annually. Variance analysis revealed significant genetic variation among cultivars and notable genotype-by-year interactions for certain phenolics. Broad-sense heritability was generally high for most compounds, although some, such as ligstroside and ligstroside aglycone, showed greater environmental sensitivity. Best linear unbiased predictions (BLUPs) were highly correlated with average relative phenotypic values. Clustering analyses identified strong associations among key phenolic compounds and highlighted distinct metabolic profiles separating wild and cultivated genotypes, reflecting differences in phenolic accumulation patterns. These findings demonstrate the genetic and environmental influences on olive fruit phenolics and provide reliable estimates to support future marker-assisted selection studies aimed at developing useful tools in olive breeding programs. Full article

(1) Background: Litter size traits are critical for pig breeding efficiency but pose challenges due to low heritability and sex-limited influences. This study aimed to elucidate the genetic architecture and identify candidate genes for these traits in Large White pigs using genomic selection (GS) and genome-wide association analyses (GWAS). (2) Methods: This study utilized phenotypic data from nine litter size traits in Large White sows. Genotyping-by-sequencing (GBS) was performed to obtain genotype data, retaining 153,782 high-quality SNPs after quality control. Genetic evaluation was conducted using single-step genomic best linear unbiased prediction (ssGBLUP), with genetic parameters (heritability and genetic correlations) estimated via an animal model (repeatability model). To assess prediction accuracy, 10-fold cross-validation was employed to compare traditional BLUP with ssGBLUP. Furthermore, a single-step genome-wide association study (ssGWAS) integrated genomic information and pedigree-based relationship matrices to screen for significant SNPs associated with litter size traits across the genome. Functional analysis of key candidate genes was subsequently conducted based on ssGWAS results. (3) Results: Heritabilities for litter traits ranged from 0.01 to 0.06. ssGBLUP improved genomic prediction accuracy by 6.38–13.33% over BLUP. Six genomic windows explaining 1.07–1.77% of genetic variance were identified via ssGWAS, highlighting GPR12 on SSC11 as a key candidate gene linked to oocyte development. (4) Conclusions: This study demonstrates the efficacy of ssGBLUP for low-heritability traits and identifies GPR12 as a pivotal gene for litter size. Prioritizing NHB and LBWT in breeding programs could enhance genetic gains while mitigating adverse effects on piglet health. These findings advance genomic strategies for improving reproductive efficiency in swine. Full article

Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF–effect size relationship (as a selection signature, S) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection (S < −1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011–0.031, while GBLUP-SMS achieved a gain of 0.005–0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in S-values, whereas GBLUP-S heavily relied on accurate S specification. When the true S was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched S in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle (S = −0.5) and pig T1, T2, and T3 (S = −1.5, −1, and −2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation. Full article

This study evaluated the genetic parameters for growth and Vibrio parahaemolyticus (Vp_AHPND) resistance in both the introduced MK strain and the self-constructed GK strain of Penaeus vannamei, investigating the impact of genotyped female parents on trait estimates under a single-parent nested mating design. A total of 32 families from the MK strain and 44 families from the GK strain were analyzed. Fifty-four female parents from both strains were genotyped using the “Yellow Sea Chip No. 1” containing 10.0 K SNPs. In the MK strain, heritability estimates ranged from 0.439 to 0.458 for body weight (Bw) and from 0.308 to 0.489 for survival time (ST) and survival rates at 36 h (36 SR), 50% mortality (SS₅₀), and 60 h (60 SR). In the GK strain, heritability for Bw ranged from 0.724 to 0.726, while ST, 36 SR, SS₅₀, and 60 SR had heritability estimates between 0.370 and 0.593. Genetic correlations between Bw and ST were 0.601 to 0.622 in the MK strain and 0.742 to 0.744 in the GK strain. For Bw and survival rates, correlations ranged from 0.120 to 0.547 in the MK strain and from 0.426 to 0.906 in the GK strain. The genetic correlation between ST and survival rates was not significantly different from 1 (p > 0.05) in both strains. High Pearson correlations (0.853 to 0.997, p < 0.01) were observed among survival rates at different points. Predictive accuracies for Bw, ST, and survival rates using single-step genomic best linear unbiased prediction (ssGBLUP) were comparable to pedigree-based best linear unbiased prediction (pBLUP) in the MK strain, while in the GK strain, ssGBLUP improved predictive accuracies for Bw, ST, and SS₅₀ by 0.20%, 0.32%, and 0.38%, respectively. The results indicate that both growth and Vp_AHPND resistance have significant breeding potential. Although the genetic correlation between weight and resistance varies across different populations, there is a positive genetic correlation between these traits, supporting the feasibility of multi-trait selection. To enhance genetic accuracy, breeding programs should include more genotyped progeny. These findings also suggest that infection frequency and observation time influence resistance performance and breeding selection, emphasizing the need for a tailored resistance evaluation program to improve breeding efficiency and reduce costs. Full article

In linear regression analysis, the independence assumption is crucial and the ordinary least square (OLS) estimator generally regarded as the Best Linear Unbiased Estimator (BLUE) is applied. However, multicollinearity can complicate the estimation of the effect of individual variables, leading to potential inaccurate statistical inferences. Because of this issue, different types of two-parameter estimators have been explored. This paper compares t-tests for assessing the significance of regression coefficients, including several two-parameter estimators. We conduct a Monte Carlo study to evaluate these methods by examining their empirical type I error and power characteristics, based on established protocols. The simulation results indicate that some two-parameter estimators achieve better power gains while preserving the nominal size at 5%. Real-life data are analyzed to illustrate the findings of this paper. Full article

Harvest weight uniformity is a critical economic trait in the production of Pacific white shrimp (Penaeus vannamei). Social interactions among individuals can significantly influence both uniformity and productivity in aquaculture. To improve harvest weight uniformity through selective breeding, it is essential to accurately partition the genetic component of social effects, known as an indirect genetic effect (IGE), from purely environmental factors. Since IGEs cannot be estimated when all individuals are kept in a single group, a specialized experimental design, such as the grouping design with three families per group (3FAM), is required. With this experimental design, the shrimp population is divided into multiple groups (cages), each containing three families. Individuals from each family are then evenly subdivided and placed in three cages, thereby enabling the estimation of both direct and social genetic effects. Additionally, integrating genomic information instead of relying solely on pedigree data improves the accuracy of genetic relatedness among individuals, leading to more precise genetic evaluation. This study employed a 3FAM experimental design involving 40 families (36 individuals per family) to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. The genotypes of all tested individuals obtained using the 55K SNP panel were incorporated into a hierarchical generalized linear model to predict direct genetic effects and indirect genetic effects (IGE) separately. The results revealed that the heritability of harvest weight uniformity was low (0.005 to 0.017). However, the genetic coefficient of variation (0.340 to 0.528) indicates that using the residual variance in harvest weight as a selection criterion for improving uniformity is feasible. Incorporating IGE into the model increased heritability estimates for uniformity by 150% to 240% and genetic coefficient of variation for uniformity by 32.11% to 55.29%, compared to the model without IGE. Moreover, the genetic correlation between harvest weight and its uniformity shifted from a strongly negative value (−0.862 to −0.683) to a weakly positive value (0.203 to 0.117), suggesting an improvement in the genetic relationship between the traits and better separation of genetic and environmental effects. The inclusion of genomic data enhanced the prediction ability of single-step best linear unbiased prediction for both harvest weight and uniformity by 6.35% and 10.53%, respectively, compared to the pedigree-based best linear unbiased prediction. These findings highlight the importance of incorporating IGE and utilizing genomic selection methods to enhance selection accuracy for obtaining harvest weight uniformity. This approach provides a theoretical foundation for guiding uniformity improvements in shrimp breeding programs and offers potential applications in other food production systems. Full article

Accurately predicting the impacts of climate change on hydrological fluxes in ungauged basins continues to be a complex task. In this study, we investigated the transferability of the model parameters SCS-SMA-LR, available in the ATHYS platform, to simulate hydrological behavior within catchments of a large South Mediterranean transboundary basin, i.e., the Medjerda bordering Tunisia and Algeria, characterized by contrasting climatic and physiographic conditions. A robustness analysis was set up for donor and receptor catchments situated in the Medjerda catchment in Tunisia. The model was initially calibrated for two donor catchments, for the 127 km² catchment of the Lakhmess watershed situated on the right bank and for the 362 km² catchment of the Raghay watershed situated on the left bank of the Medjerda basin in Tunisia, using input data from 1990 to 1994. The model performance was evaluated through multiple accuracy criteria based on the Best Linear Unbiased Estimator (BLUE) for the automatic calibration to quantify the model simulation, proving its good performance. The temporal transferability was assessed by evaluating model performance, transferring the calibrated parameters for the two catchments as validation on data for 3-year periods outside the calibration domain to test the robustness of the model through a diachronic analysis from different decades, i.e., for the periods 1994–1997, 2001–2004, and 2014–2017, respectively. The spatial transferability was assessed by transferring the parameters calibrated on the donor catchments to be applied to the receptor catchments based on similarity and data availability. The model was upgraded to a greater catchment for data from 1994 to 2016 for the right bank, the Siliana Upstream catchment, and to the nearest catchment with a similar area for the data from 2008 to 2017 for the left bank of the Medjerda basin, the Bouheurtma catchment. The capacity of the soil reservoir and the flow velocity parameters proved to have an important impact on the modeling implementations at, respectively, 123.03 mm and 1 m/s for Raghay, and 95.05 mm and 2.5 m/s for Lakhmes. The results show that the space–time transfer process of model parameters produces an acceptable simulation of flow volumes and timing. The proposed methodology proved to be a successful way to monitor ungauged catchments and strengthens the robustness of the SCS-SMA-LR model for hydrological modeling and impact studies in ungauged basins of the Southern Mediterranean region. Full article

Assessing genetic gains from historical data provides insights to improve breeding programs. This study evaluated the Mozambique National Maize Program’s (MNMP’s) genetic gains using data from advanced germplasm trials conducted at 21 locations between 2014 and 2020. Genetic gains were calculated by regressing the genotypic best linear unbiased estimates of grain yield and complementary agronomic traits against the initial year of genotype evaluation (n = 592). The annual genetic gain was expressed as a percentage of the trait mean. While grain yield, the primary breeding focus, showed no significant improvement, significant gains were observed for the plant height (0.67%), ear height (1.74%), ears per plant (1.31%), ear position coefficient (1.22%), and husk cover (4.7%). Negative genetic gains were detected for the days to anthesis (−0.5%), the anthesis–silking interval or ASI (−9.31%), and stalk lodging (−5.01%). These results indicate that while MNMP did not achieve the desired positive genetic gain for grain yield, progress was made for traits related to plant resilience, particularly the ASI and stalk lodging. MNMP should seek to incorporate new breeding technologies and human resources to enhance genetic gains for grain yield and other key traits in the maize breeding program, while developing and deploying high-yielding, climate-resilient maize varieties to address emerging food security challenges in Mozambique. Full article

This study investigates the efficacy of various genomic prediction models—Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—in predicting genomic breeding values (gEBVs). The phenotypic data include three binary health traits (anodontia, distichiasis, oral papillomatosis) and one behavioral trait (distraction) in a population of guide dogs. These traits impact the potential for success in guide dogs and are therefore routinely characterized but were chosen based on differences in heritability and case counts specifically to assess gEBV model performance. Utilizing a dataset from The Seeing Eye organization, which includes German Shepherds (n = 482), Golden Retrievers (n = 239), Labrador Retrievers (n = 1188), and Labrador and Golden Retriever crosses (n = 111), we assessed model performance within and across different breeds, trait heritability, case counts, and SNP marker densities. Our results indicate that no significant differences were found in model performance across varying heritabilities, case counts, or SNP densities, with all models performing similarly. Given its lack of need for parameter optimization, GBLUP was the most efficient model. Distichiasis showed the highest overall predictive performance, likely due to its higher heritability, while anodontia and distraction exhibited moderate accuracy, and oral papillomatosis had the lowest accuracy, correlating with its low heritability. These findings underscore that lower density SNP datasets can effectively construct gEBVs, suggesting that high-cost, high-density genotyping may not always be necessary. Additionally, the similar performance of all models indicates that simpler models like GBLUP, which requires less fine tuning, may be sufficient for genomic prediction in canine breeding programs. The research highlights the importance of standardized phenotypic assessments and carefully constructed reference populations to optimize the utility of genomic selection in canine breeding programs. Full article

Taiwan Country chickens are integral to Taiwanese culture and the poultry industry. By establishing a crossbreeding system, breeders must consider the growth-related traits of the dam line to achieve acceptable traits in commercial meat-type chickens. This study compared the accuracy of genomic estimated breeding values (GEBVs) predicted using the pedigree-based best linear unbiased prediction (PBLUP) model and the single-step genomic BLUP (ssGBLUP) model. Additionally, we conducted a genome-wide association study (GWAS) to identify single-nucleotide polymorphisms (SNPs) associated with growth, shank, and body conformation traits to support marker-assisted selection (MAS). The results showed that the ssGBLUP model achieved 4.3% to 16.4% higher prediction accuracy than the PBLUP model. GWAS identified four missense SNPs and four significant SNPs associated with body weight, shank length, and shank width at 12 weeks. These findings highlight the potential of integrating the ssGBLUP model with identified SNPs to improve genetic gain and breeding efficiency and provide preliminary results to assess the feasibility of genomic prediction and MAS in Taiwan Country chicken breeding programs. Further research is necessary to validate these findings and explore their mechanisms and broader application across different breeding programs, particularly for the NCHU-G101 breed of Taiwan Country chickens. Full article

Infection by gastrointestinal nematodes (GINs) in sheep is a significant health issue that affects animal welfare and leads to economic losses in the production sector. Genetic selection for parasite resistance has shown promise in improving animal health and productivity. This study aimed to determine if incorporating genomic data into genetic prediction models currently used in Uruguay could improve the accuracy of breeding value estimations for GIN resistance in the Australian Merino breed. This study compared the accuracy of breeding value predictions using the BLUP (Best Linear Unbiased Prediction) and ssGBLUP (single-step genomic BLUP) models on partial and complete data sets, including 32,713 phenotyped and 3238 genotyped animals. The quality of predictions was evaluated using a linear regression method, focusing on 145 rams. The inclusion of genomic data increased the average individual accuracies by 4% for genotyped and phenotyped animals. For animals with genomic and non-phenotyped data, the accuracy improvement reached 8%. Of these, one group of animals that benefited from an ssGBLUP evaluation came from a facility with a strong connection to the informative nucleus and showed an average increase of 20% in their individual accuracy. Additionally, ssGBLUP slightly outperformed BLUP in terms of prediction quality. These findings demonstrate the potential of genomic information to improve the accuracy of breeding value predictions for parasite resistance in sheep. The integration of genomic data, particularly in non-phenotyped animals, offers a promising tool for enhancing genetic selection in Australian Merino sheep to improve resistance to gastrointestinal parasites. Full article

Background: The single-step best linear unbiased predictor (ssGBLUP) has emerged as a reference method for genomic selection in recent years due to its advantages over traditional approaches. Although its application in horses remains limited, ssGBLUP has demonstrated the potential to improve the reliability of estimated breeding values in livestock species. This study aimed to assess the impact of incorporating genomic data using single-step restricted maximum likelihood (ssGREML) on reliability (R²) in the Pura Raza Española (PRE) horse breed, compared to traditional pedigree-based REML. Methods: The analysis involved 14 morphological traits from 7152 animals, including 2916 genotyped individuals. Genetic parameters were estimated using a multivariate model. Results: Results showed that heritability estimates were similar between the two approaches, ranging from 0.08 to 0.76. However, a significant increase in reliability (R²) was observed for ssGREML compared to REML across all morphological traits, with overall gains ranging from 1.56% to 13.30% depending on the trait evaluated. R² ranged from 6.93% to 22.70% in genotyped animals, significantly lower in non-genotyped animals (0.82% to 12.37%). Interestingly, individuals with low R² values in REML demonstrated the largest R² gains in ssGREML. Additionally, this improvement was much greater (5.96% to 19.25%) when only considering stallions with less than 40 controlled foals. Conclusions: Hereby, we demonstrated that the application of genomic selection can contribute to improving the reliability of mating decisions in a large horse breeding program such as the PRE breed. Full article

Developing drought-resistant alfalfa (Medicago sativa L.) that maintains high biomass yield is a key breeding goal to enhance productivity in water-limited areas. In this study, 424 alfalfa breeding families were analyzed to identify molecular markers associated with biomass yield under drought stress and to predict high-merit plants. Biomass yield was measured from 18 harvests from 2020 to 2023 in a field trial with deficit irrigation. A total of 131 significant markers were associated with biomass yield, with 80 markers specifically linked to yield under drought stress; among these, 19 markers were associated with multiple harvests. Finally, genomic best linear unbiased prediction (GBLUP) was employed to obtain predictive accuracies (PAs) and genomic estimated breeding values (GEBVs). Removing low-informative SNPs [SNPs with p-values > 0.05 from the additive Genome-Wide Association (GWAS) model] for GBLUP increased PA by 47.3%. The high number of markers associated with yield under drought stress and the highest PA (0.9) represent a significant achievement in improving yield under drought stress in alfalfa. Full article

Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (r = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ r ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ $H_{Cullis}^2$ ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model ($R^2$ = 0.26 at 20 m and $R^2$ = 0.44 at 60 m), while plant vigor ranged from $R^2$ = 0.50 at 20 m to $R^2$ = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs. Full article